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Sample proposal 2, growing food and preserving seeds : ni-vanuatu women’s relationships with food security, biodiversity and food policy, vanuatu, 2012.

PROPOSAL NARRATIVE

Problem Statement, Research Questions, and Research Objectives

More than 1000 varieties of root and tuber crops are grown across a sample group of 10 villages in the island nation of Vanuatu in the South Pacific. Maintaining this diversity is the foundation of the island inhabitants’ food security strategy (CIRAD 2008). Global agricultural and food trade policies are fostering the dominance of cash crops like cocoa, palm and coffee on small farm holdings, altering tropical cropping systems and shortening fallow periods. Due to these changes, small farm holder communities are confronting issues such as limited access to traditional staple foods, depleted finances caused by buying other more expensive grains from the market, loss of biodiversity, and the disappearance of local customs around the cultivation and preparation of food (ANR 2010). Traditionally Ni-Vanuatu women have been responsible for food cropping while men are involved in cash crop production (FAO 2003).

This ethno-botanical study, conducted in the villages located in the island of Santo in Vanuatu, will describe the farming practices followed by women farmers in farms, home gardens and community spaces and ask the following questions – 1) As primary food croppers do women farmers play a decisive role in the in-situ conservation of indigenous genetic resources, maintaining resilient farming systems, and providing their families with access to a balanced diet 2) How are women’s farming efforts perceived and valued in the male-dominated Ni-Vanuatu rural societies by the community, men, and women themselves - as a domestic chore or as an economic contribution 3) How does gender influence a) access to and control over natural resources, b) differential preferences for selecting and growing crop varieties and c) different roles in food production (Gurung, Thapa and Gurung 2000) and 4) how do these social and cultural dynamics shape the gender sensitivity of policies and programs targeted at sustainable agro-development and building food security in Vanuatu.

Literature Review

“ With equal access to productive resources and services, such as land, water and credit, women farmers can produce 20 to 30 percent more food, enough to lift 150 million people out of hunger”, (Diouf 2011). The preservation of biological diversity and plant genetic resources is key to food security. Women are responsible for providing their families with food and care and therefore have specialized knowledge of the value and diverse uses of plants for nutrition, health and income. Consequently they are often the preservers of traditional knowledge of indigenous plants (FAO 1998). Moreover, women often experiment with and adapt indigenous species and thus become experts in plant genetic resources (Karl 1996; Bunning and Hill 1996).

Despite widespread acknowledgment of the critical role that women play in sustainable agriculture and food security in developing countries, lack of synthesized data and analysis of their influence, cultural biases and gender constraints under-value and under-represent their contribution (Villarreal 2010). Much of women's work remains “invisible” (Quisumbing 1998). This lack of recognition of their efforts means that their interests and demands are often overlooked and women are excluded as an active partner and stakeholder in sustainable agro- development. For e.g., in Cameroon, an irrigated rice project did not assign women land because women were expected to work in their husbands’ fields (Quisumbing and Pandolfelli 2008).

The over-arching objective of this research using the context of women farmers in Santo Island in Vanuatu is to study if, and how women farmers contribute to the preservation of genetic resources, the cultivation of a wide diversity of crops and therefore enhance food security for small farm holder communities in developing countries. And at the same time review available evidence and documented studies that assist in understanding the representation and participation of Ni-Vanuatu women in food security and agro-development policy and decision making.

Field Site Selection and Justification

Subsistence agriculture and local food production is vital in the Pacific Islands (IPCC 2007). That small islands have high ecological dependency is well recognized (ADB 2004) with the dependence on plant genetic resources for food cropping can be as high as 80% in some island states with it being 35% in Vanuatu (Ximena 1998). Projected impacts of international food trade and climate change will hamper Vanuatu’s food security (IPCC 2007, ANR 2010). Ni-Vanuatu women are largely responsible for producing food. On average, women spend 30 percent of their time on food production (Agricultural Census, 1983-84). Yet, given the subordinated status of women in Vanuatu's culture, women's role in agriculture is not recognized in many policies, including delivery of extension services (FAO 2003). This makes the villages in the island of Santo, largely the most important farming community in Vanuatu and the principal supplier of fruits and vegetables to the Port Vila market, an ideal location for my research which looks for direct linkages between women, the preservation of bio-diversity in the form of plant genetic resources, and food security, and its subsequent representation in agricultural policy.

Methodology

My overall goal at the end of this summer is to publish a policy proposal that documents empirical evidence, data and observations from my field work and contributes suggestive policies that could help realize the rights of women farmers in policies and programs focused on building food security and preserving bio-diversity across the developing countries.

Through an in-depth field research conducted in Santo Island, Vanuatu, my goal is to gather qualitative observations and quantitative data points around these themes. Research methods include ethnographic study, unstructured interviews, oral narratives and participant observation. The first few days will be spent in familiarizing myself with research groups who are already working in these villages, identifying the villages/farm sites for fieldwork, sensitizing myself to the cultural and social practices of the communities, explaining my presence to the community’s leaders and identifying collaborators. Quantitative questions include the extent of loss of bio- diversity by cash-cropping, how much time is spent on farming for commercial purposes, how much in farming for household production, how many different crops are planted and how many different varieties, what share of natural resources is available to women for food production vis-à-vis men etc. Qualitative aspects include the traditional practices followed in preserving seeds and planting material, the community's and women's perception of their role in securing food for the family, bio-diversity management and their representation in agro-development policies, and how they are adapting their traditional practices to new environmental and socio-political changes.

Personal Qualifications and Research Collaborations

Personal qualifications include past work done with a co-operative to support women farmers in southern India on similar issues, undergraduate and graduate level skills in research methods, and pertinent coursework at Yale specifically Social sciences theory and method, international environmental policy and governance, and environmental protection law. I have been in touch since mid-December with the French research organization CIRAD and VARTC (Vanuatu Agriculture Research and Training Centre) who are working in Santo. Dr. Vincent Lebot, the head of the CIRAD Vanuatu center, well known geneticist and expert on plant breeding in the Pacific Islands, has agreed to supervise my work. Official languages of Vanuatu are English, French and Bislama. I spoke beginner level French, have been taking French classes since the first term and am currently enrolled in Intermediate level French at Yale. French and colloquial English are spoken in the villages where I will be working.

RESEARCH SCHEDULE

February-May

• Confirm contacts, secure funds and plan on the ground arrangements
• Travel logistics
• Continue literature review, prepare questionnaires, work on research methods
• Obtain approval from the Yale Human Subjects Committee
• 1 week pilot survey, finding a guide and/or collaborator, appreciation of Bislama and introduction to the communities
• Interviews – Quantitative Questions

July-August

• Participant observations, quantitative with ethno botanical focus
• Participatory Work, Qualitative study, policy reviews and examination

August-September

• Write and finalize a paper

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• Open access
• Published: 12 October 2020

A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries

• Maricelis Acevedo   ORCID: orcid.org/0000-0003-4257-9375 1 ,
• Kevin Pixley   ORCID: orcid.org/0000-0003-4068-7436 2   na1 ,
• Nkulumo Zinyengere 3   na1 ,
• Sisi Meng 4 ,
• Hale Tufan   ORCID: orcid.org/0000-0002-5323-4244 1 ,
• Karen Cichy 5 ,
• Livia Bizikova 6 ,
• Krista Isaacs   ORCID: orcid.org/0000-0002-1335-4516 7 ,
• Kate Ghezzi-Kopel   ORCID: orcid.org/0000-0002-8777-402X 1 &
• Jaron Porciello   ORCID: orcid.org/0000-0002-3179-1971 1  

Nature Plants volume  6 ,  pages 1231–1241 ( 2020 ) Cite this article

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Climate-resilient crops and crop varieties have been recommended as a way for farmers to cope with or adapt to climate change, but despite the apparent benefits, rates of adoption by smallholder farmers are highly variable. Here we present a scoping review, using PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols), examining the conditions that have led to the adoption of climate-resilient crops over the past 30 years in lower- and middle-income countries. The descriptive analysis performed on 202 papers shows that small-scale producers adopted climate-resilient crops and varieties to cope with abiotic stresses such as drought, heat, flooding and salinity. The most prevalent trait in our dataset was drought tolerance, followed by water-use efficiency. Our analysis found that the most important determinants of adoption of climate-resilient crops were the availability and effectiveness of extension services and outreach, followed by education levels of heads of households, farmers’ access to inputs—especially seeds and fertilizers—and socio-economic status of farming families. About 53% of studies reported that social differences such as sex, age, marital status and ethnicity affected the adoption of varieties or crops as climate change-adaptation strategies. On the basis of the collected evidence, this study presents a series of pathways and interventions that could contribute to higher adoption rates of climate-resilient crops and reduce dis-adoption.

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Agriculture and food production are highly vulnerable to climate change. Extreme weather events such as droughts, heat waves and flooding have far-reaching implications for food security and poverty reduction, especially in rural communities with high populations of small-scale producers who are highly dependent on rain-fed agriculture for their livelihoods and food. Climate change is expected to reduce yields of staple crops by up to 30% due to lower productivity and crop failure 1 . Moreover, the projected global population growth and changes in diets toward higher demand for meat and dairy products in developing economies will stretch natural resources even further, increasing demands on food production and food insecurity 2 . To cope with climate change, farmers need to modify production and farm management practices, such as adjusting planting time, supplementing irrigation (when possible), intercropping, adopting conservation agriculture, accessing short- and long-term crop and seed storage infrastructure, and changing crops or planting more climate-resilient crop varieties.

This scoping review examines the conditions that have led to the adoption of climate-resilient crops over the past 30 yr in lower- and middle-income countries. For all countries, but especially those that rely on domestic agriculture production for food security, one of the most critical and proactive measures that can be taken to cope with food insecurity caused by unpredictable weather patterns is for farmers to adopt climate-resilient crops. Climate-resilient crops and crop varieties have enhanced tolerance to biotic and abiotic stresses 3 (Box 1 ). They are intended to maintain or increase crop yields under stress conditions and thereby provide a means of adapting to diminishing crop yields in the face of droughts, higher average temperatures and other climatic conditions 4 . Adoption of climate-resilient crops, such as early-maturing cereal crop varieties, heat-tolerant varieties, drought-tolerant legumes or tuber crops, crops or varieties with enhanced salinity tolerance, or rice with submergence tolerance, can help farmers to better cope with climate shocks. Climate-resilient crops and crop varieties increase farmers’ resilience to climate change, but despite their benefits, adoption rates by small-scale producers are not as high as expected in some cropping systems 4 , 5 , 6 . In this study, we focus on scoping (reviewing and synthesizing) the published evidence on the adoption of climate-resilient crops and crop varieties from climate-vulnerable countries and countries that have experienced climate-related impacts as determined by 45 indicators established by the Notre Dame Global Adaptation Initiative.

Overall, we find that the most important determinants of adoption of climate-resilient crops are the availability and effectiveness of extension services and outreach, education level of heads of households, including some awareness of climate change and adaptation measures, and farmers’ access to inputs, especially seeds and fertilizers. On the basis of the collected evidence, this scoping review presents a series of pathways and interventions that can contribute to higher adoption rates of climate-resilient crops and reduce dis-adoption (Box 2 ).

Box 1 Definitions and assumptions

Small-scale food producers. Definitions of small-scale food producers in the literature are mostly based on four criteria: land size, labour input (especially of family members), market orientation and economic size 2 . Land size is the most commonly used criterion. The clear majority of definitions of small-scale food producers are based on the acreage of the farm and/or a headcount of the livestock raised. Sometimes an arbitrary size is created (commonly 2 hectares or less), but otherwise a relative measure is used, which considers the average size of landholdings in the country, as well as a poverty measure (farms that generate 40% or less of the median income). A second important criterion of small-scale producer is the source of the labour used on the farm (whether it is provided by the household that runs the farm or workers who are paid a wage). A third criterion is the extent to which the farm output is sold to market rather than consumed by the farm household or bartered with neighbours (some authors caution that this is also contextual and many small-scale producers are engaged in commercial markets). A fourth criterion is economic size (the value of the farm’s production) 56 .

Climate-vulnerable countries are countries that are considered to be vulnerable to climate change. The ND-GAIN index presents a list of countries ranked by vulnerability to climate change and readiness to respond ( https://gain.nd.edu/our-work/country-index/rankings/ ).

Climate resiliency is the capacity for a socio-ecological system to absorb stresses and maintain function in the face of external stresses imposed on it by climate change, and adapt, reorganize and evolve into more desirable configurations that improve the sustainability of the system, leaving it better prepared for future climate change impacts.

Climate change adaptation includes planned or autonomous actions that seek to lower the risks posed by climatic changes, either by reducing exposure and sensitivity to climate hazards or by reducing vulnerabilities and enhancing capacities to respond to them. Adaptation also includes exploiting any beneficial opportunities presented by changing climates.

Climate-resilient crops are crops and crop varieties that have enhanced tolerance to biotic and abiotic stresses. They are intended to maintain or increase crop yields under stress conditions such as drought, flooding (submergence), heat, chilling, freezing and salinity, and thereby provide a means of adapting to diminishing crop yields in the face of droughts, higher and lower than seasonal temperatures, and other climatic conditions 3 , 57 .

Climate-smart agriculture is an approach or set of practices aimed at increasing agricultural productivity and incomes sustainably, while building resilience and adapting to climate change conditions and reducing and/or removing greenhouse gas emissions where possible 6 .

Conservation agriculture is a farming system that promotes minimum soil disturbance (that is, no tillage), maintenance of a permanent soil cover, and diversification of plant species; for instance, through crop rotation 58 .

Adoption is the stage at which technology has been selected and is being used over a sustained period by an individual or an organization. Adoption is more than acceptance; it is inclusion of a product or innovation among the common practices of the adopter.

Gender refers to the social relations between men and women, boys and girls, and how this is socially constructed. Gender roles are dynamic and change over time.

Agricultural extension is a form of outreach that shares research-based knowledge with farmers and communities in order to improve agricultural practices and productivity. The approach to delivering these services varies in terms of farmer participation and engagement. This range includes technology transfer, advisory, experiential and iterative learning, farmer-led extension services (such as farmer field schools), and facilitation, in which farmers define their own problems and develop their own solutions.

Box 2 Summary methods

A double-blind title and abstract screening was performed on 5,650 articles that were identified through a comprehensive search of multiple databases and grey literature sources and then uploaded to the systematic review software Covidence. The full search protocol is described in the Supplementary Information .

The resulting 886 articles were subjected to a second round of full-text screening, and 684 articles that did not meet the inclusion criteria were excluded, leaving 202 articles that were read in full and included in the qualitative synthesis.

We performed data extraction on each of the 202 included studies. A data-extraction template (available in the Supplementary Information ) was developed to document the data, study type and context of each citation and all themes of interest.

The extracted data were qualitatively summarized on the basis of emerging themes and with the aim of providing recommendations to donors and policy makers.

Among the 684 articles that were excluded at the full-text screening phase, 230 were excluded because they did not include an explicit analysis of factors for climate-resilient crop adoption and 204 were excluded because there was no explicit focus on crops, varieties, seed, planting materials or germplasm.

The inclusion criteria for this study were:

The study focus includes population of small-scale food producers, as defined in the protocol

The study was published after 1990 (1990 was the year the Intergovernmental Panel on Climate Change (IPCC) produced its first report on climate change).

The study includes original research (qualitative and quantitative reports) and/or a review of existing research, including grey literature.

An explicit focus or clear relevance on climate change resilience or climate change adaptation, as defined in the protocol.

An explicit focus on crops, varieties, seed, planting materials or germplasm.

The study mentions factors for adoption, as defined in the protocol.

The area of focus of the study includes target populations in lower- and middle-income countries, as defined by the World Bank.

A scoping review aims to explore the key concepts underpinning a research area and the main sources and types of evidence available 7 . Established scoping review methods provide an evidence-based framework for systematically searching and thematically characterizing the extent, range and nature of existing evidence. A PRISMA-P protocol for this scoping review 8 was registered on 4 June 2019 on the Open Science Framework. We performed double-blind title and abstract screening of 5,649 citations, selecting 568 papers for full-text screening using a priori inclusion and exclusion criteria; 202 papers met the inclusion criteria for data extraction. The inclusion and exclusion criteria are available in the protocol (Methods and Supplementary Information ), and the data-extraction procedure and the PRISMA flow diagram of included and excluded studies are presented in the Supplementary Information .

Of the 202 papers included, 89% were published in peer-reviewed journals and 11% were published in the grey literature. Eighty-seven studies used mixed methods, 82 used quantitative methods and 33 studies used qualitative methods.

Evidence of adoption of climate-resilient crops

Of the 29 evaluated potential social and economic factors related to adoption, interventions related to the availability, effectiveness and access to agricultural extension services were the most prominent determinants of the adoption of climate-resilient crops in low- and middle-income countries. Nearly 50% of the studies identified extension services and awareness outreach as important factors for the effective adoption of climate-resilient crops in low- and middle-income countries (Fig. 1 ). The individual figures per characteristic are presented in detailed summary graphs in Extended Data Figs. 1 – 5 . The determinants are plotted in bar charts to provide additional context and visualization. The unit of analysis is per study, and a single study can report on multiple determinants.

The inner ring outlines the five broad categories to which the 29 social and economic factors are mapped. The outer ring shows the factors within each broad category that were most frequently mentioned across the included studies. The relative area occupied by categories indicates their relevance. Charts with the full data and frequencies for each category are presented in the Supplementary Information . For illustrative purposes, factors mentioned in less than 20% of studies as determinants of adoption were excluded from this figure.

The principal factors determining adoption of climate-resilient crops or crop varieties were largely consistent across the three regions with robust numbers of publications: sub-Saharan Africa, South Asia and East Asia. The most important determinants across these regions were, in order of importance: (1) access to extension services or information about options, (2) education level of head of household, (3) access to needed farm inputs, (4) experience and skills of farmer, (5) social status, and (6) access to climate information (Fig. 2 ). Access to extension services and information about options, and education level of head of household were among the top five determinants for adoption for all three regions. Access to farm inputs was the first and second most important determinants for adoption in South Asia and sub-Saharan Africa, respectively, but was only sixth most important for East Asia. Experience and skills of farmers were first and third most important determinants for adoption in East Asia and sub-Saharan Africa, respectively, and sixth most important in South Asia. Social status was highly important in South Asia and sub-Saharan Africa, but only moderately important for determining adoption of technologies in East Asia. Although there were few papers and thus limited information for Latin America and Middle East and North Africa regions, the education level of the head of household was cited as the most important determinant for adoption in both regions.

a – e , Individual determinants are ranked from highest to lowest number of studies in the regions: East Asia and Pacific ( a ), Latin America and the Caribbean ( b ), Middle East and North Africa ( c ), South Asia ( d ) and sub-Saharan Africa ( e ).

The climate-resilient crops are included in this scoping review on the basis of data found in the included papers (Fig. 3 ). We classified them as cereals (maize, rice, grain (general), wheat, millet, sorghum barley and teff), legumes (soybean, chickpeas, cowpea, common beans, mung beans and groundnut), vegetables and fruits (tomato, eggplant, pepper, cocoa, mango, clover, garlic, mustard, pea, onion, saffron, green grams and cola nut) and roots, tubers and bananas (banana, plantain, yam, sweet potato, cassava and potato). Thirty-three per cent of the studies did not report on a specific crop or variety in their research; of the studies that did report on a specific crop or variety, 67% reported on cereals only. Despite their importance for food security and nutrition, less than 1% of the studies reported on legumes only and 25% reported on a combination of cereals and legumes, roots, tubers, bananas, vegetables and fruits. We also assessed the 202 papers to determine the purpose of the crops as primarily for human consumption (44%), for human consumption and animal feed (26%) or not clearly stated (30%).

a – d , Countries are colour-coded from yellow to red based on number of relevant studies involving cereal ( a ), legumes ( b ) vegetables ( c ) and roots, tubers and bananas ( d ).

Climate-resilient crops and crop varieties were adopted to cope with abiotic stresses such as drought, heat, flooding, salinity and shorter growing season (early-maturing crops), as well as pests associated with changes in weather or climate patterns (disease and pest resistance) (Fig. 4 ). Climate-resilient crops and crop varieties were also adopted to address general challenges associated with climate change and crop system sustainability, such as to improve moisture retention in soil, improve soil quality, and reduce erosion (planting of cover crops and legumes and to reduce vulnerability to food insecurity). The most studied trait in the dataset was drought tolerance, followed by water-use efficiency and earlier maturity. Adoption of early-maturing crops enables farmers to cope with climate change-induced weather variability by allowing them to adjust planting dates when rains are delayed and reducing the chances of yield losses caused by drought or heat waves late in the growing season. Changing of planting dates was identified in 32% of the papers as a strategy to cope with climate change.

Studies are divided into the same geographical regions as in Fig. 2 .

In general, the evidence suggests that farmers do not adopt a new crop or crop variety without changing other practices. A total of 136 papers (67%) describe that farmers adopt climate-resilient crops in conjunction with other climate-resilient technologies such as climate-smart agriculture (CSA) schemes and conservation agriculture (CA). Other climate-resilient technologies included: planting of trees and shrubs, reduced or increased investment in livestock and modified planting dates and irrigation (Table 1 ).

Seed and adoption of climate-resilient crops

Seventy-three papers mentioned the topic of seed. The major themes associated with seed that emerged with direct evidence drawn from the papers are summarized in Table 2 . Access to and availability of seed were the most prevalent themes, with 60% of papers mentioning these as issues in the adoption of climate-resilient strategies. Social networks such as farmers’ organizations or co-operatives, as well as access to information, were also reported as facilitators of adoption. These themes refer to different social groups and ways in which farmers can exchange seed or get information about seed.

Social differences and adoption of climate-resilient crops

About 53% of studies reported that social differences (such as sex, education and age of household head) influence adoption of varieties or crops as mitigation strategies against the effects of climate change, whereas 30% of studies did not report any effect of social difference. Fifteen per cent of studies did not include data on social differences. Of the studies that identified social differences as influencing adoption of climate-resilient crops and crop varieties, education (22%), sex (28 %), age (24%) and family size (14%) emerged as the most important factors. Income (6%), access to information (5%), marital status (2%) and experience (2%) were also mentioned, but much less frequently. We examined the papers for sex disaggregation of data, in which sex of household heads was considered. Forty-five per cent of studies reported on the sex of respondents, with 39% reporting on both male and female household heads, 5% including men only, and only 1% of studies including only female respondents. Most of the studies explored social differences only superficially, by including variables in surveys, but few substantiated these findings with follow-up qualitative research to understand the social dynamics driving the observed adoption decisions.

The studies largely concur that socio-economic status of farmers plays a large part in their adoption of climate-resilient technologies. Thirty-one per cent of the studies highlighted the socio-economic status of farmers. Various studies indicated that a nuanced understanding of the socio-economic status of farmers is vital for the targeting of climate-resilient crop technology interventions and their adoption and sustainability in practice. Thirteen studies reported a positive effect of farmer income on adoption. Farmers with access to finance, such as risk transfers (for example, insurance or remittances) and credit (for example, bank loans or community loans), were more likely to adopt climate-resilient crop technologies. Farmers who reported constrained credit were less likely to grow modern crops and more likely to cultivate local varieties 9 . This is partly because the lack of cash or credit may prevent farmers from using purchased inputs 10 .

Evidence on the dis-adoption of climate-resilient crops

Dis-adoption of climate-resilient crops and crop varieties was discussed in 12 of the 202 papers included in our evidence synthesis. The major reasons for dis-adoption included technology not meeting expectations due to poor performance or quality of the technology or variety (8 papers), government policies (3 papers), technical constraints (2 papers), labour shortages (1 paper) or financial constraints (1 paper). Eight of the twelve studies indicated that dis-adoption was specifically due to the performance of a crop variety, and four of these eight studies indicated that the varieties’ performance under stress conditions did not meet farmers’ expectations 10 , 11 , 12 , 13 .

The primary goal of this scoping review was to identify factors in adoption of climate-resilient crops in climate-vulnerable countries. Insights into these factors may inform the design of interventions aimed at equipping farmers to adopt climate-resilient technologies before experiencing devastating impacts of climate change and encourage adoption best practices 14 , 15 .

We show that there is a predominance of cereals in reported studies on adoption of climate-resilient crops (67%). Only 1% of the studies report on legumes only; otherwise, they are considered only in combination with other crops. This may reflect the dominance of cereals in staple foods across the world and biases towards the study of such crops and in the development of improved climate-resilient crop varieties. However, this is a concerning trend given that some legumes, roots and tuber crops (for example, cassava, bambara groundnuts and beans) that are largely neglected in the studies have known climate resilience, are sources of high-quality nutrition and provide more well-established environmental benefits than cereals, such as soil enrichment.

About 50% of the studies included in this scoping review identified agricultural extension and awareness outreach as the most relevant factor for adoption of climate-resilient technologies in low- and middle-income countries. Agricultural extension links farmers with the latest research and engages in a translational practice to make complex information more accessible to farmers. It has been shown that farmers who have access to early-warning systems such as weather forecast systems can better cope and adapt to a changing climate 16 . Farmers plan better for farming activities, including choice of crop varieties to plant, after having had access to weather forecast information (for example, from a community-managed weather station). Emerging digital technologies provide an opportunity to use information and communications technology-enhanced extension and climate services that can provide timely information that farmers can use for decision making and to adapt their farming practices. These could also improve efficiencies of extension services while also reducing their cost. Poor funding for extension services in the developing world have limited farmers’ access to training and expert guidance on emerging technologies 17 . Partnerships with other emerging players in information exchange, such as telecommunications companies and non-governmental organizations, will be key.

Farmers generally tend to be risk averse, which leads to limited investment and adoption of improved agricultural production technology 18 . Experienced farmers use precautionary strategies to protect against the possibility of catastrophic loss in the event of a climatic shock and thus optimize management for average or likely conditions, but not for unfavourable conditions. These ex ante, precautionary strategies include selection of crops and cultivars and improved production technology 18 .

In general, there is widespread agreement that aside from the useful experience that farmers gain from the time they have spent in farming, their experience with climatic shocks is key to their adoption of climate-resilient technologies. Many studies showed that farming experience is influential in adoption and utilization, and previous experiences with environmental shocks such as drought can influence adoption of climate-resilient crops and crop varieties. The more experience farmers have with climatic shocks, the more likely they are to be receptive to the adoption of related climate-resilient technologies. For example, experience with drought shock in the agro-ecological zone of Brong Ahafo, Ghana, increased the probability of adoption of drought-tolerant varieties by 15%, and farmers reported that drought shock was the primary reason for adoption of drought-tolerant varieties 19 .

It has been widely acknowledged that education levels of farmers have a positive correlation with technology adoption, and our synthesis demonstrates that this is also relevant for the adoption of climate-resilient crops 16 , 20 , 21 , 22 . Highly educated heads of households are more likely to readily accept and access information about new technologies in a shorter period of time than less educated heads of households; education was measured as educational attainment and reported in 49% of the studies. A study based in Zimbabwe showed a 52% decrease in production of traditional sorghum varieties in favour of new varieties better suited to drier conditions for every additional year of schooling, and a 5% increase in growing new early-maturing varieties 23 .

Changing crop varieties is one of the most frequently cited climate-resiliency strategies for both men and women farmers, but women are more likely to adopt such strategies when they are aware of climate-adaptation options 24 . Other intersectional variables such as marital status, education and age, in combination with gender, influenced whether improved seed was grown by households 25 . A major shortcoming of the reviewed literature is that most studies included women only when they were household heads. Definitions of household headship are variable, and when women are only included as household heads, their views do not necessarily represent the views of women who live in male-headed households 26 . A large majority of women live in male-headed households, and their views are rendered invisible through this practice 27 . For example, young, poor women who were household heads were the least likely to adopt drought-tolerant maize in Uganda, whereas spouses of male household heads influenced adoption decisions on their husbands’ fields 9 . Only a few studies paid attention to intra-household dynamics, gender roles and relations, and how these shape adaptation decisions 9 , 28 . This limited attention on intra-household gender dynamics and decision making around climate-resilient seed adoption skews the conclusions and recommendations, as the literature does not equally represent the challenges and views of women.

Seed policies in many countries focus on strengthening formal, national seed systems that rely on variety-release mechanisms, seed certification policies and seed companies for distribution. These types of seed systems remain difficult to access for many farmers, and evidence from the papers in this scoping review suggests that strengthening local seed systems is essential. Local seed systems rely on social networks to ensure multiple options to access seed of a range of climate-resilient crops and varieties, including local landraces and improved seed. Thus, context specificity is important for seed systems, as it is for almost all factors influencing adoption of climate-resilient crops and varieties.

The determinants of adoption that we identified are, in many cases, context-specific and therefore implementation of specific interventions is most successful when they are tailored to their environment and the cropping system. Seemingly contradictory or opposing (positive and negative) effects of each determinant of adoption were commonly reported among—and sometimes within—studies. Sex, age, education, years of farming experience and indicators of socio-economic status or wealth (assets) all affected decisions to adopt climate-resilient technologies in context-specific and sometimes opposite ways, depending on interacting environmental, policy and household factors. For example, equal and sizable numbers of studies (13 each) identify positive and negative effects of age on adoption. Whereas some studies identified older farmers to be more reluctant to adopt new technologies, other studies found that the earned experience, broad social networks and accumulation of wealth associated with older farmers may explain a positive effect on adoption. Extension and access to information about climate-resilient technologies and weather might be exceptions to this trend, as these determinants seem to transcend context-specific implementation. The resulting conclusion is that there is no ‘one size fits all’ recommendation to ensure adoption of climate-resilient crops and crop varieties, and interventions are unlikely to uniformly benefit all climate-vulnerable farmers (Table 3 ). This is consistent with the large number of papers in this study that reported farmers adopting climate-resilient crops as part of broader climate-resilient strategies.

Climate resiliency at farm level is essential to achieve food security and improve livelihoods of rural communities, especially in countries and communities that depend on local agricultural production to ensure household income and achieve daily adequate caloric intake and balanced nutrition. Understanding the factors contributing to adoption and dis-adoption of climate-resilient crops provides opportunities to increase adoption and reduce the impact of climate change on rural communities in developing countries. The most important determinants of adoption of climate-resilient crops based on our analysis are the availability and effectiveness of extension services and outreach, followed by education levels of heads of households, farmers’ access to inputs, especially seeds and fertilizers, and socio-economic status of farming families. Building resilience to climate change requires a cropping-systems, and more often a farming-systems approach. The results from this scoping review show that the adoption of climate-resilient crops and varieties, in most cases, happens as part of whole-farm and climate-smart agriculture strategies to cope with changing climate. Farmers adopting multiple complementary strategies under climate-smart agriculture help to build highly resilient and sustainable agriculture systems that can respond to shocks associated with climate change and other agricultural challenges 29 , 30 , 31 . Single component intervention programmes or projects are therefore less likely to realize widespread adoption and improvement of resource-poor farmers’ resilience to climate change compared with more holistic, multifaceted approaches that take into consideration the physical, human and socio-economic circumstances of the targeted farmer or farming community. Specific policy recommendations are presented in Box 3 .

Box 3 Recommendations

Access and availability of climate-resilient crops seeds must be combined with relevant and timely advisory services, such as early-warning systems for weather.

Ensuring that farmers have multiple options to access seeds for a range of climate-resilient crops and varieties is essential. This can be achieved by empowering existing social networks, such as farmer organizations.

There is no single profile that applies to all farmers. Therefore, extension services will need to continue to evolve to be (1) participatory, (2) information and communications technology enhanced, and (3) partnerships based. This partnership should include various actors, such as women’s groups, universities, the private sector and non-governmental organizations in order to provide customized and appropriate information for diverse needs.

High-quality studies are needed on how members of households—and not just heads of households—make decisions about how to respond to climate change. This research will fill in the evidence gaps on gender and social differences and reasons for dis-adoption of climate-resilient crops and related technologies, and promote a more diverse group of climate-resilient crops that also provide food security and nutrition, such as legumes and root crops.

National policies need to support farmers’ access to other assets and services, such as education, land, finance services and diverse income-earning opportunities. Without these provisions, especially education, the adoption of climate-resilient crops and technologies will be limited.

A multiple-interventions approach is needed if countries want to promote adoption of climate-resilient crops. Farmers do not adopt climate-resilient crop or crop varieties without changing other practices, such as planting dates, water-conserving technologies, planting trees and shrubs, or increasing or decreasing livestock.

Farmers will not adopt climate-resilient crops solely on the basis of environmental-adaptation qualities. Development and breeding programmes must consider farmer and market trait preferences.

Mandating disaggregated data collection to identify strategies that are working and who they are working for in agricultural surveys and research will enable policy makers and donors to respond with more appropriate and informed interventions.

Unlike a typical narrative review, a scoping review strives to capture all the literature on a given topic and reduce authorial bias. Scoping reviews offer a unique opportunity to explore the evidence in agricultural fields to address questions relating to what is known about a topic, what can be synthesized from existing studies to develop policy or practice recommendations, and what aspects of a topic have yet to be addressed by researchers.

Evidence synthesis methodology and protocol pre-registration

This scoping review was prepared following guidelines from the PRISMA extension for scoping reviews (PRISMA-ScR) 32 . This framework comprises five steps: identifying the research question; identifying relevant studies; study selection; extracting and charting the data; and collating, summarizing, and reporting the results 33 . The protocol for this scoping review was registered on the Open Science Framework before study selection 8 . The full protocol is available in the Supplementary Information .

Research question

The guiding question for this scoping review was, ‘what are determinants that lead small-scale producers in low-and middle-income countries to adopt climate-resilient crops and crop varieties?’.

Information sources, search methods and citation management

An exhaustive search strategy was developed to identify all available research pertaining to facilitators that lead small-scale producers in low- and middle-income countries to adopt climate-resilient crop varieties. Search terms included variations of the key concepts in the research question: small-scale producers, germplasm and climate resilience. The search algorithms were formatted for compatibility with each database so that they may be reproduced in their entirety, and they can be accessed at https://osf.io/sfzcm/ . Searches were performed in the following electronic databases by K.G.K.: CAB Abstracts and Global Health (accessed via Web of Science), Web of Science Core Collection (accessed via Web of Science) and Scopus (accessed via Elsevier). A comprehensive search of grey literature sources was also conducted. Search results were de-duplicated to remove redundant citations identified from multiple sources. To facilitate acceleration of the screening process, machine-derived metadata were added to individual citations, for example, identifying populations, geographies, interventions and outcomes of interest. This enabled accelerated identification of potential articles for exclusion at the title- or abstract-screening stage.

Eligibility criteria and study selection

Studies were included for data extraction and analysis if (1) their focus included a population of small-scale food producers; (2) they were published between 1990 and the start of the search (1990 is when the IPCC first met and produced their first report on climate change); (3) they presented original research (qualitative and quantitative reports) and/or reviewed existing research, including grey literature; (4) they explicitly focused on or were clearly relevant to climate change resiliency or climate change adaptation; (5) they explicitly focused on crops, varieties, seed, planting materials or germplasm; (6) they mentioned factors for adoption; (7) they included target populations in countries classified as lower and middle-income by the World Bank. Studies that did not meet all of the aforementioned inclusion criteria were excluded.

Study selection was performed in two stages. In a first step, articles were uploaded to the systematic review software Covidence, and title and abstract screening was performed by all authors to exclude articles that did not meet all inclusion criteria. Each article was reviewed by two independent authors, and discrepancies were resolved by a third independent author. Full-text screening was then performed by M.A., K.C., S.M., N.Z., H.T., K.P., L.B. and K.I., and inclusion decisions were made by a single reviewer. Studies included in full-text screening were those that met all inclusion criteria or those whose eligibility could not be established during title and abstract screening. The PRIMSA flow diagram in the Supplementary Information presents the study selection process and indicates the number of articles excluded at each phase of screening.

Data extraction and analysis

A data-extraction template (available in the Supplementary Information ) was developed to document the data and study type and context of each citation and all themes of interest. The data extraction first collected data on the paper quality, study location, population socio-economic data of the population and crop and cropping system characteristics. Second, the data-extraction template was used to collect information about the determinants of adoption and associated socio-economic factors influencing the adoption or dis-adoption of the climate-resilient crops. In total, 29 factors and determinants were selected. Additional rater observations and comments were included to increase analysis depth. Finally, raters also recorded policy and programmatic information and recommendations mentioned in the papers to support the adoption of climate-resilient crops. The data-extraction template was tested by the review team before use and data were extracted by the authors. The extracted data were qualitatively summarized on the basis of emerging themes and with the aim of providing recommendations to donors and policy makers. An assessment of study quality is not typically carried out as part of a scoping review 7 , 34 .

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We thank the Federal Ministry of Economic Cooperation of Germany (BMZ) and the Bill and Melinda Gates Foundation for the support to conduct this study under the Ceres2030: Sustainable Solutions to End Hunger programme.

Author information

These authors contributed equally: Kevin Pixley, Nkulumo Zinyengere.

Authors and Affiliations

Cornell University, Ithaca, NY, USA

Maricelis Acevedo, Hale Tufan, Kate Ghezzi-Kopel & Jaron Porciello

CIMMYT, Mexico City, Mexico

Kevin Pixley

World Bank, Washington, DC, USA

Nkulumo Zinyengere

University of Notre Dame, Notre Dame, IN, USA

USDA-ARS, East Lansing, MI, USA

Karen Cichy

International Institute for Sustainable Development, Winnipeg, Manitoba, Canada

Livia Bizikova

Michigan State University, East Lansing, MI, USA

Krista Isaacs

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M.A., K.C., S.M., N.Z., H.T., K.P., L.B., K.I. and J.P. provided expertise on content, extracted data and wrote the manuscript. K.G.-K. and J.P. provided systematic review methods and information retrieval.

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Extended data

Extended data fig. 1 access to advisory networks and knowledge about climate change..

Social determinants captured in this graph are a small-scale producers access to demonstration plots, access to weather and climate info, education of the head of household or respondent if not head of household, experience and skills of head of household or respondent, access to extension and outreach, access to social networks including co-operatives, and a knowledge and perceptions of crops and traits.

Extended Data Fig. 2 Crops fit for purpose.

Social determinants captured in this graph include farmer’s selection of a CR crop or variety based on environmental and agro-ecological conditions, cultural practices and preferences about CR crops and varieties, and selection based on knowledge about a crop traits.

Extended Data Fig. 3 Education, experience and household characteristics.

The social determinants captured in this graph include age of head of household or respondent, family size, gender, social and economic status of household, and diversification of household income.

Extended Data Fig. 4 Enabling environment.

The determinants captured in this graph include a farmer’s reported power and agency, access to institutions, and access to government programs.

Extended Data Fig. 5 Access to finance and technical resources (not advisory).

The determinants in this chart include access to energy and electricity, access to labour, access to water, distance to market for inputs and outputs, farm infrastructure, farm inputs (seeds and fertilizer), land (size and tenure), non-farm infrastructure, access to finance (transfers and credit).

Extended Data Fig. 6

Prisma Flow Diagram.

Supplementary information

Supplementary information.

List of included studies, scoping review protocol and data-extraction template.

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Acevedo, M., Pixley, K., Zinyengere, N. et al. A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries. Nat. Plants 6 , 1231–1241 (2020). https://doi.org/10.1038/s41477-020-00783-z

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45 Research Project Ideas in Agriculture – Innovative Approaches to Sustainable Farming

Explore 45 research project ideas in agriculture for sustainable farming.

Table of contents

Agriculture is a vast and dynamic field that plays a critical role in feeding the world’s population. As the global population continues to grow, the demand for food production is also increasing, making agriculture one of the most important sectors for ensuring food security and sustainable development. However, the challenges facing the agriculture industry today are numerous, ranging from climate change, soil degradation, water scarcity, and pest infestation to biodiversity loss and food waste.

To tackle these issues and promote sustainable agriculture, researchers and professionals in the field are continuously exploring new and innovative ways to improve agricultural practices, increase productivity, and reduce environmental impact. In this article, we will present 45 research project ideas in agriculture that can help address some of the most pressing issues facing the industry today.

These research projects cover a wide range of topics, from soil health and crop yields to livestock farming, aquaculture, and food systems, providing a comprehensive overview of the latest trends and innovations in agricultural research.

Whether you are a student, researcher, or professional in the field, these research project ideas can help guide your work and contribute to a more sustainable and resilient agriculture industry.

• Evaluating the effectiveness of natural pest control methods in agriculture.
• Investigating the effects of climate change on crop yields and food security.
• Studying the impact of soil quality on plant growth and crop yields.
• Analyzing the potential of precision agriculture techniques to increase yields and reduce costs.
• Assessing the feasibility of vertical farming as a sustainable solution to food production.
• Investigating the impact of sustainable agriculture practices on soil health and ecosystem services.
• Exploring the potential of agroforestry to improve soil fertility and crop yields.
• Developing strategies to mitigate the effects of drought on crop production.
• Analyzing the impact of irrigation management techniques on crop yields and water use efficiency.
• Studying the potential of biochar as a soil amendment to improve crop productivity.
• Investigating the effects of soil compaction on crop yields and soil health.
• Evaluating the impact of soil erosion on agriculture and ecosystem services.
• Developing integrated pest management strategies for organic agriculture.
• Assessing the potential of cover crops to improve soil health and reduce erosion.
• Studying the effects of biofertilizers on crop yields and soil health.
• Investigating the potential of phytoremediation to mitigate soil pollution in agriculture.
• Developing sustainable practices for livestock farming and manure management.
• Studying the effects of climate change on animal health and productivity.
• Analyzing the impact of animal feeding practices on meat quality and safety.
• Investigating the potential of aquaponics to increase food production and reduce environmental impact.
• Developing strategies to reduce food waste and loss in agriculture.
• Studying the effects of nutrient management practices on crop yields and environmental impact.
• Evaluating the potential of organic agriculture to improve soil health and reduce environmental impact.
• Investigating the effects of land use change on agriculture and biodiversity.
• Developing strategies to reduce greenhouse gas emissions from agriculture.
• Analyzing the impact of agricultural policies on food security and sustainability.
• Studying the potential of precision livestock farming to improve animal welfare and productivity.
• Investigating the impact of agrochemicals on soil health and biodiversity.
• Developing sustainable practices for fisheries and aquaculture.
• Studying the potential of bioremediation to mitigate pollution in aquaculture.
• Investigating the effects of climate change on fisheries and aquaculture.
• Developing strategies to reduce water pollution from agriculture and aquaculture.
• Studying the impact of land use change on water resources and aquatic ecosystems.
• Evaluating the potential of agroecology to promote sustainable agriculture and food systems.
• Investigating the impact of climate-smart agriculture practices on food security and resilience.
• Studying the potential of agrobiodiversity to improve crop productivity and resilience.
• Analyzing the impact of agricultural trade on food security and sustainability.
• Investigating the effects of urbanization on agriculture and food systems.
• Developing strategies to promote gender equity in agriculture and food systems.
• Studying the potential of agroforestry to promote biodiversity and ecosystem services.
• Analyzing the impact of food systems on public health and nutrition.
• Investigating the effects of climate change on pollination and crop yields.
• Developing strategies to promote agrotourism and rural development.
• Studying the potential of agroforestry to promote carbon sequestration and mitigate climate change.
• Analyzing the impact of agricultural subsidies on food security and sustainability.

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research proposal on farmer groups and agricultural development in Sanga Sub county, Kiruhura District in Uganda

The study will be carried out in Sanga Sub County in Kiruhura district. It will aim at investigating the roles of farmer groups in agricultural development in the Sub County. The study will specifically identify the farmer groups in Sanga Sub County in Nyabushozi county, Kiruhura district, establish the farmer group contribution/roles in agricultural development in the area of study, analyse the challenges met by farmer groups in a bid to bring about Agricultural development in Sanga Sub county, Kiruhura district and will explore ways of overcoming challenges faced by farmer groups in Sanga sub county. The study will use a descriptive research design. Data will be analyzed using frequency counts and percentages which will be derived from questionnaires and interviews. The study will use a sample size of 120 respondents. FINDINGS OF THE STUDY WILL BE PUBLISHED IN OCTOBER, 2014.

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Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

Muhammad habib-ur-rahman.

1 Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany

2 Department of Agronomy, MNS-University of Agriculture, Multan, Pakistan

Ashfaq Ahmad

3 Asian Disaster Preparedness Center, Islamabad, Pakistan

4 Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, Pakistan

Muhammad Usama Hasnain

Hesham f. alharby.

5 Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

Yahya M. Alzahrani

Atif a. bamagoos, khalid rehman hakeem.

6 Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia

7 Department of Public Health, Daffodil International University, Dhaka, Bangladesh

Saeed Ahmad

8 Institute of Plant Breeding and Biotechnology, MNS-University of Agriculture, Multan, Pakistan

9 Department of Agronomy, The Islamia University, Bahwalpur, Pakistan

Wajid Nasim

Shafaqat ali.

10 Department of Environmental Science and Engineering, Government College University, Faisalabad, Pakistan

Fatma Mansour

11 Department of Economics, Business and Economics Faculty, Siirt University, Siirt, Turkey

Ayman EL Sabagh

12 Department of Agronomy, Faculty of Agriculture, Kafrelsheikh University, Kafrelsheikh, Egypt

13 Department of Field Crops, Faculty of Agriculture, Siirt University, Siirt, Turkey

Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040–2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040–2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Introduction

Asia is the most populous subcontinent in the world (UNO, 2015 ), comprising 4.5 billion people—about 60% of the total world population. Almost 70% of the total population lives in rural areas and 75% of the rural population are poor and most at risk due to climate change, particularly in arid and semi-arid regions (Yadav and Lal, 2018 ; Population of Asia, 2019 ). The population in Asia is projected to reach up to 5.2 billion by 2050, and it is, therefore, challenging to meet the food demands and ensure food security in Asia (Rao et al., 2019 ). In this context, Asia is the region most likely to attribute to population growth rate, and more prone to higher temperatures, drought, flooding, and rising sea level (Guo et al., 2018 ; Hasnat et al., 2019 ). In Asia, diversification in income of small and poor farmers and increasing urbanization is shocking for agricultural productivity. Asia is the home of a third of the world's population and the majority of poor families, most of which are engaged in agriculture (World Bank, 2018 ). We can expect diversification of adverse climate change effects on the agriculture sector due to diversity of farming and cropping systems with dependence on climate. According to the sixth assessment report of IPCC, higher risks of flood and drought make Asian agricultural productivity highly susceptible to changing climate (IPCC, 2019 ). Climate change has already adversely affected economic growth and development in Asia, although there is low emission of greenhouse gasses (GHG) in this region (Gouldson et al., 2016 ; Ahmed et al., 2019a ). Still, China and India are major contributors to global carbon dioxide emission; the share of each Asian country in cumulative global carbon dioxide emission is presented in Figures 1 , ​ ,2. 2 . Although GHGs emission from the agriculture sector is lower than the others, it still has a negative impact. Emission of GHGs from different agricultural components and contribution to emissions can be found in Figure 3 . However, the contribution of Asian countries in GHGs including land use changes and forestry is described in Figure 4 .

Share of each Asian country in cumulative global carbon dioxide emission (1751–2019; Source: OWID based on CDIAC and Global Carbon Project).

Carbon dioxide (CO 2 ) emission from different Asian countries (source: International Energy Statistics https://cdiac.ess-dive.lbl.gov/home.html ; Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States).

Sources of greenhouse gasses (GHGs) emission from different Asian countries with respect to agricultural components (Source: CAIT climate data explorer via . Climate Watch ( https://www.climatewatchdata.org/data-explorer/historical-emissions ).

Total greenhouse gasses (GHGs) emission includes emissions from land use changes and forestry from Asian countries (measured in tons of carbon dioxide equivalents [CO 2 -e] (Source: CAIT climate data explorer via Climate Watch).

Asia is facing alarming challenges due to climate change and variability as illustrated by various climatic models predicting the global mean temperature will increase by 1.5°C between 2030 and 2050 if it continues to increase at the current rate (IPCC, 2019 ). In arid areas of the western part of China, Pakistan, and India, it is also projected that there will be a significant increase in temperature (IPCC, 2019 ). During monsoon season, there would be an increase in erratic rainfall of high intensity across the region. In South and Southeast Asia, there would be an increase in aridity due to a reduction in winter rainfall. Due to climatic abnormalities, there will be a 0.1 m increase in sea level by 2,100 across the globe (IPCC, 2019 ). In Asia, an increase in heat waves, hot and dry days, and erratic and unsure rainfall patterns is projected, while dust storms and tropical cyclones are predicted to be worse in the future (Gouldson et al., 2016 ). Natural disasters are the main reason behind the agricultural productivity (crops and livestock) losses in Asia, including extreme temperature, storms and wildfires (23%), floods (37%), drought (19%), and pest and animal diseases infestation (9%) which accounted for 10 USD billions in amount (FAO, 2015 ). During the last few decades, tropical cyclones in the Pacific have occurred with increased frequency and intensity. South Asia consisted of 262 million malnourished inhabitants, which made South Asia the most food insecure region across the globe (FAO, 2015 ; Rasul et al., 2019 ). In remote dry lands and deserts, the rural population is more vulnerable to climate change due to the scarcity of natural resources.

In Asia, climate variability (temperature and rainfall) and climate-driven extremes (flood, drought, heat stress, cold waves, and storms) have several negative impacts on the agriculture sector (FAO, 2016 ), especially in the cropping system which has a major role in food security, and thus created the food security issues and challenges in Asia (Cai et al., 2016 ; Aryal et al., 2019 ). The rice-wheat cropping system, a major cropping system which fills half of the food demand in Asia, is under threat due to climate change (Ghaffar et al., 2022 ). Climate change adversely affects both the quantity and quality of wheat and rice crops (Din et al., 2022 ; Wasaya et al., 2022 ). For instance, the protein content and grain yield of wheat have been reduced because of the negative impacts of increasing temperature (Asseng et al., 2019 ). The temperature rise has decreased the crop-growing period, and crop evapotranspiration ultimately reduced wheat yield (Azad et al., 2018 ). Adverse impacts of climate change and variability on winter wheat yield in China are attributed to increased average temperature during the growing period (Geng et al., 2019 ). Climate change is also adversely affecting the quality traits especially protein content, and sugars and starch percentages in grains of wheat. Elevated carbon dioxide and high temperatures increase the growth traits while decreasing the protein content in wheat grains (Asseng et al., 2019 ). Similarly, drought stress also reduces the protein content and soluble sugars of the wheat crop (Rakszegi et al., 2019 ; Hussein et al., 2022 ). The decline in the starch content in wheat grains has also been observed under drought stress (Noori and Taliman, 2022 ). Similarly, heat stress also causes a decline in the protein content, soluble sugar, and starch content in wheat grains (Zahra et al., 2021 ; Iqbal et al., 2022 ; Zhao et al., 2022 ). Climate change also negatively affects the quality of wheat products as the rise in temperature causes a reduction in protein content, sugars, and starch. It is assessed that rise in temperature by 1–4°C could decrease the wheat yield up to 17.6% in the Egyptian North Nile Delta (Kheir et al., 2019 ). In China, crop phenology has changed because of both climate variability and crop management practices (Liu et al., 2018 ). Both climate change scenarios and human management practices have adversely affected wheat phenology in India and China (Lv et al., 2013 ; Ren et al., 2019 ). The elevated temperature has increased the infestation of the aphid population on wheat crops and ultimately reduced yield (Tian et al., 2019 ). There is a direct and strong correlation between diseases attached to climate change. For instance, the Fusarium head blight of wheat crops is caused by the Fusarium species and its chances of an attack were increased due to high humidity and hot environment (Shah et al., 2018 ). A similar study has shown a direct interaction between insect pests and diseases and higher temperature and carbon dioxide levels in rice production (Iannella et al., 2021 ; Tan et al., 2021 ; Tonnang et al., 2022 ).

Climate variability has marked several detriments to rice production in Asia. Climate variability has induced flood and drought, which have decreased the rice yield in South Asia and several other parts of Asia (Mottaleb et al., 2017 ). Heat stress, drought, flood, and cyclones have reduced the rice yield in South Asia (Cai et al., 2016 ; Quyen et al., 2018 ; Tariq et al., 2018 ). Thus, climate change-driven extremes, particularly heat and drought stress, have also become a serious threat for sustainable rice production globally (Xu et al., 2021 ). Higher temperatures for a longer period as well as water shortages reduce seed germination which lead to poor stand establishment and seedling vigor (Fahad et al., 2017 ; Liu et al., 2019 ). It has been reported that the exposure of rice crops to high temperatures (38°C day/30°C night) at the grain filling stage led to a reduction in grain weight of rice (Shi et al., 2017 ). Moreover, heat stress also reduces the panicle and spikelet's initiation and ultimately the number of spikelets and grains in the rice production system (Xu et al., 2020 ). Drought stress also adversely affects the reproductive stages and reduces the yield components especially spikelets per panicle, grain size, and grain weight of rice (Raman et al., 2012 ; Kumar et al., 2020 ; Sohag et al., 2020 ). GLAM-Rice model has projected rice yield will decrease ~45% in the 2080's under RCP 8.5 as compared to 1991–2000 in Southeast Asia (Chun et al., 2016 ). On the other hand, climate variability could reduce crop water productivity by 32% under RCP 4.5, or 29% under RCP 8.5 by 2080's in rice crops (Boonwichai et al., 2019 ). In China and Pakistan, high temperature adversely affects the booting and anthesis growth stages of rice ultimately resulting in yield reduction (Zafar et al., 2018 ; Nasir et al., 2020 ). Crop models like DSSAT and APSIM have projected a yield reduction of both rice and wheat crops up to 19 and 12% respectively by 2069 due to a rise of 2.8°C in maximum and 2.2°C in minimum temperature in Pakistan (Ahmad et al., 2019 ).

About 35 million farmers having 3% landholding are projected to convert their source of income (combined crop-livestock production systems) to simply livestock because of the negative impacts of climate change on the quality and quantity of pastures as predicted by future scenarios for 2050 in Asia (Thornton and Herrero, 2010 ). The livestock production sector also contributes 14.5% of global greenhouse emissions and drives climate variability (Downing et al., 2017 ). Directly, there would be higher disease infestation and reduced milk production and fertility rates in livestock because of climate extremes like heat waves (Das, 2018 ; Kumar et al., 2018 ). Indirectly, heat stress will reduce both the quantity and quality of available forage for livestock. Several studies have reported that heat stress reduces the protein and starch content in the grains of maize which is a widely used forage crop (Yang et al., 2018 ; Bheemanahalli et al., 2022 ). Similarly, heat stress also reduces the soluble sugar and protein content in the heat-sensitive cultivars of alfalfa which is also a major forage crop (Wassie et al., 2019 ). In this context, heat stress leads to a reduction in the quality of forage. There would be an increase in demand for livestock products, however, there would be a decrease in livestock heads under future climate scenarios (Downing et al., 2017 ). In Asia, a severe shortage of feed for livestock has imposed horrible effects on the livestock population which has been attributed as the result of extreme rainfall variability and drought conditions (Ma et al., 2018 ).

Timber forests have several significances in Asia, and non-timber forests are also significant sources of food, fiber, and medicines (Chitale et al., 2018 ). Unfortunately, climate change has imposed several negative impacts on forests at various levels in the form of productive traits, depletion of soil resources, carbon dynamics, and vegetation shifting in Asian countries. In India, forests are providing various services in terms of meeting the food demand of 300 million people, the energy demand of people living in rural areas up to 40%, and shelter to one-third of animals (Jhariya et al., 2019 ). In Bangladesh, forests are also vulnerable to climate variability as they are facing the increased risks of fires, rise in sea level, storm surges, coastal erosion, and landslides (Chow et al., 2019 ). Increased extreme drought events with higher frequency, intensity, and duration, and human activities, i.e., afforestation and deforestation, have adversely altered the forest structure (Xu et al., 2018 ). Hence, there is a need to evaluate climate adaptation strategies to restore forests in Asian countries in order to meet increased demands of food, fiber, and medicines. Agroforestry production is also under threat because of adverse climate change impacts such as depletion of natural resources, predominance of insect pests, diseases and unwanted species, increased damage on agriculture and forests, and enhanced food insecurity (De Zoysa and Inoue, 2014 ; Lima et al., 2022 ).

Asia also consists of good quality aquaculture (80% of aquaculture production worldwide) and fisheries (52% of wild caught fish worldwide) which are 77% of the total value addition (Nguyen, 2015 ; Suryadi, 2020 ). In Asia, various climatic extremes such as erratic rainfall, drought, floods, heat stress, salinity, cyclone, ocean acidification, and increased sea level have negatively affected aquaculture (Ahmad et al., 2019 ). For instance, Hilsailisha constituted the largest fishery in Bangladesh, India, and West Bengal and S. Yangi in China have lost their habitat because of climate variability (Jahan et al., 2017 ; Wang et al., 2019a ). Ocean acidification and warming of 1.5°C was closely associated with anthropogenic absorption of CO 2 . Increasing levels of ocean acidity is the main threat to algae and fish. Among various climate driven extremes like drought, flood, and temperature rising, drought is more dangerous as there is not sufficient rainfall especially for aquaculture (Adhikari et al., 2018 ). Similarly, erratic rainfall, irregular rainfall, storms, and temperature variability have posed late maturity in fish for breeding and other various problems (Islam and Haq, 2018 ).

The above-mentioned facts have indicated that agriculture, livestock, forestry, fishery, and aquaculture are under threat in the future and can drastically affect food security in Asia. This paper reviews the climate change and variability impacts on the cropping system (rice and wheat), livestock, forestry, fishery, and aquaculture and their issues, challenges, and opportunities. The objectives of the study are to: (i) Review the climate variability impacts on agriculture, livestock, forestry, fishery, and aquaculture in Asia; (ii) summarize the opportunities (adaptation and mitigation strategies) to minimize the drastic effects of climate variability in Asia; and (iii) evaluate the impact of climate change on rice-wheat farmer fields—A case study of Pakistan.

Impact of climate change and variability on agricultural productivity

Impact of climate change and variability on rice-wheat crops.

In many parts of Asia, a significant reduction in crop productivity is associated with a reduction in timely water and rainfall availability, and erratic and intense rainfall patterns during the last decades (Hussain et al., 2018 ; Aryal et al., 2019 ). Despite the increased crop production owing to the green revolution, there is a big challenge to sustain production and improve food security for poor rural populations in Asia under climate change scenarios (FAO, 2015 ; Ahmad et al., 2019 ). In the least developed countries, damage because of climactic changes may threaten food security and national economic productivity (Myers et al., 2017 ). Yield reductions in different crops (rice, wheat) varied within regions due to variations in climate patterns (Yu et al., 2018 ). CO 2 fertilization can increase crop productivity and balance the drastic effects of higher temperature in C 3 plants (Obermeier et al., 2017 ) but cannot reduce the effect of elevated temperature (Arunrat et al., 2018 ). Crop growth and development have been negatively influenced because of rising temperatures and rainfall variability (Rezaei et al., 2018 ; Asseng et al., 2019 ).

Rice and wheat are major contributors to food security in Asia. There is a big challenge to increase wheat production by 60% by 2050 to meet ever-enhancing food demands (Rezaei et al., 2018 ). In arid to semi-arid regions, declined crop productivity is attributed to an increase in temperature at lower latitudes. In China, drought and flood have reduced the rice, wheat, and maize yields and it is projected that these issues will affect crop productivity more significantly in the future (Chen et al., 2018 ). Rice is sensitive to a gradual rise in night temperature causing yield and biomass to reduce by 16–52% if the temperature increase is 2°C above the critical temperature of 24°C (Yang et al., 2017 ). In Asia, semi-arid to arid regions are under threat and are already facing the problem of drought stress and low productivity. The quality of wheat produce (protein content, sugars, and starch) and grain yield have reduced because of the negative impacts of increasing temperature and erratic rainfall with high intensity (Yang et al., 2017 ). In the Egyptian North Nile Delta (up to 17.6%), India, and China, the climate variability has decreased wheat yield significantly which is attributed to a rise in temperature, erratic rainfall and increasing insect pest infestation (Arunrat et al., 2018 ; Shah et al., 2018 ; Aryal et al., 2019 ; Kheir et al., 2019 ). In South Asia, rice yield in rain-fed areas has already decreased and it might reduce by 14% under the RCP 4.5 scenario while 10% under the RCP 8.5 scenario by 2080 (Chun et al., 2016 ). High temperature and drought have decreased the rice yield because of their adverse impacts on the booting and anthesis stage in Asia, especially in Pakistan and China (Zafar et al., 2018 ; Ahmad et al., 2019 ). Similarly, heat stress is a major threat to rice as it decreases the productive tillers, shrinkage of grains, and ultimately grain yield of rice (Wang et al., 2019b ). In Asia, climate change would affect upland rice (10 m ha) and rain-fed lowland rice (>13 million hectares). The projected production of rice and wheat crops by 2030 is presented in Table 1 .

Productivity shock due to climate change and variability on rice and wheat crop production by 2030.

Source: Gouldson et al. ( 2016 ), Asseng et al. ( 2019 ), Chow et al. ( 2019 ), Degani et al. ( 2019 ), Sanz-Cobena et al. ( 2019 ), and Suryadi ( 2020 ).

Minus sign (-) indicates the decrease in productivity while positive sign (+) indicates increase in productivity.

Impact of climate change and variability on livestock

In arid to semi-arid regions, the livestock sector is highly susceptible to increased temperature and reduced precipitation (Downing et al., 2017 ; Balamurugan et al., 2018 ). A temperature range of 10–30°C is comfortable for domestic livestock with a 3–5% reduction in animal feed intake with each degree rise in temperature. Similarly, the lower temperature would increase the requirement feed up to 59%. Moreover, drought and heat stress would drastically affect livestock production under climate change scenarios (Habeeb et al., 2018 ). Climate variability affects the occurrence and transmission of several diseases in livestock. For instance, Rift Valley Fever (RVF) due to an increase in precipitation, and tick-borne diseases (TBDs) due to a rise in temperature, have become epidemics for sheep, goats, cattle, buffalo, and camels (Bett et al., 2019 ). Different breeds of livestock show different responses to higher temperature and scarcity of water. In India, thermal stress has negative impacts on the reproduction traits of animals and ultimately poor growth and high mortality rates of poultry (Balamurugan et al., 2018 ; Chen et al., 2021 ; van Wettere et al., 2021 ). In dry regions of Asia, extreme variability in rainfall and drought stress would cause severe feed scarcity (Arunrat et al., 2018 ). It has been revealed that a high concentration of CO 2 reduces the quality of fodder like the reduction in protein, iron, zinc, and vitamins B1, B2, B5, and B9 (Ebi and Loladze, 2019 ). Future climate scenarios show that the pastures, grasslands, feedstuff quality and quantity, as well as biodiversity would be highly affected. Livestock productivity under future climate scenarios would affect the sustainability of rangelands, their carrying capacity and ecosystem buffering capacity, and grazing management, as well as the alteration in feed choice and emission of greenhouse gases (Nguyen et al., 2019 ).

Impact of climate change on forest

Climate variability has posed several negative impacts on forests including variations in productive traits, carbon dynamics, and vegetation shift, as well as the exhaustion of soil resources along with drought and heat stress in South Asian countries (Jhariya et al., 2019 ; Zhu et al., 2021 ). In Bangladesh, forests are vulnerable to climate variability due to increased risks of fires, rise in sea level, storm surges, coastal erosion and landslides, and ultimately reduction in forest area (Chow et al., 2019 ). Biodiversity protection, carbon sequestration, food, fiber, improvement in water quality, and medicinal products are considered major facilities provided by forests (Chitale et al., 2018 ). In contrast, trait-climate relationships and environmental conditions have drastically influenced structure, distribution, and forest ecology (Keenan, 2015 ). Higher rates of tree mortality and die-off have been induced in forest trees because of high temperature and often-dry events (Allen et al., 2015 ; Greenwood et al., 2017 ; Zhu et al., 2021 ). For instance, trees Sal, pine trees, and Garjan have been threatened by climate-driven continuing forest clearing, habitat alteration, and drought in South Asian countries (Wang et al., 2019). An increase in temperature and CO 2 fertilization has increased insect pest infestation for forest trees in North China (Bao et al., 2019 ). As rising temperature, elevated carbon dioxide (CO 2 ), and fluctuating precipitating patterns lead to the rapid development of insect pests and ultimately more progeny will attack forest trees (Raza et al., 2015 ). Hence, there is a need to develop adaptation strategies to restore forests to meet the increasing demand for food, fiber, and medicines in Asia.

Impact of climate change on aquaculture and fisheries

There is a vast difference in response to climate change scenarios of aquaculture in comparison to terrestrial agriculture due to greater control levels over the production environment under terrestrial agriculture (Ottaviani et al., 2017 ; Southgate and Lucas, 2019 ). Climatic-driven extremes such as drought, flood, cyclones, global warming, ocean acidification, irregular and erratic rainfall, salinity, and sea level rise have negatively affected aquaculture in South Asia (Islam and Haq, 2018 ; Ahmad et al., 2019 ). In Asia, various species such as Hilsa and algae have lost their habitats due to ocean acidification and temperature rise (Jahan et al., 2017 ). Increased water temperature and acidification of terrestrial agriculture have become dangerous for coral reefs and an increase in average temperature by 1°C for four successive weeks can cause bleaching of coral reefs in India and other parts of Asia (Hilmi et al., 2019 ; Lam et al., 2019 ). Ocean warming has caused severe damage to China's marine fisheries (Liang et al., 2018 ). In Pakistan, aquaculture and fisheries have lost their habitat quality, especially fish breeding grounds because of high cyclonic activity, sea level rise, temperature variability, and increased invasion of saline water near Indus Delta (Ali et al., 2019 ). It is revealed that freshwater and brackish aquaculture is susceptible to the negative effects of climate variability in several countries of Asia (Handisyde et al., 2017 ). It is also evaluated that extreme climate variability has deep impacts on wetlands and ultimately aquaculture in India (Sarkar and Borah, 2018 ).

Climate variability and change impact assessment

Agriculture has a complex structure and interactions with different components, which will make it uncertain in a future climate that is a serious risk to food security in the region. Consequently, it is essential to assess the negative impacts of climate change on agricultural productivity and develop adaptive strategies to combat climate change. Simulation models such as General Circulation Models (GCMs) and Representative Concentration Pathways (RCPs) are being used worldwide for the quantification of the negative effects of climate change on agriculture and are supporting the generation of future weather data (Rahman et al., 2018 ). Primary tools are also available that can estimate the negative impacts of changing climate on crop productivity, crucial for both availability and access to food. Crop models have the potential to describe the inside processes of crops by considering the temperature rise and elevated CO 2 at critical crop growth stages (Challinor et al., 2018 ). There are no advanced methods and technologies available to see the impact of climate variability and change on the production of livestock and crops other than the modeling approach (Asseng et al., 2014 ). There are also modeling tools available, and being used across the world, to quantify the impacts of climate change and variability on crops and livestock production (Ewert et al., 2015 ; Hoogenboom et al., 2015 ; Rahman et al., 2019 ). We decided to quantify the impacts of future climate on farmer's livelihood to study the complete agricultural system by adopting the comprehensive methodology of climate, crop, and economic modeling (RAPs) approaches and found the agricultural model inter-comparison and improvement project (AgMIP) as the best approach.

A case study—Agricultural model inter-comparison and improvement project

Impact of climate change on the productivity of rice and wheat crops.

Department for International Development (DFID) developed the Agricultural Model Inter-comparison and Improvement Project (Rosenzweig et al., 2013 ) which is an international collaborative effort to deeply investigate the influences of climate variability and change on crops' productivity in different cropping zones/systems across the world and in Pakistan. The mission of AgMIP is to improve the scientific capabilities for assessing the impact of climate variability on the agricultural production system and develop site-specific adaptation strategies to ensure food security at local to global scales. The review discussed above indicated that the agriculture sector is the most vulnerable due to climatic variability and change. Crop production is under threat in Asian countries—predominantly in developing countries. For instance, Pakistan is also highly vulnerable due to its geographical location with arid to semi-arid environmental conditions (Nasi et al., 2018 ; Ullah et al., 2019 ; Ghaffar et al., 2022 ). There would be impacts that are more adverse in arid and semi-arid regions in comparison to humid regions because of climate change and variability (Nasi et al., 2018 ; Ali et al., 2019 ). Future climate scenarios have uncertainty and the projected scenario of climate, especially precipitation, did not coincide with the production technology of crops (Rahman et al., 2018 ). Floods and drought are anticipated more due to variations in rainfall patterns, and dry seasons are expected to get drier in future. Developing regions of the globe are more sensitive to climate variability and change as these regions implement old technologies whereas developed regions can mediate climate-driven extremes through the implementation of modern technologies (Lybbert and Sumner, 2012 ). The extent of climate change and variability hazards in Pakistan is massive and may be further shocking in the future. Therefore, it is a matter of time to compute climate variability, impacts on crop production, and develop sustainable adaptation strategies to cope with the negative impact of climate change using AgMIP standards and protocols (AgMIP). The main objective is to formulate adaptation strategies to contradict potential climate change effects and support the livelihood of smallholder farmers in the identified area and circulate this particular information to farmers, extension workers, and policy-makers. Sialkot, Sheikhupura, Nankana sahib, Hafizabad, and Gujranwala are considered the hub of the rice-wheat cropping system (Ghaffar et al., 2022 ), with an area of 1.1 million hectares. The rice-wheat cropping system is a food basket and its sustainable productivity in future climates will ensure food security in the country and generally overall in the region.

Methodology of the case study

Field data collection.

Field data included the experimental trials and socio-economic data of 155 successive farmers' farms collected during an extensive survey of rice-wheat cropping zone from five-selected districts ( Figure 5 ). From each district, randomly two villages were selected from each division, randomly 30 respondents and 15 farms of true representation of the farming population from each village considered. Crop management data included all agronomic practices from sowing to harvesting such as planting time, planting density, fertilizers amount and organic matter amendment, irrigation amount and intervals, cultural operations, grain yield, and biomass production collected for both crops, rice and wheat, and overall, for all systems. Farm data for the rice-wheat cropping system were analyzed with crop and economic models to see the impact of climate variability on crop production.

Map of study location/sites in rice-wheat cropping zone of Pakistan.

Historic and future climatic data

Daily historic data was collected from the Pakistan Meteorological Department (PMD) for all study locations. The quality of observed weather data was checked following the protocol of the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols (AgMIP, 2013 ). Station-based downscaling was performed with historic weather data from all study sites/locations in the rice-wheat cropping zone. For the zone/region, five GCMs (CCSM4, GFDL-ESM2M, MIROC5, HadGEM2-ES, and MPI-ESM-MR) of the latest CMIP5 family were engaged for the generation of climate projections for the mid-century period using the RCP 8.5 concentration scenario, and using the protocols and methodology developed by AgMIP (Ruane et al., 2013 , 2015 ; Rahman et al., 2018 ). GCMs were selected on the basis of different factors such as better performance in monsoon seasons, the record of accomplishment of publications, and the status of the model-developing institute. Under the RCP 8.5 scenario, an indication of warming ranges 2–3°C might be expected in all selected districts for the five CMIP5, GCMs in comparison to the baseline between the periods of 2040–2069. However, there is no uniform warming recorded under all 5 CMIP5 GCMs. For instance, CCSM4 and GFDL-ESM-2M showed uniform increased temperatures during April and September months. The outputs of the GCMs indicated large variability in the estimated values of precipitation. The HadGEM2-ES and GFDL-ESM2M projected mean of 200 and 100 mm between times 2040–2069, respectively. On average, a minor rise in annual rainfall (mm) is indicated by five GCMs in comparison to the baseline.

Crop models (DSSAT and APSIM)

To understand the agronomic practices and the impact of climate variability on the development and growth of plants, crop simulation models like DSSATv4.6 (Hoogenboom et al., 2015 , 2019 ) and APSIMv7.5 (Keating et al., 2003 ) were applied. Three field trials were conducted on rice and wheat crops during two growing seasons, to collect the data like phenology, crop growth (leaf area index, biomass accumulation), development, yield, and agronomic management data by following the standard procedure and protocols. Crop models are calibrated with experimental field data (phenology, growth, and yield data) under local environmental conditions by using soil and weather data. Crop models were further validated with farmers' field data of rice and wheat crops. Climate variability impact on both crops was assessed with historic data (baseline) and future climate data of mid-century in this region.

Tradeoff analysis model for multi-dimensional impact assessment

For the analysis of climate change impact socio-economic indicators, version 6.0.1 of the Tradeoff Analysis Model for Multi-Dimensional Impact Assessment (TOA-MD) Beta was employed (Antle, 2011 ; Antle et al., 2014 ). It is an economical and standard model employed for the analysis of technology adoption impact assessment and ecosystem services. Schematically illustrated, showing connections between the different models and the points of contact between them in terms of input-output in a different climate, crop and economic models and climate analysis is shown in Figure 6 . Various factors that may affect the anticipated values of the production system are technology, physical environment, social environment, and representative agricultural pathways (RAPs), hence it is necessary to distinguish these factors (Rosenzweig et al., 2013 ). RAPs are the qualitative storylines that can be translated into model parameters such as farm and household size, practices, policy, and production costs. For climate impact assessment, the dimensionality of the analysis is the main threat in scenario design. Farmers employ different systems for operating a base technology. For instance, system 1 included base climate, in system 2, farmers use hybrid climate, and in system 3, farmers use perturbed climate to cope with future climate with adaptation technology. The analysis gave the answer to three core questions (Rosenzweig et al., 2013 ). First, without the application RAPs of the core question, one-climate change impact assessments (CC-IA) were formulated. Second, analysis was again executed for examining the negative effects of climate change on future production systems. Third, analysis was executed for future adapted production systems through RAPs and adaptations. Two crop models, i.e., DSSAT and APSIM, outputs were used as the inputs of TOA-MD. Different statistical analyses like root mean square error (RMSE), mean percentage difference (MPD) d-stat, percent difference (PD), and coefficient of determination (R2) were used to check the accuracy of models.

Schematic illustration showing connections between the different models (climate, crop, and economic) and the points of contact between them in terms of input-output and climate analysis.

Farmers field data validation

Crop model simulation results regarding calibration and validation of both crops (rice and wheat) were in good agreement with the field experimental data. Both models were further validated using farmers' field data of rice and wheat crops in rice-wheat cropping zone after getting robust genetic coefficients. Model validation results of 155 farmers of rice and wheat crops indicated the good accuracy of both models (DSSAT, APSIM) and have a good range of statistical indices. Both of these crop models showed an improved ratio between projected and observed rice yield in farmers' fields with RMSE 409 and 440 kg ha −1 and d-stat 0.80 and 0.78, respectively. Similarly, the performance of models DSSAT and APSIM for a yield of wheat was also predicted with RMSE of 436 and 592 kg ha −1 and d-stat of 0.87, respectively.

Quantification of climate change impact by crop models

Climate change impact assessment results in the rice-wheat cropping zone of 155 farms indicated that yield reduction varied due to differences in GCM's behavior and variability in climatic patterns. It is predicted that mean rice yield reduction would be up to 15 and 17% for DSSAT and APSIM respectively during mid-century while yield reduction variation among GCMs are presented in Figure 7 . Rice indicated a yield decline ranging from 14.5 to 19.3% for the case of APSIM while mean yield reduction of the rice crop was between 8 and 30% with DSSAT. Reduction in production of wheat varied among GCMs as well as an overall reduction in yield in rice-wheat cropping systems. For wheat, with DSSAT would be a 14% reduction whereas for APSIM, the reduction would be 12%. GCMs reduction in wheat yield for midcentury (2040–2069) is shown in Figure 8 . Reduction in wheat yield for all 5 GCMs was from 10.6 to 12.3% in the case of APSIM while mean reduction in wheat yield was between 6.2 and 19%. As rice is a summer crop where the temperature is already high and, according to climate change scenarios, there is an increase in both maximum and minimum temperature, an increase in minimum temperature leads to more reduction in yield as compared to wheat being a winter season crop. It was hypothesized that the increase in night temperature (minimum temperature) leading to more losses in the summer season may be due to high temperature, particularly at anthesis and grain formation stages in rice crops, as it is already an irrigated crop and rainfall variability (more rainfall) cannot reduce the effect of high temperature in the rice yield as compared to the wheat crop.

Reduction in rice yield of APSIM and DSSAT models for 155 farms; variation with 5-GCMs in rice-wheat cropping system of Punjab-Pakistan.

Reduction in wheat yield of APSIM and DSSAT models for 155 farms; variation with 5-GCMs in rice-wheat cropping system of Punjab-Pakistan.

Climate change economic impact assessment and adaptations

Sensitivity of current agricultural production systems to climate change.

Climate change is damaging the present vulnerabilities of poor small farmers as their livelihood depends directly on agriculture. Noting various impacts of future climate (2040–2069) on a current production system (current technologies), we examine the vulnerability of the current production system used for the assessment of the adverse impacts of climate change on crop productivity and other socio-economic factors. Climate change impacts possible outcomes for five GCMs based on the estimation of yield generated by two crop models presented in Table 2 . In Table 3 , and the grain losses and net impacts as a percentage of average net returns for the first core question are given for each GCM. The analysis clearly shows the observed values of the mean yield of wheat and rice, which are estimated to be 18,915 kg and 18,349 kg/ farm respectively in the projected area. For all GCMs, observed average milk production was 3,267 liters per farm with a 12% average decline in yield found under livestock production. Losses were about 69–83% and from 72 to 76% for DSSAT and APSIM respectively as predicted by TOA-MD analysis because of the adverse effects of climate change situations. For DSSAT, percentage losses and gains in average net farm returns were from 13 to 15% and 23 to 30%, respectively. While gains were 14–15% and losses were from 25 to 27%, respectively for APSIM. Without adverse impacts of climate change, a net income of Rs. 0.54 per farm pragmatic was predicted by DSSAT and APSIM. However, DSSAT predicted Rs. 0.42–0.48 M per farm and APSIM predicted Rs. 0.45–0.47 M net income per farm under climate change for all GCMs. An increase in the poverty rate in climate change situations would be 33–38% for DSSAT and it would be 35–37% for APSIM, respectively while the rate of poverty with no adverse impacts of climate change would be 29%.

Relative yield summary of crop models.

r = ∑s2/∑s1, ∑s2, Time averaged mean of simulated future yield; ∑s1, Time averaged mean of simulated past yield.

Aggregated gains and losses with CCSM4 GCM (without adaptation and with trend) of DSSAT and APSIM.

Impacts of climate change on future agricultural production systems

In regard to the second core question, a comparison of system 1 (current climate and future production system) with system 2 (future climate and future production system in mid-century) was analyzed with the aid of TOA-MD using 5 GCMs. Mean wheat and rice yield reduction for DSSAT was from 6.2 to 19% and 8 to 30% respectively, and APSIM indicated a decline ranging from 10.6 to 12.3% and 14 to 19%, respectively. For all analyses of Q2, the projected mean yield was 25,073 kg per farm under rice production. While in the case of livestock for all analyses, the mean projected milk production was 3,267 L/farm with its mean decline in yield estimated to be about 12%. Percentage losses for DSSAT and APSIM would fluctuate between 57 and 70% and from 61 to 71%, respectively for all five GCMs.

Mean net farm returns for gains and losses, as a percentage for DSSAT would be 11–13% and from −16 to −22%, respectively. While the percentage of gains and losses would be between 10 and 15% and −17% and −19% in the case of APSIM, respectively. DSSAT predicted Rs. 89–100 thousand per person while APSIM predicted Rs. 93–97 thousand per person per capita income in changing climatic scenarios. For both crop models, the poverty rate will be 16% without climate change. While poverty rates will be from 17 to 19% in the case of DSSAT and ranging from 18 to 19% for APSIM with climate change ( Table 3 ).

Evaluation of potential adaptation strategies and representative agricultural pathways

Adaptation technologies for rice and wheat crops ( Table 4 ) are used in crop growth models and economic TOA-MD model analysis ( Table 5 ) for simulating the sound effects of prospective adaptation strategies on both adapters and non-adapters distribution. This TOA-MD analysis compared “system 1” (incorporating RAPs) and “system 2” (incorporating RAPs and adapted technology) for the rice-wheat system in the mid-century based on crop models DSSAT and APSIM using 5 GCMs. The mean yield change of wheat and rice crops was from 60 to 72% for DSSAT and 70 to 80% for APSIM respectively, wheat crop indicated a change that ranges from 80 to 89% and 62 to 84% for all five GCMs ( Figure 9 ). Under livestock production, the estimated average production of milk exclusive of adaptation was 3,593 liters/farm for all analyses and for all cases indicates a 42% increase in average yield. The percentage of adopters due to adaptation technologies for DSSAT and APSIM in rice-wheat cropping systems would be between 92 and 93% and 93 and 94%, respectively. For DSSAT and APSIM estimated per head income with adaptation cases will be from Rs. 89 to 100 and 93 to 97 thousand and from Rs. 156 to 174 and 166 to 181 thousand per head, respectively in a year. Without and with adaptation, poverty would range between 17 and 19% and 12 and 13% respectively, for DSSAT and from 18 to 19% and 12 to 13%, respectively for APSIM ( Table 6 ). Climatic changes in the rice-wheat cropping areas of Punjab province will have less impact on the future systems after implementing the adaptation strategies, with a large and significant impact imposed by these adaptations.

Adaptation technology related to crop management used for crop models (DSSAT and PSIM) to cope with the negative impacts of climate change during mid-century (2040–2069).

Percentage change (% change) shows the percentage of farmers using the crop management practices related to crop models to reduce the adverse effects of climate change.

Adaptation technology related to socioeconomic used for crop models (DSSAT and APSIM) to cope with the negative impacts of climate change during mid-century (2040–2069).

Percentage change (% change) shows the percentage of farmers using the socioeconomic technology related to crop models to reduce the adverse effects of climate change.

Distribution of adopters and non-adopters for all 5 GCMs (with adaptation and with trend). The percentage of adopters due to adaptation technologies for DSSAT and APSIM in rice-wheat cropping system would be between 92 and 93% and 93 and 94%, respectively. For DSSAT and APSIM estimated per head income with adaptation cases will be from Rs. 89 to 100 and 93 to 97 thousand and from Rs. 156 to 174 and 166 to 181 thousand per head respectively in a year.

Projected adoption of adaptation package used in crop models for CCSM4 GCM during mid-century.

Opportunities in the era of climate change for agriculture

Scope of adaptation and mitigation strategies for sustainable agricultural production.

It is essential to assess the impact of climate variability on agricultural productivity and develop adaptation strategies/technology to cope with the negative effects to ensure sustainable production. The hazardous climate change effects can be reduced by adapting climate-smart and resilient agricultural practices, which will ensure food security and sustainable agricultural production (Zafar et al., 2018 ; Ahmad et al., 2019 ; Ahmed et al., 2019b ). Adaptation is the best way to handle climate variability and change as it has the potential to minimize hazardous climate change effects for sustainable production (IPCC, 2019 ). Innovative technologies and defensive adaptation can reduce the uncertain and harmful effects of climate on agricultural productivity.

Therefore, to survive the harmful climate change effects, the development and implementation of adaptation strategies are crucial. In developing countries, poverty, food insecurity and declined agricultural productivity are common issues, which indicate the need for mitigation and adaptation measures to sustain productivity (Clair and Lynch, 2010 ; Lybbert and Sumner, 2012 ; Mbow et al., 2014 ). At the national and regional level, the insurance of food security is the major criterion for the effectiveness of mitigation and adaptation. Integration of adaptation and mitigation strategies is a great challenge to promote sustainability and productivity. Climate resilient agricultural production systems can be developed and diversified with the integration of land, water, forest biodiversity, livestock, and aquaculture (Hanjra and Qureshi, 2010 ; Meena et al., 2019 ). Summary and overview of all below discussed potential opportunities are presented in Figure 10 .

Overview of opportunities including adaptations and mitigations strategies for sustainable agriculture production system in Asia.

Reduction in GHGs emission

Reduction in GHGs emissions from agriculture under marginal conditions and production of more food are the major challenges for the development of adaptation and mitigation measures (Smith and Olesen, 2010 ; Garnett, 2011 ; Fujimori et al., 2021 ). Similarly, it is an immediate need to control such practices in agriculture which lead to GHGs emissions, i.e., N 2 O emissions from the application of chemical fertilizers, and CH 4 emissions from livestock and rice production systems (Herrero et al., 2016 ; Allen et al., 2020 ). Similarly, alternate wetting and drying and rice intensification are important to reduce the GHGs emission from rice crops (Nasir et al., 2020 ). Carbon can be restored in soil by minimizing the tillage, reducing soil erosions, managing the acidity of the soil, and implementing crop rotation. By increasing grazing duration and rotational grazing of pastureland, sequestration of carbon can be achieved (Runkle et al., 2018 ). About 0.15 gigatonnes of CO 2 equal to the amount of CO 2 produced in 1 year globally, can be sequestered by adopting appropriate grazing measures (Henderson et al., 2015 ). Development of climate-resilient breeds of animals and plants with higher growth rates and lower GHGs emissions should be developed to survive under harsh climatic conditions. Focus further on innovative research and development for the development of climate-resilient breeds, especially for livestock (Thornton and Herrero, 2010 ; Henry et al., 2012 ; Phand and Pankaj, 2021 ).

Application of ICT and decision support system

To mitigate and adapt to the drastic effects of climate variability and change, information and communication technologies (ICTs) can also play a significant role by promoting green technologies and less energy-consuming technology (Zanamwe and Okunoye, 2013 ; Shafiq et al., 2014 ; Nizam et al., 2020 ). Timely provision of information from early warning systems (EWS) and automatic weather stations (AWS) on drought, floods, seasonal variability, and changing rainfall patterns can provide early warning about natural disasters and preventive measures (Meera et al., 2012 ; Imam et al., 2017 ), and it can also support farmers' efforts to minimize harmful effects on the ecosystems. Geographical information systems (GIS), wireless sensor networks (WSN), mobile technology (MT), web-based applications, satellite technology and UAV can be used to mitigate and adapt to the adverse effects of climate change (Kalas, 2009 ; Karanasios, 2011 ). Application of different climate, crop, and economic models may also help reduce the adverse effects of climate variability and change on crop production (Hoogenboom et al., 2011 , 2015 , 2019 ; Ewert et al., 2015 ).

Crop management and cropping system adaptations

Adaptation strategies have the potential to minimize the negative effect of climate variability by conserving water through changes in irrigation amount, timely application of irrigation water, and reliable water harvesting and conservation techniques (Zanamwe and Okunoye, 2013 ; Paricha et al., 2017 ). Crop-specific management practices like altering the sowing times (Meena et al., 2019 ), crop rotation, intercropping (Hassen et al., 2017 ; Moreira et al., 2018 ), and crop diversification and intensification have a significant positive contribution as adaptation strategies (Hisano et al., 2018 ; Degani et al., 2019 ). Meanwhile, replacement of fossil fuels by introducing new energy crops for sustainable production (Ruane et al., 2013 ) is also crucial for the sustainability of the system. Different kinds of adaptation actions (soil, water, and crop conservation, and well farm management) should be adapted in case of long-term increasing climate change and variability (Williams et al., 2019 ). Similarly, alteration in input use, changing fertilizer rates for increasing the quantity and quality of the produce, and introduction of drought resistant cultivars are some of the crucial adaptation approaches for sustainable production. Therefore, under uncertain environmental conditions, to ensure sustainable productivity, crops having climatic resilient genetic traits should also be introduced (Bailey-Serres et al., 2018 ; Raman et al., 2019 ). Similarly, to ensure the sound livelihood of farmers, it is important to develop resilient crop management as well as risk mitigation strategies.

Opportunities for a sustainable livestock production system

The integration of crop production, rearing of livestock and combined use of rice fields for both rice and fish production lead to enhancing the farmers' income through diversified farming (Alexander et al., 2018 ; Poonam et al., 2019 ). Similarly, variations in pasture rates and their rotation, alteration in grazing times, animal and forage species variation, and combination production of both crops and livestock are the activities related to livestock adaptation strategies (Kurukulasuriya and Rosenthal, 2003 ; Havlik et al., 2013 ). Under changing climate scenarios, sustainable production of livestock should coincide with supplementary feeds, management of livestock with a balanced diet, improved waste management methods, and integration with agroforestry (Thornton and Herrero, 2010 ; Renaudeau et al., 2012 ).

Carbon sequestration and soil management

Selection of more drought-resilient genotypes and combined plantation of hardwood and softwood species (Douglas-fir to species) are considered adaptive changes in forest management under future climate change scenarios (Kolstrom et al., 2011 ; Hashida and Lewis, 2019 ). Similarly, timber growth and harvesting patterns should be linked with rotation periods, and plantation in landscape patterns to reduce shifting and fire of forest tree species under climate-smart conditions for forest management to increase rural families' income for a sustainable agricultural ecosystem (Scherr et al., 2012 ). Although, conventional mitigation methods for the agriculture sector have a pivotal role in forest related strategies, some important measures are also included in which afforestation and reforestation should be increased but degradation and deforestation should be reduced and carbon sequestration can be increased (Spittlehouse, 2005 ; Seddon et al., 2018 ; Arehart et al., 2021 ). Carbon stock enhanced the carbon density of forest and wood products through longer rotation lengths and sustainable forest management (Rana et al., 2017 ; Sangareswari et al., 2018 ). Climate change impacts are reduced through adaptation strategies in agroforestry including tree cover outside the forests, increasing forest carbon stocks, conserving biodiversity, and reducing risks by maintaining soil health sustainability (Mbow et al., 2014 ; Dubey et al., 2019 ). Similarly, climate-smart soil management practices like reduction in grazing intensity, rotation-wise grazing, the inclusion of cover and legumes crops, agroforestry and conservation tillage, and organic amendments should also be promoted to enhance the carbon and nitrogen stocks in soil (Lal, 2007 ; Pineiro et al., 2010 ; Xiong et al., 2016 ; Garcia-Franco et al., 2018 ).

Opportunities for fisheries and aquaculture

Sustainable economic productivity of fisheries and aquaculture requires the adaptation of specific strategies, which leads to minimizing the risks at a small scale (Hanich et al., 2018 ). Therefore, to build up the adaptive capacity of poor rural farmers, measures should be carried out by identifying those areas where local production gets a positive response from variations in climatic conditions (Dagar and Minhas, 2016 ; Karmakar et al., 2018 ). Meanwhile, the need to build the climate-smart capacity of rural populations and other regions to mitigate the harmful impacts of climate change should be recognized. In areas which have flooded conditions and surplus water, the integration of aquaculture with agriculture in these areas provides greater advantages to saline soils through newly adapted aquaculture strategies, i.e, agroforestry (Ahmed et al., 2014 ; Dagar and Yadav, 2017 ; Suryadi, 2020 ). To enhance the food security and living standards of poor rural families, aquaculture and artificial stocking engage the water storage and irrigation structure (Prein, 2002 ; Ogello et al., 2013 ). In Asia, rice productivity is increased by providing nutrients by adapting rice-fish culture in which fish concertedly consume the rice stem borer (Poonam et al., 2019 ). Food productivity can be enhanced by the integration of pond fish culture with crop-livestock systems because it includes the utilization of residues from different systems (Prein, 2002 ; Ahmed et al., 2014 ; Dagar and Yadav, 2017 ; Garlock et al., 2022 ). It is important to compete with future challenges in the system by developing new strains which withstand high levels of salinity and poorer quality of water (Kataria and Verma, 2018 ; Lam et al., 2019 ).

Globally, and particularly in developing nations, variability in climatic patterns due to increased anthropogenic activity has become clear. Asia may face many problems because of changing climate, particularly in South Asian countries due to greater population, geographical location, and undeveloped technologies. The increased seasonal temperature would affect agricultural productivity adversely. Crop growth models with the assistance of climatic and economic models are helpful tools to predict climate change impacts and to formulate adaptation strategies. To respond to the adverse effects of climate change, sustainable productivity under climate-smart and resilient agriculture would be achieved by developing adaptation and mitigation strategies. AgMIP-Pakistan is a good specimen of climate-smart agriculture that would ensure crop productivity in changing climate. It is a multi-disciplinary plan of study for climate change impact assessment and development of the site and crop-specific adaptation technology to ensure food security. Adaptation technology, by modifications in crop management like sowing time and density, and nitrogen and irrigation application has the potential to enhance the overall productivity and profitability under climate change scenarios. The adaptive technology of the rice-wheat cropping system can be implemented in other regions in Asia with similar environmental conditions for sustainable crop production to ensure food security. Early warning systems and trans-disciplinary research across countries are needed to alleviate the harmful effects of climate change in vulnerable regions of Asia. Opportunities as discussed have the potential to minimize the negative effect of climate variability and change. This may include the promotion of agroforestry and mixed livestock and cropping systems, climate-smart water, soil, and energy-related technologies, climate resilient breeds for crops and livestock, and carbon sequestration to help enhance production under climate change. Similarly, the application of ICT-based technologies, EWS, AWS, and decision support systems for decision-making, precision water and nutrient management technologies, and crop insurance may be helpful for sustainable production and food security under climate change.

Author contributions

AA, MH-u-R, and AR: conceptualization, validation, and formal analysis. MH-u-R, SAh, AB, WN, AE, HA, KH, AA, FM, YA, and MH: methodology, editing, supervision, and project administration. Initial draft was prepared by MH-u-R and improved and read by all co-authors. All authors contributed to the article and approved the submitted version.

This research funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia under grant number (IFPRP: 530-130-1442).

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.

Acknowledgments

The authors extend their appreciation to Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPRP: 530-130-1442) and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

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Water-saving techniques: physiological responses and regulatory mechanisms of crops

• Open access
• Published: 26 October 2023
• Volume 1 , article number  3 , ( 2023 )

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• Yu Chen 1 ,
• Ya-Nan Leng 1 ,
• Fu-Yuan Zhu 1 ,
• Si-En Li 2 ,
• Tao Song 1 &
• Jianhua Zhang 3  

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Water-saving irrigation techniques play a crucial role in addressing water scarcity challenges and promoting sustainable agriculture. However, the selection of appropriate water-saving irrigation methods remains a challenge in agricultural production. Additionally, the molecular regulatory mechanisms of crops under water-saving irrigation are not yet clear. This review summarizes the latest research developments in the application of different water-saving irrigation technologies to five important crops (rice, wheat, soybeans, maize, and cotton). It provides an overview of the impact of different irrigation techniques on crop yield, water use efficiency (WUE), physiology, growth, and environmental effects. Additionally, the review compares and contrasts the molecular regulatory mechanisms of crops under water-saving irrigation techniques with those under traditional drought stress, emphasizing the significance of combining irrigation technologies with genetic engineering for developing drought-resistant varieties and improving WUE. Furthermore, the integration of various technologies can stimulate new management strategies, optimize water resource utilization, and enhance sustainability, representing a major focus for future research. In conclusion, this review underscores the importance of water-saving irrigation technologies, especially when combined with genetic engineering, in addressing water resource scarcity, increasing crop yields, and promoting sustainable agriculture.

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1 Introduction

Irrigation is the largest consumer of water in human society and an important means of increasing crop yields and mitigating the impact of drought (Wang et al. 2021a ). However, irrigation water is a scarce and expensive resource, and the scarcity of water resources poses a serious threat to sustainable agriculture. This has led to an increased demand for water-saving technologies (Rao et al. 2016 ; Cheng et al. 2021a ). The level of irrigation is closely related to crop yield and water use efficiency (WUE), which is also known as water productivity (WP) when used for yield (Tong et al. 2022 ; García-Tejera et al. 2018 ; Bozkurt Çolak 2021 ). Currently, drought and water stress are the main causes leading to a decrease in crop yield and quality (Tong et al. 2022 ; García-Tejera et al. 2018 ; Bozkurt Çolak 2021 ; Chen et al. 2021 ). The challenge lies in striking a delicate balance between meeting the water needs of crops and conserving this precious resource for sustainable use. In recent years, water-saving irrigation technologies have gradually become a crucial solution to address the conflict between agricultural production and water usage (Chen et al.  2023 ). Today, full irrigation (FI) and deficit irrigation (DI) are the two main irrigation strategies, with the former requiring sufficient water to irrigate plants and the latter allowing for a certain amount of water deficit (Peake et al. 2016 ; Wen et al. 2018 ). To meet diverse water-saving requirements and cater to different crops, these two distinct irrigation strategies (FI and DI) have given rise to various specific water-saving irrigation techniques. Simultaneously, after conducting a keyword co-occurrence network analysis and burst word detection analysis in the Web of Science Core Collection database, it was observed that water-saving irrigation techniques in crops are closely associated with WUE, grain yield, treatment, and farmers, and their importance is steadily increasing (Figure S1 ). Moreover, the data show that among these key crops, rice, wheat, soybean, maize, and cotton are at the forefront. These five crops are also the most widely grown globally, providing sustenance for the majority of the world's population (Hergert et al. 2016 ). Therefore, investigating the impact of water-saving irrigation techniques on these five crops is of utmost importance. This review delves into the significant role of various water-saving irrigation techniques in enhancing WP and crop yields and explores the benefits that water-saving irrigation technologies bring to modern agriculture. This will help producers to select the appropriate irrigation techniques in production practice to achieve better economic or ecological benefits, making this review a valuable source of theoretical reference for production practitioners and researchers.

2 Sustainable solutions of embracing water constraints: unravelling water-saving irrigation techniques

2.1 typical water-saving irrigation technologies under the fi strategy: drip irrigation technology for fi (drfi).

FI is an irrigation approach that supplies crops with the precise amount of water required for optimal growth and high yields (Fig.  1 A). Two primary quantitative benchmarks exist for FI. The first standard uses crop evapotranspiration (ETc), which calculates irrigation water demand by multiplying the reference crop evapotranspiration (ET0) with the crop coefficient (Kc), representing the crop's evapotranspiration (Hergert et al. 2016 ). The second standard employs field capacity as a reference, considering over 75% field capacity as indicative of FI (Haghighi et al. 2023 ; O'Shaughnessy et al. 2023 ). Various irrigation methods can achieve FI, among them, flood irrigation is the most traditional, but causes considerable water waste, while drip irrigation is one of the most typical water-saving forms of FI (Umair et al. 2019 ; Parthasarathi et al. 2018 ).

Utilization under drip irrigation and water saving irrigation. A  Full irrigation (FI), strategy of irrigation by evapotranspiration with the amount of water required for high crop yield; B  Impact of drip irrigation technology for full irrigation (DRFI) on crops. Where the red arrow represents an increase, the green arrow represents a decrease, and the blue rectangle represents an insignificant effect. C  Optimal drip-reduced deficit irrigation (DRDI) use chart according to crop growth stage. Applying appropriate water deficit during the plant's seedling stage, flowering period, or later growth stage, and providing sufficient water during the rapid growth or fruit ripening stage seems to be a way to save water and ensure yield

DRFI, short for drip irrigation technology for FI, involves the gradual and uniform release of water near plant roots through pipes or drippers, delivering water droplets to the soil surface (Bozkurt Çolak 2021 ; Liu et al. 2016a ; Ma et al. 2022a ; Wang et al. 2021b ) (Fig.  1 B). DRFI suits diverse crops, particularly those that demand ample water, are sensitive to water stress, or benefit from precise water and nutrient supply. However, it is unsuitable for submerged crops like rice (Bozkurt Çolak 2021 ; Parthasarathi et al. 2018 ). It can be adapted for varied soil types (Arbat et al. 2020 ), and also accommodates different climates. However, factors like farm size, layout, installation costs, maintenance needs, crop value, and potential water savings should be weighed for the economic viability of adopting DRFI (Sidhu et al. 2019 ).

DRFI presents an enticing solution for water conservation in rice cultivation. In the realm of rice, DRFI boasts double the WUE compared to traditional flood irrigation (Bozkurt Çolak 2021 ; Parthasarathi et al. 2018 ) (Fig.  1 B). Research highlights that for water conservation priorities, DRFI can save 48–64% water while potentially causing yield reductions of 15–54%. The optimum balance lies in applying DRFI to save 49% water with only a 15% yield reduction (Bozkurt Çolak 2021 ; Sidhu et al. 2019 ; Fawibe et al. 2020 ; He et al. 2013 ; Wang et al. 2023 ). When preserving yield becomes the aim, DRFI can save about 27% water without compromising grain yield (Parthasarathi et al. 2018 ). Interestingly, some studies even propose that DRFI might augment yields by 15–23.5% while concurrently saving 23.3% water (Parthasarathi et al. 2023 ). In the context of dryland crops, DRFI emerges as a water-saving measure that boosts crop yield and WUE without the usual yield reductions (Wang et al. 2021c ; Aydinsakir et al. 2021 ) (Fig.  1 B). For instance, in wheat cultivation, DRFI curbs evapotranspiration by 11–26%, while matching flood irrigation in grain yield, yield composition, and aboveground biomass (Sidhu et al. 2019 ). In soybean cultivation, DRFI consistently elevates soybean yield and aboveground dry matter (AGDM) by an average of 14% (Chomsang et al. 2021 ). Similarly, in maize cultivation, DRFI has the potential to raise grain yield by 8.2–13.3% (sometimes even up to 119%) while enhancing WUE by 3.5–8.0% (Ma et al. 2022b ; Gao et al. 2021 ). In cotton, DRFI increases average seed cotton yield by 11.5–33.5%, and saves about 30% of water (Rao et al. 2016 ; Liu et al. 2016a ), significantly improving cotton yield and WUE (24.10–89.03%) (Wang et al. 2021b ; Çetin and Kara 2019 ; Zong et al. 2021 ). In summary, for paddy crops like rice, DRFI offers substantial water savings and notable enhancements in WUE, albeit potentially accompanied by yield reductions. In the case of rainfed crops, DRFI can achieve noteworthy water savings, substantial improvements in WUE, and can maintain or even enhance crop yields.

DRFI's impact on yield varies based on crop type. Generally, except for rice, DRFI seldom leads to significant yield reductions. Well-planned DRFI strategies can even yield potential gains of about 10%. However, rice cultivation poses a unique situation where DRFI might entail yield reduction risks (Table 1 ). In rice fields, DRFI's yield influence is derived from reduced flag leaf photosynthesis during grain-filling and decreased post-flowering dry matter accumulation (Wang et al. 2023 ) (Fig.  1 B). Conversely, higher yields result from increased fertile spikelets per panicle (Fawibe et al. 2020 ). Noteworthy studies show DRFI reshaping dry matter distribution and rice photosynthesis, promoting more fertile spikelets on primary branches and tillers, thus enhancing nutritional quality (Parthasarathi et al. 2018 ; Fawibe et al. 2020 ; Wang et al. 2023 ). Additionally, DRFI stimulates rice root growth, particularly in deeper soil layers, boosting robust grain development and yield (He et al. 2013 ; Parthasarathi et al. 2017 ). For dryland crops, DRFI can enhance plant height, net photosynthesis, chlorophyll (Chl) content, leaf area index (LAI), and leaf nitrogen (N) content while reducing transpiration (Umair et al. 2019 ; Zong et al. 2021 ; Guo et al. 2022 ) (Fig.  1 B). Moreover, DRFI significantly impacts seed yield, mature aboveground dry matter (AGDM), and reproductive structures like soybean pods and cotton bolls (Wang et al. 2021c ; Chomsang et al. 2021 ; Illés et al. 2022 ). DRFI also adeptly manages soil moisture and root growth (Parthasarathi et al. 2018 ; Ma et al. 2022a ). In wheat, for instance, DRFI shapes root morphology, activity, and distribution of the active root system, prompts non-hydraulic root signals (nHRS) earlier, enhances yield, stability, and water productivity (Ma et al. 2022a ). Similar outcomes are observed in cotton, where drip-irrigated cotton excels in water use efficiency at the 40 cm depth (Çetin and Kara 2019 ). DRFI augments soil water content (SWC) in the 0–60 cm soil layer, promoting fine root proliferation, enhancing water uptake, and boosting AGDM, lint, and seed cotton yield (Wang et al. 2021b ). In summary, DRFI optimizes root growth and distribution, and influences crop yield by regulating photosynthesis and dry matter allocation (Table 2 ).

In paddy fields and water-cultivated crops, DRFI reduces methane emissions by 78% (Parthasarathi et al. 2023 ), trims nitrogen fertilizer demand by 20% while maintaining yield, enhances N use efficiency (NUE) (Sidhu et al. 2019 ). Under DRFI, rice rhizosphere soil shows lower NH 4 -N, available potassium (K), and exchangeable manganese (Mn), but higher NO 3 -N and zinc (Zn) (Zhu et al. 2013 ). In rain-fed crops, DRFI raises surface soil water, safeguards groundwater, and affects water and nitrogen distribution (Parthasarathi et al. 2018 ; Ma et al. 2022a ; Çetin and Kara 2019 ; Fan et al. 2022 ). DRFI boosts nitrogen, phosphorus, and potassium accumulation, reduces nitrogen fertilizer demand by 20%, maintains yield, and enhances nutrient use efficiency (Sidhu et al. 2019 ; Wang et al. 2021c ). Combining DRFI with fertilizers like urea and ammonium sulfate improves WUE and NUE (Li et al. 2021 ). DRFI lowers greenhouse gas (GHG) intensity (12–27%), aids global warming mitigation, and supports sustainable agriculture (Gao et al. 2021 ). Overall, DRFI enhances plant nutrient use, soil water and nutrient efficiency, and cuts GHG emissions (Table 3 ).

2.1.1 Traditional deficit irrigation techniques (TDI) based on directly reducing water usage

Traditional deficit irrigation techniques (TDI), do not use other forms of irrigation, such as drip or sprinkler irrigation, and can save a significant amount of irrigation water (García-Tejera et al. 2018 ; Peake et al. 2016 ) (Fig.  2 A). TDI suits regions with limited water resources or a focus on conservation, aiming for water efficiency and acceptable yields (Peake et al. 2016 ). It can be applied across multiple crops, but requires knowledge of the water needs and stress tolerance at all growth stages (Felisberto et al. 2022 ; Hanif et al. 2020 ). Soil characteristics influence TDI suitability, with good water-holding capacity and root permeability favouring deficit irrigation (Naghdyzadegan Jahromi et al. 2022 ). Climate, resources, evapotranspiration, and rainfall must be weighed for TDI feasibility (Allakonon et al. 2022 ). Cost–benefit analysis must also be conducted to determine if the reduced water input aligns with the economic goals of agricultural operations.

Effects of traditional deficit irrigation technique (TDI) and partial root zone drying-based deficit irrigation techniques (PRDI) on crops. A  Both TDI can improve WP of crops, but as irrigation water decreases, crop growth and development are affected and yields gradually decrease. The left half of the picture represents the wet root zone, which has increased hydraulic conductivity, and the right half of the picture represents the dry root zone, which has a larger area and explores deeper soil, along with increased ABA and antioxidant content. In addition, under PRDI, plants can better absorb nutrients and have higher soil microbial content

However, TDI leads to reduced production (Fig.  2 A). Studies reveal that 50% TDI results in yields that are about 44% of FI in wheat (Naghdyzadegan Jahromi et al. 2022 ), 6% and 60% TDI decreases corn yields by 9.0%-17.0% compared to FI (Li et al. 2022 ). Similarly, soybean yields decreased by 34.8% and 51.5% with 50% and 20% TDI, respectively (Amani Machiani et al. 2021 ). Nonetheless, TDI enhances plant WP (Peake et al. 2016 ). Studies show that reducing irrigation by 15% maintains yields similar to those of FI, and 60% TDI achieves peak WP (Painagan and Ella 2022 ; Himanshu et al. 2023 ). In wheat, 75% TDI enhances WUE by about 23%-28% (Naghdyzadegan Jahromi et al. 2022 ). For cotton, a 70%-80% deficit maintains lint yield comparable to FI (Wen et al. 2018 ). Thus, TDI balances WUE and yield, avoiding strict yield maximization. TDI significantly improves WP, and its yield impact primarily hinges on water consumption, where a 40%-50% or greater water shortage notably reduces yield for most crops, with magnitude increasing as water supply diminishes (Table 1 ).

TDI impacts crop quality and yield through various mechanisms (Fig.  2 A) and triggers a range of physiological changes in plants (Table 2 ). Initially, the plant's water-deficit tolerance mechanism is inadequate (Felisberto et al. 2022 ; Khatun et al. 2021 ). For instance, soybeans initially increase proline production to maintain acceptable leaf water content and potential under water stress, but as TDI progresses, this mechanism becomes insufficient, resulting in severe yield losses (Felisberto et al. 2022 ). Secondly, water deficit disrupts several plant physiological processes. In wheat, it affects the net photosynthetic rate, stomatal conductance (Gs), transpiration efficiency, and intrinsic water use efficiency, which together limit photosynthetic productivity and yield due to soil water deficit (Yang et al. 2023 ). In cotton, drought stress influences plant height, knot number, seed number, and yield (Wen et al. 2018 ). TDI also alters root growth and soil microbial communities. Moderate to mild stress treatments in cotton promote downward root growth, extracting water from deeper soil profiles (60–100 cm) in late season. In late-stage maize growth under TDI, there is enhanced root growth in the 40–60 cm soil layer and decreased soil microbial biomass (Pabuayon et al. 2019 ; Flynn et al. 2021 ). TDI's impact on crop WUE and irrigation WUE also depends on the growth stage during which it is applied. For cotton, growth is divided into four stages: first leaf to first square (GS1), flower initiation/early bloom (GS2), peak bloom (GS3), and cut out, late bloom, and boll opening stage (GS4). GS2 and GS3 are the most sensitive to water stress, while GS4 is least sensitive (Himanshu et al. 2023 ). For maize, TDI at any stage lowers dry matter and grain yield, with single-stage TDI impact is only on grain yield, whereas TDI spanning multiple stages impacts both biomass and grain yield (Igbadun et al. 2008 ). Maize varieties exhibit improved WP under TDI, favouring early-season TDI for yield (Allakonon et al. 2022 ). For cottonseed, in normal years, the strategy of replacing 90% ETc in GS1 to GS3 and 30% ETc in GS4 is optimal for higher yield (Himanshu et al. 2023 ).

TDI influences plant physical traits, chemical composition, and other attributes. For instance, maize experiences reduced kernel weight, density, and breakage susceptibility as irrigation levels decrease (Liu et al. 2013 ). Under low irrigation, maize starch content decreases by around 0.9% (Liu et al. 2013 ; Jones et al. 2009 ). Simultaneously, decreasing irrigation elevates starch gelatinization temperature and free amino N, while ethanol production diminishes by about 4.0% (Liu et al. 2013 ). Therefore, TDI can cause plants to respond to drought stress and maintain their growth through a series of physiological and chemical reactions.

TDI application may reduce plant NUE. Studies indicate that wheat NUE increased by 2.8%, 6.9%, and 7.8% for 50%, 75%, and 100% TDI, respectively, compared to the initial and subsequent years with 0% TDI (Naghdyzadegan Jahromi et al. 2022 ). TDI can lead to a 50% N nutrition decrease due to limited water supply (Stamatiadis et al. 2016 ). Under TDI conditions, soil organic carbon (SOC) concentration in the 0–20 cm depth diminishes with higher total irrigation, with SOC and N concentrations in the 40–60 cm depth also declining significantly. Additionally, aggregate stability in the 0–20 cm depth generally decreases with increased irrigation (Flynn et al. 2021 ). Therefore, supplementation with N fertilization is recommended when applying TDI to meet the growth and production needs of plants (Table 3 ).

2.1.2 Drip irrigation-based deficit irrigation techniques

Drip-reduced deficit irrigation (DRDI) curtails irrigation volume via DRFI, creating water deficit (Fig.  1 C). DRDI safeguards groundwater and 100% DRFI combined with moderate DRDI yields similar net benefits as FI, preserving crop yield and farm income (Fan et al. 2022 ). DRDI aligns with DRFI's applicability and is suited to most crops, except aquatic plants. It is especially fitting for water-scarce regions or water conservation is required (Zhao et al. 2022 ; Comas et al. 2019 ). Versatile across crops and soil types, DRDI strategically applies water during critical growth stages for water efficiency, maintaining acceptable yields (Wan et al. 2022 ).

In soybean, compared to 100% DRFI, yield reductions of 54%, 32%, 17%, and 8% were observed with 0%, 25%, 50%, and 75% DRDI, respectively (Aydinsakir 2018 ). In cotton, 80% DRDI conserved 17% water but only decreased yield by 6.4% (Rao et al. 2016 ). Additionally, 50% DRDI lowered seed cotton yield by 13% (Liu et al. 2016a ). Seed cotton and lint yield rose with increased DRDI water application (Wang et al. 2021c ). Yet, in maize, with 60% and 80% DRDI, grain yield was reduced by 28%-45% and 6%-9%, straw yield by 40%-66% and 14%-20%, respectively (Eissa and Negim 2018 ). DRDI influenced maize ear weight, grain weight per ear, and seed index among other properties. For instance, with 60% DRDI, ear weight, grain weight per ear, seed index shrank by 20%, 13%-23%, and 16%-25% versus 100% DRFI, while with 80% DRDI, they were reduced by 4%-5%, 2%-3%, and 8%-10%, respectively. Additionally, biological yield with 60% DRDI decreased by 47%-58% (Eissa and Negim 2018 ). However, DRDI does not always reduce maize biomass and grain yield, with 75% DRDI causing no yield reduction (Zhao et al. 2022 ). In a maize DRDI study, 70% DRDI maintained yields near 100% DRFI for two years, while 40% DRDI led to a notable reduction in yield (Singh et al. 2023 ). Similar findings have been observed in other studies. 70% DRDI had the lowest impact on sweet maize yield, with higher quality and no reduction in yield, while 40% DRDI resulted in the lowest fresh ear yield, an approximate 25% reduction, but with the highest protein and sugar content (Ertek and Kara 2013 ). As such, 70–80% DRDI does not notably affect yield, similar to DRFI, while excessive water deficit significantly reduces yield. Additionally, DRDI significantly reduced fibre strength, fibre length in cotton (Liu et al. 2016a ). In soybeans, DRDI-induced water deficit raised oil content and lowered protein content (Aydinsakir 2018 ), influencing crop quality. Overall, DRDI can enhance WP from DRFI. Yield considers water usage, crop type, with trends akin to TDI vs FI. Severe water deficit magnifies yield reduction. Yet, DRDI's yield impact varies by crop. For soybeans and cotton, ~ 20% water deficit slightly reduces yield, but maize is unaffected (Table 1 ). Generally, DRDI combines the benefits of DRFI and TDI.

Utilizing DRDI maximizes water conservation while maintaining crop yields. Research indicates that applying DI throughout maize's growth stages, except for the later vegetative ones, achieves yields akin to DRFI, curtailing ETc by 15–17% (Comas et al. 2019 ). In the late nutritional stage, water deficit leads to a slight reduction in LAI, significant leaf curling, and lowered photosystem II efficiency (quantum yield), however, recovery occurs upon rewatering (Comas et al. 2019 ; O'Toole et al. 1980 ). Favourable light conditions and photosystem II's resilience post-stress suggest biomass and yield reduction are due to temporary stomatal closure, decrease in photosynthesis, or fleeting wood conductivity loss—all of which are remedied by water replenishment (Zhao et al. 2022 ; Comas et al. 2019 ). A parallel study, revealed that water deficit during maize's nutritional stage curbs grain count, dry leaf weight, impeding the potential grain filling rate, while during mature growth, it directly truncates grain filling rate and duration, chiefly impacting yield (Zhang et al. 2019 ). A similar pattern emerges in cotton, where water deficit during initial or final growth stages barely affects seed cotton yield, but during peak flowering, it markedly reduces yield (Himanshu et al. 2019 ). Thus, judicious water deficit application during seedling, flowering, or later growth, paired with adequate water during rapid expansion or fruit maturation, conserves water and ensures yield (Cheng et al. 2021a ) (Fig.  1 C).

DRDI concurrently triggers physiological shifts in crops (Table 2 ). Moderate irrigation reduction diminishes soil moisture status while enhancing dry matter accumulation in preharvest organs, which is later distributed to grains during filling (Wan et al. 2022 ). It also bolsters plants' soil moisture extraction capabilities, with root system enhancement pivotal for crop adaptation to water scarcity (Zhao et al. 2022 ; Singh et al. 2023 ). Compared to 100% DRFI, 70% DRDI raised root length density (RLD), and both 70% and 40% DRDI boosted soil water consumption (Singh et al. 2023 ). In rice, elevated drought levels lead to reduced SWC, reducing root water uptake, prompting root growth, deepening root penetration, and elevating WUE. However, this increased WUE was accompanied diminished water availability, culminating in lower rice and grain yield, and growth traits like plant height, tiller count, panicle length, panicle weight, and grain count per panicle showing a decline (Eltarabily et al. 2021 ). Furthermore, upping DRDI frequency could augment root length, root weight, and aboveground biomass accumulation in wheat (Chen et al. 2021a ).

DRDI influences plant WUE and N, P, and K use efficiency. DRDI yields higher WUE than DRFI. Moderate irrigation reduction significantly enhances wheat WUE and returns (Wan et al. 2022 ). Cotton's overall WUE improves by about 5.3% under DRDI (Cheng et al. 2021a ). Maize's WUE under 75% DRDI surpasses 100% DRFI (Zhao et al. 2022 ) and 70% DRDI elevated WUE by 21% compared to 100% DRFI (Singh et al. 2023 ). Meanwhile, two maize growth seasons that experienced 60% DRDI saw WUE increase by 14% and 15%, while 80% DRDI elevated it by 30% and 42% (Eissa and Negim 2018 ). However, DRDI-induced water stress can lower N, P, and K uptake by plants (Parthasarathi et al. 2023 ; Eissa and Negim 2018 ). For maize, a 60% DRDI reduces total N, P, and K uptake by 21%, 25%, and 21%, while an 80% DRDI reduces it by 5%, 10%, and 13%, respectively (Eissa and Negim 2018 ). Water scarcity during the seed stage alters N distribution, causing a 50% decrease in nutrient N (Stamatiadis et al. 2016 ). In summary, DRDI is a more water-efficient irrigation method than DRFI, but water reduction can affect N, P, and K absorption by plants (Table 3 ).

2.1.3 Partial root zone drying-based deficit irrigation techniques (PRDI)

PRDI divides the root zone into irrigated and dry halves (El-Sadek 2014 ; Iqbal et al. 2021a ; Tang et al. 2005 ), inducing wet and dry areas that trigger water stress-related physiological responses (El-Sadek 2014 ; Iqbal et al. 2021a ; Tang et al. 2005 ; Iqbal et al. 2021b ). By generating chemical signals in the dry roots, PRDI significantly reduces water use and improves WUE (El-Sadek 2014 ; Iqbal et al. 2021a ; Tang et al. 2005 ). PRDI suits diverse soil textures (except sandy soils) for most plants (Lekakis et al. 2011 ). PRDI, which is applicable to crops facing drought stress, benefits from effective water distribution and permeable roots. While PRDI is adaptable to many climates, it demands vigilant monitoring and adjustments to lessen risks (Iqbal et al. 2021a ; Iqbal et al. 2021b ; Kang et al. 1998 ; Tang et al. 2010 ; Barideh et al. 2018 ).

PRDI, an efficient water-saving irrigation method, enhances WUE, root development and distribution (Fig.  2 B), and biomass production (Iqbal et al. 2021a ; Iqbal et al. 2021b ; Kang et al. 1998 ; Tang et al. 2010 ). On average, PRDI boosts WUE by 82% (Sadras 2008 ). At 50% PRDI, soybeans biomass might decreased by 25–30%, yet half of the irrigation water is conserved, elevating WUE by about 53% (Wakrim et al. 2005 ). Some studies find no significant crop yield difference (maize, wheat, cotton) with alternate partial rootzone irrigation (Cheng et al. 2021b ). For instance, maize roots alternately exposed to dry, 55%, or 65% field capacity soil reduced water intake by 34.4–36.8%. Despite a mere 6–11% reduction in total biomass and yield as compared to well-irrigated plants, WUE surged (Kang et al. 1998 ). Moreover, PRDI conserved 15.2% maize season water, increased yield by 4.62–20.71%, and elevated WUE by 38.93% (Karandish and Shahnazari 2016 ; Al-Kayssi 2023 ). In cotton, 30% irrigation reduction under PRDI led to only 4.44% yield loss, which was statistically insignificant, with earlier flowering, quicker harvesting, and higher economic returns (Tang et al. 2010 ). Cotton's PRDI WUE outpaced that of FI and TDI by 21% and 26%, yielding greater biomass and WUE (Iqbal et al. 2021a ; Li et al. 2017a ). Some studies show PRDI saving 30% water, yet still yielding as many cotton bolls as FI, and an ~ 92% total lint yield (Tang et al. 2005 ). For wheat, PRDI decreased total water consumption per plant by 11.6–17.3%, and markedly increased WUE by 17.2–20.3% (Shi et al. 2014 ). Overall, PRDI significantly bolsters WP. While limited in yield potential compared to TDI, PRDI mitigates water deficit-induced yield decline with most crops experiencing only a minor decrease in yield (30%-40% water deficit) (Table 1 ).

PRDI treatment enhances root development, which is pivotal for plant material metabolism and information exchange (Table 2 ). Roots detect and positively respond to decreasing soil moisture (Caixia et al. 2015 ). Initially, PRDI encourages maize roots to penetrate soil more vigorously (Karandish and Shahnazari 2016 ; Caixia et al. 2015 ). Spatially, PRDI significantly influences maize's RLD distribution. Maximum RLD occurs in 10–20 cm soil, decreasing with depth. Water uptake by maize roots under PRDI relies on soil moisture and RLD distribution (Caixia et al. 2015 ). During nutritional growth, root water uptake efficiency (RWA) predominantly arises from the 10–30 cm soil layer, while during reproductive growth, the 20–70 cm soil layer's root system dominates water uptake (Caixia et al. 2015 ; Hu et al. 2011 ). After the filling stage begins, the surface soil root system gradually ages, and the deep soil RLD increases slightly (Caixia et al. 2015 ; Hu et al. 2011 ). Therefore, the renewal of soil water below 70 cm benefits the filling of maize (Caixia et al. 2015 ). PRDI also shapes maize's soil-root hydraulic conductivity, as soil water absorption hinges on root growth and distribution (Mehrabi et al. 2021 ), where crop water use relates significantly to hydraulic conductivity in the irrigated root zone. PRDI markedly compensates RWA, boosting total hydraulic conductivity in the irrigated root zone by 49.0–92.0% over FI (Hu et al. 2011 ). Furthermore, PRDI's non-irrigated root zone has higher hydraulic conductivity than a completely non-irrigated zone, contributing to total crop water uptake even without irrigation, a factor in PRDI's elevated WUE (Hu et al. 2011 ). PRDI enhances soil ventilation, increasing soil microbial abundance in the root zone, with mild soil water shortage increasing soil microbial abundance in PRDI (Wang et al. 2007 ). PRDI maintains a higher microorganism count at similar soil moisture versus FI, promoting plant-microorganism coexistence and fostering ecological balance (Wang et al. 2007 ; Ke and Wan 2019 ).

Leaf Chl content links positively to plant drought tolerance, and leaf water potential assesses water-holding capacity, while Gs evaluates plant response to low water conditions (Kang et al. 1998 ; Tang et al. 2010 ; Iqbal et al. 2022 ). Compared to FI, PRDI curbs LAI, leaf dry matter, and leaf relative water content (Shi et al. 2014 ; Liu et al. 2022 ). PRDI also elevates abscisic acid (ABA), a signaling chemical for direct/indirect stomatal behavior regulation, in leaves (Li et al. 2017a ). While PRDI induces drought stress in root zones, it notably bolsters stomatal resistance to water diffusion, promoting more closure (Kang et al. 1998 ; Batool et al. 2019a ), reducing transpiration by narrowing the stomatal opening (Tang et al. 2010 ). Although transpiration rates drop significantly, maize's photosynthetic rate and leaf water content show no significant change under PRDI (Kang et al. 1998 ; Shi et al. 2014 ). In wheat, the leaf photosynthesis rate did not significantly drop within two PRDI years, but Gs was reduced by 12% and 7% (Mehrabi and Sepaskhah 2019 ). Similar behaviors are observed in cotton, with PRDI causing lower Gs and smaller LAI than FI (Tang et al. 2005 ).

PRDI mitigates the impact of water stress through the regulation of physiological parameters (Xiancan Zhu and Liu 2018 ). PRDI results in more dry matter accumulation than TDI, lowered malondialdehyde, and higher proline, soluble sugars, and proteins in maize, soybean, and wheat. PRDI concurrently curbs superoxide dismutase (SOD), peroxidase (POD), and ascorbate peroxidase (APX) activities in maize and soybean, aiding water stress coping (Shi et al. 2014 ; Liu et al. 2022 ; Xiancan Zhu and Liu 2018 ). Under PRDI cotton accumulates osmolytes (sugar, proline) and antioxidants (SOD, POD, catalase (CAT), APX), raising SOD, POD, CAT, and APX by 19.86%, 43.90%, 20.51%, and 21.11%, respectively (Iqbal et al. 2022 ). Partial root zone drought stress in wheat intensifies partial root zone stress, sparking the operation of nHRS (Liu et al. 2022 ; Batool et al. 2019a ). nHRS-mediated signal transduction ups ABA production, prompts the generation of reactive oxygen species (ROS, curbs cytokinin synthesis, amplifying plant antioxidant defences and proline content (Batool et al. 2019a ; Batool et al. 2019b ).

Compared to FI, PRDI heightens N and water utilization efficiency in plants (Table 3 ). PRDI notably enhances N absorption in the irrigated root zone, with a marked compensatory N uptake effect (Hu et al. 2009 ). Within five days post-irrigation, the root zone rapidly regains high N inflow (Hu et al. 2009 ; Xia et al. 2021 ). Additionally, PRDI elevates wheat's RLD and root mass density by 48% and 32%, respectively, within two years after nitrogen fertilization (Mehrabi et al. 2021 ). Enhanced N utilization in the root system under PRDI propels crop root development. Some reports suggest PRDI bolsters maize's N absorption, WUE, and NUE with 0.18 g N per kg, leveraging plant-soil N synergy (Fu et al. 2017 ). Thus, applying total fertilizer as basal in conjunction with PRDI has emerged as an effective water and fertilizer strategy (Liang et al. 2013 ). Compared to TDI and FI, PRDI plants exhibit superior root biomass and root-to-shoot ratio. Higher N application significantly boosts leaf N accumulation, with PRDI plants amassing more N in leaves than their TDI counterparts (Wang et al. 2012 ).

2.1.4 Crop-specific alternate wetting and drying (AWD) deficit irrigation techniques

AWD introduces unsaturated soil conditions during the growth season, intermittently halting irrigation to allow water to recede until the soil reaches a specified moisture content before reflooding occurs (Carrijo et al. 2017 ; Graham-Acquaah et al. 2019 ; Ashraf et al. 2022 ). Two main methods quantify the AWD threshold: field water level (FWL) and soil water potential (SWP) (Carrijo et al. 2017 ). Typically, AWD's threshold is FWL reaching 15–18.5 cm below the soil or SWP between -15 kPa and -20 kPa (Carrijo et al. 2017 ; Zhang et al. 2023a ). Rice is sensitive to unsaturated soil (Bouman and Tuong 2001 ) and AWD has been established in rice practices, especially in water-scarce areas, enhancing agricultural production (Fig.  3 ). Primarily for paddy or flooded rice, AWD suits clayey or loamy soils with good water retention (Carrijo et al. 2017 ; Zhang et al. 2023a ).

Impact of alternate wetting and drying (AWD) on rice. The upper part of the graph represents that AWD saves water while significantly increasing WUE and has no significant effect on yield; the lower part of the graph represents that AWD promotes plant growth; the left part of the graph represents that AWD promotes nutrient uptake and reduces harmful ions; the right part of the graph represents that AWD reduces greenhouse gas emissions

AWD in rice cultivation can save up to 35% of water, increase WUE by 31%, with a yield loss of only around 6%, as confirmed by multiple studies (Carrijo et al. 2017 ; Graham-Acquaah et al. 2019 ; Zhang et al. 2023a ; Massey et al. 2014 ; Song et al. 2021a ; Song et al. 2021b ). AWD duration has no yield impact during the vegetative and reproductive stages (Carrijo et al. 2017 ). Compared to FI, AWD reduces water use by 25.7% on average, with 23.4% water savings while maintaining yield (Carrijo et al. 2017 ; Chaurasiya et al. 2022 ). AWD's water-saving effect surpasses yield reduction, leading to 24.2% higher WP than FI (Carrijo et al. 2017 ). Additionally, AWD cuts rice irrigation by up to 28.8%, enhancing WUE (Haque et al. 2022 ), and potentially increasing yield by 9.2–12.3% (Li et al. 2018 ). In clay paddies, optimal AWD reduces water by 8%-41%, raising WP by 11%-54%, and yield by 0%-1% (Cheng et al. 2022 ). Furthermore, AWD yields larger grains, up 12.0–15.4% (Norton et al. 2017 ). Lower percolation and leakage contribute to decreased water usage in AWD (Carrijo et al. 2017 ). Increased filled grains, heavier inferior grains, and more tillers enhance yield under AWD (Li et al. 2018 ; Norton et al. 2017 ). Reports show AWD improves WP significantly, with varying yield effects (Table 1 ). AWD's impact depends on implementation conditions, as arid regions or severe deficits may affect yield. AWD timing also matters, especially during sensitive water periods, impacting yield, with careful planning having the potential to increase yield to some extent.

AWD triggers diverse physiological changes in rice (Table 2 ). Research has demonstrated that AWD treatment during both vegetative and reproductive stages elevates endogenous proline levels, a precursor for creating grain aroma compounds such as 2-acetyl-1-pyrroline (2-AP), thereby amplifying rice aroma through increased 2-AP production by 5%-15% (Ashraf et al. 2022 ; Zhang et al. 2023b ). Leaf development is affected by AWD, showing slight elevation in photosynthetic rates (Ashraf et al. 2022 ; Li et al. 2018 ). Nevertheless, AWD-influenced leaves exhibit higher ABA content and a 37% increase in isopentenyladenine, while trans-zeatin decreased by 36% (Norton et al. 2017 ). Thirdly, wetting and drying cycles promote rhizosphere ecology, delaying root senescence, and stimulating growth (Li et al. 2018 ). AWD enhances root activity pattern and root-to-shoot ratio, vital for absorption capacity and reduced transpiration (Zhang et al. 2017 ). The rise in CO 2 concentration under AWD bolsters root growth via increased root ABA synthesis (Wang et al. 2022 ). Grain quality is also impacted by AWD, with treated rice showing minorly elevated chalkiness and reduced setback viscosity (Graham-Acquaah et al. 2019 ). AWD enhances polished rice's nutritional profile by increasing amino acid and phenolic acid content, while lowering lipids and alkaloids (Song et al. 2021b ). AWD variably affects soluble sugars, proline, proteins, and enzyme activity, with higher malondialdehyde and H 2 O 2 levels than FI (Ashraf et al. 2022 ).

Moisture fluctuations during AWD enhance nutrient cycling, microbial dynamics, and nutrient mineralization by disrupting soil aggregates and triggering physical and biological changes, optimizing the resource utilization efficiency for benefiting plants (Li et al. 2018 ; Sandhu et al. 2017 ). The AWD system cyclically alters soil pH, accelerating nutrient circulation and transfer in the rhizosphere (Li et al. 2018 ). Under AWD, the addition of organic fertilizers notably enhanced N, P, and K absorption by rice, particularly promoting P transfer to panicles and grains, resulting in increased grain weight and yield (Yang et al. 2004 ). Similar reports showed AWD-grown plants with higher grain yields, improved P transport efficiency, metabolic P efficiency, and P harvest index values (Deng et al. 2021 ). SOC and N benefits increased with straw, biochar, and slow N addition under AWD, increasing yields by 2–3% (Zhang et al. 2023a ). AWD combined with compound N reduces paddy runoff and N leaching (Qi et al. 2023 ). Additionally, AWD and biochar together raise yields by approximately 15%, improving photosynthesis rate, and N, P, and K efficiency during the nutrition and maturation stages (Haque et al. 2022 ; Deng et al. 2021 ).

Microbes transform inorganic mercury to toxic methylmercury (MeHg), which then accumulates in rice (Tanner et al. 2018 ; Guo et al. 2021 ). AWD effectively reduces MeHg in rice ecosystems (Table 3 ). AWD at 35% soil moisture curtails surface water MeHg by 68% and 39% during the growth and fallow seasons, respectively, decreasing rice grain MeHg and total mercury by 60% and 32%, respectively (Tanner et al. 2018 ). Over a 2 year period, AWD lowered sulfur (S) (4% and 15%), and arsenic (As) (14% and 26%) in grains, while elevating manganese (Mn) (19% and 28%), copper (Cu) (81% and 37%), cadmium (Cd) (28% and 67%) (Norton et al. 2017 ). This aligns with AWD lowering As, raising Cd, Cu, selenium (Se), and Zn in rice (Martínez-Eixarch et al. 2021 ). Drying severity in AWD lowers the amounts of total and inorganic As in grains, while lowering inorganic As content requires lower SWP (Norton et al. 2017 ; Carrijo et al. 2022 ). Grain Cd hinges on AWD timing, where early reproductive AWD raises Cd, and AWD during multiple stages can elevate it even further (Norton et al. 2017 ; Carrijo et al. 2022 ). Combining AWD with P application lowers the amounts of heavy metal elements, like Pb and As, in grains (Song et al. 2021a ; Wu et al. 2021 ). Yet, AWD weakens rice's sodium (Na) tolerance. Alkaline soil AWD yields are lower due to high levels of exchangeable Na causing impermeable layers, making roots draw deeper water (Carrijo et al. 2017 ; Yang et al. 2004 ). Also, high Na causes toxicity, and in non-flooded soil, it's absorbed to a greater extent, affecting AWD-associated Na poor tolerance (Carrijo et al. 2017 ).

Rice yields about 10% of global agricultural GHG emissions, largely from CH 4 in flooded fields (Kraus et al. 2022 ). AWD reduces GHG emissions and water use (Carrijo et al. 2017 ; Martínez-Eixarch et al. 2021 ; Kraus et al. 2022 ). Applying AWD in rice cultivation reduces water use by 30% and methane emissions by 30–70% while maintaining yield (evidence from Bangladesh et al. 2022 ). Mild AWD reduces cumulative CH 4 emissions by 87.1% on average (Liao et al. 2020 ). A study found AWD could lower annual methane emissions by 51% while still boosting yield by 9% (Arai 2022 ). Rice under AWD showed reduced CH4 emissions, and increased yield through improved carbohydrate transport (Arai 2022 ). Also, while AWD lowered CH 4 emissions by 23%, it increased N 2 O by 8% (Kraus et al. 2022 ). Increases in N 2 O have been tied to urea application and irrigation methods. Mild AWD raised cumulative N 2 O emissions by 28.8%, and a further 17.9% in conjunction with urea, cutting global warming potential (GWP) by 62.9% (Wu et al. 2022 ). Some research has suggested that AWD might even cut GWP by 90% (Martínez-Eixarch et al. 2021 ). Hence, AWD stands as an effective method for sustainable development with strong ecological benefits.

3 Unlocking the secrets of survival: exploring a crop's molecular adaptation to drought stress under deficit strategies

3.1 unveiling molecular regulatory mechanisms: advantages of water-saving irrigation in light of crop responses.

Application of water-saving irrigation techniques brings about the reduction in water consumption. This is particularly evident in instances of DI strategies, where unavoidable drought stress can impact plants to a certain degree. An essential factor in the successful integration of water-saving irrigation methods for achieving optimal crop production lies in the judicious exploitation of plant drought stress mechanisms. Traditional responses to drought stress encompass an array of physiological reactions, including stomatal closure, root development, cellular adaptation, photosynthesis, the production of ABA and JA, ROS elimination, and the transmission of diverse signaling pathways (Ullah et al. 2017 ). Omics technologies have been widely used to study molecular physiological processes and characteristics (Song et al. 2022 ; Song et al. 2023 ). Under water-saving irrigation, a multitude of genes exhibit differential expression at the molecular level, orchestrating various metabolic pathways (Fig.  4 A). Consistent with traditional drought stress, these pathways encompass signal transduction, oxidation–reduction processes, the synthesis of secondary metabolites, non-biological stress responses, enzyme activities, and the involvement of heat shock proteins, which collectively equip plants to withstand a range of challenges (Lopez et al. 2020 ; Gregorio Jorge et al. 2020 ; Chaichi et al. 2019 ). Analyzing pivotal differentially expressed genes (DEGs) within these metabolic pathways not only enhances our understanding of crop regulation mechanisms under distinct water-saving irrigation treatments (Fig.  4 B), but also unveils the potential for uncovering distinctive response mechanisms unique to crops under water-saving irrigation conditions, which are in contrast to conventional drought scenarios.

Molecular regulatory mechanisms of crops under different water-saving irrigation techniques. A  Major metabolic pathways activated by crops to adapt to different water-saving irrigation technologies. B  Differentially expressed genes or proteins under different water-saving irrigation and their functions. Differentially expressed genes or proteins on different metabolic pathways, black arrows; orange arrows; blue arrows represent the genes and metabolic pathways mentioned in the studies on TDI & DRDI; PRDI: AWD, respectively. Red triangles represent increasing expression, green triangles represent decreasing expression

Under deficient irrigation strategies, both TDI and DRDI hinge on directly triggering the corresponding regulatory mechanisms in plants as a response to diminished water availability. When it comes to technologies aimed at directly reducing water usage, the approach often entails simulating a conventional drought scenario while harnessing the plant's inherent resilience to drought stress. This methodology facilitates the accomplishment of water conservation goals. However, in the cases of PRDI and actual AWD, the activation of regulatory networks isn't solely predicated on water reduction. Instead, it's based on a blend of distinct molecular regulatory mechanisms present in plant roots within dry and wet soils. Nevertheless, the spatial and temporal requisites for the dry-to-wet transitions in PRDI and AWD differ, thereby giving rise to some specific molecular regulatory mechanisms.

Traditionally, under conventional drought conditions, crops predominantly regulate their drought resistance through a combination of ABA-dependent and ABA-independent pathways (Du et al. 2018 ). For example, many crops, like rice, have evolved drought escape mechanisms to curtail their life cycle under drought circumstances (Du et al. 2018 ; Chen et al. 2020 ). In the context of water-saving irrigation, experimental studies have reported a substantial differential gene expression that pertains to molecular functions, biological processes, and cellular components when subjected to reduced water irrigation (Lopez et al. 2020 ; Gregorio Jorge et al. 2020 ). This concurs with the conventional drought response, wherein genes linked to ABA synthesis and degradation take the spotlight as significant features for crops facing drought stress. Furthermore, under water-saving irrigation, studies have found that concerning TDI and DRDI, genes involved in ABA responses, such as PP2C ( Phvul.001G021200 ) and a gene with a putative ABA 8'-hydroxylase ( Phvul.002G122200 ), showcase inhibition under drought conditions (Lopez et al. 2020 ). PP2C stands as a pivotal negative regulatory factor in ABA signal transduction, whereas Phvul.002G122200 is associated with ABA catabolism (Cutler et al. 2010 ). Conversely, under AWD conditions, genes like ABA biosynthesis gene OsNCED2 and signal transduction genes ( OsbZIP60 , OsPP2C08 ) were upregulated (Song et al. 2020 ). OsPP2C08 (Os01g0656200), belonging to the A subfamily of the rice OsPP2C family, actively participates in the ABA signaling pathway while responding to environmental cues (Xue et al. 2008 ). Remarkably, rice OsPP2C08 displayed an approximate 22-fold increase under AWD due to drought stress, and its counterpart in Arabidopsis (AtPP2CA enzyme) also plays a role in drought stress (Song et al. 2020 ; Yoshida et al. 2006 ). Furthermore, research has illuminated the upregulation of the HIGHLY ABA-INDUCED PP2C GENE 3 (HAI3 ) and the soybean-specific hub gene CYP707A4 in response to ABA (Fang, et al. 2023 ). HAI3 , which functions as an ABA signal suppressor gene, contributes to the early activation and heightened sensitivity of stomatal control (Fang, et al. 2023 ). On a different note, CYP707A4 represents an ABA degradation gene associated with transpiration rate (Fang, et al. 2023 ). Its upregulation under water-deficient conditions is regarded as a fine-tuning mechanism in plants, allowing for the maintenance of higher stomatal conductance (gm) through ABA degradation (Fang, et al. 2023 ). The mechanism where plants activate downstream regulation through ABA has been validated through earlier studies on plant responses to drought stress. For instance, in rice, the OsASR5 protein has been demonstrated to regulate ABA biosynthesis and encourage stomatal closure as a response to combat drought stress (Li et al. 2017b ). Moreover, the mechanism wherein ABA mediates the abbreviation of a plant's life cycle also stands as a pivotal element for the feasibility of implementing water-saving irrigation for crops. Taking rice as an example, the onset of early drought stress in rice development sparks the accumulation of ABA, which, in turn, exerts regulatory control over numerous flowering-related genes to advance early flowering (Du et al. 2018 ). Throughout this process, photoreceptors, components of the circadian rhythm, and genes linked to flowering, such as OsTOC1 , Ghd7 , and PhyB , are recognized to participate in ABA-dependent drought stress responses (Du et al. 2018 ). The strategy of expediting flowering to hasten the life cycle offers an avenue to earlier crop harvesting, underscoring one of the capacities of water-saving irrigation techniques to enhance agricultural productivity. Beyond ABA, the WRKY transcription factor has been documented as a negative regulator of plant aging (Besseau et al. 2012 ), and is implicated in steering plant growth and responding to drought through the brassinosteroid pathway (Chen et al. 2017a ). Research has revealed that, under water-saving irrigation, the expression of WRKY70 ( Phvul.008G185700 ) transcription factors experiences an upsurge during drought stress (Lopez et al. 2020 ). Furthermore, studies have highlighted that two soybean GABA transporter 1-encoding genes, implicated in regulating leaf senescence, undergo a significant upregulation during moderate or severe drought conditions in plants (Fang, et al. 2023 ). Hence, the simulation of drought conditions by water-saving irrigation to activate crop ABA synthesis and senescence mechanisms stands as a critical determinant for maintaining crop yield. In this aspect, water-saving irrigation draws parallels with traditional drought stress.

Simultaneously, keywords like drought, ABA, and senescence are intricately link with various biological synthesis and signal transduction processes. Among these, MYB transcription factors regulate the biosynthesis of secondary metabolites and mediate a plant's adaptability to abiotic stressors, including drought (Chen et al. 2021b ). In previous studies, GmMYB14 has been reported to regulate plant architecture, high-density yield, and drought tolerance in soybeans through the brassinosteroid (BR) pathway (Chen et al. 2021b ). This results in reduced plant height, internode length, leaf area, petiole length, and petiole angle, while increasing high-density yield under field conditions (Chen et al. 2021b ). Similarly, in rice, there have been reports of the OsFTIP6-OsHB22-OsMYBR57 module regulating drought response (Yang et al. 2022 ). In this module, OsMYBR57, a MYB-related protein, directly regulates the expression of several key drought-related OsbZIP genes to respond to drought treatment (Yang et al. 2022 ). There are also reports suggesting that MYB-related transcription factors can enhance plant tolerance to biotic stress through the C-repeat/dehydration-responsive element binding proteins (CBF/DREB) and ABA signaling pathways (Chen et al. 2023 ). CBF/DREB plays a crucial role in abiotic stress responses, and in wheat, it has been found that drought can induce the activity of the promoters of two DREB/CBF genes, TaDREB3 and TaCBF5L , thereby improving plant stress tolerance and maintaining yield (Yang et al. 2020a ). Furthermore, MYB ( Phvul.003G028000) is not only involved in crop drought tolerance but also indirectly participates in phosphate synthesis (Lopez et al. 2020 ). In wheat, deficient irrigation has been reported to significantly upregulate DEGs encoding 6-phosphogluconate dehydrogenase (PGD6) (Ma et al. 2019 ). Increased accumulation of PGD6 enhances the efficiency of the pentose phosphate pathway, providing more nicotinamide adenine dinucleotide phosphate (NADPH), precursors, or cofactors for biosynthesis. This enhances plants’ ability to withstand drought conditions (Ma et al. 2019 ; Chen et al. 2004 ). In addition to ABA signaling, the Mitogen-Activated Protein Kinase (MAPK) cascade is a key strategy that plants have developed to respond to various biotic and abiotic stresses. It participates in signal transduction from extracellular stimuli and regulates cellular responses (Group and M. 2002 ). The MAPK cascade consists of at least three different protein kinases: MAPKKK, MAPKK, and MAPK, which activate in sequence through phosphorylation (Group and M. 2002 ). Drought stress induces the expression of stress-related transcription factors and genes, such as those involved in ROS clearance, ABA, or MAPK signaling. These genes activate various drought-related pathways, thereby inducing plant tolerance to drought stress (Ullah et al. 2017 ). In cotton, the nuclear-localized and membrane-localized MAPK cascade pathway GhMAP3K62-GhMKK16-GhMPK32 targets and phosphorylates the nuclear-localized transcription factor GhEDT1 to activate downstream GhNCED3 (Chen et al. 2022 ). This mediates ABA-induced stomatal closure and drought response (Chen et al. 2022 ). These signals, targeted at the nuclear-localized transcription factor GhEDT1 , cause a cascade that culminates in the downstream activation of GhNCED3 (Chen et al. 2022 ). Additionally, in practical agricultural settings, the upregulation of genes in the MAPK signaling pathway has been found to enhance drought resistance in crops. For instance, GhMKK3 enhances drought tolerance in cotton (Wang et al. 2016 ), while GhMKK1 is involved in the enhancement of salt and drought tolerance (Lu et al. 2013 ). Therefore, drought stress under water-saving irrigation can induce a series of signal transduction and biosynthesis processes in crops, that increase their drought tolerance, adjust plant structures, and potentially maintaining or even increasing yield.

Moreover, the cell wall remodeling is an often-observed response in plants faced with the challenge of drought stress (Gregorio Jorge et al. 2020 ; Ezquer et al. 2020 ; Gall et al. 2015 ). Under deficient irrigation, certain DEGs participate in cell wall modification. For instance, two genes encoding cellulose synthase H1 ( Phvul.005G117833 and Phvul.005G116501 ) are upregulated in samples subjected to drought treatment, while a gene encoding an extension protein ( Phvul.004G161500 ) is downregulated (Lopez et al. 2020 ). Secondary cell walls (SCWs) contribute to improving plant drought tolerance by alleviating osmotic disturbances caused by drought (Gregorio Jorge et al. 2020 ). The cellulose synthase complex (CSC), primarily responsible for cellulose synthesis in SCWs, is predominantly composed of CesA4, CesA7, and CesA8 proteins (Gregorio Jorge et al. 2020 ; Hall et al. 2017 ). Interestingly, all core components of the CSC are found among the upregulated genes (Gregorio Jorge et al. 2020 ). Coincidentally, research has also revealed a significant downregulation of multiple FASCICLIN-LIKE ARABINOGALACTAN (FLA) genes in soybeans. These FLA genes were previously reported to be associated with cell proliferation and cell wall structure formation (Fang, et al. 2023 ; MacMillan et al. 2010 ). In rice, an interesting finding was made regarding the rice phytochrome-interacting factor-like protein OsPIL1/OsPIL13, which was identified as a crucial regulator of reduced internode elongation under drought conditions (Todaka et al. 2012 ). OsPIL1 promotes internode growth, and genes downstream of OsPIL1 are enriched in cell wall-related genes responsible for cell growth (Todaka et al. 2012 ). OsPIL1 plays a key regulatory role in reducing plant height in response to drought stress through cell wall-related genes (Todaka et al. 2012 ). Under drought conditions, the expression of OsPIL1 is inhibited during the photoperiod (Todaka et al. 2012 ). This highlights the necessity of applying water-saving irrigation based on actual conditions and selecting suitable seasons to meet the crop's requirements for light and temperature. This also explains why the effects of using water-saving irrigation techniques can vary depending on the timing of application. Furthermore, heat shock proteins (HSPs) in plants facilitate protein folding or assembly under stress conditions (Driedonks et al. 2015 ; Swindell et al. 2007 ), enhancing plant tolerance to drought and high temperatures (Burke and Chen 2015 ; Cho and Hong 2006 ). Research has indicated that a member of the HSP70 family, GhHSP70-26 , is involved in the response of cotton to drought stress (Guo et al. 2023 ). The relative expression level of GhHSP70-26 shows a linear correlation with the comprehensive drought resistance of cotton seedlings (Guo et al. 2023 ). In rice, the HSP90 family gene OsHSP50.2 has been found to have increased transcription levels in response to both heat and osmotic stress (Xiang, et al. 2018 ). This leads to reduced electrolyte leakage and malondialdehyde levels in rice, along with a smaller reduction in chlorophyll and higher SOD activity, demonstrating greater drought resistance (Xiang, et al. 2018 ). However, the expression of HSPs is rapid and transient, so after an extended period of drought, HSPs and genes involved in protein folding are downregulated, indicating that these proteins are no longer needed (Gregorio Jorge et al. 2020 ).

In addition to utilizing traditional drought response mechanisms to enhance drought tolerance and maintain yield, specific regulatory mechanisms have also been identified in PRDI and AWD. For instance, under most drought stress conditions, PIP-related genes significantly increase plant growth rate, transpiration rate, stomatal density, and photosynthetic efficiency (Chen et al. 2021c ; Aharon et al. 2003 ). However, enhanced symplastic water transport through the aquaporins facilitated by membrane water channel proteins under drought stress might have detrimental effects (Chen et al. 2021c ; Aharon et al. 2003 ). As a result, PIP-related genes in plants are often downregulated. Yet, studies have found that PRDI can induce specific responses to enhance the water uptake rate of the hydrated root, which is believed to be regulated by signals produced by the leaves (Luo et al. 2019 ; McLean et al. 2011 ; Pérez-Pérez et al. 2020 ). In cotton, the hydraulic conductivity rate (L) of roots and the water absorption in hydrated roots may be the result of increased expression of the intrinsic protein gene ( PIP ) (Luo et al. 2019 ; McLean et al. 2011 ). Moreover, the contents of jasmonic acid (JA) and jasmonic acid-isoleucine conjugate (JA-Ile), and the expression of three JA biosynthesis genes in the leaves of PRDI plants are increased (Luo et al. 2019 ). Although the expression of the three JA genes in the roots does not change, the JA/JA-Ile content increases (Luo et al. 2019 ). Therefore, under PRDI conditions, plants can induce the expression of genes related to leaf JA synthesis, synthesize more JA/JA-Ile, and transfer them to the roots through the cortex to induce the expression of GhPIP , thereby increasing the hydraulic conductivity rate of the roots (Luo et al. 2019 ; McLean et al. 2011 ; Han et al. 2023 ). Under AWD conditions, aside from the significant differential expression of genes related to ABA synthesis and metabolism, substantial differential expression of genes associated with photosynthesis contributes to rice's yield enhancement under AWD (Song et al. 2020 ). Transcriptomic analysis has revealed that these DEGs are mainly related to Chl, light-harvesting complexes (LHCs), PSI, and PSII (Song et al. 2020 ). Similar to traditional drought stress, AWD also leads to the downregulation of gene expression in pathways related to chlorophyll synthesis and other aspects of photosynthesis (Song et al. 2020 ). When rice was subjected to AWD irrigation treatment, 14 DEGs involved in Chl biosynthesis were downregulated in the flag leaves, and the enzymes encoded by these DEGs were important in the Chl biosynthesis pathway (Song et al. 2020 ). Chl plays a vital role in capturing sunlight and converting it into chemical energy, and any interference with the Chl concentration will lead to changes in photosynthesis (Vandoorne, et al. 2012 ). Secondly, the genes involved in LHCs were downregulated in AWD (Song et al. 2020 ). The primary function of the light-harvesting complex is to capture solar energy and transfer the excited energy to the reaction center (Masuda et al. 2002 ). Thirdly, the DEGs involved in photosynthetic pathways such as PSI, PSII, cytochrome b6/f complex, photosynthetic electron transfer, and F-type ATP synthase were downregulated in the flag leaves, affecting the synthesis metabolism of key components in the photosynthetic pathway (Song et al. 2020 ). However, more interesting was that under the severe drought conditions caused by the AWD irrigation technique, several genes in rice, namely, OsPIP1;1 , OsPIP1;2 , OsPIP2;3 , OsTIP2;2 , and OsTIP3;1 , play a crucial role in positively regulating Gs and gm in rice plants (He et al. 2021 ). Maintaining a high gm is one of the primary factors for sustaining high photosynthesis in rice under water stress (He et al. 2022 ). The OsPIP1;1 gene shows a clear positive correlation with Gs and gm, and its relative expression level significantly decreases under AWD conditions. Conversely, OsPIP1;2 , OsPIP2;3 , OsTIP2;2 , and OsTIP3;1 are all upregulated to mitigate the sharp decline in Gs and gm during severe drought (He et al. 2021 ; Bai et al. 2021 ). Furthermore, it has been reported that AWD enables rice to maintain a high expression of OsLHCA5 and OsCSP41B genes under water stress (He et al. 2022 ). LHCA5 is an essential component of the chloroplast NADH dehydrogenase super complex and plays a critical role in regulating cyclic electron flow (Kato et al. 2018 ). Additionally, the OsLHCA5 gene indirectly influences ATP and NADPH content in the PSI system (Selmar and Kleinwächter 2013 ). The CSP41B gene, located in the chloroplast, contributes to maintaining a deep green leaf color in crops (Mei et al. 2017 ). The OsLHCA5 and OsCSP41B genes are candidate genes that co-regulate gm and energy distribution to achieve high photosynthesis under severe water stress (He et al. 2022 ). The OsLHCA5 and OsCSP41B genes are candidate genes that co-regulate gm and energy distribution to achieve high photosynthesis under severe water stress (Li et al. 2017b ), the drought stress induced in rice by AWD allows for the maintenance of high gm and efficient energy distribution (He et al. 2022 ). This enables rice to sustain high photosynthetic activity, and it's one of the reasons why rice can maintain high yields under AWD conditions. Moreover, studies have revealed that under AWD treatment, rice can enhance enzyme activity by inducing the expression of genes such as PRODH , P5CS2 , and DAO , while inhibiting other pathways, such as BADH2, to downregulate gamma-aminobutyric acid (Bao et al. 2018 ). This leads to the upregulation of P5C and Δ1-pyrroline synthesis and promotes 2-AP biosynthesis, leading to improved rice quality (Bao et al. 2018 ). Regarding the promotion of P uptake in rice under AWD treatment, it has been found that AWD significantly enhances the activity of ionic-bonded cell-wall-located class III peroxidases (iPrx) and the expression of OsPrx24 (a gene encoding iPrx) (Acharya et al. 2016 ; Yang et al. 2020b ). iPrx is involved in the clearance of ROS in the cell wall (Acharya et al. 2016 ; Yang et al. 2020b ). OsPrx24 is primarily expressed in the root epidermis and participates in the formation of infection structures by regulating iPrx activity and H 2 O 2 concentration under AWD, thereby facilitating phosphorus absorption (Acharya et al. 2016 ; Yang et al. 2020b ). These are unique regulatory mechanisms under AWD. Therefore, understanding the similarities and differences in the regulatory mechanisms of crops under water-saving irrigation and traditional drought can further help harness the potential of crops and water-saving irrigation techniques, with the aim to create greater agricultural value.

3.2 Unlocking the potential of plants: using genetic modification and water-saving technologies

Understanding the molecular adaptability of crops to drought stress under DI strategies contributes to the breeding, selection, and introduction of actual or potential water-saving varieties to fully exploit the drought resistance potential and superior performance of crops. Currently, many excellent new varieties have been developed that are better adapted to water-saving irrigation. The combination of both, not only harnesses the inherent performance of crops, but also maximizes the advantages of water-saving irrigation. Research has shown that silencing the OsSYT-5 gene enhances the drought tolerance of rice (Shanmugam et al. 2021 ). Transgenic plants where this gene has been supressed exhibit higher photosynthetic rates, lower gm, and transpiration under water-deficit conditions, and increased ABA content in both drought and normal conditions, resulting in delayed drought stress symptoms, higher pollen vitality, and increased grain production compared to the wild type (Shanmugam et al. 2021 ). Overexpression lines of the galactinol synthase 2 gene ( OsGolS2 ) were found to enhance leaf water content, maintain higher photosynthetic activity, reduce the rate of plant growth deceleration, and exhibit stronger recovery capabilities (Selvaraj et al. 2017 ). Additionally, overexpression of OsHAK1 in rice seedlings enhances their drought tolerance compared to the wild type, while the OsHAK1 knockout mutants display lower tolerance to stress during both nutritional and reproductive stages, and exhibited delayed growth (Chen et al. 2017b ). Compared to wild-type seedlings, the lipid peroxidation level was lower, the activity of antioxidant enzymes was higher, and the accumulation of proline was higher in OsHAK1 overexpressing rice, demonstrating higher drought resistance and 35% higher grain yield under drought conditions (Chen et al. 2017b ). This greatly enhances the adaptability of rice to water-saving irrigation. Moreover, research has suggested that OsJAZ9 plays a crucial role in conferring drought tolerance in rice by affecting JA and ABA signal transduction (Singh et al. 2021 ). Overexpression of OsJAZ9 in rice increases ABA and JA content, improves osmotic pressure, reduces leaf width and stomatal density, decreases leaf transpiration rate, and exhibits better drought resistance compared to the wild type (Singh et al. 2021 ). In wheat, which is particularly susceptible to water deficiency during the jointing stage of development, sucrose non-fermenting 1-related protein kinase 2 (SnRK2) acts as a key signaling hub in response to drought stress (Zhang, et al. 2023 ). Overexpression of SnRK2 in wheat leads to drought-resistant varieties, and studies have shown that ectopic expression of SnRK2 in rice also imparts higher drought tolerance (Zhang, et al. 2023 ). Research has also explored the performance of different wheat genotypes under various levels of water-saving irrigation (Liu et al. 2016b ). The results of which indicate that Shijiazhuang 8, a drought-resistant variety, maintains a higher net photosynthetic rate, gm, and transpiration under drought stress, resulting in higher grain yield and WUE compared to Yanmai 20, a drought-sensitive variety (Liu et al. 2016b ). The isopentenyltransferase gene ( IPT ), which encodes a rate-limiting enzyme in cytokinin biosynthesis, significantly improves the drought resistance of cotton, especially during the pre-flowering (nutritional) stage under water deficit stress (Zhu et al. 2018 ). Overexpression of IPT in cotton plants demonstrates higher drought tolerance and yield compared to control plants (Zhu et al. 2018 ). LOS5/ABA3 (LOS5), encoding a molybdenum co-factor, is essential for activating aldehyde oxidase involved in ABA biosynthesis (Yue et al. 2012 ). Overexpression of AtLOS5 in cotton plants enhances ABA production, ABA-induced physiological regulation, improves antioxidant enzyme activity, and significantly improves membrane integrity under water deficit stress (Yue et al. 2012 ). In maize, genotypes with partial stomatal closure in response to elevated atmospheric vapor pressure deficit exhibit higher drought resistance and higher yield under water deficit conditions (Jafarikouhini et al. 2022 ). Studies have also suggested that maize plants with overexpression of the zeaxanthin epoxidase gene ( ZEP ) are more sensitive to water deficit (Borel, et al. 2001 ). In soybean, it has been found that soybean lines with higher expression of GmWRKY106 and GmWRKY149 genes show stronger drought tolerance under water deficit conditions (Dias et al. 2016 ). Overexpression of GmDREB2A and GmDREB2A in transgenic plants leads to higher leaf β-glucose and fructose concentrations, indicating greater drought resistance (Marinho et al. 2019 ). In conclusion, whether through hybridization or conferring individual gene functionality, the support of molecular regulatory mechanism theories is required. Understanding the molecular adaptability of crops contributes to the selection of drought-resistant crop varieties and the better integration of water-saving irrigation, thereby creating more suitable agricultural irrigation and production systems and generating higher economic and ecological value.

4 Conclusions and perspectives

This review underscores the significance of selecting appropriate irrigation technologies based on the desired water consumption reduction, improved WUE, and sustained crop yield in sustainable agriculture. DRFI is suitable for most crops other than paddy fields, improving WP, stimulating plant root growth, and increasing fertile spikelets, with a high potential for yield increase. TDI has a high water-saving potential, which improves WP, but can affect plant yield through various mechanisms. DRDI significantly enhances WP based on DRFI, but its yield-increasing potential is moderate and requires proper nutrient management. PRDI improves water use efficiency, particularly promoting root development and biomass production under dry conditions, and also influences soil microorganisms. AWD is suitable for paddy crops, enhancing WP with minimal yield impact, and can regulate harmful ion concentrations and greenhouse gas emissions. Each technique offers specific advantages and disadvantages, and agricultural practitioners can use this review as a guide to determine the most suitable irrigation method for their circumstances.

Furthermore, there are a variety of methods that can be used in future research to further realize the potential of water-saving irrigation techniques, including the integration of multiple irrigation techniques to maximize water conservation and crop performance, such as incorporating drip irrigation to supplement PRDI or utilizing a root zone irrigation strategy for AWD. Additionally, exploring the use of innovative materials for irrigation infrastructure, such as integrating biodegradable mulches or smart irrigation membranes into water-saving irrigation technologies, could contribute to more sustainable and eco-friendly practices. Furthermore, research efforts should focus on understanding the molecular regulatory mechanisms underlying crop responses to different water-saving techniques. Many studies have already focused on exploring the functions of genes to cultivate drought-resistant and water-efficient crop varieties. Integrating these varieties into water-saving irrigation techniques can greatly enhance the water-saving potential of both approaches. By pursuing these avenues of research and innovation, we can address the challenges of water scarcity and food security while promoting a sustainable and resilient agricultural sector for the future.

Availability of data and materials

No datasets were generated or analysed during the current study.

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This work was supported by the Natural Science Foundation of Jiangsu Province (BK20221334, SBK2020042924), National Key Research and Development Program of China (2022YFD1900801), the Jiangsu Agricultural Science and Technology Innovation Fund [CX (21) 2023], the National Natural Science Foundation of China (No.41977354), the Science Technology and Innovation Committee of Shenzhen (JCYJ20210324115408023), the Major Project of Natural Science Research in Colleges of Jiangsu Province (20KJA220001).

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State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing, 210037, China

Yu Chen, Ya-Nan Leng, Fu-Yuan Zhu & Tao Song

Center for Agricultural Water Research in China, China Agricultural University, Beijing, China

Department of Biology, Hong Kong Baptist University, and State Key Laboratory of Agro-Biotechnology, Chinese University of Hong Kong, Hong Kong, 999077, China

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yu Chen and Ya-Nan Leng. The first draft of the manuscript was written by Yu Chen. Fu-Yuan Zhu, Si-En Li, Tao Song and Jianhua Zhang corrected and approved the final manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Additional file 1. figure s1..

Web of science core collection database keyword analysis from January 2012 to April 2023 and patterns of water-saving irrigation techniques. A: Keyword co-occurrence network analyzed by BibExcel and VOSviewer. Node colors represent modularity, node size represents how often keywords appear. B: Burst keyword analysis. The length of colored boxes represents burst status duration. Colors represent burst strength. The keywords "treatment" and"mission" are diluted by a factor of 20 and 5, respectively, due to the large difference in values with the other keywords in the figure. C: Statistical table of relevant publications from 2012 to 2022. The horizontal coordinate represents the year and the vertical coordinate represents the frequency of keywords. 

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Chen, Y., Leng, YN., Zhu, FY. et al. Water-saving techniques: physiological responses and regulatory mechanisms of crops. Adv. Biotechnol. 1 , 3 (2023). https://doi.org/10.1007/s44307-023-00003-7

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Received : 06 August 2023

Revised : 11 September 2023

Accepted : 15 September 2023

Published : 26 October 2023

DOI : https://doi.org/10.1007/s44307-023-00003-7

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