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Research Article

A scoping review on technology applications in agricultural extension

Contributed equally to this work with: Zhihong Xu, Anjorin Ezekiel Adeyemi

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Agricultural Leadership, Education and Communications, Texas A&M University, College Station, Texas, United States of America

Roles Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing

Roles Investigation, Writing – original draft

Roles Formal analysis, Investigation, Visualization, Writing – original draft

Roles Resources, Writing – review & editing

Affiliation Department of Teaching, Learning, and Culture, Texas A&M University College, Station, Texas, United States of America

• Zhihong Xu, 
• Anjorin Ezekiel Adeyemi, 
• Emily Catalan, 
• Shuai Ma, 
• Ashlynn Kogut, 
• Cristina Guzman

• Published: November 6, 2023
• https://doi.org/10.1371/journal.pone.0292877
• Peer Review
• Reader Comments

Agricultural extension plays a crucial role in disseminating knowledge, empowering farmers, and advancing agricultural development. The effectiveness of these roles can be greatly improved by integrating technology. These technologies, often grouped into two categories–agricultural technology and educational technology–work together to yield the best outcomes. While several studies have been conducted using technologies in agricultural extension programs, no previous reviews have solely examined the impact of these technologies in agricultural extension, and this leaves a significant knowledge gap especially for professionals in this field. For this scoping review, we searched the five most relevant, reliable, and comprehensive databases (CAB Abstracts (Ovid), AGRICOLA (EBSCO), ERIC (EBSCO), Education Source (EBSCO), and Web of Science Core Collection) for articles focused on the use of technology for training farmers in agricultural extension settings. Fifty-four studies published between 2000 and 2022 on the use of technology in agricultural extension programs were included in this review. Our findings show that: (1) most studies were conducted in the last seven years (2016–2022) in the field of agronomy, with India being the most frequent country and Africa being the most notable region for the studies; (2) the quantitative research method was the most employed, while most of the included studies used more than one data collection approach; (3) multimedia was the most widely used educational technology, while most of the studies combined more than one agricultural technology such as pest and disease control, crop cultivation and harvesting practices; (4) the impacts of technology in agricultural extension were mostly mixed, while only the educational technology type had a statistically significant effect or impact of the intervention outcome. From an analysis of the results, we identified potential limitations in included studies’ methodology and reporting that should be considered in the future like the need to further analyze the specific interactions between the two technology types and their impacts of some aspects of agricultural extension. We also looked at the characteristics of interventions, the impact of technology on agricultural extension programs, and current and future trends. We emphasized the gaps in the literature that need to be addressed.

Citation: Xu Z, Adeyemi AE, Catalan E, Ma S, Kogut A, Guzman C (2023) A scoping review on technology applications in agricultural extension. PLoS ONE 18(11): e0292877. https://doi.org/10.1371/journal.pone.0292877

Editor: Mojtaba Kordrostami, Nuclear Science and Technology Research Institute, ISLAMIC REPUBLIC OF IRAN

Received: June 27, 2023; Accepted: October 1, 2023; Published: November 6, 2023

Copyright: © 2023 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data for this study is publicly available from the Texas Data Repository ( https://doi.org/10.18738/T8/VNLOTC ).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Agricultural extension programs play a crucial role in disseminating knowledge, empowering farmers, and driving agricultural development. From the earliest times, agricultural extension has been noted to traditionally, through the research scientists, develop products and methods which are transferred to the farmers through the extension agents for adoption. The transfer process which was mostly in-person or through radio communications [ 1 ] became largely inadequate to catch up with the expanding population as well as the rapid pace of development. This was further compounded by reduced government funding, uncertainties of the effectiveness of the methods, the extent of the relevance of the knowledge disseminated, and the appropriateness of the models [ 2 ] giving rise to introspection for paradigm shifts in the extension methods and practices. Hence, to enhance the effectiveness of these extension programs, the use of technology such as information and communication technologies (ICTs), digital technologies, farm simulation, and others became very much necessary. Rajkhowa & Qaim [ 3 ] noted that technology application has the potentials for improving the delivery of agricultural extension programs and disseminating agricultural research to farmers and producers since they can lower communication costs, improving smallholder market access and household welfare. By leveraging technology, agricultural extension can overcome geographical barriers, reach a wider audience, and provide access to valuable information and resources, leading to improved farming practices, increased productivity, and enhanced agricultural outcomes [ 4 ].

By exploring the application of technology in agricultural extension programs, this scoping review aims to shed light on the current state of research, identify gaps, and map the overall landscape of this rapidly evolving field. By examining journal articles, conference proceedings, and dissertations, this review specifically describes the outcomes of technology application in agricultural extension under three objectives which are the substantive features, methodological features, and characteristics of technology application in the context of agricultural extension. The findings of this scoping review will provide valuable insights for policymakers who are faced with the decision of expending their resources on the most effective yet economical technology. It can also provide researchers with empirical evidence supporting their decisions when designing adoption and diffusions models for agricultural innovations, as well as practitioners in the field of agricultural extension who will come face-to-face with the users of these innovations. The review will facilitate evidence-based decision-making and inform the development of effective policies and practices by offering an overview of the impact of technology application in agricultural extension. Moreover, it will foster collaboration among stakeholders, encouraging partnerships and knowledge-sharing to drive agricultural development.

Furthermore, the findings of this research will make significant contributions to establish a foundation for future studies. Through this study, we envisage a knowledge synthesis from included studies that could lead to a better understanding of the types, usage and effectiveness of the technology used in agricultural extension. By synthesizing existing knowledge, the review will identify areas where additional research is needed, thereby paving the way for further exploration and discovery. This contribution will advance the application of technology in agricultural extension and shape the future direction of the rapidly evolving field, ultimately leading to improved agricultural outcomes and sustainable development in farming communities worldwide.

2. Literature review

2.1. agricultural extension.

Throughout the history of agricultural extension, there have been a variety of definitions of agricultural extension based on who is involved, the location, and the method used. For example, Msuya et al. [ 5 ] described agricultural extension as a way for small farmers to access new technologies, while Birkhaeuser et al. [ 6 ] viewed it as a common form of public-sector support for spreading knowledge. Rivera et al. [ 7 ] (5) on the other hand explained how agricultural extension serves as a link to increase productivity and efficiency for farmers and researchers and makes it easier to share innovations among farmers. Of all these, however, the most often cited is Maunder’s [ 8 ] comprehensive definition where agricultural extension was defined as “a service or system which assists farm people, through educational procedures, in improving farming methods and techniques, increasing production efficiency and income, bettering their levels of living, and lifting the social and educational standards of rural life.” From these definitions, to achieve its goals, agricultural extension must incorporate key components such as farmers and/or farming households, knowledge diffusion/education, and willingness to change on the part of the farmer.

This scoping review will align with the definition given by Maunder [ 8 ] within the framework that the studies included involve farmers and/or farming households with the aim of mobilizing resources towards their farming objectives.

2.2. Technology application in agricultural extension

The importance of technology in enhancing agricultural productivity cannot be overstated, and agricultural extension plays a crucial role in achieving this objective. Technology, with its innovative tools and applications, has been identified as a game-changer in the agricultural sector [ 9 ]. It has revolutionized farming practices, empowered farmers to increase productivity [ 10 – 12 ], optimized resource utilization [ 13 , 14 ], and addressed sustainability challenges [ 15 – 17 ].

Technology application (TA) in agriculture has been extensively explored from two distinct yet interconnected perspectives. The first viewpoint focuses on the use of technology/innovation as a factor or component of production. Studies falling under this theme investigate aspects such as improved seed varieties [ 18 – 20 ]; farm machinery, including tractors, plows, harvesters, and similar equipment [ 21 , 22 ]; drones, animal trackers [ 23 ] and more recently robots [ 24 ]; and the Internet of Things (IoT) [ 25 ]. These studies perceive these technologies as resources that are consumed or incorporated into the farming system, recognizing that their absence may hinder one or more crucial stages of the production process.

The second perspective regards technology in agriculture primarily as a means of enhancing knowledge transfer and skills development, often referred to as educational technology (ET). These studies, which often focus on technology-enabled information dissemination, training, and capacity building, incorporate technologies such as virtual reality (VR) and augmented reality (AR), Information and Communication Technology (ICT) [ 26 – 29 ], smartphones/mobile applications [ 30 – 32 ], online platforms and websites [ 33 , 34 ], e-learning and webinars [ 35 – 37 ], and social media and online communities [ 38 – 40 ].

In this scoping review, we categorize the TA in agricultural extension into two groups: 1) agricultural technologies/innovations used during production and 2) educational technologies employed for training and facilitating the adoption of these agricultural technologies.

By integrating ET tools such as videos, smartphones, online training, and tablets, agricultural extension services/agents can significantly enhance the effectiveness of information transfer while reducing costs. This approach helps farmers in remote areas easily access timely information, such as weather variables and market factors. Studies have demonstrated the efficacy of these tools, including videos, smartphones, and tablets, in improving agricultural practices among farmers [ 38 – 41 ].

The potential of combining ET and agricultural technology/innovations is highly promising. However, a comprehensive review of previous studies to ascertain the practical outcomes is still lacking. By examining the existing literature, this scoping review aims to bridge the gap in understanding the practical implications of integrating ET and agricultural technology/innovations (ATI). The findings of this study will shed light on the effectiveness and impact of these combined approaches in agricultural extension services.

2.3. Previous studies and research gap

While previous studies have explored agricultural extension and TA separately [ 42 ], there is a lack of examination of the relationship between these two topics. This scoping review aims to address this gap by examining them simultaneously. Existing literature reviews have touched upon related aspects, such as the role of agricultural extension in the transfer and adoption of AT [ 43 ] and the use of ICT for agricultural extension in developing countries [ 44 ]. However, these prior reviews do not fully examine the relationship between agricultural extension and TA. Altalb et al. [ 43 ] highlight the importance of agricultural extension in the development of the agricultural sector and how it aids in transferring necessary knowledge to farmers. Although Altalb et al. [ 43 ] discussed various technologies and innovations in the agricultural sector, their objective was to explore how agricultural extension could transfer that information to the farmers. In contrast, our study focuses on not just information transfer but goes ahead to examine the extension system.

Aker [ 44 ] highlights the need to adopt better AT like fertilizer, seeds, and other farming methods in developing countries and the potential of technology mechanisms, such as voice, text, internet, and mobile phone, to reach farmers and enhance knowledge, ultimately leading to an increase in the economy. However, the study did not delve into the direct TA within agricultural extension; and failed to provide examples or results that demonstrate how technology can be implemented through agricultural extension.

By addressing these gaps and incorporating potential recommendations derived from a comprehensive analysis of previous studies, this scoping review aims to contribute to enhancing productivity and bridging the divide between TA and agricultural extension practices by providing empirical evidence of amongst other things, the impact technology can make in agricultural extension.

3. Research questions

This present scoping review aims to investigate the effect of technology application on agricultural extension by examining existing empirical studies. The study focuses on analyzing the substantive features, methodological features, and characteristics of technology application in the context of agricultural extension. The research questions guiding this study are as follows:

• What are the substantive features of the included studies, including publication information (year of publication and journal name), country/region information, and agricultural field?
• What are the methodological features of the included studies, such as the research methods employed, data collection approaches, and sample size?
• What are the characteristics of the technology used in agricultural extension, including the type of educational technology, agricultural technology, and the overall effect of technology on agricultural extension?

4. Research method

4.1. search strategies.

To comprehensively search for studies, we searched five databases: CAB Abstracts (Ovid), AGRICOLA (EBSCO), ERIC (EBSCO), Education Source (EBSCO), and Web of Science Core Collection. These databases cover literature in the agriculture, applied life sciences, and education disciplines. The database search was developed in CAB Abstracts and run on October 28, 2022. The original search was modified for the additional databases, and the additional databases were searched on November 1, 2022. The search consisted of subject terms and keywords related to the two core concepts of educational technology and agricultural extension. Keywords were searched for in the title and abstract fields. The full search strategies for CAB Abstracts and the other four databases can be found in Appendix A in S2 File .

4.2. Inclusion and exclusion criteria

This scoping review used specific inclusion and exclusion criteria to identify studies examining the impact of technology application on agricultural extension.

• The included studies must have examined the effect of technology application on agricultural extension. Articles were excluded if they were not about technology, were not within the agricultural extension context, and did not examine the effect of technology on agricultural extension.
• Technologies for this study were defined as the educational technology such as multimedia, smartphones, iPads and tablets, digital simulation devices and others used by the agricultural extension services/specialists/agents to facilitate educational training under which knowledge, skills, and content are transferred in the form of agricultural technology/innovations such as seed planting knowledge, disease and pest prevention practices, improved varieties, record keeping and others to the farmers and other stakeholders in an agricultural extension setting. Unless agricultural technology also qualifies as an educational technology (e.g., GPS), such studies were excluded.
• Included studies must have been conducted under the context of agricultural extension programs, which take place in an informal, out-of-school setting; directly involve farmers and/or farming households; and pertain to their farming enterprises.
• Included studies must report detailed information on the effect of technology on agricultural extension, which should include the sample size, experimental design, and detailed results (either quantitative or qualitative). Conference abstracts on this topic will be excluded.
• Included studies must have reported an assessment of technology’s impact/effect on agricultural extension, qualitatively or quantitatively, such as an empirical study (intervention or case studies). Articles that generally discuss the trends or the importance of technology in agricultural extension were excluded.
• The included studies must have been published in a journal, as a conference proceeding, or policy paper from January 1, 2000, to November 1, 2022, and available in English. We selected this period to ensure that we covered the latest studies and documented the rapid progressions of technology in agricultural extension since 2000 [ 45 , 46 ]. Secondary data analysis, literature reviews, book reviews, book chapters, and reports were excluded.

4.3. Coding scheme

To ensure efficient data extraction and analysis, a comprehensive coding system was developed to categorize and organize the information from the included studies. The coding system facilitated the examination of substantive and methodological features of the studies, specifically focusing on the impact of technology application on agricultural extension. Sub-categories were created within the coding system to distinguish between agricultural technologies and educational technologies, enabling a detailed analysis of the key features of each. This coding played a crucial role in understanding and interpreting the findings of the included studies.

4.3.1 Substantive features of the studies

The substantive features of the studies included their publication information, geographic location (country/region), and the included studies’ agricultural field/enterprise concentrations. Our primary objective was to comprehensively analyze publication patterns within the discipline. We aimed to identify journals that had a significant impact based on their titles and publication dates. Furthermore, we sought to compare agricultural extension technology trends across various countries and regions.

We categorized the agricultural fields/enterprises in which educational technologies were predominantly utilized. The coding scheme ( Table 1 ) classified the agricultural fields/enterprise as follows: agricultural economics, including food processing such as making raisins and any value-addition processing; agricultural engineering, including mechanization; agronomy, encompassing crop production and other crop-related enterprises; animal husbandry, incorporating animal production, fisheries, and other livestock-related enterprises; and mixed when the agricultural field/enterprise included more than one.

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https://doi.org/10.1371/journal.pone.0292877.t001

4.3.2 Methodological features of the studies.

The methodological features of the included studies encompassed several aspects, including the research methods employed, data collection approaches, the use of inferential statistics, and units of sample size. These components were examined to gain insights into the study design and methodology employed in investigating the impact of technology on agricultural extension.

The research methods were grouped into quantitative, qualitative, and mixed methods. Quantitative studies used descriptive and inferential statistics, while qualitative studies followed Denzin & Lincoln’s [ 47 ] definition of interpretive practices across different disciplines. We categorized the research methods into quantitative and qualitative because these are the primary categories of educational research. Since studies use both quantitative and qualitative methods, we categorized mixed methods studies as those studies that used both quantitative and qualitative approaches to collect and analyze data.

The data collection approaches included surveys, questionnaires, interviews, focus group discussions (FGD), and assessments. If the study applied more than one data collection approach, it was coded as a mixed method. We also documented whether the researchers employed inferential statistics to examine the impact of educational technology on agricultural extension.

We also considered the sample size units for the selected studies. The sample units were varied, making it difficult to unify the sample sizes. Therefore, we coded the sample size units as households, individuals, and villages, and in studies that used more than one unit, we coded them as mixed.

4.3.3 Characteristics of technology in agricultural extension.

We categorized the characteristics of the technology applications used in agricultural extension. The types of educational technologies were coded under the following categories: multimedia (video, audio, photographs, video animation, radio); mobile apps/smartphones; online/web-based; digital simulations; and mixed for those studies that used more than one.

We distinguished between ET and an AT/I were being transferred to the farmers. We categorized the agricultural technology/innovation into various groups: crop cultivation/harvesting practices, product processing, pest and disease control, and knowledge/skill/general agricultural education. The first category was crop cultivation/harvesting practices including spacing and fertilizer application, castor cultivation, cotton production, protection technology, rice intensification system, integrated soil fertility management, soil modules, and sugarcane ratoon management practices. Another category pertained to product processing, specifically the storage of beans in jerrycans. Furthermore, we grouped pest and disease control methods such as the use of neem as an insecticide, disease management, weed control practices, and the management of Fall armyworms. Knowledge/skill/general agricultural education was another category, including topics like insurance advisory, record keeping, knowledge sharing and joint decision making, climate adaptation strategies, and practices. In cases where multiple agricultural technologies/innovations were identified, they were classified under a mixed category.

For characteristics of the intervention, we coded the duration (how long) and the intensity (how often) of the technology intervention as well as the timing of the measurement of the impact /effect. For the duration and intensity of the intervention, we considered how many studies provided the information and reported how they were reported. The interval between intervention and measurement of effect was coded as immediate, short-term, long-term, mixed, and unspecified for those studies that did not clearly state the timing for the measurement. Additionally, we coded whether the use of technologies had a positive, negative, non-significant, or mixed impact and whether the effect sizes were reported.

4.4. Data collection and data analysis

To identify eligible studies, we followed the screening process illustrated in Fig 1 . After removing duplicates, we screened 4,170 unique references for eligibility. The research team screened the article titles and abstracts using the inclusion/exclusion criteria. After the first round of screening, 69.71% (2,808 articles) were excluded. After an initial screening, the authors reviewed the full text of 1,319 articles. Out of these, 61 articles were found to be eligible for inclusion in the review. During the coding process, seven articles were excluded for different reasons. Finally, 54 articles were included in the final coding stage.

https://doi.org/10.1371/journal.pone.0292877.g001

The coding scheme was created by the first two authors, who independently coded a set of 20 randomly selected articles. Subsequently, the coding scheme was employed by the first five authors to code the articles using Microsoft Excel. As noted by Belur et al. [ 48 ], the interrater reliability (IRR) of a good systematic review strengthens the transparency and replicability of the process leading to the results from such reviews. Thus, to calibrate the coding, the 54 articles were initially coded independently, resulting in an initial round of IRR of 81.30% which was calculated by the percentage of agreement between the coders. In case of conflicts, the first author acted as the arbiter and resolved the discrepancies. Eventually, a unanimous agreement was achieved regarding the coding of the articles. Descriptive statistical analyses were conducted to address our research questions.

For the data analysis, simple descriptive statistics such as frequencies, percentages, charts, and graphs were used to analyze and present the results for an understanding of the substantive and methodological features of the studies. For the characteristics of technology in agricultural extension, we conducted a crosstabulation and Chi-square analyses of the type of educational and agricultural technology used on the effect/impact of the intervention.

5. Results and discussion

5.1. substantive features of the studies, publication information..

Among the 54 included studies, a noteworthy observation was the concentrated distribution of publications within the past six years (2016–2022). As depicted in Fig 2 , two studies (3.70%) were published from 2001–2005. Six studies (11.11%) were published from 2006 to 2010; eight studies (14.81%) were published from 2011–2015. The majority of publications, comprising 38 studies (70.38%), were published between 2016 and 2022. This trend indicates a significant increase in research activity from 2001 to 2022, with a surge in studies focusing on educational technology after 2016. The rapid development and adoption of various training platforms for farmers accentuated by the global impact of the Covid-19 pandemic, has underscored the pressing need for technology-assisted agricultural extension [ 49 – 51 ].

https://doi.org/10.1371/journal.pone.0292877.g002

Out of the 54 studies examined, the majority (n = 49; 90.74%) were published in journals. Policy/discussion papers constituted 7.41% (n = 4) of the studies, while conference papers represented 1.85% (n = 1). Notably, no theses nor dissertations were included in the present scoping review. The absence of such works highlights a potential avenue for graduate students to explore the intersection of educational technology and agricultural extension. Among the journals, 61.22% (n = 30) appeared only once, while the remaining 38.78% (n = 19) published multiple articles included in this review. Details on the number of articles per journal can be found in Table 2 . The journals that published the most papers in the field are: The Journal of Agricultural Education and Extension (JAEE) (n = 4), JIAEE (Journal of International Agricultural and Extension Education) (n = 3), International Journal of Agricultural Sustainability (n = 3), Information Technology for Development (n = 3), International Food Policy Research Institute (n = 3).

https://doi.org/10.1371/journal.pone.0292877.t002

The included studies encompassed a diverse range of countries, with notable concentrations in India, Uganda, Benin, and the U.S.A. Overall, research were conducted in 17 different countries. India accounted for 29.63% (n = 16) of the studies, while Uganda represented 16.67% (n = 9). Both Benin and the U.S.A. had five studies (9.25%) conducted in each country. Kenya accounted for 7.40% (n = 4) of the studies, while Mali, Ethiopia, and Bangladesh each had two studies (3.70%) conducted in each of these countries. The other 16.67% (n = 9) of the studies were conducted in Nigeria, Mozambique, Malawi, Bolivia, Ghana, France, Senegal, China, and Burkina Faso, with one study in each country respectively. The prevalence of studies conducted in the predominantly developing countries (excluding USA), is indicative of the high number of farm families in these regions compared to extension services. Technology therefore plays a crucial role in bridging the gap in effectively reaching a large population of farmers in these areas within a short period [ 52 – 54 ].

The research primarily focused on the regions of Africa, Asia, and North America. Out of 54 studies analyzed, 28 studies (51.85%) were conducted in Africa, while 19 studies (35.19%) were carried out in Asia. Additionally, five studies (9.26%) were conducted in North America, only one study (1.85%) was conducted in South America, and one study (1.85%) was conducted in Europe. Notably, no studies specifically targeted Antarctica or Australia/Oceania. These findings highlight the active contributions of Africa, Asia, and North America to research in the field of educational technology in agricultural extension. However, the dearth of research from Australia/Oceania and Europe in our included studies suggests a need for further investigation in these regions. For instance, Australia/Oceania, renowned for its expertise in animal husbandry due to the combination of large land areas, a substantial livestock population but relatively limited investment in infrastructure and human resources [ 55 ], presents a particularly interesting area for future researchers to explore.

Agricultural field.

The majority of the included studies exhibited a strong focus on agronomy. As shown in Fig 3 , 43 studies (79.63%) were centered around agronomy. Additionally, six studies (11.11%) pertained to animal husbandry, three studies (5.56%) involved a mixed focus, and two studies (3.70%) were related to agricultural economics. The imbalance in the distribution of studies suggests a potential opportunity to explore and utilize educational technology in fields such as animal husbandry, agricultural economics and engineering, and other mixed areas. By expanding the application of technology to these underrepresented domains, a more comprehensive and inclusive approach can be adopted within the agricultural extension.

https://doi.org/10.1371/journal.pone.0292877.g003

5.2. Methodological features of the studies

Research methods..

The analysis of the research methods employed in the included studies indicated a predominant use of the quantitative research method. Thirty-seven studies (68.52%) utilized the quantitative method, while 14 studies (25.93%) employed a mixed-method approach. Only three studies (5.55%) used the qualitative method. These findings align with a scoping review (authors, under review) on educational technology in agricultural education, which also observed a prevalence of quantitative and mixed methods research as the commonly adopted approaches in this field. The rationale behind the prevalent use quantitative research methods may stem from several factors. Firstly, researchers may have already recognized the importance and advantages of educational technology in the agricultural extension field, largely owing to the extensive body of research on the of educational technology in general education. Consequently, their inclination might have been to substantiate their existing hypotheses within the extension field. It is worth noting that quantitative research often leans towards a confirmatory and deductive approach, in contrast to the more exploratory nature often associated qualitative research [ 56 ]. Additionally, another possible reason for favoring quantitative methods could be attributed to the inherent limitations of qualitative methods. Qualitative findings are typically context-specific and may not readily generalize to a broader population [ 56 ].

We recommended that researchers employ more mixed methods research designs, which combine both quantitative and qualitative approaches, because it offers additional advantages in social science research [ 57 ]. For example, mixed methods research allows researchers to obtain a more comprehensive understanding of complex social phenomena by integrating numerical data with in-depth qualitative insights. The predominant use of mixed research methods in social sciences research is driven by the need for empirical evidence, objectivity, generalizability, and the ability to establish causal relationships and test theories [ 57 , 58 ].

Data collection approaches.

The analysis of data collection approaches revealed that mixed approaches were the most utilized among the included studies. Out of the 54 studies, 19 studies (35.19%) used mixed approaches, 13 studies (24.07%) relied on assessments as their primary data collection approach, and 11 studies (20.37%) utilized surveys. Additionally, eight studies (14.81%) used interviews, two studies (3.70%) employed questionnaires, and one study (1.86%) did not specify the data collection method used. This diversity in data collection methods highlights the importance of employing a range of approaches to gather comprehensive and nuanced information within the field of educational technology in agricultural extension.

Inferential statistics.

The analysis of inferential statistics showed that a majority of the studies included employed this statistical approach. Among the 54 included studies, 70.37% ( n = 38) of the studies utilized inferential statistics to analyze their data. On the other hand, 29.63% ( n = 16) of the studies did not use inferential statistics in their data analysis. The prevalent use of inferential statistics reflects the researchers’ intention to make inferences and draw broader conclusions about the relationship between educational technology and agricultural extension based on their data.

Unit of sample size.

The included studies employed a variety of units for reporting sample size, with individuals being the most prevalent sample size unit. Out of the 54 included studies, 41 studies (75.94%) used individuals as the sample size unit. Additionally, six studies (11.11%) used households, four studies (7.40%) employed mixed units, one study (1.85%) used villages, and two studies (3.70%) did not report the sample size unit. The diversity in sample size units may be attributed to the specific characteristics of the agricultural field and the grouping involved, such as considering households or villages as a whole when studying agricultural practices. In future research endeavors, it would be beneficial to adopt a diverse array of sample size units, given the intricacy and distinctiveness of the agricultural extension field. Furthermore, there is room for investigation into the effectiveness of employing various sample size units. It is worth considering that social interaction within the households, villages, or communities within the group might be a significant factor contributing to the learning outcomes, in addition to individual interactions with technology. To gain a deeper understanding of this aspect, both quantitative or qualitative research approaches can be employed to explore the dynamics of human interaction within a shared learning community in the context of agricultural extension.

Among the 41 studies that employed individuals as the sample size unit, we adhered to the commonly used quantitative research guidelines: studies with less than 100 participants were considered small samples, studies between 100–250 participants were classified as medium samples, and studies with over 250 participants were considered large samples [ 59 , 60 ]. The sample size for studies using individuals as the unit ranged from 6 to 58872 participants. Among these studies, 58.54% ( n = 24) of the studies had a medium sample size, 24.39% ( n = 10) of the studies had a small sample size, and 17.07% ( n = 7) of the studies had a large sample size. Our findings suggest that most studies used a medium sample size when using individuals as the sample size unit. However, specific studies focusing on ET in educational settings suggested a prevalence of small sample size studies (60). This divergence could be attributed to contextual variations, particularly since agricultural extension studies typically involve a larger number of participants.

5.3. Characteristics of technology in agricultural extension

Educational technology..

In our review of 54 studies, we discovered the utilization of various ET in agricultural extension. As shown in Fig 4 , multimedia emerged as the most frequently used ( n = 27, 50.00%), followed by studies that incorporated multiple types ( n = 15, 27.78%). Additionally, mobile apps/smartphones were used in nine studies (16.67%), online/web-based applications in two studies (3.70%), and digital games/simulations in only one study (1.85%).

https://doi.org/10.1371/journal.pone.0292877.g004

These findings differ from a similar review conducted on the use of ET in agricultural education by Xu et al. [ 61 ] Among the 83 included studies in their review, they found that the most used ET was online/distance education, followed by simulation/digital games and then, multimedia and traditional technology. This stark contrast may be attributable to the different contexts or settings in which agricultural education and agricultural extension are practiced. Agricultural education primarily takes place within formal educational institutions, involving students, academics, and professionals with higher levels of academic qualifications. On the other hand, agricultural extension often occurs in non-formal settings, predominantly involving farmers who may have varying levels of academic attainment. This is further supported by Mwololo et al.’s [ 62 ] finding that socio-economic factors such as age, education, and gender influenced farmers’ preference for agricultural extension methods, specifically farmers’ field schools (FFS), farmer to farmer (F2F), or mass media. In addition, the role and characteristic of multimedia contributed to the most frequent use as ET for farmers in the extension field. Multimedia plays an important role in agricultural extension serving as the most powerful opinion maker in this information era, and can help transfer agricultural information [ 63 ]. Multimedia is simple, direct, and intuitive in nature thereby making it very comprehensive for farmers who have limited educational level and technology literacy to attain knowledge and skills competency. The majority of our included studies were conducted in Africa and Asia with representative countries like India and Nigeria. In developing countries, farmers’ educational level and current technology literacy remains limited due to the lag of development of the whole country economically, socially and technology and limited funding opportunities/resources for further improvement. Simple and cost-effective ET like multimedia would be preferred compared to complex ones.

Among the various forms of ET used in agricultural extension, video or video-mediated extension emerged as the most prominent. Horner et al. [ 64 ] conducted an experimental study in Ethiopia to assess the effectiveness of video-based extension. They compared traditional agricultural extension methods with the incorporation of videos and found that the latter was more effective in increasing farmers’ knowledge and adoption of complex agricultural technologies such as composting, blended fertilizer, improved seeds, line seeding, and lime. Chowdhury et al. [ 65 ] conducted a study in Bangladesh focusing on enhancing farmers’ capacity for botanical pesticide innovation through video-mediated learning. They observed a significant increase in knowledge about botanical pesticides in both male and female farmers who participated in the video-mediated group. Several other studies [ 38 , 66 – 70 ] have also incorporated video-based multimedia in their agricultural extension programs.

The prevalence of video-mediated extension in agricultural extension programs underscores its effectiveness in delivering information and promoting knowledge acquisition among farmers. By utilizing videos, extension practitioners can visually demonstrate agricultural techniques, showcase best practices, and present success stories, thereby enhancing farmers’ understanding and motivation to adopt agricultural practices. This multimedia approach is particularly beneficial in non-formal settings where farmers may have varying levels of education and diverse learning preferences.

In our analysis of 54 articles exploring the use of educational technology for transmitting agricultural technology/innovation to farmers, we identified multiple themes in the types of agricultural technologies. Most of the articles ( n = 21, 38.89%) discussed a combination of agricultural technologies, indicating a mixed approach. Pest/disease control technology was the next most used agricultural technology ( n = 11, 20.37%). Another 10 articles (18.52%) focused on crop cultivation/harvesting practices, six articles (11.11%) covered product processing technology, and the remaining six articles (11.11%) focused on knowledge/skill/general agricultural education.

The agricultural technology and innovations covered in our included studies varied. Some studies incorporated a combination of technologies like row planting, precise seeding rates, and urea dressing [ 68 ]; tillage and sowing machinery [ 71 ], planting methods, weeding and fertilizer application [ 72 , 73 ]; identifying growth stages and improving yield predictions [ 74 ]; and seed selection, storage and handling [ 67 ].

Several studies also examined technologies and innovations for controlling pests and diseases. For instance, Chowdhry et al. [ 65 ] explored the use of botanical pesticides, Bentley et al. [ 75 ] investigated methods for controlling bacterial wilt (BW) in potatoes, and Dione et al. [ 76 ] focused on biosecurity messages for managing African swine fever. Other studies have been conducted on crop cultivation and harvesting practices. Dechamma et al. [ 77 ] studied the production practices of tomato crops, and Ding et al. [ 78 ] focused on nitrogen management practices in crop production. Additionally, Bello-Bravo et al. [ 79 ] and Sidam et al. [ 80 ] researched technologies related to product processing, such as storing beans in jerry cans and making raisins.

The last category of studies included those that focused on knowledge and skills/general agricultural education such as knowledge and awareness about agricultural credit [ 31 ], climate information [ 81 ], information about cattle handling [ 82 ], and backyard poultry farming [ 83 ].

Intervention characteristics of technology.

We classified the duration of the technology intervention, the intensity of the intervention, and the interval between the intervention and the measurement of its effect. Regarding the duration of the technology intervention, nine studies (16.68%) did not provide information on the duration. Eight studies (14.81%) implemented interventions that lasted less than a week, while seven studies (12.96%) had interventions that ranged from one week to 12 weeks (3 months). Eleven studies (20.37%) reported interventions lasting between 13 weeks to 24 weeks (6 months), while eight studies (14.81%) had interventions lasting between 25 weeks to 48 weeks (1 year). Furthermore, eleven studies (20.37%) documented interventions lasting from 48 weeks (1 year) to 192 weeks (4 years).

As for the intensity of the intervention, 64.81% ( n = 35) of the studies did not provide information on the intensity, while 35.19% ( n = 19) did include details on the intensity. Out of the 19 studies that reported the intensity of the intervention, six (31.58%) specified the frequency of the intervention, such as two sessions per week or two messages per week. Thirteen studies (68.42%) provided precise information on the exact time of each session or video of the intervention, which varied from two minutes to as long as two days. These findings indicate that a significant majority of studies should have included more detailed information on the intensity and duration of the intervention. As the intensity and duration are crucial components of an intervention, they play a significant role in interventions’ effectiveness. Future research should place greater emphasis on exploring intensity and duration in greater depth and on detailed reporting of intervention components.

Regarding the interval between the intervention and the measurement of its effect, researchers exhibited a preference for measuring immediate effects, followed by long-term effects, short-term effects, and a mixed approach. Among the reviewed studies, 22 studies (40.74%) measured the immediate effect, 16 studies (29.64%) focused on the long-term effects (more than three months), seven studies (12.96%) assessed the short-term effects (within three months), two studies (3.70%) used a mixed interval between the intervention and the measurement of its effect, and seven studies (12.96%) did not specify the interval between the intervention and the measurement of its effects.

Effect of technology application in agricultural extension.

The effect or impact of using technologies in agricultural extension showed diverse outcomes across the 54 studies. Among those studies, 35 articles (64.82%) recorded positive outcomes, while 15 articles (27.78%) documented mixed outcomes, suggesting a combination of positive and potentially less favorable results. Two articles (3.70%) reported non-significant outcomes, indicating that the technologies did not have a statistically significant impact on agricultural extension. Finally, the last two articles (3.70%) did not specify the outcomes achieved.

In one study with mixed outcomes, Bentley et al. [ 75 ] compared three agricultural extension methods (FFS, community workshops, and radio) for their effectiveness in teaching Bolivian farmers about BW of potato. Their findings found that while radio listeners received information about topics like diagnosing BW, crop sanitation practices, use of healthy seed, crop rotation, and incorporation of manure first from the radio, they never took any concrete action that led to the actual adoption of those agricultural technologies when compared to the FFS groups and the workshop attendees. So, while radio increased awareness about the AT, it fell short in the actual adoption.

Another study that reported mixed outcomes was that of Ding et al. [ 78 ] where ICT-based agricultural advisory services were used for nitrogen management in wheat production in China. The study sought to examine the effects of ICT-based extension services on the adoption of sustainable farming practices like nitrogen control in wheat production and found that while there was no reduction in the use of N-fertilizer for wheat production, the ICT-based services prompted farmers to adopt N-fertilizer use towards site-specific management. So, whereas the educational technology fell short of convincing the farmers to reduce their N-fertilizer usage in wheat production, it achieved the unintended goal of making the farmers adopt some site-specific management practices of N-usage.

In addition, we conducted cross-tabulation analyses and employed Chi-square tests to assess the associations between different types of educational technology, agricultural technology, and the resulting effects or impacts of the implemented technology interventions. Among the 54 articles, two articles did not specify the intervention effect.

Based on the findings presented in Table 3 , a significant relationship was observed between the type of educational technology utilized and the resulting effect or impact of the intervention. The statistical analysis revealed a significant result of χ2 (8, n = 52) = 28.67, p < .001, indicating that the type of educational technology employed influenced the outcomes of the interventions. Interestingly, articles that predominantly utilized multimedia and a combination of multiple ET ( n = 30) recorded more positive intervention outcomes. Research studies, such as those conducted by Chowdhury et al. [ 65 ] in Bangladesh, which used video-mediated learning to improve farmers’ understanding of botanical pesticide usage, and by Bello-Bravo et al. [ 79 ], which found an 89% adoption rate when animated agricultural videos was used for the dissemination of postharvest bean storage, clearly demonstrate the effectiveness of multimedia as a reliable tool for promoting the adoption of agricultural technologies. Several studies have examined the effectiveness of mobile apps and smartphones, and four of them reported positive results. One such study was conducted by Dione et al. [ 76 ], where the use of interactive voice response (IVR) was found to significantly enhance the knowledge gains of 408 smallholder pig farmers who received biosecurity messages. While the results of the other four were mixed, one study conducted using digital games/simulation also reported a positive outcome which was the study by Dernat et al. [ 84 ] where a game-based methodology was found to be very effective in facilitating farmers’ collective decision making and continued engagement. Notably, the only article that did not report a positive outcome was a single study that used online/web-based applications. The implications of these findings are that stakeholders in the field of agriculture can collaboratively work together to design a targeted, cost-effective and guaranteed communication channels that could yield greater positive results in the nearest future.

https://doi.org/10.1371/journal.pone.0292877.t003

In contrast to the analysis on educational technology, the cross-tabulation and Chi-square analysis examining the relationship between the type of agricultural technology provided to farmers and the resulting impact of the intervention did not yield a statistically significant result χ2 (8, n = 52) = 7.52 ( p = .482), as shown in Table 4 . Despite the lack of statistical significance, patterns can still be observed between the two variables. Out of the 52 articles, 35 reported a positive outcome, while 15 reported mixed results, regardless of the specific agricultural technology/innovation utilized. These findings suggest that, in the context of agricultural extension, the method of communication or transmission of agricultural information through educational technology may play a more crucial role in determining the overall success of the interventions than the specific agricultural technology employed.

https://doi.org/10.1371/journal.pone.0292877.t004

The previous research (44) focused on explaining the process of transferring and adoption of agricultural technology while our study focused on the application/usage of the AT. This study found that simple technology like multimedia served as the most frequently used and video/video-mediated extension served as the most prominent, which is consistent with the previous research [ 43 ] stating that technologies that are more complex to comprehend and use have lower rates of adoption. Previous review [ 44 ] focused on how one specific type of ET (ICT) affects AT adoption in developing countries while our study investigated diverse kinds of educational technology. Our findings suggested that the use of multimedia as an ET might be due to the characteristics of limited educational level and economic level of farmers in developing countries. It is consistent with previous review [ 44 ] indicating that farmers have limited access to resources and infrastructure investments remain low in many developing countries. While these reviews concentrated on measuring the impact of ICT-based agriculture extension programs, our study focused on summarizing the effect/impact of using technologies in agricultural extension with most studies reporting positive outcomes.

6. Conclusion and future directions

In conclusion, this scoping review underscores the critical role of TA in agricultural extension, presenting valuable insights into technology’s potential to enhance extension programs and stimulate future research. Maunder’s [ 8 ] definition of agricultural extension guided this scoping review, emphasizing the characteristics of the service and its potential impact on improving and educating farmers. As explained by Rivera et al. [ 7 ], agricultural extension serves as a vital link to increase productivity and efficiency among farmers and researchers, facilitating the sharing of innovations. Technological applications within agricultural extension have the power to transform farming practices [ 12 , 13 , 16 ].

Through our comprehensive coding, we categorized the TA within agricultural extension into two domains: use of technology/innovation as a factor of production and as an ET. While our study included various agricultural fields, such as agricultural economics, agricultural engineering, animal husbandry, and agronomy, it should be noted that some studies lacked detailed information that could have provided valuable insights into the impact of technology applications on farmers through agricultural extension programs.

Furthermore, this research establishes a foundation for future studies, innovation, and informed practices by identifying areas that warrant further exploration and discovery. The significant increase in research activity in technology applications, particularly after 2016, highlights its growing importance. Advancing the application of technology in agricultural extension contributes to improved agricultural outcomes and sustainable development in farming communities worldwide. Future research on technology applications in agricultural extension should address limitations that may be inherent in the research designs, data collection instruments and the units for the measurement of the intervention outcomes. Future studies should also identify technological effectiveness, delve into mechanisms and contextual factors related to positive outcomes, and aim to support farmers and farm households more effectively.

Supporting information

S1 file. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..

https://doi.org/10.1371/journal.pone.0292877.s001

S2 File. Appendix A- database search strategies.

https://doi.org/10.1371/journal.pone.0292877.s002

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• Published: 28 February 2024

Influence of university agricultural technology extension on efficient and sustainable agriculture

• Zhaoli Dai 1 ,
• Qing Wang 1 ,
• Jiyu Jiang 1 &
• Yan Lu 1 , 2  

Scientific Reports volume  14 , Article number:  4874 ( 2024 ) Cite this article

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• Plant sciences

Agricultural extension, as an important part of modern agriculture, can promote the scientific transformation of the traditional agricultural production model. This paper analysed the impact of university agricultural technology extension on efficient and sustainable agriculture using difference-in-differences model (DID). The results showed that university agricultural technology extension plays a facilitating role by influencing the coordinated development and green development dimensions in efficient and sustainable agriculture; there is a moderating effect of modern agricultural industrial parked in university agricultural technology extension and efficient and sustainable agriculture; there are significant differences in the impact of university agricultural technology extension on efficient and sustainable agriculture across regions and different levels of development. The findings have important implications for evaluating the effectiveness of current university agricultural extension policies and how to further promote university agricultural extension. The study also established an evaluation index system for efficient and sustainable agriculture, explored the mechanism of university agricultural extension in promoting efficient and sustainable agriculture, and enriched relevant theoretical research.

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Introduction

Technological innovation and entrepreneurship play an important role in driving economic and social development 1 . Agricultural technology extension is crucial to alleviating the resource and environmental constraints faced by developing countries and transforming the mode of agricultural development. With the advancement of agricultural technology extension, extension entities are becoming more diverse. Universities are utilizing their technological and R&D advantages to conduct practical trials to further agricultural technology extension. The objective is to encourage efficient and sustainable development of local agriculture using this technology extension approach. As the world's largest developing country, China has entered a new normal of economic development, and its macroeconomic goal has shifted from high-speed growth to high-quality growth. As the main pillar industry, the development of efficient and sustainable agriculture has become the key task of China in the new period, but the international situation it faces is extremely severe. With the deepening of globalization, on the one hand, developed countries are squeezing and controlling the development of China's agricultural industry by taking high-level agricultural technology and agricultural subsidies as carriers and relying on capital flow and market expansion 2 . On the other hand, China faces resource and environmental constraints and inefficient resource management and use 3 . Therefore, China urgently needs to change its development mode through agricultural technology, push agriculture to the path of efficient and sustainable development, and finally realize the transformation from a large agricultural country to a powerful agricultural country. The investigation of Chinese universities' role in agricultural technology extension is highly significant in facilitating the effective application of agricultural science and technology, transforming conventional agricultural practices and ultimately supporting the efficient and sustainable development of regional agriculture. Anhui Agricultural University is among the first 10 pilot projects in China to adopt the new model of university agricultural technology extension. Anhui Agricultural University is an exemplar of the "one-stop, one-alliance, one-centre" typology for agricultural technology extension, this research focuses on the following questions: Does the extension of agricultural technology in universities help push agriculture towards efficient and sustainable development? If so, what is its specific mechanism of action?

Some scholars have found that socialized service plays an increasingly important role in improving agricultural productivity and increasing farmers’ income, which can promote efficient and sustainable development 4 . Anhui Agricultural University’s “one station, one alliance, and one center” new university agricultural technology extension is only a pilot policy. Since 2012, Anhui Agricultural University has established eight comprehensive experimental stations in Dabie Mountain, central Anhui, eastern Anhui, southwestern Anhui, northwestern Anhui, southern Anhui, northern Anhui, and the Yangtze-Huai river basin. The differences of this policy pilot in different regions and years constitute a “quasi-natural experiment.” Therefore, this research aims to explore the influence and mechanism of university agricultural technology extension on efficient and sustainable agriculture.

Literature review

The references closely related to the research question can be divided into two main branches. Existing research on agricultural extension in universities focuses primarily on two topics: agricultural extension and efficient, sustainable agriculture. The first branch is the research on agricultural technology extension. Agricultural technology extension has been studied from three aspects. Firstly, the impact analysis of agricultural technology extension on agricultural production. Some scholars have found that agricultural technology extension plays a positive role in improving the productivity of agricultural production and technology adoption by farmers 5 , 6 . Second, to study the construction of agricultural technology extension systems. To build and improve agricultural technology extension systems, it is first necessary to develop a critical mass of new generation, committed and well-trained men and women to serve the agricultural sector 7 . Third, the mode of agricultural technology extension should be explored. In the process of exploring the mode of agricultural technology extension, many innovative achievements have been made. Therefore, some scholars have analyzed the influence of the type and mode of agricultural technology extension on technical efficiency in the transition period 8 .

The second branch concentrates on efficient and sustainable agriculture. In the existing literature, research on sustainable agriculture is mainly concerned with the facilitating effects of modern agricultural technology on sustainable agriculture. Sharma et al. showed how knowledge-based agriculture can improve sustainable productivity 9 . Singh et al. critically highlighted the material application of NPs and pointed out the crucial gaps in the use of nanotechnology for sustainable agriculture 10 . Pereira et al. explore the potential applications of lignin nanoparticles in the agricultural sector 11 , highlighting the lignin extraction processes, nanoparticle production methods, biological activity analysis and emerging applications relevant to sustainable agriculture. In terms of constructing a measurement indicator system for sustainable agriculture, Zhang et al. consider that agricultural production cannot be achieved without natural resources such as arable land, water and the environment 3 .

While the literature has examined the impact of agricultural technology extension on agricultural production and the creation of sustainable agriculture indicators, scholars have generally agreed that the extension of technology is crucial to the high-quality advancement and maintainable growth of the industry. Additionally, it is believed that the extension of technology enhances accounting and auditing practices in the agricultural sector, resulting in a beneficial influence on such activities in the sector 12 . The utilization of mobile phone technology amplifies farmers' capacity for agribusiness and has the potential to enhance agribusiness performance 13 . Nevertheless, the literature on the impact of agricultural extension programs in universities towards sustainable and effective agricultural development, their mechanism of action, and the contribution of modern agricultural industrial parks, lacks comprehensive and in-depth analysis. This paper concentrates on the impact mechanism of promoting agricultural technology by universities in achieving efficient and sustainable agricultural development, the moderating effects of contemporary agricultural industrial parks, and the disparities in the role of university agricultural technology promotion in various regions. The study fills some gaps in existing research by distinguished scholars. Compared with the existing literature, the possible contributions of this research are as follows. First, the entropy method is used to calculate the efficient and sustainable agriculture of 90 counties and districts in Anhui Province, China during 2008–2020 from three dimensions: coordinated development, efficient development and green development. Then, the influence of university agricultural technology extension on efficient and sustainable agriculture is explored, which provides an empirical basis for the necessity of university agricultural technology extension. Second, the action mechanism of university agricultural technology extension on efficient and sustainable agriculture is discussed from the specific influences of university agricultural technology extension in three dimensions, namely coordinated development, efficient development and green development, and the modulating effect of modern agricultural industrial parks. Third, the heterogeneity of the influence of university agricultural technology extension on efficient and sustainable agriculture is explored from two aspects: three regions (northern, central, and southern Anhui) and the local agricultural development foundation. This research provides an empirical reference from China for perfecting the university agricultural technology extension system and developing efficient and sustainable agriculture.

Difference-in-differences model (DID)

Difference-in-differences model (DID) is a quasi-natural experimental method currently used in policy evaluation. It is based on the same principle of dividing the sample into a treatment group that is affected by the policy and a control group that is not affected by the policy, and then comparing the treatment group with the control group using counterfactual thinking to infer the effect of the policy. The prerequisite for employing the DID model stipulates that the introduction of the policy under scrutiny has an impact on certain samples but not others, with the former forming the treatment group and the latter constituting the control group. Anhui Agricultural University functions as a university agricultural technology extension through the establishment of extensive agricultural experimental stations. Consequently, the policy of university agricultural technology extension will impact areas with comprehensive agricultural experimental stations, while those without such amenities shall remain unaffected. In this study, the sample was selected from all municipalities and counties in Anhui Province, with the treatment group being the areas with an integrated agricultural experiment station built by Anhui Agricultural University, and the control group being other areas without an integrated agricultural experiment station.

The use of the DID method for policy evaluation can remove the interference of other factors, such as the macroeconomic environment, other than policy shocks. In this study, since the development of efficient and sustainable agriculture in a region may be influenced by other potential factors such as the level of regional economic development and the level of resources, the use of this method can accurately identify the policy impact of university agricultural technology extension on efficient and sustainable agriculture and obtain more scientific results. The prior application of the parallel trend test guaranteed that both the treatment and control groups exhibited equal progress in effective and sustainable agricultural development before encountering any shocks resulting from the agricultural extension policy of the university. As a result, all the prerequisites for utilizing the DID method were met. Furthermore, to alleviate the impact of sample selection bias and other unobserved random factors, this paper utilises propensity score-matching double-difference (PSM-DID) and placebo tests to guarantee the dependability of the test outcomes.

Analytical framework

Under the policy advocacy and active practice of universities, agricultural technology extension in universities can play an important role in the development of efficient and sustainable agriculture. On the one hand, colleges and universities are at the nexus of the first productivity of science and technology, the first resource of talents, and the first force of innovation. Their research results can provide new methods and technologies for local agricultural development and provide technicians and technical training for local agricultural production through the cultivation and transportation of agricultural professionals. Therefore, with the rich resources of colleges and universities and the local development conditions, local agriculture can be put on the track of efficient and sustainable development. On the other hand, the development of efficient and sustainable agriculture depends on government support, including policy guidance, capital investment and infrastructure construction. Agricultural technology extension in colleges and universities is supported by local governments in many aspects, such as policies and funds, through the cooperation and co-construction mode between colleges and universities and local governments, which helps to build the platform of agricultural technology extension in colleges and universities. Efficient and sustainable agriculture is promoted through experiments, demonstrations, training, information and technical support by relying on the platform of agricultural technology extension in universities and a complete extension system.

From the specific action mechanism, efficient and sustainable agriculture can be promoted through agricultural technology extension in universities through the following aspects. First, it improves the level of coordinated agricultural development. Agricultural technology extension in universities provides guidance and technology for the adjustment of local agricultural industry structure, promotes the adjustment of industrial structure, develops local characteristic breeding industry and builds local characteristic agricultural product brands, and facilitates the coordinated development of agricultural industry, thereby promoting efficient and sustainable agriculture 14 . Second, it improves the level of efficient agricultural development. Agricultural technology extension in universities can improve the production level or quality of crops using local advanced agricultural technology, which can reduce the losses caused by improper practices in the production process, realize the efficient development of agriculture, and promote efficient and sustainable agriculture. Thirdly, the green development level of agriculture will be improved. The key technologies of agricultural green production, such as fertilizer reduction and Sulphur-free fermentation 15 , which are involved in the extension of agricultural technology in universities, can reduce environmental damage and resource loss in the process of local agricultural production, help improve the green development level of agriculture, and promote efficient and sustainable agriculture. In addition, modern agricultural industrial parks are a modern agricultural development platform based on large-scale planting and breeding bases and driven by industrialized leading enterprises 16 , which can play a strong role in promoting efficient and sustainable agriculture. Agricultural technology extension in universities can expand its influence by supporting the establishment of modern agricultural industrial parks. It can also rely on industrial parks to provide more services, such as agricultural technology extension and farmer training, and exert the agglomeration effect of industrial parks. Finally, it can enhance the facilitating effect of agricultural technology extension in universities on efficient and sustainable agriculture.

University agricultural technology extension may influence efficient and sustainable agriculture differently due to differences in resource endowment and regional agricultural development foundation. On the one hand, the difference in geographical location leads to an uneven distribution of resources, which are partially concentrated in the central and southern parts of Anhui Province, creating a better external environment for efficient and sustainable agriculture in this region. However, the northern part is a traditional agricultural development area with a large proportion of rural population and a large economic gap between urban and rural areas. This area also has a weak economic base, backward agricultural infrastructure and a poor external environment compared to the central and southern parts, which may hinder the expansion of agricultural technology extension in universities. On the other hand, the regions with a weak development base are less willing to adopt new technologies and have a weak ability to apply new technologies. The extension of agricultural technology in universities has no significant effect on the development of high-quality agriculture in this region, and the regions with a good development foundation are more likely to be positively influenced by the extension of agricultural technology in universities.

County-level data for Anhui Province, China, from 2008 to 2020 are taken from the Anhui Statistical Yearbook and the China County Statistical Yearbook throughout these years. The supplementary data of each district and county from 2008 to 2020 are obtained from the statistical yearbooks of various prefecture-level cities. The missing data of some years in the above statistical yearbooks are supplemented by interpolation. After removing the areas with severe data loss and administrative changes, the data in this paper cover 90 areas and 878 observations.

Empirical model

In 2012, Anhui Agricultural University launched the pilot project of university agricultural technology extension in Anhui Province. It selected Jinzhai District of Lu'an City, Lujiang District of Hefei City, Mingguang City of Chuzhou City, Huaining District of Anqing City, Linquan District of Fuyang City, Huangshan District of Huangshan City, Yongqiao District of Suzhou City and Dingyuan District of Chuzhou City as the pilot areas. The university established comprehensive agricultural experiment stations in the above areas to provide technical guidance and talent incubation services for local agricultural development. From the perspective of policy evaluation, the extension of agricultural technology in universities can be regarded as a quasi-natural experiment. The pilot areas serve as the treatment group and the other areas in Anhui Province as the control group. In this research, the difference-in-differences (DID) method is used to analyze the influence of university agricultural technology extension on efficient and sustainable agriculture. The econometric model is as follows:

where \(ESA_{it}\) is the comprehensive index of efficient and sustainable agriculture, which is calculated by entropy method; \(policy_{it}\) is a virtual variable for agricultural technology extension in universities, which is taken as 1 for the pilot area in the year when or after the agricultural comprehensive experimental station is established, and 0 otherwise; \(Z_{it}\) is a control variable that affects efficient and sustainable agriculture; \(\delta_{i}\) is the regional fixed effect; \(\varphi_{t}\) is the fixed effect of the year; \(\varepsilon_{{i{\text{t}}}}\) is a random disturbance term; and subscripts i and t denote the region and year, respectively. Therefore, \(\alpha_{1}\) in Formula ( 1 ) measures the influence of university agricultural technology extension on efficient and sustainable agriculture. If its coefficient is significantly positive, then the university agricultural technology extension is helpful in promoting efficient and sustainable agriculture.

Parallel trend test and dynamic influence of university agricultural technology extension on efficient and sustainable agriculture

An important premise of the DID method used in policy evaluation is that the outcome variables used in the treatment and control groups meet the parallel trend before being influenced by the policy. In addition, the policy effect of university agricultural technology extension may not be obvious in a short time, so the dynamic influence of university agricultural technology extension on agricultural efficiency and sustainability needs to be analyzed. To test the parallel trend hypothesis and analyze the dynamic influence of university agricultural technology extension on efficient and sustainable agriculture, the following econometric model is constructed:

where \(policy_{it}^{ \pm j}\) is a series of dummy variables, which is taken as 1 for pilot areas in the first j years before the year when the agricultural comprehensive experimental station is established, and 0 otherwise; and the year when the comprehensive agricultural experimental station built is the reference group. The meanings of other variables are the same as those in Formula ( 1 ). Given the short period before the pilot extension of agricultural technology in the university in the sample, five years before and six years after the implementation are selected. In Formula ( 2 ), \(policy_{it}^{ - j}\) measures whether a significant difference exists in the change trend of agricultural high-quality development level between the pilot area and other areas in Anhui Province before the university agricultural technology extension. If the coefficient is insignificant, then the change in the efficient and sustainable agriculture level between the pilot area and other areas in Anhui Province before the university agricultural technology extension meets the parallel trend. \(policy_{it}^{ + j}\) measures the dynamic influence of university agricultural technology extension on efficient and sustainable agriculture.

In this research, the explained variable is the comprehensive index of efficient and sustainable agriculture (ESA), which is solved by entropy method. In this research, a set of evaluation index system of county-level efficient and sustainable agriculture is constructed, which includes 3 dimensions and 10 first-level indexes, by referring to previous research results 17 , 18 and considering the availability and continuity of data, see Table 1 .

The main calculation steps are as follows. First, given that the dimensions and orders of magnitude of each evaluation index in the evaluation system are not completely consistent, the original data are standardized to eliminate the influence of dimensional differences and orders of magnitude differences. Second, the entropy method in the objective weighting method is adopted to determine the weight of each evaluation index in the evaluation system of efficient and sustainable agriculture. Third, the multi-objective linear weighting function method is used to weigh all the evaluation indexes, and the comprehensive index of efficient and sustainable agriculture and the index of each criterion layer are obtained.

Data standardization–extremum method

The extremum method is used to standardize the positive and negative indexes in the evaluation system, as shown as follows:

where \(Y_{ij}\) is the standardized index value; \(x_{ij}\) represents the original data of index j of district/county i ; and \(x_{i,\max }\) and \(x_{i,\min }\) are the maximum and minimum values of index j , respectively.

Weight determination–entropy weight:

The proportion of index j of district/county i is calculated as

where m is the number of samples. In this research, the samples include the data of 90 districts and counties in Anhui province: thus, m = 90.

The entropy value of index j is solved as

The weight of index j is calculated as

Comprehensive index–multi-objective linear weighting function method:

The index score at the criterion layer s of district/county i is calculated as

where \(Z_{is}\) is the index score at the criterion layer s of district/county i , and q is the total number of indexes at this index layer.

The total score of agricultural high-quality development level in district/county i is

As per Formulas ( 3 )–( 8 ), the comprehensive index \(ESA_{i}\) of agricultural high-quality development in 90 districts and counties of Anhui Province are acquired. A greater \(ESA_{i}\) value of efficient and sustainable agriculture indicates a better efficient and sustainable agriculture level.

Explanatory variable. In this research, the explanatory variable \(policy_{it}\) aims to measure the influence of university agricultural technology extension on efficient and sustainable agriculture.

Moderator variable. As previously stated, modern agricultural industrial parks are used as a moderating variable due to their status as new platforms built upon resource endowment and large-scale farming in the area. As a result, they attract modern production factors and entrepreneurs, enabling broader dissemination of agricultural policies and services. Moreover, such parks enhance the role of agricultural technology promotion in colleges and universities, contributing to more efficient and sustainable agriculture. Modern agricultural industrial parks serve as the moderator variable, which is measured based on whether a provincial-level modern agricultural industrial park is constructed.

Control variables. Several control variables are also selected in this research to control for the influence of other factors on efficient and sustainable agriculture. Referring to the existing relevant literature, the control variables are the logarithm of per capita disposable income of rural residents (lnincome), government financial support for agriculture (fis), crop disaster level (dis), agricultural mechanization (power), urbanization rate (urban), employment structure of rural population (emp, share of agriculture, forestry, animal husbandry and fishery in rural employment) and industrialization (indu). The disposable income per capita of the rural population reflects the ability of agricultural development to raise the income level of the rural population and influences efficient and sustainable agriculture. This index is measured by the logarithm of the per capita disposable income of rural residents. Government financial support for agricultural development is an important driver for promoting efficient and sustainable agriculture, measured by the share of total local financial expenditure on agriculture, forestry and water affairs. Uncontrollable natural phenomena and climatic conditions will have a negative impact on agricultural production. In this study, the proportion of the affected area of crops in the sown area of crops is used to indicate the level of crop disaster. Agricultural mechanization is the power source of modern agricultural development and the necessary guarantee for realizing efficient and sustainable agriculture 19 . In this study, the total power of agricultural machinery per unit of cultivated area is adopted to measure agricultural mechanization. To some extent, the urbanization rate reflects the level of economic development of a region 20 ; therefore, it may affect the development of agriculture, which is measured by the urbanization rate of the population. The change in the employment structure of the rural labor force can inject new vitality into the development of regional agriculture and promote efficient and sustainable agriculture, which is reflected in the share of agriculture, forestry, animal husbandry and fishing in the rural labor force. The level of industrialization can also affect efficient and sustainable agriculture 21 , which is measured by the ratio of the output value of secondary industry to regional GDP. The change in the social and institutional environment of a county in a given year may also affect efficient and sustainable agriculture. To control for the above factors, the dummy variables for year and region are selected as control variables. Table 2 shows the associated variables and the descriptive statistical results.

Results and discussion

Benchmark regression.

Table 3 shows the estimated results of the influence of university agricultural technology extension on agricultural efficient and sustainable agriculture. Column (1) is the estimation result with only the fixed effect of areas and years controlled, and the coefficient at this point is 0.033, which is significant at the 1% level. Column (2) is the estimation result after adding other control variables based on column (1). In this case, the coefficient is 0.033, which is significant at the 1% level. This estimation result shows that agricultural technology extension in universities has increased the level of efficient and sustainable agriculture by 3.3%. That is, an increase of one unit in agricultural extension by the university results in a 0.033 rise in the level of both efficient and sustainable agriculture. Agricultural technology extension in universities is based on regional resources and characteristic industries. It provides various kinds of technical guidance and resource support to adjust the agricultural industrial structure, improve the efficiency of agricultural production, and promote the green and low-carbon development of agriculture. Ultimately, it achieves the goal of promoting efficient and sustainable agriculture.

In addition, the level of crop disaster has a significant negative impact on efficient and sustainable agriculture, significant at the 10% level with a coefficient of − 0.018, and natural disasters can have a negative impact on crop planting and thus on efficient and sustainable agriculture. The employment structure of the rural population has a significant negative impact on efficient and sustainable agriculture, significant at the 1% level with a coefficient of − 0.23. suggesting that reducing the share of rural employment in agriculture, forestry, animal husbandry and fishing is conducive to promoting efficient and sustainable agriculture. Changing the initial mono-employment structure in rural areas can bring fresh blood into agriculture and promote efficient and sustainable agriculture. The effects of per capita disposable income of rural residents, government financial support for agricultural inputs, the degree of agricultural mechanisation, the urbanisation rate and the degree of industrialisation are not significant, indicating that the effects of per capita disposable income of rural residents, government financial support for agricultural inputs, the degree of agricultural mechanisation, the urbanisation rate and the degree of industrialisation are negligible after controlling for university support for agricultural technology, and also illustrating the important effects of university support for agricultural technology on the development of efficient and sustainable agriculture.

Parallel trend test

As shown in Table 4 , when j = − 5, − 4, − 3, …, − 1, the coefficient of \(policy_{it}^{ - j}\) is insignificant, indicating no significant difference in the changing trend of efficient and sustainable agriculture level between the treatment and control groups before the pilot project of agricultural technology extension in universities. Hence, the possibility of the parallel trend hypothesis cannot be rejected. In the years after the pilot agricultural technology extension in universities, the influence coefficient of \(policy_{it}^{ + j}\) on efficient and sustainable agriculture is positive and significant, except for the third year. This result indicates that the agricultural technology extension in universities has a long-term promoting effect on efficient and sustainable agriculture. Moreover, the change in the coefficient indicates that its promoting effect generally shows an expanding trend. The reason may be that the effect of agricultural technology extension in universities gives farmers confidence in this policy and increases their willingness to adopt technology extension. Moreover, farmers’ ability to adopt technology is improved by early guidance and training, and the facilitating effect of agricultural technology extension in universities on efficient and sustainable agriculture is expanded accordingly.

Robustness test

In this research, two placebo tests are used as robustness tests: the propensity score matching-difference-in-differences (PSM-DID) and the placebo test that changes the year of policy implementation and switches the treatment and control groups.

PSM–DID. In the benchmark regression, the DID method is used to estimate the effect of agricultural technology extension in universities on efficient and sustainable agriculture. However, this policy pilot is not an actual natural experiment; that is, there may be some differences in observable variables between pilot and non-pilot areas, leading to biased estimation results. To solve this problem, the DID method based on PSM is adopted to re-estimate the influence of university agricultural technology extension on efficient and sustainable agriculture. First, the logit model and 1:1 nearest neighbor matching method in 0.05 caliper are used in matching the propensity scores between pilot and non-pilot areas to eliminate the differences in observable covariates between them as much as possible. Then, the common support areas with propensity scores are selected for DID regression. The estimated results are shown in Column (1) of Table 5 . Column (1) shows that the coefficient of \(policy_{it}\) is 0.038, which is significant at the 5% level, indicating that agricultural technology extension in universities positively affects efficient and sustainable agriculture.

Placebo test with transformation of the treatment group. If university agricultural technology extension has a positive impact on efficient and sustainable agriculture, then the level of efficient and sustainable agriculture in the pilot areas will not be positively affected by university agricultural technology extension. Otherwise, it is doubtful whether university agricultural technology extension has a positive impact on efficient and sustainable agriculture. Therefore, a placebo test is conducted with a transformation of the treatment group: The area selected for pilot university agricultural technology extension is assumed to be a non-pilot area, i.e. a non-pilot area. Agricultural technology extension in universities does not take place in the non-pilot area, so in theory its level of efficient and sustainable agriculture should not be affected by a false attitude. To verify the above analysis, the samples of non-pilot areas are classified according to regional codes, and the areas with even regional codes are tested. Similarly, the DID method is used to estimate the effect of university agricultural technology extension on efficient and sustainable agriculture under this false setting. The estimated results are shown in Column (2) of Table 5 . Column (2) shows that the coefficient of \(policy_{it}\) is insignificant, proving, from a side view, that the extension of agricultural technology in universities positively affects efficient and sustainable agriculture.

Placebo test in dummy pilot year. The extension of efficient and sustainable agriculture level brought by agricultural technology extension in universities may be caused by other unobservable random factors unrelated to this pilot policy, leading to biased estimation results in this research. To eliminate the influence of unobservable random factors on the estimation results, the dummy pilot year placebo test is implemented: Assuming that the pilot year of agricultural technology extension in universities is one year before the actual implementation year, the DID method is also used to estimate the influence of agricultural technology extension in universities in this dummy year on efficient and sustainable agriculture. If it has no significant impact on efficient and sustainable agriculture at that time, then no unobservable random factors have interfered with the estimation results of this research, i.e. the estimation results are credible. On the contrary, it indicates that some unobservable random factors may affect efficient and sustainable agriculture, leading to the fact that the estimation results are not credible. Assuming that agricultural technology is promoted in universities in 2010, the estimated results are shown in column (3) of Table 5 . Column (3) shows that the coefficient is insignificant, indicating that no unobservable random factors have interfered with the estimation results of this research. This means that agricultural technology extension in universities has a positive impact on efficient and sustainable agriculture.

Mechanism analysis and the modulating effect of modem agricultural industrial parks

To analyze the mechanism of action of agricultural technology extension in universities in promoting efficient and sustainable agriculture, the influence of agricultural technology extension in universities on coordinated, efficient and green agricultural development is estimated. The estimated results in columns (1)–(3) of Table 6 show that agricultural technology extension in universities has a significantly positive effect on coordinated and green agricultural development. However, the regression coefficient for efficient development is insignificant, indicating that agricultural technology extension in universities promotes efficient and sustainable agriculture by promoting the coordinated and green development of agriculture. On the one hand, agricultural technology extension in universities facilitates the coordinated development of agriculture by combining the planting of local characteristic agricultural products. Thus, it contributes to the creation of local characteristic agricultural product brands through special technology research and development and promotes the planned adjustment of the internal structure of agriculture under the premise of ensuring food security. On the other hand, the development of agricultural technology in universities also promotes the development of agricultural auxiliary industries, such as agricultural product processing, storage and logistics, and increases the output value of agricultural auxiliary activities. The facilitating effect of university agricultural technology extension on the green development of agriculture can reduce the consumption of resources and the destruction of nature by agricultural production through the extension of green agricultural technologies, such as the reduction of fertilizers and the green control of diseases and pests. Conversely, the cultivation of crops with better traits can reduce the subsequent consumption of resources to ensure crop growth. The extension of agricultural technology in universities has not had a significant impact on the efficient development of agriculture. This may be because agricultural production activities have a long cycle and are strongly influenced by the natural environment. The improvement in agricultural production efficiency brought about by new technologies and methods is not obvious in a short period of time.

Modern agricultural industrial parks are an important carrier for gathering essential resources, innovating institutional mechanisms and promoting the development of modern agriculture. Since the establishment and accreditation of the National Modern Agricultural Industrial Park in 2017, a great deal of exploration and practice has been carried out in the establishment of modern agricultural industrial parks across the country. Moreover, remarkable achievements have been made in the construction of the whole agricultural industrial chain, the establishment of the mechanism of linking agriculture and industry, the extension of the transformation of agricultural production mode, and the acceleration of the innovation of agricultural science and technology, which has effectively promoted efficient and sustainable agriculture everywhere 22 . This study examines the role of modern agricultural industrial parks in the extension of agricultural technology in universities and the extension of efficient and sustainable agriculture. In column (4) of Table 6 , the interactive term between university agricultural technology extension and modern agricultural industrial parks is added based on the benchmark regression. The regression result is significantly positive at the 5% level, indicating that modern agro-industrial parks exert a positive moderating effect that can strengthen the facilitating effect of university agro-technology extension on efficient and sustainable agriculture. Through the aggregation effect of modern agro-industrial parks, university agro-technology extension can promote a wider range of activities, such as technology extension and farmer training. In this way, the scope and influence of university agricultural technology extension will be expanded, the beneficiary groups of university agricultural technology extension will be enlarged, and the role of university agricultural technology extension in promoting efficient and sustainable agriculture will be enhanced as a whole.

Heterogeneity analysis

To explore the heterogeneity of the influence of university agricultural technology extension in different regions on efficient and sustainable agriculture, regression analysis is conducted on the southern, central and northern regions of Anhui Province using the DID method. The estimated results in columns (1)–(3) of Table 7 show that university agricultural technology extension has a significantly positive effect on efficient and sustainable agriculture in the southern and central regions but has no significant effect in the northern regions. The reason for this difference may be that, compared with the northern region, the southern and central regions have a better economic environment and better agricultural infrastructure; thus, these regions can better integrate the resources of all sectors of society and provide the basic conditions for efficient and sustainable agriculture in the process of agricultural technology extension in universities. However, the traditional agricultural development model in the northern region is deeply rooted, with a large proportion of agricultural carbon emissions but low agricultural ecological efficiency, unreasonable inputs of various factors and unbalanced development 23 . The effect of agricultural technology extension in universities will take a long time.

To explore the heterogeneity of the influence of agricultural technology extension on efficient and sustainable agriculture in regional universities with different levels of efficient and sustainable agriculture, the DID method is also used to perform regression analysis on three groups of regions with low, medium, and high levels of efficient and sustainable agriculture. The estimated results in Columns (4)–(6) of Table 7 show that the extension of agricultural technology in universities has a significantly positive influence on areas with highly efficient and sustainable agriculture level, but not on areas with low or medium level. A possible reason is that in the areas with highly efficient and sustainable agriculture levels, university agricultural technologies can be popularized and applied to actual production more quickly. In comparison with the farmers in the areas with low efficient and sustainable agriculture level, the comprehensive quality of farmers in such areas is generally higher, and they have higher digestion and absorption ability for new technologies and methods. After training, guidance, and demonstration, they can use the achievements from agricultural technology extension in a short time, thereby improving the agricultural quality development level. For areas with low levels of efficient and sustainable agriculture due to their weak development strength, weak acceptance ability, and low application efficiency for the same extension technology, applying new methods and technologies to solve problems in practice in a short time is difficult. Thus, the effect of agricultural technology extension in universities is not significant for such areas.

Baseline regression results

The results of the baseline regression indicate that university agricultural technology extension has increased the level of efficient and sustainable agriculture by 3.3%. Based on regional resources and special industries, university agricultural technology extension provides various kinds of technical guidance and resource support to regulate the structure of agricultural industry, improve agricultural production efficiency, and promote green and low-carbon agricultural development, ultimately achieving the goal of promoting efficient and sustainable agriculture. Existing research indicates that the implementation of new technologies significantly contributes to the high-quality and sustainable development of industries 12 . This study on agricultural technology extension in universities establishes that it enhances both the efficiency and sustainability of agricultural production. Correspondingly, by providing guidance and demonstration, technology extension programmes implemented by universities can assist farmers in employing novel technologies to achieve efficient and sustainable production. In addition, the level of crop damage has a significant negative impact on efficient and sustainable agriculture, with natural disasters having a negative impact on crop production, which in turn has a negative impact on efficient and sustainable agriculture. The employment structure of the rural population has a significant negative impact on efficient and sustainable agriculture, suggesting that reducing the proportion of rural workers employed in agriculture, forestry, animal husbandry and fishing is conducive to efficient and sustainable agriculture, and that changing the initially homogeneous employment structure in rural areas can bring new blood into agriculture and promote efficient and sustainable agriculture.

Analysis of the mechanism and the moderating effect of modern agro-industrial parks

The results in Table 6 show that university agricultural technology extension promotes efficient and sustainable agriculture by fostering coordinated and green agricultural development. The role of university agricultural extension in promoting coordinated agricultural development is, on the one hand, due to the fact that university agricultural extension incorporates the local special agricultural farming industry, helps to build local brands of special agricultural products through special technology research and development, and promotes internal agricultural restructuring in a planned manner while ensuring food security; on the other hand, university agricultural extension also promotes the development of agricultural auxiliary industries, such as agricultural products On the other hand, university agricultural extension also promotes the development of ancillary agricultural industries, such as the processing industry, storage, logistics, etc., and increases the output value of ancillary agricultural activities. On the one hand, it reduces the consumption of resources and damage to nature through the extension of green agricultural technologies such as fertilizer and pest control, and on the other hand, it reduces the subsequent consumption of resources to ensure the growth of crops by producing crops with better traits. The lack of significant promotion of efficient agricultural development by the University's agricultural technology diffusion may be due to the long cycle of agricultural production activities and the fact that they are highly influenced by the natural environment, and the increase in agricultural production efficiency brought about by new technologies and methods is not obvious in a short period of time. This mechanism of action has not been discussed in existing studies, which have discussed how knowledge-based agriculture can improve sustainable productivity and product quality for sustainable agriculture by monitoring crop quality and yield assessment through modern computer technology 9 , while some studies have used nanotechnology as an example of a mechanism of action to explore the impact of agricultural technology on sustainable agriculture by illustrating that the development of nano-pesticides using nanocarriers can increase the biological activity of synthetic or natural (plant) pesticides while reducing their adverse effects on the environment as a mechanism of action to explore the impact of agricultural technology on sustainable agriculture 11 . However, this study is different from previous studies in that it illustrates the specific mechanism of action by exploring the impact of university agricultural technology extension on specific dimensions of efficient and sustainable agriculture, and it is found that the impact of university agricultural technology extension on efficient and sustainable agriculture is, on the one hand, to improve the efficiency of agricultural production by adjusting the internal structure of agriculture, and on the other hand, to maintain the sustainability of development by reducing consumption and improving crops to ultimately realise efficient and sustainable agriculture, and this mechanism discussion is somewhat innovative.

Modern agricultural industrial parks are an important carrier for gathering factor resources, innovating institutional mechanisms and promoting the development of modern agriculture. Since the establishment and recognition of national modern agricultural industrial parks was launched in 2017, a great deal of exploration and practice has been carried out in the establishment of modern agricultural industrial parks across the country, and significant results have been achieved in creating whole agricultural industrial chains, building mechanisms to link and guide farmers, promoting the transformation of agricultural production methods, and accelerating innovation in agricultural science and technology, which have strongly promoted efficient and sustainable agriculture in various regions 22 . This paper examines the role of modern agricultural industrial parks in the process of promoting efficient and sustainable agriculture through university agricultural technology diffusion. Column (4) of Table 6 adds the interaction term between university agricultural extension and modern agricultural industrial parks to the baseline regression, and the regression results are significantly positive at the 5% level, indicating that there is a positive moderating effect of modern agricultural industrial parks, which can strengthen the role of university agricultural extension in promoting efficient and sustainable agriculture. With the aggregation effect of modern agricultural industrial parks, university agricultural extension can conduct a wider range of technical extension and farmer training activities, thus expanding the coverage and influence of university agricultural extension, and increasing the number of beneficiaries of university agricultural extension, which ultimately enhances the promotion of university agricultural extension for efficient and sustainable agriculture in general. This is in contrast to previous studies which have focused on how agricultural technology affects specific agricultural production and which parts of production it affects, while this study explores how, in promoting efficient and sustainable agriculture through university agricultural technology extension, modern agricultural industrial parks can enhance this promotion through the platform's convergence leadership.

Analysis of heterogeneity test results

The differences between the three regions of southern, central and northern Anhui may be due to the fact that the economic environment and agricultural infrastructure in southern and central Anhui are better than those in northern Anhui, and they can better integrate the resources of the community into the agricultural development process and provide the basic conditions for efficient and sustainable agriculture. In contrast, the traditional agricultural development model in northern Anhui is deeply rooted, with a high proportion of agricultural carbon emissions but low agricultural eco-efficiency, unreasonable input of various factors, and uncoordinated and unbalanced development, and it will take a longer period of time before the role of university agricultural technology extension can be brought into play.

The difference in the results of different levels of development of efficient and sustainable agriculture may be due to the fact that areas with a high level of efficient and sustainable agriculture are able to apply university agricultural technologies more quickly in production, as the overall quality of farmers in these areas is generally higher compared to farmers in areas with a lower level of development, and they have a higher capacity to digest and absorb new technologies and methods, and are able to maturely apply the results from agricultural technology extension in a shorter period of time after training, guidance and demonstration. They are able to use the results from agricultural extension in a mature manner within a relatively short period of time after training, guidance and demonstrations, thus improving the level of efficient and sustainable agriculture. For areas with a low level of efficient and sustainable agriculture, the effectiveness of university agricultural extension is not significant due to the weakness of their own development, and their weak ability to accept and apply the same extension techniques, making it difficult to apply new methods and techniques in practice in a short period of time. Existing research is less for sub-region and development level to discuss the role of university agricultural technology extension on efficient and sustainable agriculture, which is better for the university agricultural technology promotion to play an important role in the impact of agricultural development, this study through the differences in the discussion, we can improve the university agricultural technology extension to adapt to the specific conditions of different regions to realise the effect of its promotion of efficient and sustainable agricultural development.

Conclusions

Based on the quasi-natural experiment of university agricultural technology extension conducted by Anhui Agricultural University after 2012, and using the county-level panel data from 2008 to 2020, this research empirically analyses the influence of university agricultural technology extension on efficient and sustainable agriculture. The conclusions are as follows. First, the extension of agricultural technology in universities has a positive effect on efficient and sustainable agriculture, which increases the comprehensive index of efficient and sustainable agriculture level by 3.3%. Universities utilise their own resources, including human capital and technological research and development capabilities, to advance scientific and technological achievements in agriculture. They provide guidance to local agricultural production activities to enable practical application of agricultural technology, leading to enhanced productivity and sustainable, environmentally conscious production methods, thus achieving efficiency and sustainability in agriculture. Second, the extension of agricultural technology in universities can promote efficient and sustainable agriculture by influencing the coordinated and green development dimensions in efficient and sustainable agriculture. On one hand, university agricultural technology extension brings together local specialty agricultural plantations and promotes coordinated development by adjusting the internal structural adjustment of agriculture through specialty technology guidance. On the other hand, it employs green agricultural technology and improved varieties to foster environmentally conscious development. Third, agricultural industrial parks have a moderating effect on the extension of agricultural technology in universities and efficient and sustainable agriculture. The presentation and influential impact of modern agricultural industrial parks have the potential to broaden the scope of agricultural technology extension in universities. Fourth, the extension of agricultural technology in universities has a significant positive effect on the development of high-quality agriculture in the southern and central areas of Anhui Province, but not in the northern areas. Fifth, the pilot project of agricultural technology extension in universities has a significant positive effect on areas with a high level of efficient and sustainable agriculture, but it has no significant effect on areas with a low and medium level.

The policy implications of this research are mainly fourfold. First, the agricultural technology extension system in universities should be pursued and improved. Agricultural technology extension in universities can improve the quality of farmers through experiments, demonstrations, farmer training and technical support. In addition, agricultural science and technology should be popularized to inject vitality into agricultural development, thereby promoting efficient and sustainable agriculture. Second, the role of university agricultural technology extension in promoting efficient agricultural development is not significant. In the future, university agricultural technology extension services should pay more attention to technical research on improving agricultural production efficiency, reducing unnecessary human and material consumption in the agricultural production process, and realizing the optimal allocation of input factors. Third, modern agricultural industrial parks can enhance the facilitating effect of agricultural technology extension in universities on efficient and sustainable agriculture. Once established, modern agricultural industrial parks provide a better platform for agricultural technology extension in universities. With the radiation of this modern agricultural development platform, it can spread the training and demonstration role of agricultural technology extension in universities to a greater extent and strengthen its effect in promoting efficient and sustainable agriculture. Fourth, there are obvious differences in the effects of agricultural technology extension in universities on efficient and sustainable agriculture in different regions. Before technology extension, the problems faced by local farmers in agricultural production should be fully understood, and targeted research on key problems should be conducted. Introduce new technologies into agricultural production practices. Provide focused instructions on soil amelioration and fertilisation techniques for regions with poor soil quality. Allocate additional technical extension staff in regions with rudimentary and unsustainable farming practices to offer comprehensive guidance. Better extension is achieved by adapting the type of extension technology and the scale of extension to different regions at the time of extension.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

The Central Committee of the Chinese Communist Party

Difference-in-differences

The comprehensive index of efficient and sustainable agriculture

Per capita disposable income of rural residents

Government financial support for agriculture

Crop disaster level

Agricultural mechanization

Urbanization rate

Employment structure of rural population

Industrialization

The propensity score matching-difference-in-differences

The propensity score matching

The National Modern Agricultural Industrial Park

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This work was supported by Anhui Provincial Science and Technology Plan Project (202006f01050004), Anhui Provincial Quality Engineering Project (2022jyxm465), Humanities and Social Sciences Program for Colleges and Universities, Department of Education of Anhui Province (SK2019A0151).

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Dai, Z., Wang, Q., Jiang, J. et al. Influence of university agricultural technology extension on efficient and sustainable agriculture. Sci Rep 14 , 4874 (2024). https://doi.org/10.1038/s41598-024-55641-1

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Farmer’s perceptions of effectiveness of public agricultural extension services in South Africa: an exploratory analysis of associated factors

• Matome Moshobane Simeon Maake   ORCID: orcid.org/0000-0001-5007-2588 1 &
• Michael Akwasi Antwi 1  

Agriculture & Food Security volume  11 , Article number:  34 ( 2022 ) Cite this article

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Metrics details

Effective public extension and advisory services have the potential to improve agricultural productivity; net farm income; and food security amongst resource-poor farmers. However, studies conducted to measure the effectiveness of extension and advisory services, offered by the Government of South Africa, have focused on the methods used, instead of the guiding principles, such as demand-driven services; equity; prioritization of farmer’s needs; and social and human capital development. The aim of this research paper was to determine farmers’ perceptions regarding the effectiveness of public extension and advisory services and associated factors. Perceptions of the effectiveness were measured using sixteen variables. A group of 442 farmers, in the Gauteng province, receiving government agricultural extension and advisory services, were randomly selected to participate in the study. Using a semi-structured survey instrument, primary data was collected through physical interviews and then analysed using computer software.

The study found that public extension and advisory services in Gauteng were perceived as ineffective. Three socio-demographic factors (education level, age and farm/plot size) significantly influenced farmer’s perceptions towards public extension and advisory services. Moreover, the Principle Axis Factoring (PAF) results indicated that there were three underlying factors of the perceived effectiveness of public extension services, namely: relevance and good quality services; provision of information on improving agricultural production; and availability of the technologies required by farmers.

Conclusions

Large-scale farmers perceived public extension services to be less effective. The exploratory factor analysis extracted three underlying factors which accounted for 81.81% of the variance of the perceived effectiveness of public extension services. Farmers recommended that public extension and advisory services should be of good quality; relevant; and should improve agricultural production to be considered as effective by the farmers. Moreover, provision of extension and advisory services should be determined by farm/plot size.

Agricultural extension is a source of information for most farmers with low literacy levels and poor access to Information and Communication Technology (ICT) in developing countries. Through access to extension and advisory services, farmers receive diverse information about cultivation practices; fertilisation; plant protection (pests, weeds and disease control); marketing; livestock and crop management; climate change; and so forth. Because of the important role and benefits of agricultural extension, access to public extension and advisory services is imperative for most farmers, especially those who cannot afford private extension services. As a result, government is the main provider of extension services in most developing countries [ 1 , 2 , 3 ]. One of the reasons that the government is highly involved in rendering extension services, is to ensure that farmers receive the support which will enable them to produce adequate and quality produce, and thus enabling the country to be food secure. Therefore, effective public extension services play an important role in agricultural sustainability and food security of a country. Effectiveness of extension services have been widely investigated globally using various methods. Most scholars have measured the effectiveness of extension services using delivery methods, such as farmer trainings, farm/home visits, office calls, field demonstrations, field/farmers days, workshops/open discussions [ 4 , 5 , 6 , 7 ]. Facilitation of study groups and distribution of printed materials has also been used as a way to determine effectiveness of extension services [ 4 ]. The ability of extension personnel to manage orientation, expose farmers to mass media, provide scientific orientation and innovate farmers could be used to determine whether extension services are effective or not [ 8 ]. Extension system capable to utilizing Information and Communication Technologies such as televisions, radio, telephones, helpline and social media (Facebook, Twitter, etc.) are considered as effective [ 5 ]. Moreover, other scholars have measured the impact of extension services on farmers’ income and agricultural production [ 9 , 10 ]; innovation adoption rate, food safety and nutrition [ 7 ]; campaigns, lectures, exhibitions, literature and signboards [ 11 ]; and transferring crop production and management knowledge to farmers [ 12 ], as measures of the effectiveness of extension services. The above background indicates that different methods have been employed by scholars to measure effectiveness of extension services.

The results of the effectiveness of extension services vary from one place to the other; even though there are some commonalities in some instances. In Ekurhuleni Metropolitan and Sedibeng District Municipalities, Gauteng province of South Africa, the results of a focus group involving smallholder farmers showed that public extension services were not effective in sharing printed information, nor communicating and facilitating workshops, but were, however, effective in utilizing methods, such as trainings, demonstrations, farmers’ days, individual farm visits and on-farm trials and research [ 4 ]. In an experimental research conducted amongst smallholder poultry farmers in Dakhalia governorate, Egypt, it was found that public extension services were most effective in demonstrations; meetings; and the distributions of pamphlets [ 13 ]. However, in Khyber Pakhtunkhwa province, Pakistan, the findings of a survey showed that majority of farmers perceived extension services as ineffective in the following methods farm/home visit, phone office calls, demonstration plots, field days, demonstration plots, farmer trainings, local agriculture fair and workshop/open discussion [ 6 ]. The T test results of a survey that sampled rice growers (farmers) receiving extension services from government and private sector in Pakistan indicated that public extension services were moderately effective in the dissemination of information through demonstrations and farm/home visits [ 5 ]. However, in the said study, it was found that public extension services were less effective in agricultural campaigns; Farmers’ days; and signboards aimed at building farmers’ capacity. Again, maize growers in Kilindi District of Tanzania held the opinion that agricultural extension agents were ineffective in transferring knowledge about selection of cultivars, choosing planting date, seed treatment, crop protection (weed, pest and disease control), fertilizer application, irrigation and harvesting practices, and demonstration methods [ 12 ]. The T test results of the experimental research that involved recipients and non-recipient of extension services in Jordan found that net income and agricultural production were not statistically significant ( p  > 0.05); thus, extension services were ineffective in improving farmers’ net income an production [ 9 ]. On the contrary, survey results from Kaduna state, Nigeria showed that effective extension services enhanced productivity and farmers’ income [ 10 ]. A survey that involved extension practitioners in the Eastern Cape province of South Africa found that public agricultural extension was ineffective in uplifting farmers from poverty and in providing necessary resources [ 14 ]. Literature presented above shows that information about effectiveness of extension services can be collected through a survey, experimental research and focus groups involving farmers and extension practitioners. Moreover, different methods were used to measure effectiveness of extension services.

In addition, several studies that investigated the effectiveness of extension services have also explored factors influencing effectiveness or determinants [ 6 , 8 , 15 , 16 , 17 ]. Information about the determinant of the effectiveness of extension services has been analysed using methods, such as, principal component analysis (PCA); Regression models (Ordered Logistic, Binary Logit; Probit; Ordinary Least Squares; and Multiple logistic); descriptive statistics; T test; and qualitative analysis. The results of PCA extracted the following factors underlying factors associated with the effectiveness of extension services, policy-making factors, which accounts for 17.2% of the variance; followed by socio-cultural factors (16.4%); and structural and economic factors which accounts for 14.1% and 13.3%, respectively [ 18 ]. In a study whose participants were extension personnel, the findings of Ordinary Least Squares (OLS) regression showed that the effectiveness of extension services is influenced by factors, such as age; marital status; work experience of extension personnel; acquisition of extension education; field of expertise; and number of villages served by extension personnel [ 15 ]. Studies that involved farmers and analysed data using various regression methods (Binary Logistic, Multiple regression and Ordered Logistic) found that perceived effectiveness of extension services was positively and significantly influenced by farmers’ socio-demographic characteristics, such as age [ 17 ]; farming experience [ 8 , 17 ]; gender [ 16 , 17 ]; farm size [ 16 ] and educational status [ 8 , 17 ]. Other significant factors influencing farmers’ perceptions about the effectiveness of extension services are knowledgeable extension personnel [ 19 ]; farmer’s attitudes towards extension services, and extension services received [ 17 ]. Moreover, training received; contact with extension agents; scientific orientation; information source utilisation; and innovativeness are positive and significant predictors of perceived effectiveness of extension services [ 8 ]. In a study that utilised the Delphi Technique and subjected data to descriptive statistical analysis, it was discovered that quality of training and lack of resources influenced the performance of most extension agents [ 20 ]. The performance of extension personnel influences access to extension services by farmers. Likewise, access to extension services is a significant predictor of farmers’ perceptions about the effectiveness of extension services [ 17 ]. Based on the above-mentioned studies conducted on agricultural extension services, it is evident, that globally, scholars have employed various data analysis methods to identify important factors influencing perceived effectiveness of extension services.

In South Africa, the provision of extension services is guided by the principles, norms and standards for extension advisory services in agriculture, as developed by the Ministry of Agriculture. The guiding principles are demand-driven services; promotion of equity; flexibility to changing needs; monitoring and evaluation; participatory approaches; prioritization of farmers’ needs; social and human capital development; strengthening structural partnerships; facilitating skills development and access to technology; improved planning and decision-making; sustainable income generation; and the conservation of natural resource [ 21 ]. Therefore, it is important to measure the effectiveness of public extension and advisory services against the guiding principles, because they are the key drivers of extension services in South Africa. The above background prompted the researchers to measure the effectiveness of extension and advisory services, using the South African guiding principles as developed by government. The objectives of the study were to determine the perceived effectiveness of public agricultural extension and advisory services and to ascertain the determinants (influencing factors). The theoretical framework of the study is presented in Fig.  1 .

Theoretical framework of the study

Materials and methods

The study was carried out in the Gauteng province of the Republic of South Africa. Gauteng province covers an estimated 18,179 km 2 [ 22 ] of the country and is the smallest of the nine provinces in South Africa. However, it is the most populous province, with an estimated population of 15.4 million [ 23 ]. The province is subdivided into three metropolitan municipalities and two district municipalities, namely: the City of Johannesburg Metropolitan Municipality; the City of Tshwane Metropolitan Municipality; the City of Ekurhuleni Metropolitan Municipality; the Sedibeng District Municipality; and the West Rand District Municipality. Gauteng is the economic hub of South Africa, and contributes 35% of the gross domestic product (GDP) in the country, as well as 11% on the African continent [ 24 ]. As a result, the province is highly urbanized due to an influx of labour migrants from other provinces of South Africa, as well as the Southern African region. About 25.5% of the 57.7 million people in South Africa, resides in Gauteng [ 25 ]. The key economic drivers in the province are government services, manufacturing, trade, mining, transport, finance, electricity, construction, personal services and agriculture. Although agriculture is one of the economic sectors in Gauteng, it contributes only 1% of the GDP in the province [ 24 ]. Agriculture in the province mainly consists of livestock and crop production; as well as fishery at both small- and large-scale farming. There are 2291 commercial farming units in Gauteng, which creates about 16,420 skilled and unskilled employment opportunities [ 26 ].

Conceptual framework

Conceptual framework refers to the structure developed by the researcher to explain the development of the phenomenon to be studied [ 27 ]. Moreover, framework indicate the logic that will be followed to undertake the research [ 28 ]. The research approach used in the study was quantitative. Quantitative research approach was adopted, because it enables collection, capturing and analysing of numerical data [ 29 ]. In addition, a descriptive survey design was employed to undertake the research. A survey was chosen, because it describes how the perceptions of the respondents are associated with their characteristics [ 30 ]. The focus of the study was to assess farmers’ perceived effectiveness of public extension and advisory services through descriptive assessment. The conceptual framework employed in the study is presented in Fig.  2 .

Conceptual framework of the study

There are about 9000 farmers in the Gauteng province of South Africa [ 31 ]. A sample ( n ) of 368 would have had to be drawn from a population ( N ) of 9000 to achieve a margin error of 5% [ 32 ]. Because of the above description, a sample of 368 farmers receiving agricultural extension and advisory services from the Provincial Department of Agriculture, were targeted for participation in the study. However, more farmers showed interest to participate in the study. As a result, a sample ( n ) of 442 was randomly selected to participate in the study. Participants were selected after the study had received permission from the Gauteng Department of Agriculture and Rural Development (GDARD) as well as ethical approval from the CAES Research Ethics Review Committee at the University of South Africa. The ethics reference number for the project is 2016/CAES/073. The study participants were black African, coloured and white farmers aged 18 years and above.

Data collection

Collection of primary data was carried out using a semi-structured questionnaire (interview guide), which was validated and pilot tested to ensure its reliability. The researcher completed the questionnaire during face-to-face interviews with the participants. The aim of the study, the objectives, the ethical implications, as well as the rights of the participants were explained to the participants before the interviews commenced. Furthermore, each participant was required to give consent for the interview by signing the informed consent form. The questions focused on the effectiveness of the extension services which emanated from the guiding principles for extension support and advisory services as developed by the National Department of Agriculture in the Republic of South Africa. The questions were presented as five-point Likert scale questions: 1 = Very ineffective; 2 = Ineffective; 3 = Average; 4 = Effective; and 5 = Very effective. The measurements of the effectiveness of public extension and advisory services were quality of extension services; relevance of extension approaches used; and rendering of demand-driven, good quality services and goods (Batho Pele); promotion of equity; flexibility in responding to farmers’ changing needs; effectiveness in monitoring and evaluation tools; prioritising the needs of the beneficiaries; focusing on human and social capital development; use of participatory approaches; facilitating access to technology and services which sustains income generation; improving planning and decision-making; sustainability of agricultural production; agricultural skills development; and strengthening of institutional arrangements.

Statistical analysis

The Statistical Package for the Social Sciences (SPSS) version 27, was used to analyse the data. Because a Likert-scale survey instrument was used to collect the data, the data was treated as interval data. The first analysis, performed in SPSS, measured the reliability and internal consistency of the survey scale used to collect the data. To achieve this, Cronbach’s alpha’s coefficient was determined. All 16 variables which measured perceived effectiveness of extension and advisory services in the survey instrument (questionnaire), were loaded for analysis in the reliability test. The Cronbach’s alpha coefficient value obtained in the analysis, was 0.97. Because of that,  the internal consistency was satisfactory; and thus, the questionnaire was reliable. Cronbach’s alpha coefficient values between 0.58 and 0.97 are considered satisfactory [ 33 ]. Furthermore, the mean scores for all the variables ranged between 3.12 and 3.45. As a result, all the questions in the survey instrument were retained for principal Exploratory Factor Analysis (EFA) and descriptive statistical analysis. After it was found that the survey instrument was reliable, the descriptive and inferential statistical analyses were performed. The descriptive statistical analysis included mean, median, frequencies, percentages and interquartile range (IQR). The proportions of very ineffective and ineffective, were grouped together and categorised as ineffective, whereas average was considered as moderately effective. Furthermore, the proportions of effective and very effective, were grouped together and defined as effective.

In addition, the following inferential statistical analyses were performed: Ordered Logistic Regression (OLR); and PAF analysis and correlation. OLR was used to analyse data of the socio-demographic factors influencing farmers’ perceptions about the effectiveness of public agricultural extension and advisory services. The average mean score was used as a dependent variable in the OLR model. In OLR, a polychotomous-ranked dependant variable is predicted as a function of explanatory factors, describing individual or unit characteristics [ 34 ]. The basic principle of estimating OLR descried by [ 35 ], is as follow:

In the aforementioned equation, the probability is that \(Y_{{\text{i}}}\) (dependant variable) is within category \(j\) and below. Therefore, \(Y_{{\text{i}}}\) is in category 1, 2, …, or \(j\) , whereas \(u_{{\text{i}}}\) is the error term. In the current study, the empirical model estimated, using OLR is as follows:

whereby PEPEAS = perceived effectiveness of public extension and advisory services; E = education level; G = gender; AG = age group; FS = farm/plot size; U = error term.

The perceived effectiveness of public extension and advisory services was categorised as 1 = Very ineffective; 2 = Ineffective; 3 = Average; 4 = Effective and 5 = Very effective.

Exploratory Factor Analysis (EPA) was performed to reduce the number of variables and to assess multicollinearity that exists between the correlated factors [ 19 ]. The type of EPA employed in the study was PAF. PAF is used to determine the underlying factors related to a set of items [ 36 ]. The purpose of the PAF analysis in the study was to determine underlying dimensions of the perceived effectiveness of public extension services. The first step was to determine the adequacy of the sample size for PAF analysis using the Kaiser–Meyer–Olkin (KMO) measure. Bartlett’s test of sphericity, was also performed as part of the analysis of variance. Bartlett’s test of sphericity is used to test whether the data is suitable for factor analysis [ 37 ]. Again, Bartlett’s test measures the correlation matrix. The value of the KMO measure obtained was 0.97, which indicates that the sample size was adequate for PAF analysis. A value of ≥ 0.90 is considered excellent for factor analysis [ 38 ]. The results of the Bartlett’s test were as follows: the Chi-square value obtained was 7262.68 with 120 degrees of freedom (df), and the significant value was 0.00. This means that the Bartlett’s test of sphericity was statistically significant at 120 degrees of freedom. Because Bartlett’s test of sphericity was statistically significant ( p  < 0.01), the data was suitable for factor analysis.

Thereafter, all 16 variables which measured perceived effectiveness of extension and advisory services in the survey questionnaire, were loaded for PAF analysis. PAF with oblique promax rotation was employed. Oblique rotations (direct oblimin, quartimin and promax) gives more accurate results in social science research compared to orthogonal rotations (Varimax, quartimax and equamax) which may lose valuable information [ 39 ]. Moreover, oblique promax rotation was selected, because it gives better results than oblimin [ 40 ]. Different criteria was used to retain the factors for further analysis. A scree plot was used to select the total percentage variance accounted for (PVAF) in the transformed variables. In the scree plots, factors located, where the size of the eigenvalues started to make an elbow, or break, were retained [ 39 , 41 ]. Factor loadings above 0.50 were also retained [ 41 , 42 ]. After retaining the factors which met the above-mentioned criteria, a correlation analysis of the factors was performed.

Socio-demographic characteristics of the respondents

The socio-demographic information of the respondents collected in the study was racial affiliation, gender, age, educational background and farm/plot size. The results of socio-demographic characteristics of the respondents are presented in Table 1 . The results showed that largest proportion of the respondents were black Africans. Thus, the recipients of public extension and advisory services in the study area were black African farmers of which majority (51.8%) were females. The findings of educational level indicated that more than two-thirds (72.8%) of the participants had basic education (primary, secondary education and ABET), less than one-fifth (13.8%) had no formal education and 13.4% had acquired tertiary qualifications (diploma, bachelor’s degree, honours degree/BTech, master’s and doctoral degrees). It implied that most farmers could read and write, because they had formal education (tertiary and basic education). The results of farm/plot size showed that on average, the respondents occupied farming land of 4.6 ha with a minimum of less than one hectare (< 1 ha) and maximum of more than seventy hectares (> 70 ha). Therefore, the recipients of government extension and advisory services in Gauteng province were both large and small-scale farmers.

Effectiveness of public extension and advisory services

The perceived effectiveness of public extension and advisory services were determined using different variables derived from the South African norms and standards for extension and advisory services in agriculture. The results of the farmers’ perceived effectiveness of public extension and advisory services in the study area are presented in Table 2 . The results showed that, of the 16 variables measured in the study, public extension and advisory services were perceived as effective in five variables. This is shown by more than half (> 50%) of the respondents who agreed that public extension services were effective and very effective. A median of five (5) also support the notion that public extension services were perceived to be effective in all five variables. Moreover, all five variables had IQR between 3.2 and 3.6 for 95% CI lower bound and upper bound, respectively. Most importantly, public extension and advisory services were perceived by 55.0% as effective in complying with the principles of Batho Pele (rendering good quality services and goods) when dealing with people and planning activities; followed by promoting equity through subsistence small-scale farmers, women farmers, disabled farmers and commercial farmers with 54% of the respondents. About 53% of the respondents perceived public extension services as being effective in providing and facilitating advice on skills development in agriculture. Furthermore, 52% and 51% of them held the opinion that public extension services were effective in providing and facilitating access to agricultural information for improved planning and decision-making, and using extension approaches that are relevant to the beneficiaries, respectively. Finally, 50.4% of them were of the opinion that the government was effective in rendering high quality extension and advisory services. In general, public extension and advisory services in the Gauteng province, were perceived as ineffective, because 49% of the respondents indicated that the services rendered were average. The median score of 3.3 is also in support of the above explanation. In support, extension services were perceived to be ineffective in most of the variables, with a median of ≤ 3.5 and < 50% of the respondents who perceived the services as effective.

Factors influencing effectiveness of public extension and advisory services

The overall effectiveness of public extension services was measured using the average score of all 16 variables which measured the perceived effectiveness of public extension and advisory services. The descriptive statistic results showed that, in general, about 43.7%, 33.5%, 10.2%, 7.2% and 5.4% of the respondents perceived public extension services as effective, average, ineffective, very ineffective and effective, respectively. It implied that a minority (49.1%) of the respondents’ perceived public extension services as effective, as shown by the proportions of very effective and effective combined. A median value of 3 and IQR (3.2–3.4) indicates and supports the notion that public extension services were perceived as ineffective. Moreover, 33.5% of the respondents held the opinion that public extension and advisory services were moderately effective, while 17.4% indicated that the services were ineffective. The results of the OLR model fitting, achieved a chi-square value of 37.994 with a degrees of freedom (df) of four (4). Moreover, the model was statistically significant at 1% interval level ( p  < 0.01). It implied that the model could significantly predict the threshold [ p  < 0.00; χ 2 (4) = 37.99]; therefore, the model is suitable for the data. Again, the chi-square outputs of Pearson and Deviance achieved for goodness-of-fit were 1489.20 and 925.44, respectively. The degrees of freedom (df) for both chi-square outputs (Pearson & Deviance) was 1252. However, Pearson chi-square was statistically significant ( p  = 0.00), while Deviance was insignificant ( p  = 1.00). According to [ 43 ], non-significant results of Pearson and Deviance chi-square implied that the data fit the model well. However, they do not always have to be similar. Therefore, the model fit the data, because Pearson chi-square was not statistically significant. The values of Pseudo R -Square were 0.082, 0.089 and 0.033 for Cox and Snell, Nagelkerke, and McFadden, respectively. Unlike in Multiple Regression Models, the Pseudo R -Squares measures have limitations in evaluating the overall model fit [ 44 ]. As a result, the values are accepted as they are, without further interpretation.

The results of the parameter estimates of the Ordered Logistic Regression (OLR) model of the factors influencing perceptions towards the effectiveness of public extension and advisory services are presented in Table 3 . The results showed that only two of the four independent variables (education level and age), fitted in the regression model, were positive, while the others were negative (gender and farm/plot size). Both positive variables (education level and age group) were statistically significant at 1% and 5% levels of significance (99% and 95% confidence interval), respectively. Education level had a positive ( β  = 0.35) and significant relationship ( p  < 0.02) with perceived effectiveness of public extension and advisory services, with all other factors being constant. Furthermore, there was a positive (β = 0.35) and significant correlation ( p  < 0.00) between age and perceived effectiveness of public extension services. Therefore, when farmers’ age increased, they perceived extension services as more effective.

Nevertheless, the relationship between farm/plot size and farmers’ perceptions toward public extension and advisory services, was negative ( β  = − 0.04) and statistically significant ( p  < 0.00). It means that when farm/plot size increases, farmers perceive public extension services as less effective, with all things being equal.

Exploratory factor analysis

This section presents the results of the exploratory factor analysis which was performed using PAF. The purpose was to identify underlying factors regarding the perceived effectiveness of public extension and advisory services in the study area (Gauteng province). First, the results of the adequacy of the sample size for PAF analysis and the test of sphericity are presented, followed by the scree plot; the cumulative column explaining total variance; the exploratory factor analysis; and the factor correlation matrix. After the first analysis, three factors were extracted from the exploratory factor analysis. Furthermore, 12 variables were retained for further analysis after dropping those with loadings less than 0.50. The KMO score obtained was 0.96, which implied that the sample size was still adequate for factor analysis. Furthermore, Bartlett’s test of sphericity was statistically significant ( p  < 0.01), meaning the data was also appropriate for factor analysis. The Chi-square value obtained, was 5113.89 with 66 degrees of freedom (df).

Figure  3 , presents the scree plot that indicates how eigenvalues were plotted against factors. The results in the scree plot showed that the elbow started to decrease at Factor 4 with an eigenvalue of 0.35. Therefore, the first three factors on the slope, before the graph started decreasing to form an elbow, were retained. A detailed explanation regarding the names of the factors that were retained is provided in Table  4 .

Scree plot for factor analysis

The results of the cumulative column explaining total variance is presented in Table  4 . The results depict that the three extracted factors contributed 81.81% of the variance. Individually, factors 1, 2 and 3 contributed 70.72%, 6.10% and 5.00% to the total variance, respectively. Factor 1 demonstrated the highest eigenvalue with 8.49, followed by Factor 2 with 0.73 and 0.60 for Factor 3. Descriptions of all the factors, loading values and their communalities are presented in Table 5 .

Table 5 presents the results of the exploratory factor analysis of the effectiveness of public extension and advisory services. The results show that the analysis extracted three factors for the effectiveness of public extension and advisory services, in the study area. Factor 1 consisted of six variables, followed by Factor 2 and Factor 3 with four and two variables, respectively. The three extracted factors are labelled as follows: Factor 1 is relevant and good quality extension and advisory services (Promoting equity when rendering relevant and good quality extension services; and using appropriate approaches that are flexible and effective in monitoring and evaluation). Factor 2 is the provision of information which improves agricultural production (Facilitating and providing access to information which improves agricultural skills; planning and decision-making; and which sustains agricultural production and strengthens institutional relationships). Factor 3 is providing technologies required by farmers (Facilitating and providing access to technology that prioritises farmers’ needs). Factor loading for a large proportion of the participants was more than 0.60; therefore, the correlation between the extracted factors and the items associated with them was high. In addition, most variation was extracted, because the communalities of all the items were between 0.63 and 0.79. The results of the communalities showed that 63–79% of the variability in the perceived effectiveness of public extension and advisory services, is explained by the three factors (1–3). Therefore, the factor analysis explains the variation in eleven of the twelve (11 out of 12) variables very well.

After extracting all the factors and their individual variables, the factor correlation matrix was generated. The results indicated that relevant and good quality extension and advisory services (Factor 1) was positively correlated with provision of information that improves agricultural production (Factor 2), r  = 0.74. This implied that participants, who were of the opinion that public extension and advisory services were effective in rendering relevant and good quality extension services, perceived the provision of relevant information that improves agricultural production as an important measure of effective extension services. Factors 1 (rendering relevant and good quality extension and advisory services) and 3 (Providing technologies required by farmers) were correlated ( r  = 0.74). This means that farmers who perceived relevant and good quality extension and advisory services as a measure of effectiveness, held the opinion that extension services should provide technologies required by farmers to be considered effective. Finally, factors 2 (providing information that improves agricultural production) and 3 (Providing technologies required by farmers) were positively correlated ( r  = 0.71). Therefore, farmers who perceived public extension and advisory services as effective in providing information that improves agricultural production, held the opinion that extension services that provide technologies to the farmers are effective.

The aim of the study was to determine farmers’ perceived effectiveness of public extension and advisory services in the Gauteng province and the underlying factors. The study found that in general, farmers perceived public extension and advisory services in the province as ineffective. However, extension services were perceived to be effective in six out of sixteen variables (6/16) measured in the study (see Table 2 ). Therefore, government extension officers did not meet all the expectations in the the norms and standards for extension and  advisory services in agriculture developed by the Ministry of Agriculture in South Africa. The implications of the perceived ineffective extension and advisory services, in some of the variables measured, may negatively affect agricultural activities of farmers. For example, ineffectiveness in rendering demand driven services, inflexibility, and poor prioritisation of farmers’ needs, may result in rendering extension services that are irrelevant to farmers. A demand-led and flexible system will enable government to render services that are responsive to farmers’ needs. In addition, the perceived ineffectiveness of public extension in facilitating and providing access to technology and advice that sustains agricultural production, is a major concern. In support, it has been reported that in Kilindi District of Tanzania, public extension services were not effective in transferring information that improved maize production of the farmers [ 12 ]. Parallel to that, access to extension services had insignificant impact on agricultural production of farmers in Jordan [ 9 ]. In contrast, farmers in Kaduna State, Nigeria indicated that effective extension services enhanced their agricultural productivity [ 10 ]. Extension services that do not promote adoption of innovations that sustain agricultural production may negatively affect farmers’ productivity. Research has shown that the adoption of agricultural innovations and farm production, have a positive and significant correlation [ 45 ]. Meaning, farmers who adopt innovations are more likely to achieve higher agricultural outputs. Furthermore, adoption of new technologies has a positive and significant relationship with farm income [ 46 ]. Thus, in the current study, extension services were unlikely to help farmers achieve higher agricultural productivity through adoption of new technologies.

On the other hand, public extension services were effective in addressing some of the farmers’ needs. This is an indication that public extension officers effectively rendered some of the expected services to the farmers in the study area. For example, effective in compliance with the principles of Batho Pele (good quality services and goods) when dealing with people and planning activities; as well as rendering high quality extension and advisory services, is positive. The findings by [ 12 , 47 ] were in disagreement, because they found that in Tanzania and Pakistan, most farmers held the opinion that government was not effective in rendering extension services of good quality. In addition, studies conducted in South Africa (West Coast and Amathole District Municipalities) showed that public extension services were not satisfactory to most farmer [ 48 , 49 ]. Thus, farmers perceived the quality of public extension services to be poor. Moreover, the study findings in Table 2 showed that farmers perceived public extension services to be effective in providing and facilitating access to agricultural information for improved planning and decision-making, and using relevant extension approaches. Similarly, studies conducted in South Africa [ 48 , 49 ]; Ghana and Zambia [ 7 ]; Egypt [ 13 ] found that most farmers perceived public extension services as effective in the dissemination of information. On the contrary, farmers in South Africa and Pakistan indicated that public extension services were not effective in the dissemination of information through print material [ 9 ]; agricultural campaigns, farmer’s days, and signboards [ 5 ]. Moreover, in Pakistan it was also discovered that agricultural extension services provided insufficient information to most farmers [ 47 ]. Information access enables farmers to make decisions that improve their farming and solve problems [ 50 ]; moreover, information is essential in improving agricultural outputs, marketing and distribution strategies [ 51 ]. Thus, through public extension and advisory services, farmers in the study area held the opinion that they were able to make informed decisions when planning their agricultural activities. In addition, the majority of the farmers held the opinion that government extension officers were not discriminating when rendering extension services. This is evident, because public extension services were perceived to be effective in promoting equity through subsistence small-scale farmers, women farmers, disabled farmers and commercial farmers. This is in contrast to the study that discovered that female farmers were less likely to receive extension services of good quality [ 52 ]. Thus, the respondents in the current study were of the opinion that public extension services did not exclude farmers because of scale of operation, gender and physical abilities. It showed that the respondents have full confidence about the approaches used by government extension officers to promote equality through extension and advisory services.

Through the OLR model, education level and age were identified as the factors that positively and significantly influenced farmers’ perceptions about the effectiveness of public extension services in the study area. It implied that farmers with higher education levels perceived public extension services as `effective compared to those who had lower education levels. The reason could be that highly educated people are well informed about the role of extension services; hence, they do not have high expectations from government extension officers. As a result, they were satisfied with the extension and advisory services rendered and considered public extension effective. On contrary, education had a negative and significant correlation with perceived effectiveness of extension services in promoting modern technologies [ 47 ]. Again, with all things being equal, older farmers perceived public extension services to be more effective than younger farmers did. This may be because older farmers are well experienced about farming, thus, they have less expectations from extension officers. Moreover, they may be unaware about the kind of services that should be rendered to them in accordance with the norms and standards for extension and advisory services prescribed by the Ministry of Agriculture. In support to the study findings, [ 17 ] also reported a positive and significant relationship between age and perceived effectiveness of extension services. However, in another study, age was found to be positive and insignificant on farmers’ perceptions towards the effectiveness of extension services [ 15 ]. On the other hand, farm/plot size had a negative and significant correlation with perceived effectiveness of extension services. Thus, large-scale farmers perceived public extension services as less effective, with all things being equal. The motivation could be that large-scale farmers expected extension officers to visit them regularly, allocate more resources in accordance with their farm size and give them special preference. Therefore, when such expectations were not met, such farmers perceived extension services to be less effective. In contrast to what was discovered in the study, farm size had a positive and significant influence on the perceived effectiveness of extension services [ 16 ].

The results of PAF analysis generated three important factors underlying the perceived effectiveness of public extension and advisory services (see Table 5 ). The findings showed that relevant and good quality extension and advisory services (factor 1) was the most important predictor of the perceived effectiveness of public extension services. It was followed by the provision of information which improves agricultural production (factor 2), and providing technologies required by farmers (factor 3). In contrast to the current findings, [ 18 ] found that the important factors influencing the effectiveness of extension services were structural, socio-cultural and economic factors, as well as factors relating to policy-making. In the current study, the most important predictor (factor 1) included providing appropriate, good quality and flexible extension and advisory services to all farmers using relevant extension approaches and effective monitoring and evaluation tools. It implied that extension services using flexible approaches that have clearly defined and effective monitoring and evaluation systems, were perceived to be the most effective. Therefore, farmers in the study area perceived a participatory extension approach as effective compared to a top-down approach, which is not flexible. This is not surprising, because globally, agricultural extension has been shifting from top-down towards participatory approaches. Participatory approaches enable farmers to play a critical role in the generation of knowledge and change of practice [ 53 ]. The approach involves farmers in the planning of activities and ensures that their needs are catered for, as opposed to the needs perceived by government [ 54 ]. Moreover, monitoring and evaluation of the extension services was an important variable that determined the perceived effectiveness of public extension services in factor 1. The reason could be that monitoring and evaluation enables farmers and extension agents to identify the shortfalls of the services, to revise the extension methods, and to improve the services rendered. Factor 2 shows that extension and advisory services which enabled farmers to acquire farming information and skills that improve and sustain their agricultural production and relationships with stakeholders, and were perceived as effective. This could be motivated by the fact that access to agricultural information has a positive correlation with agricultural production [ 10 , 51 ]. Again, the respondents perceived their relationship with various stakeholders as an important variable that determines the effectiveness of extension services in factor 2. It implied that farmers expected extension officers to link them with various stakeholders that play an integral role in farming. Therefore, extension officers who linked farmers with corporate, financial institutions and other relevant stakeholders were perceived as effective. Measuring the effectiveness of extension services, by evaluating the relationship with various stakeholders, is an indication that farmers are in favour of a pluralistic extension delivery system. Globally, a pluralistic delivery system has gained popularity, because extension approaches have evolved from linear approaches to an agricultural innovation system that requires participation of various stakeholders. Agricultural innovation systems bring all potential public and private sectors in creation, diffusion, adoption and use of all types of agricultural knowledge relevant to production and marketing of produce [ 55 ]. Factor 3 is providing technologies required by farmers. Thus, farmers perceived extension services that facilitate and provide access to technology that prioritises farmers’ needs, as effective. Transfer of technology through extension agents to the farmers, include critical information from research and development [ 56 ]. Hence, farmers in the study area valued the role that extension agents can play in the transfer of technology. Adoption of technology has the potential to improve agricultural production of the farmers [ 57 ]. However, not all technologies brought to the farmers, improve agricultural production, because some of them are irrelevant. As a result, farmers noted the importance of providing technologies that prioritizes their needs as an important measure to determine effectiveness of extension services.

The study found that, in general, public agricultural extension and advisory services in the Gauteng province were perceived as ineffective. However, extension services were effective in six principles in the norms and standards for extension advisory services in agriculture, as developed by the Ministry of Agriculture. Through the OLR model, the study identified three socio-demographic factors (education level, age and farm/plot size) that significantly influenced farmers’ perceptions about the effectiveness of public agricultural extension and advisory services. The identified socio-demographic factors had positive (education level and age) and negative (farm/plot size) influences on farmers’ perceptions. Large-scale farmers were of the opinion that public extension and advisory services were less effective; however, highly educated and older farmers perceived extension services to be more effective. Moreover, three underlying factors (dimensions) of the perceived effectiveness of public extension services were extracted through PAF analysis. The three underlying factors accounted for 81.81% of the variance of the perceived effectiveness of public extension services. The three underlying factors may serve as a basis for informed policy decisions to improve agricultural extension and advisory services. The current study suggests that, for public extension and advisory services to be effective, extension agents should render relevant, good quality services and provide information that improves agricultural production and facilitates access to the technologies required by farmers. Again, farmers should receive extension and advisory services that are proportional to their scale of operation (farm/plot size). Moreover, other researchers could use the identified underlying factors to develop detailed survey instruments that measure the effectiveness of public extension and advisory services.

Availability of data and materials

The data used for the manuscript is attached in the documents submitted. The primary data is in Microsoft Excel. Furthermore, the statistical outputs from SPSS are attached. The names of the attached files for data and SPSS outputs are Primary data and SPSS Output_BMC.

Abbreviations

College of Agriculture and Environmental Sciences

Department of Agriculture

Degrees of Freedom

Gauteng Department of Agriculture and Rural Development

Information and Communication Technology

Kaiser–Meyer–Olkin

Principal axis factoring

Principal component analysis

Public–private partnership

Statistical Package for the Social Sciences

University of South Africa

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Acknowledgements

The authors would like to thank all farmers in Gauteng province who participated in the study, Gauteng Department of Agriculture and Rural Development granting permission to conduct the study, agricultural advisors from GDARD and research assistants who assisted the researchers with data collection.

The College of Agriculture and Environmental Sciences (CAES) of the University of South Africa provided funding for the study through the College Research Fund.

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Maake, M.M.S., Antwi, M.A. Farmer’s perceptions of effectiveness of public agricultural extension services in South Africa: an exploratory analysis of associated factors. Agric & Food Secur 11 , 34 (2022). https://doi.org/10.1186/s40066-022-00372-7

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A scoping review on technology applications in agricultural extension

1 Department of Agricultural Leadership, Education and Communications, Texas A&M University, College Station, Texas, United States of America

Anjorin Ezekiel Adeyemi

Emily catalan, ashlynn kogut.

2 Department of Teaching, Learning, and Culture, Texas A&M University College, Station, Texas, United States of America

Cristina Guzman

Associated data.

All relevant data for this study is publicly available from the Texas Data Repository ( https://doi.org/10.18738/T8/VNLOTC ).

Agricultural extension plays a crucial role in disseminating knowledge, empowering farmers, and advancing agricultural development. The effectiveness of these roles can be greatly improved by integrating technology. These technologies, often grouped into two categories–agricultural technology and educational technology–work together to yield the best outcomes. While several studies have been conducted using technologies in agricultural extension programs, no previous reviews have solely examined the impact of these technologies in agricultural extension, and this leaves a significant knowledge gap especially for professionals in this field. For this scoping review, we searched the five most relevant, reliable, and comprehensive databases (CAB Abstracts (Ovid), AGRICOLA (EBSCO), ERIC (EBSCO), Education Source (EBSCO), and Web of Science Core Collection) for articles focused on the use of technology for training farmers in agricultural extension settings. Fifty-four studies published between 2000 and 2022 on the use of technology in agricultural extension programs were included in this review. Our findings show that: (1) most studies were conducted in the last seven years (2016–2022) in the field of agronomy, with India being the most frequent country and Africa being the most notable region for the studies; (2) the quantitative research method was the most employed, while most of the included studies used more than one data collection approach; (3) multimedia was the most widely used educational technology, while most of the studies combined more than one agricultural technology such as pest and disease control, crop cultivation and harvesting practices; (4) the impacts of technology in agricultural extension were mostly mixed, while only the educational technology type had a statistically significant effect or impact of the intervention outcome. From an analysis of the results, we identified potential limitations in included studies’ methodology and reporting that should be considered in the future like the need to further analyze the specific interactions between the two technology types and their impacts of some aspects of agricultural extension. We also looked at the characteristics of interventions, the impact of technology on agricultural extension programs, and current and future trends. We emphasized the gaps in the literature that need to be addressed.

1. Introduction

Agricultural extension programs play a crucial role in disseminating knowledge, empowering farmers, and driving agricultural development. From the earliest times, agricultural extension has been noted to traditionally, through the research scientists, develop products and methods which are transferred to the farmers through the extension agents for adoption. The transfer process which was mostly in-person or through radio communications [ 1 ] became largely inadequate to catch up with the expanding population as well as the rapid pace of development. This was further compounded by reduced government funding, uncertainties of the effectiveness of the methods, the extent of the relevance of the knowledge disseminated, and the appropriateness of the models [ 2 ] giving rise to introspection for paradigm shifts in the extension methods and practices. Hence, to enhance the effectiveness of these extension programs, the use of technology such as information and communication technologies (ICTs), digital technologies, farm simulation, and others became very much necessary. Rajkhowa & Qaim [ 3 ] noted that technology application has the potentials for improving the delivery of agricultural extension programs and disseminating agricultural research to farmers and producers since they can lower communication costs, improving smallholder market access and household welfare. By leveraging technology, agricultural extension can overcome geographical barriers, reach a wider audience, and provide access to valuable information and resources, leading to improved farming practices, increased productivity, and enhanced agricultural outcomes [ 4 ].

By exploring the application of technology in agricultural extension programs, this scoping review aims to shed light on the current state of research, identify gaps, and map the overall landscape of this rapidly evolving field. By examining journal articles, conference proceedings, and dissertations, this review specifically describes the outcomes of technology application in agricultural extension under three objectives which are the substantive features, methodological features, and characteristics of technology application in the context of agricultural extension. The findings of this scoping review will provide valuable insights for policymakers who are faced with the decision of expending their resources on the most effective yet economical technology. It can also provide researchers with empirical evidence supporting their decisions when designing adoption and diffusions models for agricultural innovations, as well as practitioners in the field of agricultural extension who will come face-to-face with the users of these innovations. The review will facilitate evidence-based decision-making and inform the development of effective policies and practices by offering an overview of the impact of technology application in agricultural extension. Moreover, it will foster collaboration among stakeholders, encouraging partnerships and knowledge-sharing to drive agricultural development.

Furthermore, the findings of this research will make significant contributions to establish a foundation for future studies. Through this study, we envisage a knowledge synthesis from included studies that could lead to a better understanding of the types, usage and effectiveness of the technology used in agricultural extension. By synthesizing existing knowledge, the review will identify areas where additional research is needed, thereby paving the way for further exploration and discovery. This contribution will advance the application of technology in agricultural extension and shape the future direction of the rapidly evolving field, ultimately leading to improved agricultural outcomes and sustainable development in farming communities worldwide.

2. Literature review

2.1. agricultural extension.

Throughout the history of agricultural extension, there have been a variety of definitions of agricultural extension based on who is involved, the location, and the method used. For example, Msuya et al. [ 5 ] described agricultural extension as a way for small farmers to access new technologies, while Birkhaeuser et al. [ 6 ] viewed it as a common form of public-sector support for spreading knowledge. Rivera et al. [ 7 ] (5) on the other hand explained how agricultural extension serves as a link to increase productivity and efficiency for farmers and researchers and makes it easier to share innovations among farmers. Of all these, however, the most often cited is Maunder’s [ 8 ] comprehensive definition where agricultural extension was defined as “a service or system which assists farm people, through educational procedures, in improving farming methods and techniques, increasing production efficiency and income, bettering their levels of living, and lifting the social and educational standards of rural life.” From these definitions, to achieve its goals, agricultural extension must incorporate key components such as farmers and/or farming households, knowledge diffusion/education, and willingness to change on the part of the farmer.

This scoping review will align with the definition given by Maunder [ 8 ] within the framework that the studies included involve farmers and/or farming households with the aim of mobilizing resources towards their farming objectives.

2.2. Technology application in agricultural extension

The importance of technology in enhancing agricultural productivity cannot be overstated, and agricultural extension plays a crucial role in achieving this objective. Technology, with its innovative tools and applications, has been identified as a game-changer in the agricultural sector [ 9 ]. It has revolutionized farming practices, empowered farmers to increase productivity [ 10 – 12 ], optimized resource utilization [ 13 , 14 ], and addressed sustainability challenges [ 15 – 17 ].

Technology application (TA) in agriculture has been extensively explored from two distinct yet interconnected perspectives. The first viewpoint focuses on the use of technology/innovation as a factor or component of production. Studies falling under this theme investigate aspects such as improved seed varieties [ 18 – 20 ]; farm machinery, including tractors, plows, harvesters, and similar equipment [ 21 , 22 ]; drones, animal trackers [ 23 ] and more recently robots [ 24 ]; and the Internet of Things (IoT) [ 25 ]. These studies perceive these technologies as resources that are consumed or incorporated into the farming system, recognizing that their absence may hinder one or more crucial stages of the production process.

The second perspective regards technology in agriculture primarily as a means of enhancing knowledge transfer and skills development, often referred to as educational technology (ET). These studies, which often focus on technology-enabled information dissemination, training, and capacity building, incorporate technologies such as virtual reality (VR) and augmented reality (AR), Information and Communication Technology (ICT) [ 26 – 29 ], smartphones/mobile applications [ 30 – 32 ], online platforms and websites [ 33 , 34 ], e-learning and webinars [ 35 – 37 ], and social media and online communities [ 38 – 40 ].

In this scoping review, we categorize the TA in agricultural extension into two groups: 1) agricultural technologies/innovations used during production and 2) educational technologies employed for training and facilitating the adoption of these agricultural technologies.

By integrating ET tools such as videos, smartphones, online training, and tablets, agricultural extension services/agents can significantly enhance the effectiveness of information transfer while reducing costs. This approach helps farmers in remote areas easily access timely information, such as weather variables and market factors. Studies have demonstrated the efficacy of these tools, including videos, smartphones, and tablets, in improving agricultural practices among farmers [ 38 – 41 ].

The potential of combining ET and agricultural technology/innovations is highly promising. However, a comprehensive review of previous studies to ascertain the practical outcomes is still lacking. By examining the existing literature, this scoping review aims to bridge the gap in understanding the practical implications of integrating ET and agricultural technology/innovations (ATI). The findings of this study will shed light on the effectiveness and impact of these combined approaches in agricultural extension services.

2.3. Previous studies and research gap

While previous studies have explored agricultural extension and TA separately [ 42 ], there is a lack of examination of the relationship between these two topics. This scoping review aims to address this gap by examining them simultaneously. Existing literature reviews have touched upon related aspects, such as the role of agricultural extension in the transfer and adoption of AT [ 43 ] and the use of ICT for agricultural extension in developing countries [ 44 ]. However, these prior reviews do not fully examine the relationship between agricultural extension and TA. Altalb et al. [ 43 ] highlight the importance of agricultural extension in the development of the agricultural sector and how it aids in transferring necessary knowledge to farmers. Although Altalb et al. [ 43 ] discussed various technologies and innovations in the agricultural sector, their objective was to explore how agricultural extension could transfer that information to the farmers. In contrast, our study focuses on not just information transfer but goes ahead to examine the extension system.

Aker [ 44 ] highlights the need to adopt better AT like fertilizer, seeds, and other farming methods in developing countries and the potential of technology mechanisms, such as voice, text, internet, and mobile phone, to reach farmers and enhance knowledge, ultimately leading to an increase in the economy. However, the study did not delve into the direct TA within agricultural extension; and failed to provide examples or results that demonstrate how technology can be implemented through agricultural extension.

By addressing these gaps and incorporating potential recommendations derived from a comprehensive analysis of previous studies, this scoping review aims to contribute to enhancing productivity and bridging the divide between TA and agricultural extension practices by providing empirical evidence of amongst other things, the impact technology can make in agricultural extension.

3. Research questions

This present scoping review aims to investigate the effect of technology application on agricultural extension by examining existing empirical studies. The study focuses on analyzing the substantive features, methodological features, and characteristics of technology application in the context of agricultural extension. The research questions guiding this study are as follows:

• What are the substantive features of the included studies, including publication information (year of publication and journal name), country/region information, and agricultural field?
• What are the methodological features of the included studies, such as the research methods employed, data collection approaches, and sample size?
• What are the characteristics of the technology used in agricultural extension, including the type of educational technology, agricultural technology, and the overall effect of technology on agricultural extension?

4. Research method

4.1. search strategies.

To comprehensively search for studies, we searched five databases: CAB Abstracts (Ovid), AGRICOLA (EBSCO), ERIC (EBSCO), Education Source (EBSCO), and Web of Science Core Collection. These databases cover literature in the agriculture, applied life sciences, and education disciplines. The database search was developed in CAB Abstracts and run on October 28, 2022. The original search was modified for the additional databases, and the additional databases were searched on November 1, 2022. The search consisted of subject terms and keywords related to the two core concepts of educational technology and agricultural extension. Keywords were searched for in the title and abstract fields. The full search strategies for CAB Abstracts and the other four databases can be found in Appendix A in S2 File .

4.2. Inclusion and exclusion criteria

This scoping review used specific inclusion and exclusion criteria to identify studies examining the impact of technology application on agricultural extension.

• The included studies must have examined the effect of technology application on agricultural extension. Articles were excluded if they were not about technology, were not within the agricultural extension context, and did not examine the effect of technology on agricultural extension.
• Technologies for this study were defined as the educational technology such as multimedia, smartphones, iPads and tablets, digital simulation devices and others used by the agricultural extension services/specialists/agents to facilitate educational training under which knowledge, skills, and content are transferred in the form of agricultural technology/innovations such as seed planting knowledge, disease and pest prevention practices, improved varieties, record keeping and others to the farmers and other stakeholders in an agricultural extension setting. Unless agricultural technology also qualifies as an educational technology (e.g., GPS), such studies were excluded.
• Included studies must have been conducted under the context of agricultural extension programs, which take place in an informal, out-of-school setting; directly involve farmers and/or farming households; and pertain to their farming enterprises.
• Included studies must report detailed information on the effect of technology on agricultural extension, which should include the sample size, experimental design, and detailed results (either quantitative or qualitative). Conference abstracts on this topic will be excluded.
• Included studies must have reported an assessment of technology’s impact/effect on agricultural extension, qualitatively or quantitatively, such as an empirical study (intervention or case studies). Articles that generally discuss the trends or the importance of technology in agricultural extension were excluded.
• The included studies must have been published in a journal, as a conference proceeding, or policy paper from January 1, 2000, to November 1, 2022, and available in English. We selected this period to ensure that we covered the latest studies and documented the rapid progressions of technology in agricultural extension since 2000 [ 45 , 46 ]. Secondary data analysis, literature reviews, book reviews, book chapters, and reports were excluded.

4.3. Coding scheme

To ensure efficient data extraction and analysis, a comprehensive coding system was developed to categorize and organize the information from the included studies. The coding system facilitated the examination of substantive and methodological features of the studies, specifically focusing on the impact of technology application on agricultural extension. Sub-categories were created within the coding system to distinguish between agricultural technologies and educational technologies, enabling a detailed analysis of the key features of each. This coding played a crucial role in understanding and interpreting the findings of the included studies.

4.3.1 Substantive features of the studies

The substantive features of the studies included their publication information, geographic location (country/region), and the included studies’ agricultural field/enterprise concentrations. Our primary objective was to comprehensively analyze publication patterns within the discipline. We aimed to identify journals that had a significant impact based on their titles and publication dates. Furthermore, we sought to compare agricultural extension technology trends across various countries and regions.

We categorized the agricultural fields/enterprises in which educational technologies were predominantly utilized. The coding scheme ( Table 1 ) classified the agricultural fields/enterprise as follows: agricultural economics, including food processing such as making raisins and any value-addition processing; agricultural engineering, including mechanization; agronomy, encompassing crop production and other crop-related enterprises; animal husbandry, incorporating animal production, fisheries, and other livestock-related enterprises; and mixed when the agricultural field/enterprise included more than one.

4.3.2 Methodological features of the studies

The methodological features of the included studies encompassed several aspects, including the research methods employed, data collection approaches, the use of inferential statistics, and units of sample size. These components were examined to gain insights into the study design and methodology employed in investigating the impact of technology on agricultural extension.

The research methods were grouped into quantitative, qualitative, and mixed methods. Quantitative studies used descriptive and inferential statistics, while qualitative studies followed Denzin & Lincoln’s [ 47 ] definition of interpretive practices across different disciplines. We categorized the research methods into quantitative and qualitative because these are the primary categories of educational research. Since studies use both quantitative and qualitative methods, we categorized mixed methods studies as those studies that used both quantitative and qualitative approaches to collect and analyze data.

The data collection approaches included surveys, questionnaires, interviews, focus group discussions (FGD), and assessments. If the study applied more than one data collection approach, it was coded as a mixed method. We also documented whether the researchers employed inferential statistics to examine the impact of educational technology on agricultural extension.

We also considered the sample size units for the selected studies. The sample units were varied, making it difficult to unify the sample sizes. Therefore, we coded the sample size units as households, individuals, and villages, and in studies that used more than one unit, we coded them as mixed.

4.3.3 Characteristics of technology in agricultural extension

We categorized the characteristics of the technology applications used in agricultural extension. The types of educational technologies were coded under the following categories: multimedia (video, audio, photographs, video animation, radio); mobile apps/smartphones; online/web-based; digital simulations; and mixed for those studies that used more than one.

We distinguished between ET and an AT/I were being transferred to the farmers. We categorized the agricultural technology/innovation into various groups: crop cultivation/harvesting practices, product processing, pest and disease control, and knowledge/skill/general agricultural education. The first category was crop cultivation/harvesting practices including spacing and fertilizer application, castor cultivation, cotton production, protection technology, rice intensification system, integrated soil fertility management, soil modules, and sugarcane ratoon management practices. Another category pertained to product processing, specifically the storage of beans in jerrycans. Furthermore, we grouped pest and disease control methods such as the use of neem as an insecticide, disease management, weed control practices, and the management of Fall armyworms. Knowledge/skill/general agricultural education was another category, including topics like insurance advisory, record keeping, knowledge sharing and joint decision making, climate adaptation strategies, and practices. In cases where multiple agricultural technologies/innovations were identified, they were classified under a mixed category.

For characteristics of the intervention, we coded the duration (how long) and the intensity (how often) of the technology intervention as well as the timing of the measurement of the impact /effect. For the duration and intensity of the intervention, we considered how many studies provided the information and reported how they were reported. The interval between intervention and measurement of effect was coded as immediate, short-term, long-term, mixed, and unspecified for those studies that did not clearly state the timing for the measurement. Additionally, we coded whether the use of technologies had a positive, negative, non-significant, or mixed impact and whether the effect sizes were reported.

4.4. Data collection and data analysis

To identify eligible studies, we followed the screening process illustrated in Fig 1 . After removing duplicates, we screened 4,170 unique references for eligibility. The research team screened the article titles and abstracts using the inclusion/exclusion criteria. After the first round of screening, 69.71% (2,808 articles) were excluded. After an initial screening, the authors reviewed the full text of 1,319 articles. Out of these, 61 articles were found to be eligible for inclusion in the review. During the coding process, seven articles were excluded for different reasons. Finally, 54 articles were included in the final coding stage.

The coding scheme was created by the first two authors, who independently coded a set of 20 randomly selected articles. Subsequently, the coding scheme was employed by the first five authors to code the articles using Microsoft Excel. As noted by Belur et al. [ 48 ], the interrater reliability (IRR) of a good systematic review strengthens the transparency and replicability of the process leading to the results from such reviews. Thus, to calibrate the coding, the 54 articles were initially coded independently, resulting in an initial round of IRR of 81.30% which was calculated by the percentage of agreement between the coders. In case of conflicts, the first author acted as the arbiter and resolved the discrepancies. Eventually, a unanimous agreement was achieved regarding the coding of the articles. Descriptive statistical analyses were conducted to address our research questions.

For the data analysis, simple descriptive statistics such as frequencies, percentages, charts, and graphs were used to analyze and present the results for an understanding of the substantive and methodological features of the studies. For the characteristics of technology in agricultural extension, we conducted a crosstabulation and Chi-square analyses of the type of educational and agricultural technology used on the effect/impact of the intervention.

5. Results and discussion

5.1. substantive features of the studies, publication information.

Among the 54 included studies, a noteworthy observation was the concentrated distribution of publications within the past six years (2016–2022). As depicted in Fig 2 , two studies (3.70%) were published from 2001–2005. Six studies (11.11%) were published from 2006 to 2010; eight studies (14.81%) were published from 2011–2015. The majority of publications, comprising 38 studies (70.38%), were published between 2016 and 2022. This trend indicates a significant increase in research activity from 2001 to 2022, with a surge in studies focusing on educational technology after 2016. The rapid development and adoption of various training platforms for farmers accentuated by the global impact of the Covid-19 pandemic, has underscored the pressing need for technology-assisted agricultural extension [ 49 – 51 ].

Out of the 54 studies examined, the majority (n = 49; 90.74%) were published in journals. Policy/discussion papers constituted 7.41% (n = 4) of the studies, while conference papers represented 1.85% (n = 1). Notably, no theses nor dissertations were included in the present scoping review. The absence of such works highlights a potential avenue for graduate students to explore the intersection of educational technology and agricultural extension. Among the journals, 61.22% (n = 30) appeared only once, while the remaining 38.78% (n = 19) published multiple articles included in this review. Details on the number of articles per journal can be found in Table 2 . The journals that published the most papers in the field are: The Journal of Agricultural Education and Extension (JAEE) (n = 4), JIAEE (Journal of International Agricultural and Extension Education) (n = 3), International Journal of Agricultural Sustainability (n = 3), Information Technology for Development (n = 3), International Food Policy Research Institute (n = 3).

Note: *other articles only appear once but in separate journals.

The included studies encompassed a diverse range of countries, with notable concentrations in India, Uganda, Benin, and the U.S.A. Overall, research were conducted in 17 different countries. India accounted for 29.63% (n = 16) of the studies, while Uganda represented 16.67% (n = 9). Both Benin and the U.S.A. had five studies (9.25%) conducted in each country. Kenya accounted for 7.40% (n = 4) of the studies, while Mali, Ethiopia, and Bangladesh each had two studies (3.70%) conducted in each of these countries. The other 16.67% (n = 9) of the studies were conducted in Nigeria, Mozambique, Malawi, Bolivia, Ghana, France, Senegal, China, and Burkina Faso, with one study in each country respectively. The prevalence of studies conducted in the predominantly developing countries (excluding USA), is indicative of the high number of farm families in these regions compared to extension services. Technology therefore plays a crucial role in bridging the gap in effectively reaching a large population of farmers in these areas within a short period [ 52 – 54 ].

The research primarily focused on the regions of Africa, Asia, and North America. Out of 54 studies analyzed, 28 studies (51.85%) were conducted in Africa, while 19 studies (35.19%) were carried out in Asia. Additionally, five studies (9.26%) were conducted in North America, only one study (1.85%) was conducted in South America, and one study (1.85%) was conducted in Europe. Notably, no studies specifically targeted Antarctica or Australia/Oceania. These findings highlight the active contributions of Africa, Asia, and North America to research in the field of educational technology in agricultural extension. However, the dearth of research from Australia/Oceania and Europe in our included studies suggests a need for further investigation in these regions. For instance, Australia/Oceania, renowned for its expertise in animal husbandry due to the combination of large land areas, a substantial livestock population but relatively limited investment in infrastructure and human resources [ 55 ], presents a particularly interesting area for future researchers to explore.

Agricultural field

The majority of the included studies exhibited a strong focus on agronomy. As shown in Fig 3 , 43 studies (79.63%) were centered around agronomy. Additionally, six studies (11.11%) pertained to animal husbandry, three studies (5.56%) involved a mixed focus, and two studies (3.70%) were related to agricultural economics. The imbalance in the distribution of studies suggests a potential opportunity to explore and utilize educational technology in fields such as animal husbandry, agricultural economics and engineering, and other mixed areas. By expanding the application of technology to these underrepresented domains, a more comprehensive and inclusive approach can be adopted within the agricultural extension.

5.2. Methodological features of the studies

Research methods.

The analysis of the research methods employed in the included studies indicated a predominant use of the quantitative research method. Thirty-seven studies (68.52%) utilized the quantitative method, while 14 studies (25.93%) employed a mixed-method approach. Only three studies (5.55%) used the qualitative method. These findings align with a scoping review (authors, under review) on educational technology in agricultural education, which also observed a prevalence of quantitative and mixed methods research as the commonly adopted approaches in this field. The rationale behind the prevalent use quantitative research methods may stem from several factors. Firstly, researchers may have already recognized the importance and advantages of educational technology in the agricultural extension field, largely owing to the extensive body of research on the of educational technology in general education. Consequently, their inclination might have been to substantiate their existing hypotheses within the extension field. It is worth noting that quantitative research often leans towards a confirmatory and deductive approach, in contrast to the more exploratory nature often associated qualitative research [ 56 ]. Additionally, another possible reason for favoring quantitative methods could be attributed to the inherent limitations of qualitative methods. Qualitative findings are typically context-specific and may not readily generalize to a broader population [ 56 ].

We recommended that researchers employ more mixed methods research designs, which combine both quantitative and qualitative approaches, because it offers additional advantages in social science research [ 57 ]. For example, mixed methods research allows researchers to obtain a more comprehensive understanding of complex social phenomena by integrating numerical data with in-depth qualitative insights. The predominant use of mixed research methods in social sciences research is driven by the need for empirical evidence, objectivity, generalizability, and the ability to establish causal relationships and test theories [ 57 , 58 ].

Data collection approaches

The analysis of data collection approaches revealed that mixed approaches were the most utilized among the included studies. Out of the 54 studies, 19 studies (35.19%) used mixed approaches, 13 studies (24.07%) relied on assessments as their primary data collection approach, and 11 studies (20.37%) utilized surveys. Additionally, eight studies (14.81%) used interviews, two studies (3.70%) employed questionnaires, and one study (1.86%) did not specify the data collection method used. This diversity in data collection methods highlights the importance of employing a range of approaches to gather comprehensive and nuanced information within the field of educational technology in agricultural extension.

Inferential statistics

The analysis of inferential statistics showed that a majority of the studies included employed this statistical approach. Among the 54 included studies, 70.37% ( n = 38) of the studies utilized inferential statistics to analyze their data. On the other hand, 29.63% ( n = 16) of the studies did not use inferential statistics in their data analysis. The prevalent use of inferential statistics reflects the researchers’ intention to make inferences and draw broader conclusions about the relationship between educational technology and agricultural extension based on their data.

Unit of sample size

The included studies employed a variety of units for reporting sample size, with individuals being the most prevalent sample size unit. Out of the 54 included studies, 41 studies (75.94%) used individuals as the sample size unit. Additionally, six studies (11.11%) used households, four studies (7.40%) employed mixed units, one study (1.85%) used villages, and two studies (3.70%) did not report the sample size unit. The diversity in sample size units may be attributed to the specific characteristics of the agricultural field and the grouping involved, such as considering households or villages as a whole when studying agricultural practices. In future research endeavors, it would be beneficial to adopt a diverse array of sample size units, given the intricacy and distinctiveness of the agricultural extension field. Furthermore, there is room for investigation into the effectiveness of employing various sample size units. It is worth considering that social interaction within the households, villages, or communities within the group might be a significant factor contributing to the learning outcomes, in addition to individual interactions with technology. To gain a deeper understanding of this aspect, both quantitative or qualitative research approaches can be employed to explore the dynamics of human interaction within a shared learning community in the context of agricultural extension.

Among the 41 studies that employed individuals as the sample size unit, we adhered to the commonly used quantitative research guidelines: studies with less than 100 participants were considered small samples, studies between 100–250 participants were classified as medium samples, and studies with over 250 participants were considered large samples [ 59 , 60 ]. The sample size for studies using individuals as the unit ranged from 6 to 58872 participants. Among these studies, 58.54% ( n = 24) of the studies had a medium sample size, 24.39% ( n = 10) of the studies had a small sample size, and 17.07% ( n = 7) of the studies had a large sample size. Our findings suggest that most studies used a medium sample size when using individuals as the sample size unit. However, specific studies focusing on ET in educational settings suggested a prevalence of small sample size studies (60). This divergence could be attributed to contextual variations, particularly since agricultural extension studies typically involve a larger number of participants.

5.3. Characteristics of technology in agricultural extension

Educational technology.

In our review of 54 studies, we discovered the utilization of various ET in agricultural extension. As shown in Fig 4 , multimedia emerged as the most frequently used ( n = 27, 50.00%), followed by studies that incorporated multiple types ( n = 15, 27.78%). Additionally, mobile apps/smartphones were used in nine studies (16.67%), online/web-based applications in two studies (3.70%), and digital games/simulations in only one study (1.85%).

These findings differ from a similar review conducted on the use of ET in agricultural education by Xu et al. [ 61 ] Among the 83 included studies in their review, they found that the most used ET was online/distance education, followed by simulation/digital games and then, multimedia and traditional technology. This stark contrast may be attributable to the different contexts or settings in which agricultural education and agricultural extension are practiced. Agricultural education primarily takes place within formal educational institutions, involving students, academics, and professionals with higher levels of academic qualifications. On the other hand, agricultural extension often occurs in non-formal settings, predominantly involving farmers who may have varying levels of academic attainment. This is further supported by Mwololo et al.’s [ 62 ] finding that socio-economic factors such as age, education, and gender influenced farmers’ preference for agricultural extension methods, specifically farmers’ field schools (FFS), farmer to farmer (F2F), or mass media. In addition, the role and characteristic of multimedia contributed to the most frequent use as ET for farmers in the extension field. Multimedia plays an important role in agricultural extension serving as the most powerful opinion maker in this information era, and can help transfer agricultural information [ 63 ]. Multimedia is simple, direct, and intuitive in nature thereby making it very comprehensive for farmers who have limited educational level and technology literacy to attain knowledge and skills competency. The majority of our included studies were conducted in Africa and Asia with representative countries like India and Nigeria. In developing countries, farmers’ educational level and current technology literacy remains limited due to the lag of development of the whole country economically, socially and technology and limited funding opportunities/resources for further improvement. Simple and cost-effective ET like multimedia would be preferred compared to complex ones.

Among the various forms of ET used in agricultural extension, video or video-mediated extension emerged as the most prominent. Horner et al. [ 64 ] conducted an experimental study in Ethiopia to assess the effectiveness of video-based extension. They compared traditional agricultural extension methods with the incorporation of videos and found that the latter was more effective in increasing farmers’ knowledge and adoption of complex agricultural technologies such as composting, blended fertilizer, improved seeds, line seeding, and lime. Chowdhury et al. [ 65 ] conducted a study in Bangladesh focusing on enhancing farmers’ capacity for botanical pesticide innovation through video-mediated learning. They observed a significant increase in knowledge about botanical pesticides in both male and female farmers who participated in the video-mediated group. Several other studies [ 38 , 66 – 70 ] have also incorporated video-based multimedia in their agricultural extension programs.

The prevalence of video-mediated extension in agricultural extension programs underscores its effectiveness in delivering information and promoting knowledge acquisition among farmers. By utilizing videos, extension practitioners can visually demonstrate agricultural techniques, showcase best practices, and present success stories, thereby enhancing farmers’ understanding and motivation to adopt agricultural practices. This multimedia approach is particularly beneficial in non-formal settings where farmers may have varying levels of education and diverse learning preferences.

In our analysis of 54 articles exploring the use of educational technology for transmitting agricultural technology/innovation to farmers, we identified multiple themes in the types of agricultural technologies. Most of the articles ( n = 21, 38.89%) discussed a combination of agricultural technologies, indicating a mixed approach. Pest/disease control technology was the next most used agricultural technology ( n = 11, 20.37%). Another 10 articles (18.52%) focused on crop cultivation/harvesting practices, six articles (11.11%) covered product processing technology, and the remaining six articles (11.11%) focused on knowledge/skill/general agricultural education.

The agricultural technology and innovations covered in our included studies varied. Some studies incorporated a combination of technologies like row planting, precise seeding rates, and urea dressing [ 68 ]; tillage and sowing machinery [ 71 ], planting methods, weeding and fertilizer application [ 72 , 73 ]; identifying growth stages and improving yield predictions [ 74 ]; and seed selection, storage and handling [ 67 ].

Several studies also examined technologies and innovations for controlling pests and diseases. For instance, Chowdhry et al. [ 65 ] explored the use of botanical pesticides, Bentley et al. [ 75 ] investigated methods for controlling bacterial wilt (BW) in potatoes, and Dione et al. [ 76 ] focused on biosecurity messages for managing African swine fever. Other studies have been conducted on crop cultivation and harvesting practices. Dechamma et al. [ 77 ] studied the production practices of tomato crops, and Ding et al. [ 78 ] focused on nitrogen management practices in crop production. Additionally, Bello-Bravo et al. [ 79 ] and Sidam et al. [ 80 ] researched technologies related to product processing, such as storing beans in jerry cans and making raisins.

The last category of studies included those that focused on knowledge and skills/general agricultural education such as knowledge and awareness about agricultural credit [ 31 ], climate information [ 81 ], information about cattle handling [ 82 ], and backyard poultry farming [ 83 ].

Intervention characteristics of technology

We classified the duration of the technology intervention, the intensity of the intervention, and the interval between the intervention and the measurement of its effect. Regarding the duration of the technology intervention, nine studies (16.68%) did not provide information on the duration. Eight studies (14.81%) implemented interventions that lasted less than a week, while seven studies (12.96%) had interventions that ranged from one week to 12 weeks (3 months). Eleven studies (20.37%) reported interventions lasting between 13 weeks to 24 weeks (6 months), while eight studies (14.81%) had interventions lasting between 25 weeks to 48 weeks (1 year). Furthermore, eleven studies (20.37%) documented interventions lasting from 48 weeks (1 year) to 192 weeks (4 years).

As for the intensity of the intervention, 64.81% ( n = 35) of the studies did not provide information on the intensity, while 35.19% ( n = 19) did include details on the intensity. Out of the 19 studies that reported the intensity of the intervention, six (31.58%) specified the frequency of the intervention, such as two sessions per week or two messages per week. Thirteen studies (68.42%) provided precise information on the exact time of each session or video of the intervention, which varied from two minutes to as long as two days. These findings indicate that a significant majority of studies should have included more detailed information on the intensity and duration of the intervention. As the intensity and duration are crucial components of an intervention, they play a significant role in interventions’ effectiveness. Future research should place greater emphasis on exploring intensity and duration in greater depth and on detailed reporting of intervention components.

Regarding the interval between the intervention and the measurement of its effect, researchers exhibited a preference for measuring immediate effects, followed by long-term effects, short-term effects, and a mixed approach. Among the reviewed studies, 22 studies (40.74%) measured the immediate effect, 16 studies (29.64%) focused on the long-term effects (more than three months), seven studies (12.96%) assessed the short-term effects (within three months), two studies (3.70%) used a mixed interval between the intervention and the measurement of its effect, and seven studies (12.96%) did not specify the interval between the intervention and the measurement of its effects.

Effect of technology application in agricultural extension

The effect or impact of using technologies in agricultural extension showed diverse outcomes across the 54 studies. Among those studies, 35 articles (64.82%) recorded positive outcomes, while 15 articles (27.78%) documented mixed outcomes, suggesting a combination of positive and potentially less favorable results. Two articles (3.70%) reported non-significant outcomes, indicating that the technologies did not have a statistically significant impact on agricultural extension. Finally, the last two articles (3.70%) did not specify the outcomes achieved.

In one study with mixed outcomes, Bentley et al. [ 75 ] compared three agricultural extension methods (FFS, community workshops, and radio) for their effectiveness in teaching Bolivian farmers about BW of potato. Their findings found that while radio listeners received information about topics like diagnosing BW, crop sanitation practices, use of healthy seed, crop rotation, and incorporation of manure first from the radio, they never took any concrete action that led to the actual adoption of those agricultural technologies when compared to the FFS groups and the workshop attendees. So, while radio increased awareness about the AT, it fell short in the actual adoption.

Another study that reported mixed outcomes was that of Ding et al. [ 78 ] where ICT-based agricultural advisory services were used for nitrogen management in wheat production in China. The study sought to examine the effects of ICT-based extension services on the adoption of sustainable farming practices like nitrogen control in wheat production and found that while there was no reduction in the use of N-fertilizer for wheat production, the ICT-based services prompted farmers to adopt N-fertilizer use towards site-specific management. So, whereas the educational technology fell short of convincing the farmers to reduce their N-fertilizer usage in wheat production, it achieved the unintended goal of making the farmers adopt some site-specific management practices of N-usage.

In addition, we conducted cross-tabulation analyses and employed Chi-square tests to assess the associations between different types of educational technology, agricultural technology, and the resulting effects or impacts of the implemented technology interventions. Among the 54 articles, two articles did not specify the intervention effect.

Based on the findings presented in Table 3 , a significant relationship was observed between the type of educational technology utilized and the resulting effect or impact of the intervention. The statistical analysis revealed a significant result of χ2 (8, n = 52) = 28.67, p < .001, indicating that the type of educational technology employed influenced the outcomes of the interventions. Interestingly, articles that predominantly utilized multimedia and a combination of multiple ET ( n = 30) recorded more positive intervention outcomes. Research studies, such as those conducted by Chowdhury et al. [ 65 ] in Bangladesh, which used video-mediated learning to improve farmers’ understanding of botanical pesticide usage, and by Bello-Bravo et al. [ 79 ], which found an 89% adoption rate when animated agricultural videos was used for the dissemination of postharvest bean storage, clearly demonstrate the effectiveness of multimedia as a reliable tool for promoting the adoption of agricultural technologies. Several studies have examined the effectiveness of mobile apps and smartphones, and four of them reported positive results. One such study was conducted by Dione et al. [ 76 ], where the use of interactive voice response (IVR) was found to significantly enhance the knowledge gains of 408 smallholder pig farmers who received biosecurity messages. While the results of the other four were mixed, one study conducted using digital games/simulation also reported a positive outcome which was the study by Dernat et al. [ 84 ] where a game-based methodology was found to be very effective in facilitating farmers’ collective decision making and continued engagement. Notably, the only article that did not report a positive outcome was a single study that used online/web-based applications. The implications of these findings are that stakeholders in the field of agriculture can collaboratively work together to design a targeted, cost-effective and guaranteed communication channels that could yield greater positive results in the nearest future.

Note *** = p < .001.

In contrast to the analysis on educational technology, the cross-tabulation and Chi-square analysis examining the relationship between the type of agricultural technology provided to farmers and the resulting impact of the intervention did not yield a statistically significant result χ2 (8, n = 52) = 7.52 ( p = .482), as shown in Table 4 . Despite the lack of statistical significance, patterns can still be observed between the two variables. Out of the 52 articles, 35 reported a positive outcome, while 15 reported mixed results, regardless of the specific agricultural technology/innovation utilized. These findings suggest that, in the context of agricultural extension, the method of communication or transmission of agricultural information through educational technology may play a more crucial role in determining the overall success of the interventions than the specific agricultural technology employed.

Note: χ 2 (8, n = 52) = 7.52, p = .482.

The previous research (44) focused on explaining the process of transferring and adoption of agricultural technology while our study focused on the application/usage of the AT. This study found that simple technology like multimedia served as the most frequently used and video/video-mediated extension served as the most prominent, which is consistent with the previous research [ 43 ] stating that technologies that are more complex to comprehend and use have lower rates of adoption. Previous review [ 44 ] focused on how one specific type of ET (ICT) affects AT adoption in developing countries while our study investigated diverse kinds of educational technology. Our findings suggested that the use of multimedia as an ET might be due to the characteristics of limited educational level and economic level of farmers in developing countries. It is consistent with previous review [ 44 ] indicating that farmers have limited access to resources and infrastructure investments remain low in many developing countries. While these reviews concentrated on measuring the impact of ICT-based agriculture extension programs, our study focused on summarizing the effect/impact of using technologies in agricultural extension with most studies reporting positive outcomes.

6. Conclusion and future directions

In conclusion, this scoping review underscores the critical role of TA in agricultural extension, presenting valuable insights into technology’s potential to enhance extension programs and stimulate future research. Maunder’s [ 8 ] definition of agricultural extension guided this scoping review, emphasizing the characteristics of the service and its potential impact on improving and educating farmers. As explained by Rivera et al. [ 7 ], agricultural extension serves as a vital link to increase productivity and efficiency among farmers and researchers, facilitating the sharing of innovations. Technological applications within agricultural extension have the power to transform farming practices [ 12 , 13 , 16 ].

Through our comprehensive coding, we categorized the TA within agricultural extension into two domains: use of technology/innovation as a factor of production and as an ET. While our study included various agricultural fields, such as agricultural economics, agricultural engineering, animal husbandry, and agronomy, it should be noted that some studies lacked detailed information that could have provided valuable insights into the impact of technology applications on farmers through agricultural extension programs.

Furthermore, this research establishes a foundation for future studies, innovation, and informed practices by identifying areas that warrant further exploration and discovery. The significant increase in research activity in technology applications, particularly after 2016, highlights its growing importance. Advancing the application of technology in agricultural extension contributes to improved agricultural outcomes and sustainable development in farming communities worldwide. Future research on technology applications in agricultural extension should address limitations that may be inherent in the research designs, data collection instruments and the units for the measurement of the intervention outcomes. Future studies should also identify technological effectiveness, delve into mechanisms and contextual factors related to positive outcomes, and aim to support farmers and farm households more effectively.

Supporting information

Funding statement.

The author(s) received no specific funding for this work.

Data Availability

• PLoS One. 2023; 18(11): e0292877.

Decision Letter 0

14 Aug 2023

PONE-D-23-20077A scoping review on technology applications in agricultural extensionPLOS ONE

Dear Dr. Xu,

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Additional Editor Comments:

Dear Authors,

I hope this letter finds you well. I would like to extend my gratitude to you for submitting your article to the PLOS ONE journal. Your work has been reviewed by two experts in the field, and I have taken their comments into consideration for the following decision.

Your article presents a compelling exploration of the role of technology in agricultural extension programs. Based on the feedback from the reviewers, the consensus is that your paper is of considerable value to the academic community. The depth of your research, the rigor of your methodology, and the clarity of your writing have been particularly appreciated.

However, both reviewers have pointed out specific areas that could benefit from further clarity, elaboration, or adjustment. These comments are aimed at refining your manuscript to ensure that it provides the most significant value to our readers and the broader academic community.

Reviewer 1:

Reviewer 1 was quite impressed with your manuscript and recommends its acceptance in its current form. They particularly commended the depth of your research, the clarity of writing, and the logical flow of your arguments.

Reviewer 2:

Reviewer 2 provided a detailed breakdown of suggestions and potential areas of improvement. Their feedback spans across several sections of your manuscript, including:

Abstract: Emphasizing the significance of your focus, justifying database choices, providing more context on regional mentions, clarifying the distinction between research methods, and expanding on the impacts and limitations.

Introduction: Incorporating a historical perspective, giving examples of technological integrations, ensuring accurate references, and refining the presentation of objectives.

Literature Review: Streamlining definitions, elaborating on technological impacts, and refining the presentation to avoid redundancy.

Research Questions: Enhancing the specificity of your questions and ensuring that the breadth is maintained throughout the manuscript.

Research Method: Expanding on the search strategy, clarifying methodological choices, incorporating a PRISMA flow diagram, and reflecting on challenges faced during research.

Results and Discussion: Providing insights on publication platforms, discussing regional research discrepancies, interpreting statistical results, and drawing comparisons with other literature.

In light of the above, I am returning your manuscript with a decision of "Revise". I believe that by addressing the reviewers' comments, your manuscript can be further enhanced, making it an even more valuable contribution to our journal and the field at large.

Please ensure that you address each point raised by the reviewers. Upon resubmission, kindly include a detailed point-by-point response indicating how you have addressed the reviewers' comments or provide a rationale if certain suggestions were not incorporated.

We appreciate the time and effort you have put into your research and manuscript. I hope you find the reviewers' feedback constructive. I am looking forward to receiving your revised manuscript.

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Reviewers' comments:

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Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #1: This is a very interesting article. The author has delved deep into the subject matter, presenting well-researched insights and thoughtful arguments. The clarity of writing and logical flow make it an engaging read. The article effectively captures the reader's attention from the beginning till the end. The supporting evidence and references add credibility to the claims made. Overall, it's a valuable contribution to the field. I highly recommend accepting it in the current form. Great work!

Reviewer #2: Dear Editor

Please find my comment below:

General Comments

The abstract presented offers an in-depth scoping review of technology's role in agricultural extension programs. The approach is comprehensive, and the narrative effectively captures the integration of both agricultural and educational technologies. The use of multiple databases for sourcing articles provides a robust foundation for the findings. The structured presentation of findings is also commendable.

However, certain aspects of the abstract would benefit from added clarity or additional information. Specific details and clearer articulation in some areas would enhance the reader's understanding and make the abstract even more impactful.

Specific Comments

1. The abstract briefly touches upon the lack of previous reviews on the impact of technology in agricultural extension. It would be beneficial if the authors briefly indicate why this particular focus is of significance.

2. Justifying the choice of the five databases, or mentioning if these are the most prominent databases in this field, would enhance the credibility of the study.

3. The mention of India and Africa requires more context. It would be useful to know if this observation indicates a trend or if it merely represents the scope of available literature.

4. The distinction between the quantitative research method being the most employed and the mixed methods being the most used data collection approach might be confusing. It would be helpful if the authors could provide a brief explanation or example of this distinction.

5. While the most widely used educational technology is mentioned, the abstract could benefit from highlighting a few of the most common agricultural technologies that appeared in the reviewed studies.

6. The statement that the impacts were "mostly mixed" requires further specificity. Providing a brief example or elaborating on what areas showed positive or negative impacts would be beneficial.

7. It's commendable to acknowledge potential limitations. A brief mention of one or two key limitations would be insightful.

8. The abstract concludes with an emphasis on gaps in the literature. Mentioning one or two primary gaps or areas for future research would provide readers with a clear takeaway.

Introduction

The introduction offers a clear context and rationale for the importance of integrating technology into agricultural extension programs. The progression from the significance of technology in enhancing extension programs to the purpose of the scoping review is logical. The emphasis on the potential benefits for policymakers, researchers, and practitioners provides a broad perspective on the review's relevance.

However, some areas could benefit from further elaboration, and the structure might be enhanced to offer a more concise and direct presentation of the main points.

1. The introduction starts strongly by emphasizing the importance of agricultural extension programs. However, it could benefit from a brief mention of the historical or traditional methods of agricultural extension for context.

2. While the importance of technology in agricultural extension is emphasized, it would be beneficial to provide examples or categories of such technologies. This would offer readers a clearer picture of what technological integrations are being discussed.

3. The references (1) and (2) are placeholders. In the final manuscript, it would be crucial to ensure that these references are accurately representing the claims made.

4. The statement about shedding light on the "current state of research" and mapping the field is clear. However, distinguishing between the broader goals of the review and the specific objectives could provide more clarity.

5. The mention of policymakers, researchers, and practitioners is appropriate. Still, it might be enhanced by briefly discussing the specific challenges or questions each of these groups faces that the review can address.

6. The final part of the introduction discusses the research's aims to lay a foundation for future studies. While this is a strong ending, it might be enhanced by presenting a more concise summary of the intended contributions and outcomes of the review.

Literature Review

General Comment

The literature review offers a comprehensive overview of the integration of technology in agricultural extension programs. The authors have meticulously categorized the research into the historical perspectives of agricultural extension, the role of technology in agriculture, and the intersection of both. The reference to prior studies and the identification of gaps in existing literature lend robustness to the review.

However, some areas could benefit from further clarity, and the structure might be enhanced to offer a more concise presentation of the main points.

1. Agricultural Extension Definitions: The various definitions of agricultural extension provided are comprehensive. However, the transition to the definition that the review aligns with could be smoother. Perhaps a brief rationale for choosing Maunder’s definition would be beneficial.

2. Technological Integration: The distinction between agricultural technology as a component of production and as an educational tool is clear. Yet, more explicit connections between the tools and their practical impacts would enhance understanding. For instance, how do drones or IoT directly influence agricultural extension?

3. Previous Studies and Research Gap: While the section thoroughly identifies gaps in existing research, it could benefit from a more streamlined presentation. The repeated mention of "technology application in agricultural extension" and the emphasis on the review's unique approach can be condensed to avoid redundancy.

4. Citation and Referencing: The placeholders for references are well-placed, providing a strong foundation for the claims made. In the final manuscript, ensuring that these references are comprehensive and up-to-date will be critical.

5. Relevance of Previous Studies: The review does well to distinguish itself from the works of Altalb et al. and Aker. However, a brief mention of why these studies are particularly relevant or how they shaped the current review's approach might provide more context.

6. The literature review concludes with a forward-looking statement about enhancing productivity and bridging divides. This is effective but could be enhanced with a brief mention of the expected outcomes or implications of the scoping review.

Research Questions

The research questions section offers a structured breakdown of the areas that the scoping review aims to address. The categorization into substantive features, methodological features, and characteristics of technology application provides a clear roadmap of the study's approach. The questions themselves are well-formulated and adequately detailed, promising a comprehensive exploration of the topic.

However, certain areas could benefit from further specificity or clarity to ensure that the subsequent sections of the manuscript align seamlessly with these guiding questions.

1. While the query about publication information is clear, it might be helpful to specify what particular publication information is of interest (e.g., publisher, year, journal name). Additionally, the inclusion of "agricultural field" is relevant, but the term might benefit from elaboration or examples for clarity.

2. The question is comprehensive in covering research methods, data collection approaches, and sample size. However, it might be enhanced by adding inquiries about potential research biases, limitations, or challenges identified in the included studies.

3. The distinction between educational technology and agricultural technology is clear and aligns with the literature review. Yet, the question might benefit from an exploration of the integration or interaction of these technologies. For instance, how does the use of educational technology influence the adoption or effectiveness of agricultural technology?

4. The query about the "overall effect of technology on agricultural extension" is broad. It would be beneficial to specify if this effect is being measured in terms of productivity, knowledge transfer, farmer satisfaction, or any other specific metrics.

5. The research questions set a broad scope for the review. Ensuring that this breadth is maintained throughout the manuscript will be crucial, especially in the results and discussion sections.

Research Method

The research method section is comprehensive, providing detailed insights into the procedures followed in the scoping review. The use of multiple databases, clearly defined inclusion and exclusion criteria, and a structured coding scheme reflects the systematic approach the authors have taken. The use of PRISMA flow and the description of inter-rater reliability further emphasize the rigor with which the study has been conducted.

While the overall methodology appears robust, certain areas could benefit from further clarity or elaboration to ensure the methodological choices are entirely transparent and replicable.

o It is commendable that the authors have provided the date of the search to ensure the recency of the data.

o While the Appendix A contains the full search strategy for CAB Abstracts, the modifications made to fit other databases would be useful for replication. It would be beneficial to briefly describe or provide these modified strategies in an additional appendix.

o The criteria are well-defined and comprehensive. However, the delineation between what qualifies as an educational technology versus agricultural technology might benefit from additional examples or elaboration.

o It would be helpful to know why the authors chose the specific date range of January 1, 2000, to November 1, 2022. While technological advancements since 2000 are mentioned, a brief rationale for this specific range could enhance clarity.

o The coding scheme is extensive and well-structured. However, the categorization of agricultural field/enterprise could benefit from a more exhaustive list or examples, given that only a few are mentioned.

o Under the section on methodological features, while the grouping of research methods is clear, a brief rationale for these groupings (especially what constitutes mixed methods) would be useful.

o The distinction between educational technology and agricultural technology/innovation is clear. However, the list of technologies and their sub-categories might benefit from further examples or references to ensure clarity.

o The PRISMA flow diagram, while mentioned, is not provided within the section. If feasible, it would be helpful to include this diagram directly within the manuscript or provide a clearer direction to its location.

o The inter-rater reliability is commendably high, but a brief discussion on how discrepancies were resolved (other than the first author acting as arbiter) would provide additional transparency.

o It might be helpful to provide a brief overview of the descriptive statistical analyses planned or executed to address the research questions.

o The section could benefit from a brief discussion or reflection on any anticipated or encountered challenges during the research method, especially during data collection or coding.

Results and Discussion

The Results and Discussion section is extensively detailed, covering a wide range of aspects concerning the substantive features, methodological features, and characteristics of educational technology in agricultural extension. The use of figures, tables, and statistical analyses enhances the rigor and depth of the presented findings. The section is well-organized, with clear sub-sections that aid in understanding the progression of results.

However, there are areas that could benefit from further elaboration or explanation to ensure clarity and completeness.

o The graphical representation of publication distribution (Fig 2) and the breakdown of journals, conferences, and policy papers (Table 2) provide a clear overview of the landscape of research in the field. It would be beneficial to include comments or insights on the top journals or platforms publishing in this area.

o The distribution by country and region paints a clear picture of where the research is focused. Some insight into the potential reasons behind the lack of research from certain regions, like Europe or Australia/Oceania, beyond what is provided, might enhance the discussion.

o The breakdown of research methods, data collection approaches, inferential statistics, and sample size units are comprehensive. It would be interesting to see a further discussion on the implications or reasons behind the prevalent use of quantitative methods over qualitative ones.

o The discussion on sample size units and the categorization based on the number of participants adds depth to the results. However, a brief discussion on the implications of these findings for future research would enhance this section.

o The breakdown of different types of educational technologies and agricultural technologies is detailed and clear. It would be helpful to delve deeper into the reasons behind the prevalent use of certain technologies over others.

o The findings on the intervention characteristics of technology are insightful. The relationship between the duration, intensity, and outcomes of interventions could benefit from further exploration.

o The cross-tabulation analyses and the Chi-square tests add depth to the results, providing a clear understanding of the relationships between variables. However, some additional interpretation of these results in the context of the broader research landscape would be useful.

o The section might benefit from a summarization of the main findings and their implications for both researchers and practitioners in the field.

o While the section is quite detailed, it would be beneficial to see more connections or comparisons with other studies or literature in the field, providing a broader context for the presented findings.

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Reviewer #1:  Yes:  Cristiano Matos

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Author response to Decision Letter 0

28 Sep 2023

Dear reviewers,

We are delighted to revise our manuscript based on the excellent feedback from the reviewers and you. We believe the suggestions and corresponding revisions have significantly improved our research, findings, and manuscript. We responded to the comments one by one and highlighted what we revised in the manuscript. We also created a comments and response table to explain how we addressed all of the comments. We will be happy to receive any additional comments and make revisions if necessary. Thanks again for the opportunity to revise and resubmit.

Detailed information can be found in our Comments and Response table and the revised manuscript.

Submitted filename: comments and response table.docx

Decision Letter 1

PONE-D-23-20077R1

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27 Oct 2023

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Cultivating the Future: Agricultural Innovations for Climate Resilience

As the world grapples with the impacts of climate change, extreme temperatures and tragic weather events tend to dominate the news. But a slower moving, less conspicuous threat looms in our future: the challenge of maintaining a sustainable, resilient agricultural system. 

Supported in part by funding from USDA’s National Institute of Food and Agriculture as well as funding from other federal agencies, the University of Maryland College of Agriculture and Natural Resources’ (AGNR) research and Extension programs take a comprehensive, multi-disciplinary approach to that challenge. 

“Climate smart agricultural practices that reduce environmental impact while also building system resilience will ensure that we maintain productive agricultural and forestry systems,” said Dr. Rachel Melnick, division director for Global Climate Change in NIFA’s Institute of Bioenergy, Climate and Environment.  “The work highlighted by the University of Maryland are some excellent examples of how Land-grant Institutions are addressing the climate crisis.”   

This following article was published by the University of Maryland College of Agriculture and is reprinted here with permission.  

Slowing the Burn 

According to the Environmental Protection Agency, agriculture of all forms accounts for approximately 10-11% of the total U.S. contribution to the greenhouse gasses that are heating Earth’s atmosphere. Finding ways to reduce emissions from agriculture while maintaining, and even increasing, yields is crucial to helping slow the warming process. 

With over $1 million in funding from NASA, Associate Professor Stephanie Yarwood is leading an effort to use satellite data in unraveling the complex relationship between farming practices and nitrogen emissions. The primary contributors to greenhouse gasses from farmland0 are nitrogen compounds from animal waste and fertilizers. But the effects of different farming practices on nitrogen loss from farms, at various times and under diverse conditions, remains unclear. Yarwood’s project uses satellite data to identify “hot spots” and “hot moments” in time, when nitrogen compound levels rise in the atmosphere above farmlands. Her team will then examine the soil microbes to determine how they control the amount and kind of nitrogen released into the air and water, and the effect of various conservation practices on those processes. 

“As a microbial ecologist who often thinks in scales no bigger than a shovel full of soil, it is exciting to be working with collaborators using satellites to think at much larger scales and understand these large, atmospheric effects,” Yarwood said. 

The team’s ultimate goal is to create models to guide policymakers and stakeholders in reducing nitrogen emissions through climate-smart farming. 

Meanwhile, Professor Richard Kohn is tackling farm emissions from another angle. Kohn is studying exactly how metabolism works in the guts of cattle, which burp up methane and account for nearly half of U.S. agriculture’s contribution to greenhouse gas. 

Kohn’s lab has looked at alternative feeds, like algae that is purported to reduce methane from cows. But algae may just shift the composition of cow’s waste products away from methane to toxic compounds that are harmful to the cows and the environment. Kohn and his colleagues are evaluating different algae supplements to see if they can help cows produce less waste overall and convert more food to muscle, or meat. 

“The goal is ultimately to feed cattle better, so we decrease methane emissions and at the same time make digestion more efficient,” Kohn said. 

Adapting To Change 

Already feeling the impacts of climate change, many farmers need help adapting to unpredictable conditions now, as well as in the future, because climate change not only brings new temperature and moisture levels, but it allows pests and diseases to spread into new territories. Researcher Chris Walsh began thinking about that decades ago. Now, through years of careful crossbreeding, he has developed two new breeds of apples that address a growing suite of problems for apple growers. His apples are heat-tolerant, blight-resistant, low-maintenance, and delicious-tasting. 

While orchard fruits play a significant role in the world’s economy and diet, wheat and corn fill the nation’s granaries and provide a significant portion of the world’s calories. Both are facing environmental threats around the world. 

After thousands of years of breeding for large grains and high yields, modern wheat lacks the genetic diversity essential to adapt to those emerging threats. Fortunately, an international team led by Professor Vijay Tiwari has sequenced the complete genome of an ancient variety of wheat known as einkorn. This breakthrough allows researchers to identify genetic traits like disease- and drought-tolerance, and potentially reintroduce those resilience genes into modern bread wheat. 

When It’s Time to Pivot 

Even with adapted crops and more efficient growing methods, there are places where change has already happened too fast to continue supporting crops. Across the globe, sea level is rising, and in the mid-Atlantic region, land is also sinking due to large geological shifts caused by climate change. The result is that saltwater intrudes into surface and groundwaters in low-lying areas, making the soils too salty for farming. 

Alongside collaborators at University of Delaware and George Washington University, AGNR researchers Kate Tully and Rebecca Epanchin-Neil recently found that the area covered by visible salt patches on Delaware, Maryland and Virginia farmland nearly doubled from 2011 to 2019. 

They estimated economic losses from the salt patches to be over $427,000, and what’s more, high salinity soils within 200 meters of salt patches accounted for an estimated crop loss of between $39-70 million annually. This is an especially acute problem for corn farmers, because corn is not very salt tolerant, yet it makes up a substantial portion of the crops grown in the region. 

“Saltwater intrusion often happens in advance of sea level rise,” said Associate Professor Tully. “This research is the first visualization of this often-invisible symptom of climate change.” 

Epanchin-Niell, also an associate professor, said their study can “help identify high risk areas and better target resources and support to regions where transitions are occurring.” 

A Grassroots Solution 

In the meantime, finding crops that can withstand salty soils could help keep agricultural lands profitable. And AGNR Extension agents are helping with that. Sarah Hirsh and Haley Sater just completed a two-year experiment planting Giant Miscanthus in fields belonging to a soy farmer who had three consecutive years of failed crops. Miscanthus is a tall, perennial grass that is often used for bedding in poultry operations, but it could be marketed for other animals, and for making paper and biofuels. 

“Our experimental plots yielded successful harvests,” said Sater, “suggesting this could be an alternative crop that is easy-growing and low-maintenance.” 

As a perennial, miscanthus doesn’t need replanting, and once it gets established, it outcompetes weeds and isn’t eaten by deer.  

It sounds like a perfect solution, but of course, there is no silver bullet to solving the diverse and complex problems brought on by climate change. Miscanthus is just one tool among many that can help farmers stay profitable and sustainable. Whether it’s through a new view of satellite data, innovative cattle feed, genetically informed breeding, or a host of other initiatives, AGNR is helping pave the way for a resilient and adaptable agricultural future. 

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Original research article, how has the rural digital economy influenced agricultural carbon emissions agricultural green technology change as a mediated variable.

• 1 Guiyang Institute of Humanities and Technology, Guiyang, China
• 2 Binary University of Management and Entrepreneurship, Selangor, Malaysia
• 3 Business School, Nanjing Normal University, Nanjing, China
• 4 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
• 5 School of Politics and Economic Administration, Guizhou Minzu University, Guiyang, China

Digital economy is being closely integrated with agricultural development and tapping into its unique potential to alleviate agriculture’s carbon emissions To explore the mechanism of how digital economy reduce the agricultural carbon emissions, this paper constructs a systematic evaluation method with extend STIRPAT model and panel data drawn from 29 provinces (or municipalities and autonomous regions) in the Chinese mainland from 2013–2020. The results show that the development of the rural digital economy has a significant negative influence on agricultural CEs, and this result is still valid given robustness tests. Second, the alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. Third, the influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect. This paper not only provide new empirical evidence for understanding nexus between digital economy and agricultural carbon reduction, but also give constructive policy implication to improve agricultural green development.

1 Introduction

Alleviating carbon emission is receiving more and more attention globally ( Ma S. et al., 2022 ). To maintain harmonious coexistence between humans and natures and realize the United Nations’ Sustainable Development Goals, Chinese central government pledged the global stakeholders that the Chinese people will try their best to have CEs peak before 2030 and achieve carbon neutrality before 2060, which demonstrates a strong determination to solve the problem of climate change. Activities of agricultural sector not only release CO 2 but also hold carbon sequestration function, and the CEs and sequestration function make agricultural production activities have function of maintaining the carbon balance in the atmospheric. However, agricultural CEs have obvious spatial heterogeneity ( Charkovska et al., 2019 ). Faced with issues such as global economic instability, rising energy demand, frequent adverse weather conditions, and expanding food demand ( Fahad et al., 2022 ), the Chinese government should attach importance to cutting agricultural CEs. China is a large and longstanding agricultural country with widespread and extensive agricultural production activities. In the traditional agricultural production mode, the overuse of pesticides and chemical fertilizers, land ploughing and irrigation, as well as the problems of low production efficiency and unreasonable resource allocation in the agricultural production process, will directly or indirectly lead to more agricultural CEs and their higher intensity, thereby seriously restricting the development of low-carbon and high-quality agriculture. The 14th Five-Year Plan for National Agricultural Green Development emphasizes building an agricultural industry system with characteristic of green, low-carbon, and circular, while the 2023 Government Work Report further emphasizes the need to continuously improve the ecological environment and achieve low-carbon and sustainable development.

The digital economy plays an important role in promoting the full and balanced development between urban and rural areas, and its development has driven the economic development of agricultural and rural areas ( Zhao et al., 2023 ). In China, the digital transformation of agriculture sector has shown initial results. According to the Information Center of the Ministry of Agriculture and Rural Affairs, the informatization level of national agricultural production in 2020 was 22.5% and the national level of agricultural product quality and safety traceability informatization was 22.1%. In 2021, the online retail sales agricultural production nationwide has reached 2.05 trillion Yuan, with growth rate of 11.3% compared to the level of the previous year. The construction of digital rural areas has been promoted extensively, with 117 digital rural pilot projects established nationwide, nine agricultural IoT demonstration provinces delineated, and 100 digital agriculture pilot projects established. Alongside these tremendous achievements, the digital economy has a positive impact on carbon emissions from agricultural production ( Zhao et al., 2023 ). Thus, the problem is how to realize the coordinated relationship between them. Would the rural digital economy development bring fresh momentum to reducing agricultural CEs? Meanwhile, how can the rural digital economy empower the reduction of agricultural CEs? Exploring these issues has important practical value for the development of the rural digital economy and improving the reduction of agricultural CEs while also contributing to policy enlightenment in terms of achieving the great mission of China’s “Carbon Peak and Carbon Neutrality”.

The main contribution of this paper comparing to the existing literature are as following. First, we use the extend STIRPAT model to explore the influence mechanism of agricultural digital economy on the agricultural carbon emission. Second, the agricultural green production efficiency is used as a proxy for agricultural green technology change, which not only considering the quantity of the agricultural green development, but also capture the quality of agricultural green development. Third, this paper use three dimensions to measure the agricultural digital economy. Digital infrastructure in rural areas, digitalization of agriculture, and rural digital finance).

The rest of this paper is organized as follows. Section 2 is the literature review. The theory base and research hypnosis are showed in Section 3 . Section 4 describes the models and data used in this paper. Section 5 analysis the estimation results. Section 6 gave the conclusion and presents the policy implications.

2 Literature review

So far, the relevant studies relating to CEs focus on the challenges faced by China in realizing its CEs reduction strategy and corresponding countermeasures. Hu (2021) , OuYang (2021) and others have analyzed the severe challenges faced by China in realizing the goals of dual carbon strategy in terms of international and domestic perspectives, respectively. Liu et al. (2021) and others have analyzed the problems that exist in China in the context of carbon neutrality from on the viewpoint of energy structure, and have put forward countermeasures such as energy conservation and efficiency improvement, while accelerating the transformation and further promotion of energy structures. Adopting another approach, some scholars have conducted empirical analysis on the CEs reduction effect of the carbon trading pilot policy implemented by the Chinese government through the synthetic control method ( Li et al., 2021 ; Yang et al., 2021 ), and have argued that China’s carbon trading pilot policy has played a significant role in the reduction of CEs, but there are problems such as insufficient market driving force for low-carbon innovation, poor pilot policy incentives, and regional heterogeneity. At the same time, Chen et al. (2016) have emphasized that increasing CEs reduces green total factor productivity (GTFP) based on studying the relationship between CEs and GTFP and economic development, and Wang et al. (2019) have also reached the same conclusion in relation to GTFP in agriculture economy development.

In addition, many researchers have devoted attention to agricultural CEs and carried out relevant research on the characteristics and calculation of agricultural CEs, agricultural CEs reduction policies, and influencing factors. Jin and other authors (2021) have explored the structural characteristics of China’s agricultural CEs, and drawn the conclusion that agricultural CEs in China have a phased upward trend alongside regional and provincial heterogeneity. In terms of policy research, Zhang et al. (2001) compared different environmental and economic instruments and argued that the environmental tax system has been more advantageous; Zheng et al. (2011) elaborated on a number of low-carbon special plans and proposed relevant recommendations, such as the establishment of a Chinese low-carbon agricultural model. Based on evolutionary game theory, Fan et al. (2011) suggested that government support and intervention can guide agricultural source farmers to choose CEs reduction strategies. In terms of influencing factors, the empirical studies of Xu et al. (2022a) and Xu et al. (2022b) have suggested that agricultural mechanization and the rural finance service have significant preventative effects on agricultural CEs. Furthermore, He et al. (2020) have discussed the status and role of green production efficiency in agriculture in various provinces.

The digital economy, a new engine of high-quality economic growth, has also attracted extensive attention and discussion in the academic community in recent years. On the one hand, there is research on the definition of the digital economy. Li et al. (2021a) characterize the digital economy on macro, meso- and micro-levels, asserting it includes four levels, namely, broad, middle, narrow and narrowest, and explored the mechanism and evolution process involved in data becoming a production factor ( Li et al., 2021b ). On the other hand, researches about digital economy are mainly about the comprehensive effect of digital economy, and they have put forward the argument that the digital economy can reduce environmental pollution ( Deng, 2022 ), while driving high-quality urban development and promoting a specific economic pattern, which aim to coordinate development between regions ( Zhao et al., 2020 ).

Especially since the strategy “Carbon Peak and carbon neutrality” was put forward, the relationship between the digital economy and carbon emission has become an important topic, and academia has also carried out extensive research ( Yu et al., 2022 ). While researchers hold different conclusion on the nexus between digital economy and carbon emissions. Most studies show that the digital economy has improved the environmental situation, and provided impetus for emission reduction, Wang (2022) point out the digital economy is helpful to reducing the carbon emissions. Zhang (2022a) find that the digital economy plays a significant role in carbon emission reduction. They all conduct their research based on China’s urban data. However, some studies hold that the digital economy has a heterogeneous influence on CEs. Some scholars ( Salahuddin et al., 2015 ; Avom et al., 2020 ) believe that, as the core foundation of the digital economy, the development of digital technology will lead to a large amount of power consumption and energy consumption, thereby increasing carbon emissions.

Furthermore, there are many researches focusing on the development of the digital economy in rural areas. According to theoretical analysis, the existing literature mainly pays attention to the mechanisms or development paths of the rural digital economy. Wang et al. (2021) , Yin and others (2020) and others have explored the significance, practice mode and mechanism of the digital economy development in agriculture production and rural regions, and believe that it should be promoted by, respectively, accelerating the construction of rural digital infrastructure, promoting agricultural digitalization, and developing rural e-commerce. Some researches on digital inclusive finance (DIF) have argued that DIF can push the regional convergence of green economic growth while less developed regions experience a more significant convergence effect ( Wang et al., 2022 ).

Many studies have also been carried out focusing on the influence of digital economy on CEs, mainly adopting the empirical analysis method with panel data based on province- or city-level contexts in China, and have found that digital economy growth can significantly alleviate the intensity of CEs ( Xu et al., 2022 ; Guo et al., 2023 ), however, there exist certain regional differences ( Miao et al., 2022 ; Xie, 2022 ).

A few researches have focused on the correlation between digital economy growth and agricultural CEs in China or foreign countries, and these literature mainly concentrate on the introduction of information and communications technology (ICT) into the field of smart agriculture, the promotion of sustainable agriculture, and the reduction of chemical use on the basis of embedding artificial intelligence ( Patrício and Rieder, 2018 ), sensors ( Basnet and Bang, 2018 ), robotics, and remote sensing technologies ( Huang et al., 2018 ) into agricultural modernization processes. ICT, as a main focus of advanced technology trends, can promote comprehensive productivity efficiency, total factor efficiency (TFP) and agricultural sustainability ( Dlodlo and Kalezhi, 2015 ). The prevalence of ICT not only promotes agricultural productivity and TFP, but also improves the progress of sustainable agricultural development. Ma S. Z. et al. (2022) focus on the nexus between the development of the agricultural digital economy and agricultural CEs; their conclusions emphasize that digital economy development inhibits agricultural CEs. In addition, advances in agricultural technologies, the optimization of agricultural industrial structure, and improvements in rural education all significantly inhibit the agricultural CEs in the research area. Adding to the influence factors outlined above, Zhang J. et al. (2022) emphasize that the development of DIF has significantly reduced agricultural CEs. Unlike other countries or regions, China’s agricultural digital economy mostly stresses the digital transformation of rural industrial models ( Wu, 2021 ), agricultural industries ( Zhao MJ. et al., 2022 ; Zhao YL. et al., 2022 ) and the effectiveness of the digital economy ( Xie, 2020 ). These studies all pay attention to the innovative developments in digital agriculture ( Wang et al., 2020 ). Through the systematic review of the literature outlined above, three main shortcomings can be found in the existing research: First, although many researchers have devoted attention to the correlation between the digital economy and CEs, more of them have studied this on urban level, and rarely extended this correlation to the rural development context, hence there is a lack of research that directly and empirically tests the correlations between the rural digital economy and agricultural CEs. Second, when analyzing heterogeneity, most existing studies only conduct sub-sample studies by region, and consider to a lesser extent the role of R&D in leading the high-quality development of the digital economy. Third, the path or mechanisms of the digital economy in rural areas in relation to the reduction of agricultural CEs is unclear, hence this requires further research. Considering the three points mentioned above, this article measures the intensity and amount of agricultural CEs, the progress in agricultural green technology and the development level of rural digital economy at a provincial level in China and tests empirically the nexus between rural digital economy and agricultural CEs. Meanwhile, this study not only examines the regional heterogeneity of the rural digital economy on agricultural CEs, it also analyzes the heterogeneity of this in relation to the science and technology investment level.

3 The mechanism and research hypotheses

The digital economy is an advanced economic mode with data as the important production factor and its development depends on the ability to obtain data. The establishment of a digital infrastructure not only realizes the utilization and transmission of data information, but also improves the efficiency of data circulation, thereby accelerating the process of digital infrastructure construction, the latter having become an indispensable foundation for the promotion of the growth of the digital economy. China has ascribed importance to the construction of digital infrastructure, and since 2018, the Politburo of the Central Committee has repeatedly stressed the need to accelerate the roll out and promotion of new digital infrastructure and its construction. At the same time, the construction of digital infrastructure is an important prerequisite for the integration of the digital and rural economies; whether it is agricultural informatization, agricultural product trading e-commerce, or the rural digital finance development, the prerequisite is it must be a complete rural digital infrastructure construction.

The reports of the China Academy of Information and Communications Technology believe that the definition of the digital economy can be divided into industrial digitization and digital industrialization, whereby industrial digitalization means the output and efficiency improvement brought about by the introduction of ICT into traditional industries. With the empowerment of digital technology, an environmental monitoring system for agricultural pre-production and production can be established, while new formats such as rural e-commerce goods can be formed after production, thereby realizing the transformation of traditional agriculture into a scientifically based and efficient modern model.

The integration of the digital and rural economies has improved the practice model of digital financial services in China’s “San Nong” field. The development of the digital economy has spawned updated financial models while the innovative development of digital finance has continuously added new momentum to the digital economy. The integration of ICT and traditional finance provides the possibility of opening up the farmers’ “last mile”. Furthermore, digital finance enables rural areas to address difficulties in accessing affordable financing at a low cost, fully leveraging the inclusive and the sharing advantages of digital finance, thereby contributing to the rural revitalization strategy while promoting the in-depth and comprehensive growth of the digital economy in today’s China.

Based on these insights, this article mainly explores the effect and mechanism of the rural digital economy growth level (explained from three aspects: rural digital infrastructure construction, agricultural digitalization, and development of the rural digital finance development) on agricultural CEs while also examining the intermediary effect of green technologies progress, which was measured by the agricultural green technological efficiency (see Figure 1 ).

Figure 1 . Model of the impact of the rural digital economy on agricultural CEs.

3.1 Digital infrastructure in rural areas

Digital infrastructure as a foundation for the development of the digital economy plays an important role in realizing agricultural digitalization and rural digital finance. It contributes to promoting the deep development of the digital economy while limiting the digital economy’s CEs. The agricultural CEs reduction effect of rural digital infrastructure construction is mainly manifested in the following two aspects: First, rural digital infrastructure construction can guide residents in rural areas to form green environmental protection concepts. The development of ICT enables rural residents to accelerate their access to the online environment, understand news and public opinion related to environmental pollution, and develop green and environmental protection concepts, thereby promoting the formation of informal environmental regulations on the Internet ( Xu, 2014 ) while helping to alleviate agricultural CEs and reshaping patterns of rural environmental governance. Second, the establishment of perfect rural digital infrastructures can reduce the limitations of geographical space, promote information interconnections and sharing, and help achieve a rational allocation of resources, thereby reducing the energy consumption caused by spatial and time factors in production and life, improving energy efficiency while unleashing CEs reduction effects.

3.2 Digitalization of agriculture

In terms of agricultural production management, the technology of big data analysis can promote the establishment of large-scale and standardized agricultural production bases, realize scientific analysis and reasonable predictions of crop sowing, output and demand, while reducing the imbalance between supply and demand and the waste of resources caused by insufficient and asymmetric information. In addition, through modern information processing technologies such as remote sensing satellites, real-time data collection, monitoring and analysis of agricultural production can be realized, and a scientific environmental monitoring system can be established so as to improve the allocation efficiency of production factor, grasp changes in the ecological environment, accurately measure CEs and trace them in time, thereby promoting effective governance and green development.

Digital technology can also continuously enrich the marketing methods of agricultural products, forming new sales models, i.e., rural e-commerce and live streaming. The continuous popularization of the rural Internet has connected farmers to online consumption cyberspace, realized “point-to-point” transactions, and reduced resource waste and CEs caused by the problems of information asymmetry and high transaction costs in traditional agricultural sales models. In terms of logistics and distribution, low-carbon logistics has become an important future development direction. The Vision 2035 Plan points out that green and low-carbon development should be promoted in the transportation industry while low-carbon freight logistics should also be realized. Aim to achieve development of the low-carbon logistics, relying on digital technology, the logistics and distribution industry is gradually replacing traditional fuel vehicles with clean energy electric vehicles, and accelerating the application of drones in rural areas for logistics distribution to reduce CEs. Regarding the latter, Jingdong drones have been used in some rural areas of Suqian City, Jiangsu Province, and this has already achieved normalized delivery ( Lin et al., 2020 ). Relying on artificial intelligence technology can also promote the intelligence of agricultural product logistics systems, while the establishment of rural smart logistics information platform can optimize distribution routes, achieve resource intensification, continuously save costs, improve efficiency, and deepen the digital economy’s Carbon reduction effect.

3.3 Rural digital finance

The development of rural digital finance has promoted the establishment of rural environmental protection service platforms. Participation in environmental governance and other activities has effectively increased farmers’ enthusiasm for engaging in environmental protection and has helped to improve their sense of social responsibility ( Meng et al., 2022 ; Dong et al., 2023 ). Taking the “Ant Forest” in Alipay’s personal carbon account platform as an example, users collect online energy and plant virtual trees to achieve real afforestation projects in reality, which attracts lots of subscribers to participate in environmental protection actions. In addition, it not only provides a sense of gain for the masses, but also promotes agricultural green development and reduces CEs. Furthermore, the rural environmental protection service platform built by relying on the digital finance development can also analyze the information of platform users through big data technology while rationally allocating resources, thereby reducing agricultural CEs. For example, Alipay’s “garbage sorting and recycling platform” is specially set up for problems such as the low recycling rate of domestic waste, supporting door-to-door collection of waste items so that the resource recycling rate is improved. Digital finance promotes green growth and green technological significantly ( Wu et al., 2022 ; Razzaq and Yang, 2023 ). Mobile payment and online financial services can continuously reduce farmers’ dependence on financial institutions, not only reducing the transaction costs of paper money but also promoting the rational layout of financial business outlets, lowering resource consumption, while uniting both economic and environmental benefits.

In addition, digital finance can effectively compensate for the neglect of traditional finance in rural areas. In the traditional financial environment, farmers have difficulty in financing and own single source of funds, which is not conducive to introduce new agricultural technologies and form the extensive production methods, resulting in more agricultural CEs, hence more serious agricultural pollution problems. The promotion and application of digital finance has broadened the channels of farmers’ capital sources, assisted them to introduce efficient and low-carbon new agricultural technologies, and formed a green agricultural business model, thereby continuously reducing agricultural CEs’ intensity and promoting green agricultural development. Besides, digital finance can also alleviate the misallocation of financial resources and provide more career options for rural residents.

3.4 The progress of agricultural green technology

Generally speaking, a valuable way to achieve high-quality agricultural development is via green agricultural technological change ( Deng et al., 2022 ).

In the existing agricultural economics research, more studies focus on green technological change or environmental technological change using different methods to assess agricultural green technology’s efficiency or that of environmentally friendly technology’s efficiency. According to the existing study on agricultural green technology change (AGTC) of China, the improvement of China’s agricultural productivity is overestimated due to ignoring the influence of environmental factors. Considering the regional heterogeneity of environmental conditions, agricultural technological change in rural China shows an increase trend, while there is a descending trend in the eastern, western, and central regions respectively. The northeast region has experienced an obvious decline in levels of technological change, while technological change without environmental constraints has exhibited a descending trend from eastern to western China ( Jiang et al., 2022 ). He et al. (2021) have identified some important factors affecting agricultural green innovation efficiency, such as the level of agricultural technologies’ diffusion, absorption, implementation, and informatization, the amounts of agricultural extension workers, the average schooling of households, and levels of agricultural mechanization.

To estimate the green efficiency of agricultural production, Korhonen and Luptacik (2004) developed and extended the DEA considering environmental aspects. Existing literature usually through two ways to calculate the green efficiency, one is choosing the environmental factors as the inputs, the other is taking the environmental factors, especially the bad environmental results as bad outputs. The SBM-DEA taking account undesirable outputs is a widely used model to deal with economic and ecological issues ( Liu et al., 2022 ). In this paper, we also chose the SBM-DEA model to estimate the agricultural green production efficiency, taking the carbon emission as the bad output in the DEA model.

In view of the above analysis regarding how the rural digital economy influences agricultural CEs, this article puts forward two research hypothesizes.

Hypothesis 1:. The rural digital economy may reduce the level and intensity of agricultural CEs significantly.

Hypothesis 2:. The rural digital economy may reduce CEs through green technological innovation efficiencies.

4 Research design

4.1 constructing the modelling.

The STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model initially proposed by Dietz and Rosa (1994) explores the factors influencing atmospheric emissions, such as socioeconomic, demographic, and technological issues. In the existing literature, the STIRPAT model mainly has been introduced to explore the causes of CEs in different industries, countries or cross-government economic organizations. These researches have concluded that certain factors such as rising population and affluence levels, the growth of urbanization, the structure of economic development and energy consumption as well as the energy mix and related technological issues are all responsible for increasing emissions. The STIRPAT model is in introduced in our study and is extended from a base IPAT model, which was initially proposed by Ehrlich and Holdren (1971) . The advantage of this is that it allows for appropriate decomposition of population, technology, and wealth, while also adding other issues when analyzing environmental impact factors. The expression is:

where I i is the influence in observational unit i from population P , affluence A and technology T . μ i is the random error term, α、η、κ and φ are the parameters.

The fixed-effects model can be used to control regional invisible differentiation, so the endogeneity issue generated by invisible or unchanging is addressed ( Liu et al., 2024 ). Because of the advantages of fixed-effects, here we choose the fixed-effects model.

To effectively avoid the heteroscedasticity of the model, this article converts the terms in Equation 1 into their logarithms as follows:

where i indicates province; t indicates time; λ i indicates provincial fixed effects; and ε i t represents random error terms. β is the coefficient that this article focuses on, and it is expected to be negative.

A E i t stands for the agricultural CEs intensity of the i th province (city) in the t year; A D I G i t represents the comprehensive level of rural digital economy growth in the t year of i th province (city), which is the core explanatory variable of this paper. In York et al. (2003) , the STIRPAT model was introduced to interpret the technology term, which can be composed of more than one variable considering the needs of a given study. In the STIRPAT model, the estimated coefficients of core explanatory variables can be clarified as environmental effect elasticities, which means the percentage change of CEs for one percentage change in digital economy growth.

Thus in our paper we choose certain control variables, including urbanization rate ( U R B A N i t ), level of agricultural mechanization ( M E C H i t ), planting structure ( S T R U i t ), agrochemical input intensity ( C H E M i t ), traffic ( T R A N i t ), rural electricity use ( E L E C i t ) to represent the population, affluence and technology of a given rural area.

Digital agriculture is conducive to the green transformation of agricultural industry, meanwhile, the progress of green technologies can decrease the CEs level of agricultural production. Thus, the influence path of digital agricultural economy on CEs can be expressed as the following models, as shown in (3) to (5) .

Here, Eq. 5 is the total effect model, Eq. 4 is the estimated model of the agricultural digital economy on agricultural green production efficiency, and Eq. 3 is the estimated model that considers both the agricultural digital economy and the mediating mechanism. Where, the mediator variable is the variable GTFP, the green agricultural production efficiency. The coefficient ω 1 in the formula (5) reflects the overall effect of the digital economy on the agricultural CEs, the coefficient λ 2 represents the direct effect of digital economy on the agricultural CEs, and the magnitude of the mediating effect can be determined by ω 1 − λ 2 . If the coefficient ω 1 , λ 2 and ζ 1 are all significant, and λ 2 < ω 1 or the significance of λ 2 is lower than ω 1 , it can be inferred that the mediating effect exists.

4.2 Variable selection

1. Variable to be explained: Agricultural carbon intensity (AE). In this study, agricultural CEs intensity is chosen to measure the level of agricultural CEs in provinces. Agricultural CEs intensity is expressed by the ratio of total agricultural CEs to agricultural added value. The total amounts of agricultural CEs of each province were calculated from six dimensions: agricultural fertilizer, pesticide, farm PE film, agricultural diesel, tilling and irrigation ( Li et al., 2011 ).

The CEs estimation formula is:

where variable E is the total CEs generated by agriculture production. E i stands for the CEs amount of various carbon sources, T i is the amount of i th carbon source, and δ i is the CEs coefficient of i th carbon source. The CEs coefficients of different carbon sources are listed as follows: 0.896 kg kg -1 for agricultural fertilizers, 4.934 kg kg -1 for pesticides, 5.180 kg kg -1 for agricultural film, 0.593 kg kg -1 for agricultural diesel, and 312.600 kg km -2 for ploughing. Agricultural irrigation is 25 kg hm -2 ( Dubey and Lal, 2009 ). After calculating the total agricultural CEs of each province, divide by the agricultural added value of each province to get the agricultural CEs intensity of each province (kg/10,000 yuan). The average values of total agricultural CEs and agricultural CEs’ intensity from 2013–2020 in each province (municipality) are shown in Figure 2 . The top five average agricultural CEs are Henan, Shandong, Heilongjiang, Hebei and Anhui, mainly in the major agricultural provinces. Nearly half of whole country have agricultural carbon emissions exceeding five million tons. From the viewpoint of agricultural CEs’ intensity, the top five areas are Gansu, Jilin, Inner Mongolia, Shanxi and Xinjiang, which produce large volumes of CEs per 10,000 yuan of agricultural added value, all exceeding 180kg, on the one hand because they may be dominated by extensive agricultural production methods, while on the other hand it is also related to the less development level of the agricultural digital economy.

2. Core explanatory variable: Rural Digital Economy Development Index (ADIG). Based on the existing research, this paper selects 10 indicators such as rural Internet penetration rate and agricultural meteorological observation stations from the three aspects of rural digital economy infrastructure construction, agricultural digitalization, and rural digital services, and constructs an evaluation index system for the growth level of the digital economy in rural areas, as shown in Table 1 . The Internet penetration rate in rural areas is assessed using the proportion of rural Internet broadband access users to the rural population in an area, while the number of Taobao villages is taken from the Ali Research Institute’s China Taobao Village Research Report , 1 the DIF coverage breadth index is obtained from the digital inclusive financial index data of Peking University ( Guo et al., 2020 ) measured by account coverage status, including the number of Alipay accounts per 10,000 people, the ratio of Alipay card users, and the average amounts of bank cards bound to an Alipay account. Other metric data is available directly. Among these, the average population served by postal outlets is a negative indicator while the others are positive indicators. In this research, the entropy method is introduced to measure 10 indicators of rural digital economy growth at three dimensions in order to get the rural digital economy development index of each province (city).

Figure 2 . Average level of total agricultural CEs amounts and intensity in each province (city), 2013–2020.

Table 1 . Evaluation index system of rural digital economy development.

The growth level of the rural digital economy in every province (city) in 2013 and 2020 are shown in Figure 3 . It is found that there is significant heterogeneity in the growth level of the rural digital economy between different regions and different years.

3. Mediated variables: Green efficiency agricultural development (GE). In the existing literature, the total factor productivity (TFP) calculated by DEA-Malmquist index is always used to measure the technological change, while using the Malmquist index will sacrifice time information. Thus, this paper uses agricultural green technological efficiency with environmental constraints. In the DEA model of this paper, agricultural added value was defined as the good output, agricultural CEs constitute the bad output, meanwhile the sown area of crops, fixed capital investment and the agricultural workers were set as the input variables.

Figure 3 . Comparison of comprehensive scores of rural digital economy development in 29 provinces (municipalities and districts) in China, 2013–2020.

From Figure 4 , it is obvious that the green agricultural technological efficiency of less than half province is more than 1, which means that more than half of provinces have less efficient green agricultural technologies. Thus, for China, there is still more space to improve the green technologies. In this paper, we use GE to stand for green technological efficiency.

4. Control variables. Due to the complexity of factors influencing the agricultural carbon emission, considering only the impact of the agricultural digital economy on agricultural CEs might lead to bias, and even serious endogeneity issues. Therefore, the following variables are selected to ensure the comprehensiveness and accuracy of empirical analysis. Is complexity and variables: 1) Urbanization rate (URBAN), measured by the proportion of urban population in a region to total population in the same area; 2) The level of agricultural mechanization (MECH), expressed as the total power of agricultural machinery; 3) Planting structure (STRU), expressed as the ratio of the grain sown area to the crop sown area; 4) Agricultural chemical input intensity (CHEM), expressed as the ratio of fertilizer use to the crop sown area; 5) Traffic conditions (TRAN), expressed as the sum of railway operating mileage and highway mileage; 6) Rural electricity consumption (ELEC), expressed in terms of agricultural power generation. The above variables are logarithmic.

Figure 4 . Average green agricultural technological efficiency of 29 provinces, 2013–2020.

Considering the availability of data, the Institute uses all data for 29 provinces (cities) in China from 2013–2020 (excluding Shanghai, Tibet, Taiwan, Hong Kong and Macao), which are derived from the China Statistical Yearbook (2014–2021) 2 and China Rural Statistical Yearbook (2014–2021), the EPS data platform, the Ali Research Institute Report, and the Peking University Digital Inclusive Finance Index (2011–2020). The descriptive results for all variables chosen are shown in Table 2 .

Table 2 . Description of main variables and descriptive statistical analysis.

As shown in Table 2 , except for lnELEC , all other variables have very small fluctuation trends, namely, less than 1.

5 Empirical results and analysis

5.1 estimates of basic regression model.

Firstly, only the core explanatory variable, namely, rural digital economy development composite score (ADIG) is considered, while the mixed-, fixed- and random-effects model is selected, and the F-test is 25.04 and the p -value is 0.0000, and the fixed-effect model should be selected. The Hausmann test shows that χ 2 is 4.77 and the p -value is 0.029, choosing a fixed-effect model. The other control variables were then added, and mixed-, fixed-, and random-effects models were selected, and the F-test was 42.79 and the p -value was 0.0000, and the fixed-effect model should be selected. The Hausmann test showed that χ 2 was 17.29 and the p -value was 0.0156, choosing a fixed-effect model.

Table 3 reports the baseline estimation of the influence effect of the rural digital economy development on the intensity of agricultural CEs. 1) considers only the core explanatory variable, and finds that the rural digital economy growth significantly reduces agricultural CEs intensity at the 1% level. Adding control variables to column 2), it is found that for every 1 unit increase in the growth level of rural digital economy, agricultural CEs intensity decreases by 40.01%, and this negative impact is still significant at the 1% level, thus validating the research hypothesis. For one thing, the development of the rural digital economy accelerates rural residents’ access to the network environment, not only promoting information interconnection and sharing while realizing the rational allocation of resources, but also helps rural residents establish the concept of green consumption and to develop informal network environment regulations, thereby reducing agricultural CEs intensity. And for another, the close combination of digital technology and agriculture helps farmers to, respectively, grasp agricultural production data accurately, improve production efficiency, and effectively reduce agricultural pollution caused by waste of resources. In addition, in an environment marked by the continuous development of rural digital finance, rural residents can broaden financing channels, introduce efficient and low-carbon new agricultural technologies, form a green business model, and promote the transformation of traditional extensive agricultural production methods to intensive ones, thereby realizing the agricultural CEs reduction effect of the rural digital economy.

Table 3 . Baseline regression results.

5.2 Endogeneity test

To alleviate the impact of endogeneity on empirical results, this article also verifies the relationship between agricultural digital economy with a lag of one period and agricultural CEs, the results are in the column 3) in Table 3 . The results of Table 3 have verified the negative impact of agricultural digital economy on agricultural carbon emissions. If the digital economy is an endogenous variable, then the estimation results in this paper are biased. This paper will test the core explanatory variable and each control variable with a lag of one period to overcome the possible reverse causal relationship between contemporaneous variables. The corresponding empirical results are shown in column 4) of Table 3 . The regression results show that the coefficient of the core explanatory variable is −0.4564, with a p -value of 0.047, excluding the possibility that agricultural digital economy is an endogenous variable.

5.3 Robustness test

1. Replace the explanatory variable. In the baseline regression, the logarithmic form of agricultural CEs intensity was used as the explanatory variable. In order to further enhance the robustness of the conclusion, the dependent variable was replaced with the total amounts of agricultural CEs (logarithmic value) for robustness testing, and the results are shown in columns 1) and 2), Table 4 . With the variables to be replaced, the growth of the rural digital economy still has a significant negative impact on agricultural CEs.

2. Exclude part of sampling. Considering substantial heterogeneity in the levels digital economy growth among Chinese provinces, in order to further strength the robustness of the conclusions, the data of two provinces with a digital economy scale of more than 15 trillion yuan and 12 provinces (cities) with a digital economy scale of more than one trillion yuan of 2020 are excluded. The results in column 3) and column 4) of Table 4 show that the development of rural digital economy still has a significant negative impact on agricultural CEs, and this negative impact has become stronger, which may be due to the fact that the digital economy in these provinces is on the rise, with accelerated development speed and greater development potential, so it is easier to reduce agricultural CEs intensity.

Table 4 . Robustness test results.

5.4 Heterogeneity analysis

1. Regional heterogeneity. This study categorizes the samples into four parts: eastern, central, western and northeastern regions for sub-sample regression, and discusses the regional heterogeneous impact of rural digital economy development on agricultural CEs intensity in the four parts. The estimations of regional heterogeneity analysis are shown in Table 5 ; for the eastern and central China, the development of rural digital economy still has a significant negative impact on agricultural CEs intensity and the central China have greater influence than their eastern counterparts while the western China is not significant. Possible explanations are: the eastern region has a good economic development foundation; the digital economy came early; it has a relatively complete rural digital economy infrastructure; and the integration and development of digital technology and agriculture is higher. Meanwhile, the central region is China’s most important agricultural production zone, the central government places greater focus on agricultural input, especially its green agricultural policy and finance support, which may lead to a larger and more significant negative impact on the intensity of agricultural CEs. The development and application of digital technology in the western region started late, that is might the reason why the impact is not significant. But it is not rational to deny its rapid upward phase and the low-carbon development potential of agriculture. The results also show that the coefficient of the rural digital economy development in the northeast region is positive, indicating that the development of the rural digital economy may increase the intensity of agricultural CEs. The development of the digital economy in northeast China is relatively backward, its digital infrastructure is not yet perfect, the coverage of rural digital finance is small, the proportion of secondary industry is large, while the integration of digital technology and agriculture is not complete.

2. Heterogeneity of scientific investment. As the primary productive and innovative force, the increased science and technology investment plays an important supporting role in the reduction of CEs and the growth of the digital economy. On the one hand, advances in science and technology have a direct impact on CEs’ reduction. At present, technological progress is an important driving force for the reduction of CEs and green development, while investment in science and technology helps to promote green technology innovations ( Yang et al., 2019 ; Gu et al., 2022 ), saving production costs, promoting the professional division of labor in various fields, and improving productivity, thereby directly reducing CEs. On the other hand, the progress of science and technology will also promote the progress of digital technologies such as AI and big data, accelerating the development process of industrial digitalization and digital industrialization, thereby promoting the high-quality development of the digital economy, thus further reducing CEs.

Table 5 . Results of regional heterogeneity analysis.

To examine the impact of rural digital economy development on agricultural CEs’ intensity against the background of different scientific and technological inputs, this paper divides 29 provinces (municipalities) into high and low sample groups for heterogeneity analysis based on the average science and technology expenditures in each province (municipality) over 2013–2020, and the results are shown in Table 6 . For the high-tech input group, the development of the rural digital economy still had a significant negative impact on the intensity of agricultural CEs, while the low-tech input group was not significantly negative. This shows that high scientific and technological investment can help promote the green development of agriculture while reducing the intensity of agricultural CEs. The development of the rural digital economy is premised on the completion and improvement of rural digital infrastructure as well as the production, transportation, sales of agricultural products, as well as the supervision, measurement, and traceability of CEs in the whole process of agricultural digitalization, which depends on sound digital infrastructure. High levels of investment in science and technology is conducive to promoting scientific and technological innovation and building a higher quality digital economy infrastructure, thereby providing the realization method and technical guarantee required for the close integration of digital technology and agriculture while promoting the reduction of agricultural CEs. At the same time, the continuous inflow of high-tech labor as a result of government investment in science and technology in the form of subsidies can enhance the level of local innovation, thereby promoting the sustainable and high-quality development of the digital economy and realizing the digital economy’s capacity to reduce CEs. Therefore, local governments should vigorously promote innovation-driven development strategies, increase financial support for science and technology, establish a sound incentive system, and encourage applied research and technological innovation in key fields. In addition, local governments can also increase the weight and proportion of indicators such as scientific and technological investment and their application in the government assessment index system, design a sound talent introduction system, and pay attention to cultivating high-quality talent ( Bian et al., 2020 ), so as to achieve high-quality development and deepen the digital economy’s CEs reduction effects.

Table 6 . Analysis results of scientific and technological inputs’ heterogeneity.

5.5 Mediated effect analysis

From above analysis, it is obvious that the digital economy development has ability to decrease the agriculture CEs intensity and amounts. Further to explore the influence mechanism of the digital economy development on the agriculture CEs, the model 3) and model 4) mentioned in Section 4.1 is run using Stata software. To directly and conveniently compare the mediating effects with the estimates of the basic model of digital economy influence on agricultural CEs’ intensity, the baseline regression results in Table 3 were listed again in column 1), Table 7 . The dependent variable in column 2) is the mediator variable agriculture green efficiency (GE), while the explanatory variable focused on in this paper, agricultural digital economy (ADIG), is significantly positive, consistent with expectations. The dependent variable in column 3) is the agricultural CEs intensity (lnAE). After adding the mediating variable GE, the explanatory variable agricultural digital economy (ADIG) remained significantly negative at the 1% level, while the mediating variable agricultural green efficiency (GE) was significantly negative.

Table 7 . Analysis results of mediating effect.

Comparing the results of Table 3 and Table 7 , the coefficient β = 0.4001 with 1% significance, the coefficient λ 2 = -0.3375 is significant at 1% level, besides the coefficient ζ 1 = 0.9143 is significant at 5% level, the mediating effect is β − λ 2 = -0.0626, and the mediating effect of green agricultural technology exists through the empirically analysis. The coefficient −0.4001 show the total effect, and means when the agricultural digital economy increases one unit, the agricultural CEs will decrease 40.01%. The coefficient −0.3375 is the direct effect of agricultural digital economy with one unit increase on the agricultural CEs reduction is 33.75%. The gap between the total and direct effect is the mediating effect.

6 Discussion

6.1 the construction of agricultural digital economy indicators.

Based on the existing researches, this paper mainly focuses on the three aspects of rural digital economy infrastructure, digitalization of agriculture and rural digital services to construct the indicator of agricultural digital economy. This indicator not only consider the hardware and software agricultural digital economy level, but also digital service level. In Zhao et al. (2023) study, the indicators of digitalization level mainly focus on two aspects of digital economy infrastructure and digital economy service level, while they choose the digitization levels to substitute the rural digitalization index. In our study, we use the agricultural digital economy, which is closely related to the development agriculture and rural areas, and can better reflect the digitization level of agriculture.

6.2 The main effect of agricultural digital economy on agricultural carbon emission

In the existing studies, the level of digitalization can significantly reduce the agricultural carbon emission ( Zhao et al., 2023 ), although their research chose the carbon emission intensity of different agricultural sector, cropping and livestock sector respectively. Even in the city level or other sector of China, most studies also hold the same conclusion as our study, such as Wang et al. (2022) , Zhang W. et al. (2022) . And our study also support the carbon emission reduction effect of digital economy.

6.3 The mediating effect of agricultural digital economy on agricultural carbon emission intensity

Through the mediating effect analysis, it is obvious that the agricultural green production technology is an important mechanism for the development of the digital economy’s capacity to alleviate agricultural CEs. The same results are also evident in the research of Rong et al. (2023) . They emphasize that green technology can effectively suppress agricultural CEs directly, which has significantly negative spatial spillover effects on agricultural CEs in both the short and long term. Except for the influence mechanism, Guo et al. (2023) underline that the role of agricultural green technology in reducing agricultural CEs is particularly dominant in the main grain-producing areas. Zhao et al. (2023) emphasis digitalization can reduce China’s carbon intensity by promoting the agricultural technological input. This can support our influence mechanism of agricultural digital economy on the agricultural carbon emission. Except for the agricultural technology inputs, Zhao et al. (2023) also emphasis the role of human capital level and urbanization rate. In our research we use the agricultural green production efficiency as the mediating variable, which both considering the input and output of agricultural technology, and considering the agricultural green transformation.

6.4 Discussion of heterogeneity in the impact of agricultural digital economy on the agricultural carbon emissions

In Zhao et al. (2023) study, the carbon reduction effect is slightly greater in the central and western regions than that in the eastern regions, which is slightly different with our results, one reason is the different research period, the former chose the 2006–2018, while we chose the 2013–2020, considering the fact China’s digital economy has entered a mature period since the year 2013, thus we choose the 2013 is more rational for agricultural digital economy. Other reasons such as the region and province chosen difference also would lead to the less reduction effect of west region.

7 Conclusion and policy implications

This study uses the data of 29 provinces (cities) in China from 2013–2020 in order to measure the intensity of agricultural CEs as well as the development level of rural digital economy in each province. On this basis, the influence of the development of the rural digital economy on agricultural CEs is empirically estimated. The results show that: 1) the development of the rural digital economy could significantly reduce the intensity of agricultural CEs, a conclusion which is still valid after robustness test such as replacing the explanatory variables and removing some samples. The overall environmental effect is 40.01%, which means the agricultural CEs would decrease 40.01% when the agricultural digital economy increase one unit, the direct effect of digital economy on the agricultural CEs reduction is 33.75%; 2) The alleviation of CEs based on the rural digital economy is more significant in the higher technological investment zones than that in the lower technological investment zones, and the central and eastern regions also have more significant CEs reduction effect. 3) The influence mechanism analysis shows that agricultural green technology change is an effective means to promote the rural digital economy’s CEs reduction effect, and the mediating effect is −6.26%, which means the agricultural CEs would decrease 6.26% for one unit agricultural digital economy increase, through mediating effect of the agricultural green technology. Based on the above conclusions, this article puts forward the policy recommendations as follows.

Firstly, continuously improve the level of agricultural digital economy. Including build a complete rural digital economy infrastructure, strength the agricultural digitalization and promote the agricultural finance service. Further promote the full coverage of rural Internet, accelerate the construction of rural 5G networks, realize the in-depth application of agricultural Internet, and establish a smart agricultural technology system. Accelerate information interconnection and sharing, build a unified Big Data platform for agricultural and rural development, and provide solid information infrastructure support for the rural digital economy and agricultural digitalization, so as to accelerate the agricultural CEs reduction effect of the rural digital economy. Besides, increase the accessibility and coverage of agricultural finance is crucial for the green transformation of agricultural industry. The agricultural green development balances the agricultural industry growth and the sustainability of the rural environment.

Secondly, focus on achieving the balanced the rural digital economy development in various regions and better effect of agricultural CEs reduction. On the one hand, it is necessary to strengthen the interconnection and information sharing of various regions while deepening cooperation to promote the establishment of data sharing platforms. On the other hand, it is necessary to raise financial investment in the central, western and northeast regions, implement coordinated and sustainable digital economy development policies in accordance with local conditions, strive to eliminate the digital divide between regions, and bring into play the CEs reduction effect of digital economy. Meanwhile, the central China and western China can also take the initiative to expand foreign cooperation, such as introducing information technology to empower agriculture through free trade zone cooperation, thereby giving full scope to local comparative advantages, hence accelerating the digitization transformation of agriculture ( Guo, 2021 ) while realizing the coordinated the digital economy development between regions.

Thirdly, the government should pay attention to agricultural green development, because the agricultural carbon reduction effect of digital economy needs to be achieved through the mediating variable of agricultural green technology change. Considering the peculiarity of agricultural development, there is a need to increase financial support and incentives for science and technology, set up special funds to encourage agricultural green technology R&D and innovation levels, continuously strengthen the scientific and technological research and technology research capacity of low-carbon technologies, while promoting agriculture’s turn to low-carbon and green development.

8 Limitations

This paper has some shortcomings and can be further analyzed. The assessment of agricultural digital economy has consistently constituted an important issue and challenge in related research. Although this paper assesses the agricultural digital economy by establishing a novel evaluation framework, because of the availability and measurability of data, some regions and some indicators cannot be included in the evaluation system in this paper. Thus, there is still space to further improve the evaluation methodology in the future, to enhance the comprehensiveness and scientific rigor of the research. Furthermore, since the agricultural digitalization and CEs are highly influence by the grassroots government, the role of township-level government played in the agricultural green development and agricultural digital economy is very direct and important. While the related data on the grassroots government is relatively incomplete, which would not provide sufficient evidence for our study. If we would get enough data of township level government, we would conduct more comprehensive research in this area.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

HZ: Writing–original draft, Conceptualization, Funding acquisition, Investigation, Resources. KG: Conceptualization, Data curation, Formal Analysis, Methodology, Writing–original draft, Resources. ZL: Conceptualization, Funding acquisition, Investigation, Writing–original draft, Data curation, Formal Analysis, Methodology, Validation. ZJ: Data curation, Formal Analysis, Methodology, Project administration, Resources, Visualization, Writing–original draft. JY: Data curation, Formal Analysis, Software, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Guizhou Planning Office of Philosophy and Social Science grant numbers 22GZQN28.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1 Taobao Village: The Alibaba Research Institute’s recognition criteria for “Taobao Village” mainly includes: 1) business premises (in rural areas, administrative villages are the unit); 2) sales scale (the annual sales volume of e-commerce reaches 10 million yuan); 3) scale of online merchants (the number of active online stores in this village reaches 100, or the number of active online stores reaches 10% of the local household size).

2 The China Statistical Yearbook (2014) shows the development of whole economic and social status in the year 2013, the rest can be done in the same manner.

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Keywords: agricultural carbon emissions, agricultural green technology efficiency, rural digital economy, rural digital finance, digitalization of agriculture

Citation: Zhang H, Guo K, Liu Z, Ji Z and Yu J (2024) How has the rural digital economy influenced agricultural carbon emissions? Agricultural green technology change as a mediated variable. Front. Environ. Sci. 12:1372500. doi: 10.3389/fenvs.2024.1372500

Received: 18 January 2024; Accepted: 20 March 2024; Published: 08 April 2024.

Reviewed by:

Copyright © 2024 Zhang, Guo, Liu, Ji and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jinna Yu, [email protected]

This article is part of the Research Topic

Low-Carbon Economy and Sustainable Development: Driving Force, Synergistic Mechanism, and Implementation Path

Submissions are open for Volume 7, Number 3 - September 2021

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University of Missouri

College of agriculture, food and natural resources, mu extension seeks farmers for strip trial program.

April 10, 2024

Written by Linda Geist

COLUMBIA, Mo. – Farmers are being sought to participate in the 2017-2018 University of Missouri Extension Strip Trial Program, which helps farmers and crop advisers compare on-farm management decisions and practices.

The program uses on-farm, field-scale research to give growers farm-specific data to guide decisions that can protect or improve the bottom line, said MU Extension nutrient management specialist John Lory.

MU Extension conducts the Strip Trial Program in collaboration with the Missouri Soybean Merchandising Council, the Missouri Corn Merchandising Council and their checkoffs.

Farmers work with an MU Extension specialist or crop consultant of their choice and use their own equipment. Trials are long strips of land laid out side by side in a field with different management practices or treatments. Participants receive a personalized report on the results on their farm. They also have access to the results of other trials.

A farmer panel sets trial priorities each year. This year, the farmer panel prioritized cover crop management trials and nitrogen management trials.

One cover crop trial allows farmers to test if winter wheat can be planted successfully instead of cereal rye without lowering yields of corn or soybean.

In a new trial for 2018, farmers can test a cover crop of their choice to plant after soybean and before corn. This will help farmers and MU Extension collect real-world data on cover crop systems that might provide sufficient cover without compromising corn management after soybean. Farmers choose the type of cover crop and whether to drill or broadcast.

A third cover crop trial lets farmers test alternative termination dates for an existing cover crop. This is a great option for farmers who already have a cover crop planted and have questions about the risks and rewards with different timings for killing the cover crop, Lory said.

Researchers in the Strip Trial Program are willing to work with any farmer interested in testing delayed nitrogen applications, such as side-dressing nitrogen, or any nitrogen decision-support tool in corn, milo or wheat fields. MU specialists will be matched to individual farmers in northeastern and central Missouri.

A fifth trial examines crop response to phosphorous fertilizer. Farmers will have strips with and without phosphorous fertilizer to see where in the field yield benefits are seen.

Finally, 2018 will be the second year to test the effectiveness of ILeVO seed treatment on yield and nematode numbers.

If you are interested in having a trial on your farm, contact your local MU Extension center or one of the contacts below.

“Our goal is to have a local extension person work with you to lay out the trial,” said Lory. “In all trials, we need to get the yield map for the field after harvest. All fields are surveyed with aerial photography at least once during the growing season. And for specialized trials, such as the phosphorus trial, there may be soil sampling done by the program.”

Farmers prioritize which trials are important and volunteer to have tests on their fields.

“This program is a great collaboration between MU Extension, the corn and soybean organizations and farmers,” Lory said. “MU brings expertise to help farmers implement the trial and bring scientific rigor to the tests and the reported results for each trial. Ultimately, Missourians all benefit from this collaboration as we integrate lessons learned about cover crops and nutrients across multiple trials and multiple years.”

Lory said farmers participated in 55 trials in both 2016 and 2017. In 2018, the goal is at least 60 strip trials across Missouri.

For more information, contact Lory at [email protected] , Greg Luce at [email protected] or Darrick Steen at [email protected] .

Southern Piedmont Agricultural Research and Extension Center celebrates 50 years of research and Extension programming

As the center prepares for a golden anniversary celebration event this November, its vision for the future remains rooted in service to the agricultural sector and the residents of the commonwealth.

• Suzanne M. Pruitt

10 Apr 2024

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Established in 1974, Southern Piedmont Agricultural Research and Extension Center (AREC) celebrates a significant milestone throughout 2024 – a half-century of applied research and Virginia Cooperative Extension education supporting the region’s integrated and diverse production systems.

Focused on aiding the 23 counties that surround the Southern Piedmont AREC, the center incorporates the latest scientific discoveries and technological advancements into the region’s production systems. Its research and Extension efforts cover tobacco, forage crops, beef cattle, and a variety of other field and specialty crops, offering research-based support and guidance for sustainable production.

As one of 11 Agricultural Research and Extension Centers strategically located across the commonwealth, Southern Piedmont stands out as the sole center supporting tobacco producers in the state.

“This AREC has been vital to my farming career,” said Richard Hite, a Lunenburg County producer. “Their research in tobacco has been key to my success and helped me keep up with changes in the industry. With the information the staff provides, I can fix or prevent a problem that could cause me financial loss. I also depend on their research in small grains and beef cattle. When I make decisions for the upcoming year, I use the information provided to me by the AREC to direct my purchases.”

A history that dates back to 1906

The need for agricultural research in the Southern Piedmont region of Virginia was first recognized in a significant manner by the General Assembly in 1906, when an appropriation of $2,500 was made to help Virginia Tech finance the region's first off-campus field stations in Appomattox and Chatham. The necessity of field stations operating as a complement to the work on Virginia Tech’s Blacksburg campus is of no less importance today.

Expanding from the original two field stations, three additional research sites were later established in the region. These included two experiment stations at Chatham and one at Charlotte Court House. Initially, these stations had limited acreage and were staffed by one or two professionals who lacked sufficient technical support, facilities, and equipment.

To address these challenges, Virginia Tech decided to centralize programs, facilities, and staff at a more suitable location with ample land for research activities. This new site would not only provide an opportunity for tobacco research but all major agricultural ventures prevalent in Southside Virginia. Facilities would be established to conduct various educational activities such as short courses, seminars, workshops, and graduate instruction.

Southern Piedmont AREC tobacco agronomist T. David Reed photographed as a graduate student during the 1986-1987 growing season in Blackstone, Virginia. Photo courtesy of Southern Piedmont AREC.

Graduate student Usha Panta, Ph.D. candidates Atoosa Nikoukar and Hadi Farrokhzadeh, and undergraduate intern Ashish Tammisetti in one of the greenhouses at Southern Piedmont AREC, Blackstone, Virginia. Photo by Shirin Parizad for Virginia Tech.

A glimpse into a Southern Piedmont AREC laboratory circa 1984. Photo courtesy of Southern Piedmont AREC.

Analytical chemist/biologist Grace Rogers in the laboratory at Southern Piedmont AREC. Photo by Sam Dean for Virginia Tech.

Early success paves the way for expansion

In 1972, the Virginia General Assembly allocated $800,000 to establish a center in the Blackstone area. These funds covered the construction of office facilities, tobacco curing and handling facilities, and field service buildings. After evaluating 10 potential locations, an area at Fort Barfoot, formerly Fort Pickett, in Nottoway County was chosen. A 25-year lease was signed on June 30, 1972, between the Department of the Army and Virginia Tech for the creation, use, operation, and maintenance of a versatile agricultural research and educational center.

The Southern Piedmont ARECs first director, James L. Tramel, began his tenure in July of that year, and under his leadership, approximately 125 acres were initially cleared for field research and a 6-acre irrigation pond and irrigation system were installed.

Eight acres of flue-cured and dark tobacco were planted in the spring of 1974 for research that included breeding and variety development, chemical and sucker control, and fertilizer testing. The first pulling and curing of this crop took place during the first week of August 1974. Throughout the late 1970s and early 1980s, the research and programming efforts at the center continued to expand, incorporating forage systems for livestock, and small fruits.

In 1982, the Virginia General Assembly allocated $450,000 for an extension to the office and laboratory building, including new greenhouse facilities, which were finished in 1983. Subsequently, an experimental pond facility was set up in 1987 made up of 12 ponds and a supply reservoir.

Following the retirement of James Tramel in 1989, James L. Jones became the second director in September 1990, serving through June 2002. During this time, Fort Barfoot was released from Army inventory in 1997, and Virginia Tech later obtained approximately 1,180 acres in September 2002 through a public benefit conveyance from the U.S. Department of Education.

A solid foundation leads to a successful future

Carol A. Wilkinson served as Southern Piedmont AREC’s third director from March 2004 to June 2022 and is currently an agronomist for the center. Under her leadership, the center’s Agriculture Awareness Days have grown into annual spring events for local third and fifth grade students. The award-winning program is designed for students and contributes to the preparation for the Standards of Learning test.

Hands-on, inquiry-based learning activities are conducted to educate the next generation about the exciting aspects of agriculture and science. Ideas and issues discussed in the classroom are brought to life for students by collaborations with Virginia Cooperative Extension , Piedmont Soil and Water Conservation District, Fort Barfoot Departments of Forestry and Environmental Office, Natural Resources Conservation Service , Virginia Farm Bureau , Nottoway Chapter of Future Farmers of America , Richlands Dairy, and Cedar Hill Farm.

In September, the center hosts another of its highly anticipated community outreach events , the annual Family and Farm Day, which offers numerous hands-on agricultural activities and demonstrations suitable for all ages. Last year's event attracted over 1,000 visitors.

Today, the Southern Piedmont AREC team consists of six resident faculty supported by 11 full-time staff members and additional seasonal employees. The 1,180-acre farm includes 130 acres for crop research, 120 acres for research grazing, 16 acres of certified organic land, and a 40-acre silvopasture area.

Specialized facilities for tobacco curing, extensive greenhouses, high tunnels, and innovative technologies such as automation for monitoring and curing tobacco, silvopasture ,   and precision agriculture techniques are utilized to evaluate crop development. Silvopasture is the integration of trees and grazing livestock operations on the same land.

To date, Southern Piedmont AREC has welcomed more than 70 graduate students in research areas covering agronomy, plant pathology, plant genetics and breeding, forages, and entomology.

“Through diverse research and training opportunities, Southern Piedmont AREC strives to educate and prepare the next generation of specialists, educators, and professionals,” said Arash Rashed , who has been the AREC’s director since 2022. “Our specialists and researchers are committed to programs focused on sustainable production and profitability for Virginia’s producers. We work closely with our regional extension agents and Virginia Cooperative Extension to reach out to our stakeholders and communicate our latest findings.”

As the center prepares for a 50th anniversary celebration event scheduled for Nov. 1, its vision for the future remains rooted in service to the agricultural sector and the residents of the commonwealth.  

Tom Soladay

540-232-2501

• Agricultural Research and Extension Centers
• College of Agriculture and Life Sciences
• Southern Piedmont Agricultural Research and Extension Center
• Virginia Cooperative Extension

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