Pub Date : 2025-11-09DOI: 10.1016/j.agsy.2025.104562
Ankita Kumari , Tinesh Pathania
Context:
The efficient and coordinated management of water resource systems is crucial to address increasing demands and resource uncertainties. As a result, good water resources management in agricultural and reservoir systems is vital for ensuring water security, better resource utilization, and sustaining agricultural productivity.
Objective:
In this respect, optimization models are employed to analyze several management scenarios, allowing decision-makers to identify optimal strategies under varying conditions. Therefore, we examined optimization-based approaches to address interconnected challenges to promote better understanding of systemic balances between agricultural water and reservoir-based systems.
Methods:
We conducted a comprehensive review of more than 400 research articles. The review article is grouped into sections, which include the applications of optimization techniques in irrigation, surface water and groundwater management, water-energy- food (WEF) nexus, and reservoir management.
Results and conclusions:
The review emphasizes the importance of agricultural water resources and reservoir management for developing an efficient water resource system. This includes simulation–optimization (SO) models that facilitate sustained water policies for conflicting objectives. It also highlights the increasing applications of machine learning (ML) based surrogate models to reduce computational efforts. Further, several reviewed studies indicate that the participation of different stakeholders in the optimization process leads to better water resource utilization.
Significance:
The review presents an overview containing numerous case studies throughout the world, demonstrating the applicability of optimization and ML methodologies. This serves as an important document for academicians, industry experts, and policymakers with the advantages of sustainable water system operations and advances in optimization modeling techniques.
{"title":"Comprehensive review of optimization and surrogate models for agricultural water resources and reservoir water management","authors":"Ankita Kumari , Tinesh Pathania","doi":"10.1016/j.agsy.2025.104562","DOIUrl":"10.1016/j.agsy.2025.104562","url":null,"abstract":"<div><h3>Context:</h3><div>The efficient and coordinated management of water resource systems is crucial to address increasing demands and resource uncertainties. As a result, good water resources management in agricultural and reservoir systems is vital for ensuring water security, better resource utilization, and sustaining agricultural productivity.</div></div><div><h3>Objective:</h3><div>In this respect, optimization models are employed to analyze several management scenarios, allowing decision-makers to identify optimal strategies under varying conditions. Therefore, we examined optimization-based approaches to address interconnected challenges to promote better understanding of systemic balances between agricultural water and reservoir-based systems.</div></div><div><h3>Methods:</h3><div>We conducted a comprehensive review of more than 400 research articles. The review article is grouped into sections, which include the applications of optimization techniques in irrigation, surface water and groundwater management, water-energy- food (WEF) nexus, and reservoir management.</div></div><div><h3>Results and conclusions:</h3><div>The review emphasizes the importance of agricultural water resources and reservoir management for developing an efficient water resource system. This includes simulation–optimization (SO) models that facilitate sustained water policies for conflicting objectives. It also highlights the increasing applications of machine learning (ML) based surrogate models to reduce computational efforts. Further, several reviewed studies indicate that the participation of different stakeholders in the optimization process leads to better water resource utilization.</div></div><div><h3>Significance:</h3><div>The review presents an overview containing numerous case studies throughout the world, demonstrating the applicability of optimization and ML methodologies. This serves as an important document for academicians, industry experts, and policymakers with the advantages of sustainable water system operations and advances in optimization modeling techniques.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104562"},"PeriodicalIF":6.1,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-09DOI: 10.1016/j.agsy.2025.104558
Xue Yang , Yijie Yao , Yingxu Fan , Vilma Sandström
CONTEXT
With rising global demand for animal-sourced foods, controlling greenhouse gas (GHG) emissions from livestock production has become critical to mitigating climate change. While prior research has quantified the global potential of using agricultural by-products to replace crop feeds, a comprehensive evaluation of the associated GHG mitigation benefits—particularly when considering both land-use change (LUC) and agricultural production emissions—has not been addressed.
OBJECTIVE
This study aims to evaluate the global integrated GHG mitigation potential of replacing livestock crop feeds with agricultural by-products, by combining emissions from both land-use change and agricultural activities, while also identifying regional variations in this potential.
METHODS
Five major agricultural by-products (cereal bran, molasses, sugar beet pulp, citrus pulp, and distillers' grains) were selected to replace six energy-intensive crop feeds across 19 global regions. The methodology comprised four key steps: (1) developing region-specific substitution matrices based on previous literature; (2) estimating land-use change emissions of replaced crop feeds and replacing by-products using spatially explicit data; (3) assessing agricultural emissions of replaced crop feeds and replacing by-products with statistical data; and (4) calculating global GHG mitigation potential as the difference between emissions from replaced crop feeds and those from replacing by-products.
RESULTS AND CONCLUSIONS
Globally, replacing crop feeds with agricultural by-products was projected to reduce GHG emissions by 167 Mt. CO₂-eq. (80–276 Mt. CO₂-eq) annually over a 30-year timeframe, which accounts for 9 % (4 %–14 %) of the total annual emissions from crop feeds; 80 % of this reduction is attributed to land carbon restoration. Regionally, South-eastern Asia (31 Mt. CO₂-eq) and Southern Asia (30 Mt. CO₂-eq) emerge as mitigation hotspots, primarily due to their status as tropical and subtropical regions with high native land carbon stocks. Eastern Asia, Eastern Europe, Southern America, and Western Europe exhibit mitigation potentials ranging from 14 to 20 Mt. CO₂-eq, driven mainly by their large volumes of replaced crop feeds.
SIGNIFICANCE
This study advances the development of a framework for assessing land-use and agricultural emission reductions from crop feed substitution with by-products. It provides a foundational reference for further research on mitigation strategies in the global livestock feed sector.
{"title":"Integrated land-use and agricultural emissions modeling reveals untapped mitigation potential in livestock feed substitution","authors":"Xue Yang , Yijie Yao , Yingxu Fan , Vilma Sandström","doi":"10.1016/j.agsy.2025.104558","DOIUrl":"10.1016/j.agsy.2025.104558","url":null,"abstract":"<div><div>CONTEXT</div><div>With rising global demand for animal-sourced foods, controlling greenhouse gas (GHG) emissions from livestock production has become critical to mitigating climate change. While prior research has quantified the global potential of using agricultural by-products to replace crop feeds, a comprehensive evaluation of the associated GHG mitigation benefits—particularly when considering both land-use change (LUC) and agricultural production emissions—has not been addressed.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to evaluate the global integrated GHG mitigation potential of replacing livestock crop feeds with agricultural by-products, by combining emissions from both land-use change and agricultural activities, while also identifying regional variations in this potential.</div></div><div><h3>METHODS</h3><div>Five major agricultural by-products (cereal bran, molasses, sugar beet pulp, citrus pulp, and distillers' grains) were selected to replace six energy-intensive crop feeds across 19 global regions. The methodology comprised four key steps: (1) developing region-specific substitution matrices based on previous literature; (2) estimating land-use change emissions of replaced crop feeds and replacing by-products using spatially explicit data; (3) assessing agricultural emissions of replaced crop feeds and replacing by-products with statistical data; and (4) calculating global GHG mitigation potential as the difference between emissions from replaced crop feeds and those from replacing by-products.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Globally, replacing crop feeds with agricultural by-products was projected to reduce GHG emissions by 167 Mt. CO₂-eq. (80–276 Mt. CO₂-eq) annually over a 30-year timeframe, which accounts for 9 % (4 %–14 %) of the total annual emissions from crop feeds; 80 % of this reduction is attributed to land carbon restoration. Regionally, South-eastern Asia (31 Mt. CO₂-eq) and Southern Asia (30 Mt. CO₂-eq) emerge as mitigation hotspots, primarily due to their status as tropical and subtropical regions with high native land carbon stocks. Eastern Asia, Eastern Europe, Southern America, and Western Europe exhibit mitigation potentials ranging from 14 to 20 Mt. CO₂-eq, driven mainly by their large volumes of replaced crop feeds.</div></div><div><h3>SIGNIFICANCE</h3><div>This study advances the development of a framework for assessing land-use and agricultural emission reductions from crop feed substitution with by-products. It provides a foundational reference for further research on mitigation strategies in the global livestock feed sector.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104558"},"PeriodicalIF":6.1,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-08DOI: 10.1016/j.agsy.2025.104554
Kieron Moller , A. Pouyan Nejadhashemi , Mohammad Tirgari , Nilson Vieira Junior , Ana Julia Paula Carcedo , Ignacio Ciampitti , P.V. Vara Prasad , Amadiane Diallo
<div><h3>CONTEXT</h3><div>Many challenges, such as climate change, conflict, and economic fluctuations, pose a significant threat to global agriculture and food systems. Therefore, it is crucial to develop and evaluate methods to enhance agricultural resilience.</div></div><div><h3>OBJECTIVES</h3><div>This study aims to identify knowledge gaps in commonly used methods for determining farm household resilience. It addresses these gaps by developing a novel method for determining resilience. Therefore, the objectives of this study were to (1) identify knowledge gaps within existing resilience determination literature and develop a resilience quantification approach and (2) incorporate the shock-failure-recovery sequence to measure resilience. Incorporating this sequence allows understanding the dynamic relationship between failure and recovery phases and how it varies across study units.</div></div><div><h3>METHODS</h3><div>A comprehensive resilience measure for farmers' livelihoods is established by integrating two fundamental dimensions: (1) the system's failure dynamics caused by external shocks and (2) the recovery path following these shocks. This study extends these aspects within the theoretical framework of the agricultural household model, which simultaneously captures farmers' production and consumption decisions within rural economies. The agricultural household model is particularly relevant in contexts characterized by incomplete or missing markets, which often arise due to high transaction costs. By leveraging this framework, we conceptualize failure and recovery as intrinsic components of farmers' resilience, systematically linking household decision-making processes to resilience assessment.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The proposed resilience measurement procedure offers two key advantages over conventional methodologies: (1) it conceptualizes resilience as an explicitly quantifiable variable rather than a latent construct; and (2) it mitigates biases stemming from perception-based assessments and subjective valuations. The econometric framework improves methodological flexibility. It estimates the marginal effect of the farmer indicator variable on recovery probabilities and accommodates diverse data structures. However, ensuring the procedure's accuracy requires further refinement through rigorous validation studies, multidimensional assessment, context-specific indicator selection, and the development of standardized analytical models to enhance methodological consistency and policy relevance.</div></div><div><h3>SIGNIFICANCE</h3><div>This study contributes to the field of agricultural resilience by introducing a novel, data-driven framework. The framework can integrate farmers' indicator variables related to economic, nutritional, risk, and other livelihood aspects to measure resilience. The results from this resilience measurement approach can enhance farmers' resilience, helping achieve sustainable dev
{"title":"A novel framework for evaluating farmer resilience: Methodological advances and theoretical foundations","authors":"Kieron Moller , A. Pouyan Nejadhashemi , Mohammad Tirgari , Nilson Vieira Junior , Ana Julia Paula Carcedo , Ignacio Ciampitti , P.V. Vara Prasad , Amadiane Diallo","doi":"10.1016/j.agsy.2025.104554","DOIUrl":"10.1016/j.agsy.2025.104554","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Many challenges, such as climate change, conflict, and economic fluctuations, pose a significant threat to global agriculture and food systems. Therefore, it is crucial to develop and evaluate methods to enhance agricultural resilience.</div></div><div><h3>OBJECTIVES</h3><div>This study aims to identify knowledge gaps in commonly used methods for determining farm household resilience. It addresses these gaps by developing a novel method for determining resilience. Therefore, the objectives of this study were to (1) identify knowledge gaps within existing resilience determination literature and develop a resilience quantification approach and (2) incorporate the shock-failure-recovery sequence to measure resilience. Incorporating this sequence allows understanding the dynamic relationship between failure and recovery phases and how it varies across study units.</div></div><div><h3>METHODS</h3><div>A comprehensive resilience measure for farmers' livelihoods is established by integrating two fundamental dimensions: (1) the system's failure dynamics caused by external shocks and (2) the recovery path following these shocks. This study extends these aspects within the theoretical framework of the agricultural household model, which simultaneously captures farmers' production and consumption decisions within rural economies. The agricultural household model is particularly relevant in contexts characterized by incomplete or missing markets, which often arise due to high transaction costs. By leveraging this framework, we conceptualize failure and recovery as intrinsic components of farmers' resilience, systematically linking household decision-making processes to resilience assessment.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The proposed resilience measurement procedure offers two key advantages over conventional methodologies: (1) it conceptualizes resilience as an explicitly quantifiable variable rather than a latent construct; and (2) it mitigates biases stemming from perception-based assessments and subjective valuations. The econometric framework improves methodological flexibility. It estimates the marginal effect of the farmer indicator variable on recovery probabilities and accommodates diverse data structures. However, ensuring the procedure's accuracy requires further refinement through rigorous validation studies, multidimensional assessment, context-specific indicator selection, and the development of standardized analytical models to enhance methodological consistency and policy relevance.</div></div><div><h3>SIGNIFICANCE</h3><div>This study contributes to the field of agricultural resilience by introducing a novel, data-driven framework. The framework can integrate farmers' indicator variables related to economic, nutritional, risk, and other livelihood aspects to measure resilience. The results from this resilience measurement approach can enhance farmers' resilience, helping achieve sustainable dev","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104554"},"PeriodicalIF":6.1,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-08DOI: 10.1016/j.agsy.2025.104556
Li Luo , Matthew J. Knowling , Aaron C. Zecchin , Glenn K. McDonald
<div><h3>CONTEXT</h3><div>Expectations are mounting on cropping systems to satisfy objectives across social, economic, and environmental dimensions. To identify cropping systems with an enhanced capacity to deliver on these diverse objectives, it is essential to understand both how these systems perform for a wide range of quantifiable performance metrics and the extent of synergies and trade-offs between these metrics, as they describe the underlying system's processes and properties. Such knowledge is presently lacking for dryland cropping systems in the southern grains region of Australia (SGR), one of the largest global grain production regions.</div></div><div><h3>OBJECTIVE</h3><div>This study (1) evaluates the performance of different cropping systems in the SGR for a range of performance metrics and (2) explores the relationships between these metrics for the range of case studies considered.</div></div><div><h3>METHODS</h3><div>Using the process-based crop model, APSIM, we simulated the water-limited production potential of different cropping systems across diverse environments, from which performance metrics were computed to describe key system processes and properties. A total of 16 systems, with varying cropping diversity and intensity, were evaluated using 14 performance metrics spanning six objectives. Three case studies collectively represent key variabilities in climate and soil within the SGR.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>No single cropping system consistently outperformed others across all performance metrics. The ‘baseline’ systems, which reflect the most commonly adopted system in given regions, were generally not the top performers across several key metrics, highlighting some potential opportunities for performance improvements. Systems achieving higher productivity (e.g., water use efficiency (WUE)), were generally associated with improved environmental outcomes across all study areas, including lower CO<sub>2</sub>-e emissions and decreased relative soil loss. In addition, while higher WUE was linked to greater gross margins (GM) in the low- and high-rainfall zones, this relationship was not as evident in the mid-rainfall zone. Synergies were strongest for the high rainfall zone case study, where the correlation coefficients were 0.94 between WUE and GM, and −0.72 between WUE and CO<sub>2</sub>-e emissions. Trade-offs and synergies were influenced by both site- and system-specific factors.</div></div><div><h3>SIGNIFICANCE</h3><div>Our findings highlight that identified synergies between economic and environmental metrics could serve to enhance confidence in growers regarding strategic cropping decisions. Our findings offer region-specific insights that can help inform decisions towards more sustainable and profitable cropping systems, while some relationships that were found to be independent of system and sites may be applicable to other cropping regions in similar dryland farming worldwide.</div></
{"title":"Identification of synergies and trade-offs in cropping system performance in southern Australia","authors":"Li Luo , Matthew J. Knowling , Aaron C. Zecchin , Glenn K. McDonald","doi":"10.1016/j.agsy.2025.104556","DOIUrl":"10.1016/j.agsy.2025.104556","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Expectations are mounting on cropping systems to satisfy objectives across social, economic, and environmental dimensions. To identify cropping systems with an enhanced capacity to deliver on these diverse objectives, it is essential to understand both how these systems perform for a wide range of quantifiable performance metrics and the extent of synergies and trade-offs between these metrics, as they describe the underlying system's processes and properties. Such knowledge is presently lacking for dryland cropping systems in the southern grains region of Australia (SGR), one of the largest global grain production regions.</div></div><div><h3>OBJECTIVE</h3><div>This study (1) evaluates the performance of different cropping systems in the SGR for a range of performance metrics and (2) explores the relationships between these metrics for the range of case studies considered.</div></div><div><h3>METHODS</h3><div>Using the process-based crop model, APSIM, we simulated the water-limited production potential of different cropping systems across diverse environments, from which performance metrics were computed to describe key system processes and properties. A total of 16 systems, with varying cropping diversity and intensity, were evaluated using 14 performance metrics spanning six objectives. Three case studies collectively represent key variabilities in climate and soil within the SGR.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>No single cropping system consistently outperformed others across all performance metrics. The ‘baseline’ systems, which reflect the most commonly adopted system in given regions, were generally not the top performers across several key metrics, highlighting some potential opportunities for performance improvements. Systems achieving higher productivity (e.g., water use efficiency (WUE)), were generally associated with improved environmental outcomes across all study areas, including lower CO<sub>2</sub>-e emissions and decreased relative soil loss. In addition, while higher WUE was linked to greater gross margins (GM) in the low- and high-rainfall zones, this relationship was not as evident in the mid-rainfall zone. Synergies were strongest for the high rainfall zone case study, where the correlation coefficients were 0.94 between WUE and GM, and −0.72 between WUE and CO<sub>2</sub>-e emissions. Trade-offs and synergies were influenced by both site- and system-specific factors.</div></div><div><h3>SIGNIFICANCE</h3><div>Our findings highlight that identified synergies between economic and environmental metrics could serve to enhance confidence in growers regarding strategic cropping decisions. Our findings offer region-specific insights that can help inform decisions towards more sustainable and profitable cropping systems, while some relationships that were found to be independent of system and sites may be applicable to other cropping regions in similar dryland farming worldwide.</div></","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104556"},"PeriodicalIF":6.1,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-06DOI: 10.1016/j.agsy.2025.104545
William Sylvain, Timothé Lalonde, Danielle Monfet, Didier Haillot
CONTEXT
There is a growing interest in sustainable and year-round food production through protected agriculture. Greenhouses play a key role in this transition, but their performance varies significantly with climate conditions and operational strategies.
OBJECTIVE
As a result, this study proposes a standardised and practical framework for evaluating greenhouse performance, grounded in a systematic analysis of key performance indicators (KPI).
METHODS
A total of 16 key performance indicators (KPI) were identified from the literature and classified into three main categories: thermal, daylighting, and energy. From these, a refined set of 10 KPI was selected based on their applicability, non-redundancy, and relevance for both passive and active greenhouses. These KPI were applied to a case study involving a naturally ventilated, free-standing Gothic arch greenhouse, modelled using the TRNSYS dynamic simulation software. The model was validated using measured data and used to assess greenhouse performance under three distinct Canadian climates: cold (Montréal), very cold (Baie-Comeau), and subarctic (Kuujjuaq).
RESULTS AND CONCLUSION
The analysis revealed that while some KPI, such as the average indoor air temperature () and daily light integral (), are essential for assessing crop survival, others provided insights into growing potential, operational climate control or the environmental and economic viability of the system. This study introduced two refined indicators for greenhouse cultivation in cold climates: , which excludes lethal short-term cold events, and , which combines temperature and daylight to define realistic growing conditions. These demonstrated that combining different classes of KPI enabled more meaningful, comparative assessments of greenhouse suitability, offering practical guidance for optimising crop production and energy use under diverse climates.
SIGNIFICANCE
This work contributes to a standardised and practical framework for evaluating greenhouse performance, paving the way for more informed decision-making in controlled environment agriculture.
{"title":"Standardised framework for analysis of greenhouse performance using key performance indicators","authors":"William Sylvain, Timothé Lalonde, Danielle Monfet, Didier Haillot","doi":"10.1016/j.agsy.2025.104545","DOIUrl":"10.1016/j.agsy.2025.104545","url":null,"abstract":"<div><h3>CONTEXT</h3><div>There is a growing interest in sustainable and year-round food production through protected agriculture. Greenhouses play a key role in this transition, but their performance varies significantly with climate conditions and operational strategies.</div></div><div><h3>OBJECTIVE</h3><div>As a result, this study proposes a standardised and practical framework for evaluating greenhouse performance, grounded in a systematic analysis of key performance indicators (KPI).</div></div><div><h3>METHODS</h3><div>A total of 16 key performance indicators (KPI) were identified from the literature and classified into three main categories: thermal, daylighting, and energy. From these, a refined set of 10 KPI was selected based on their applicability, non-redundancy, and relevance for both passive and active greenhouses. These KPI were applied to a case study involving a naturally ventilated, free-standing Gothic arch greenhouse, modelled using the TRNSYS dynamic simulation software. The model was validated using measured data and used to assess greenhouse performance under three distinct Canadian climates: cold (Montréal), very cold (Baie-Comeau), and subarctic (Kuujjuaq).</div></div><div><h3>RESULTS AND CONCLUSION</h3><div>The analysis revealed that while some KPI, such as the average indoor air temperature (<span><math><msub><mover><mi>T</mi><mo>¯</mo></mover><mrow><msub><mi>a</mi><mi>i</mi></msub><mo>,</mo><mi>ND</mi></mrow></msub></math></span>) and daily light integral (<span><math><mi>DLI</mi></math></span>), are essential for assessing crop survival, others provided insights into growing potential, operational climate control or the environmental and economic viability of the system. This study introduced two refined indicators for greenhouse cultivation in cold climates: <span><math><msub><mi>TGSL</mi><mi>limit</mi></msub></math></span>, which excludes lethal short-term cold events, and <span><math><mi>OGSL</mi></math></span>, which combines temperature and daylight to define realistic growing conditions. These demonstrated that combining different classes of KPI enabled more meaningful, comparative assessments of greenhouse suitability, offering practical guidance for optimising crop production and energy use under diverse climates.</div></div><div><h3>SIGNIFICANCE</h3><div>This work contributes to a standardised and practical framework for evaluating greenhouse performance, paving the way for more informed decision-making in controlled environment agriculture.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104545"},"PeriodicalIF":6.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145463027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1016/j.agsy.2025.104547
Mohamed Ghali , Nejla Ben Arfa , Giffona Justinia , Soazig Di Bianco , Abdul Rahman Saili
<div><h3>CONTEXT</h3><div>Digital tools are increasingly recognized for their essential roles in enhancing farm productivity, sustainability, and competitiveness. However, their adoption remains uneven across different farming systems due to multiple structural and strategic constraints.</div></div><div><h3>OBJECTIVE</h3><div>This study analyzed the adoption of digital tools on French beef cattle, pig, and vegetable farms, three production systems that have received limited research attention. The objectives are twofold: to distinguish between farmers’ stated motives and the structural factors influencing adoption decisions and to formulate recommendations for targeted public policies.</div></div><div><h3>METHODS</h3><div>A mixed-methods approach was employed: semi-structured interviews with 49 farmers and logistic regression models using data from the 2020 French agricultural census. Two regions with a high prevalence of the three production systems - Pays de la Loire and Brittany- were selected and compared to the national level.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Qualitative analysis identified key motives such as improved productivity, reduced workload, and environmental sustainability. Quantitative results showed that adoption was more likely among larger farms, and farmers involved in collaborative networks and collectives that facilitate resource sharing, participation in expert groups, training, and knowledge exchange, as well as among farmers with higher education levels. Conversely, smaller farms and those engaged in short supply chains faced greater barriers, including high costs, technological complexity, and limited internet access. However, in certain cases—such as vegetable farming—adoption requires higher levels of education and advanced technical and digital skills, particularly for decision-support and automation tools where precision and responsiveness are critical. In contrast, in livestock sectors such as pig and beef production, automation tools are often adopted by older and less-educated farmers as a response to labor shortages, primarily to reduce drudgery and automate repetitive, low-value tasks rather than to transform management practices. The study underscores the need for differentiated policy strategies to support equitable digital transitions across farm types.</div></div><div><h3>SIGNIFICANCE</h3><div>The findings provide actionable insights for policymakers seeking to foster an inclusive and sustainable digital transition, which requires differentiated policy responses. Small farms, particularly those in vegetable production, need target financial and technical support to overcome cost-related and technological barriers. Beef and pig farms face structural and infrastructural constraints, underscoring the importance of broadband investment in rural areas.</div><div>The factors influencing the adoption of digital tools are highly context-dependent, varying across production sectors and tool types.
{"title":"Adoption of digital tools in french beef cattle, pig, and vegetable farming: A mixed-methods analysis of motives, barriers, and structural determinants","authors":"Mohamed Ghali , Nejla Ben Arfa , Giffona Justinia , Soazig Di Bianco , Abdul Rahman Saili","doi":"10.1016/j.agsy.2025.104547","DOIUrl":"10.1016/j.agsy.2025.104547","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Digital tools are increasingly recognized for their essential roles in enhancing farm productivity, sustainability, and competitiveness. However, their adoption remains uneven across different farming systems due to multiple structural and strategic constraints.</div></div><div><h3>OBJECTIVE</h3><div>This study analyzed the adoption of digital tools on French beef cattle, pig, and vegetable farms, three production systems that have received limited research attention. The objectives are twofold: to distinguish between farmers’ stated motives and the structural factors influencing adoption decisions and to formulate recommendations for targeted public policies.</div></div><div><h3>METHODS</h3><div>A mixed-methods approach was employed: semi-structured interviews with 49 farmers and logistic regression models using data from the 2020 French agricultural census. Two regions with a high prevalence of the three production systems - Pays de la Loire and Brittany- were selected and compared to the national level.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Qualitative analysis identified key motives such as improved productivity, reduced workload, and environmental sustainability. Quantitative results showed that adoption was more likely among larger farms, and farmers involved in collaborative networks and collectives that facilitate resource sharing, participation in expert groups, training, and knowledge exchange, as well as among farmers with higher education levels. Conversely, smaller farms and those engaged in short supply chains faced greater barriers, including high costs, technological complexity, and limited internet access. However, in certain cases—such as vegetable farming—adoption requires higher levels of education and advanced technical and digital skills, particularly for decision-support and automation tools where precision and responsiveness are critical. In contrast, in livestock sectors such as pig and beef production, automation tools are often adopted by older and less-educated farmers as a response to labor shortages, primarily to reduce drudgery and automate repetitive, low-value tasks rather than to transform management practices. The study underscores the need for differentiated policy strategies to support equitable digital transitions across farm types.</div></div><div><h3>SIGNIFICANCE</h3><div>The findings provide actionable insights for policymakers seeking to foster an inclusive and sustainable digital transition, which requires differentiated policy responses. Small farms, particularly those in vegetable production, need target financial and technical support to overcome cost-related and technological barriers. Beef and pig farms face structural and infrastructural constraints, underscoring the importance of broadband investment in rural areas.</div><div>The factors influencing the adoption of digital tools are highly context-dependent, varying across production sectors and tool types.","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104547"},"PeriodicalIF":6.1,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.agsy.2025.104541
Isabel Pinheiro , Pedro Moura , Leandro Rodrigues , Abílio Pereira Pacheco , Jorge Teixeira , António Valente , Mário Cunha , Filipe Neves dos Santos
In 2023, global kiwifruit production reached over 4.4 million tonnes, highlighting the crop’s significant economic importance. However, achieving high yields depends on adequate pollination. In Actinidia species, pollen is transferred by insects from male to female flowers on separate plants. Natural pollination faces increasing challenges due to the decline in pollinator populations and climate variability, driving the adoption of assisted pollination methods. This study examines the Portuguese kiwifruit sector, one of the world’s top 12 producers, using a novel mixed-methods approach that integrates both qualitative and quantitative analyses to assess the feasibility of robotic pollination. The qualitative study identifies the benefits and challenges of current methods and explores how robotic pollination could address these challenges. The quantitative analysis explores the cost-effectiveness and practicality of implementing robotic pollination as a product and service. Findings indicate that most farmers use handheld pollination devices but face pollen wastage and application timing challenges. Economic analysis establishes a break-even point of €685 per hectare for an annual single application, with a first robotic pollination of €17 146 becoming cost-effective for orchards of at least 3.5 hectares and a second robotic solution of €34 293 becoming cost-effective for orchards up to 7 hectares. A robotic pollination service priced at €685 per hectare per application presents a low-risk and a viable alternative for growers. This study provides robust economic insights supporting the adoption of robotic pollination technologies. This study is crucial to make informed decisions to enhance kiwifruit production’s productivity and sustainability through precise robotic-assisted pollination.
{"title":"Economic benchmarking of assisted pollination methods for kiwifruit flowers: Assessment of cost-effectiveness of robotic solution","authors":"Isabel Pinheiro , Pedro Moura , Leandro Rodrigues , Abílio Pereira Pacheco , Jorge Teixeira , António Valente , Mário Cunha , Filipe Neves dos Santos","doi":"10.1016/j.agsy.2025.104541","DOIUrl":"10.1016/j.agsy.2025.104541","url":null,"abstract":"<div><div>In 2023, global kiwifruit production reached over 4.4 million tonnes, highlighting the crop’s significant economic importance. However, achieving high yields depends on adequate pollination. In Actinidia species, pollen is transferred by insects from male to female flowers on separate plants. Natural pollination faces increasing challenges due to the decline in pollinator populations and climate variability, driving the adoption of assisted pollination methods. This study examines the Portuguese kiwifruit sector, one of the world’s top 12 producers, using a novel mixed-methods approach that integrates both qualitative and quantitative analyses to assess the feasibility of robotic pollination. The qualitative study identifies the benefits and challenges of current methods and explores how robotic pollination could address these challenges. The quantitative analysis explores the cost-effectiveness and practicality of implementing robotic pollination as a product and service. Findings indicate that most farmers use handheld pollination devices but face pollen wastage and application timing challenges. Economic analysis establishes a break-even point of €685 per hectare for an annual single application, with a first robotic pollination of €17 146 becoming cost-effective for orchards of at least 3.5 hectares and a second robotic solution of €34 293 becoming cost-effective for orchards up to 7 hectares. A robotic pollination service priced at €685 per hectare per application presents a low-risk and a viable alternative for growers. This study provides robust economic insights supporting the adoption of robotic pollination technologies. This study is crucial to make informed decisions to enhance kiwifruit production’s productivity and sustainability through precise robotic-assisted pollination.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104541"},"PeriodicalIF":6.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.agsy.2025.104551
Fatemeh Yaghoubi, Mohammad Bannayan
Context
Climate change poses significant challenges to food security, especially in arid and semi-arid regions like Iran. Identifying resilient crops and effective adaptation strategies is crucial to maintaining agricultural productivity.
Objective
This study aims to evaluate the viability of quinoa, a stress-tolerant and nutritionally rich crop, as a climate-resilient alternative in Iran's diverse agro-climatic zones under projected climate change scenarios.
Methods
Ten CMIP6 global climate models (GCMs) were assessed for their performance in simulating historical climate data (1990–2014) across 25 sites. Seven high-skill models were selected and downscaled under SSP2–4.5 and SSP5–8.5 pathways to project future climate in Iran through 2100. The CROPGRO-quinoa model simulated yield responses with and without elevated CO₂. The effect of planting date adjustment as an adaptation measure was also analyzed.
Results and conclusions
The CROPGRO-quinoa accurately simulated yields (R2 = 0.95; NSE = 0.94) under existing weather in Iran. Without CO₂ enrichment, quinoa yields declined on average by 22.6 % (SSP2–4.5) and 19.8 % (SSP5–8.5) during 2026–2050, though reductions eased over time relative to the 1990–2014 baseline. Accounting for CO₂ effects alleviated yield losses, with a potential average gain of 4.7 % under SSP5–8.5 in 2076–2100. Optimizing planting dates improved yields across most zones, demonstrating its value as a practical adaptation measure.
Significance
This research supports quinoa as a promising crop for climate adaptation in dryland agriculture. It offers actionable insights for policymakers and practitioners aiming to enhance agricultural resilience and implement climate-smart strategies in similar vulnerable regions.
{"title":"Toward climate-resilient agriculture in Iran: Modeling quinoa viability under future climate scenarios","authors":"Fatemeh Yaghoubi, Mohammad Bannayan","doi":"10.1016/j.agsy.2025.104551","DOIUrl":"10.1016/j.agsy.2025.104551","url":null,"abstract":"<div><h3>Context</h3><div>Climate change poses significant challenges to food security, especially in arid and semi-arid regions like Iran. Identifying resilient crops and effective adaptation strategies is crucial to maintaining agricultural productivity.</div></div><div><h3>Objective</h3><div>This study aims to evaluate the viability of quinoa, a stress-tolerant and nutritionally rich crop, as a climate-resilient alternative in Iran's diverse agro-climatic zones under projected climate change scenarios.</div></div><div><h3>Methods</h3><div>Ten CMIP6 global climate models (GCMs) were assessed for their performance in simulating historical climate data (1990–2014) across 25 sites. Seven high-skill models were selected and downscaled under SSP2–4.5 and SSP5–8.5 pathways to project future climate in Iran through 2100. The CROPGRO-quinoa model simulated yield responses with and without elevated CO₂. The effect of planting date adjustment as an adaptation measure was also analyzed.</div></div><div><h3>Results and conclusions</h3><div>The CROPGRO-quinoa accurately simulated yields (R<sup>2</sup> = 0.95; NSE = 0.94) under existing weather in Iran. Without CO₂ enrichment, quinoa yields declined on average by 22.6 % (SSP2–4.5) and 19.8 % (SSP5–8.5) during 2026–2050, though reductions eased over time relative to the 1990–2014 baseline. Accounting for CO₂ effects alleviated yield losses, with a potential average gain of 4.7 % under SSP5–8.5 in 2076–2100. Optimizing planting dates improved yields across most zones, demonstrating its value as a practical adaptation measure.</div></div><div><h3>Significance</h3><div>This research supports quinoa as a promising crop for climate adaptation in dryland agriculture. It offers actionable insights for policymakers and practitioners aiming to enhance agricultural resilience and implement climate-smart strategies in similar vulnerable regions.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104551"},"PeriodicalIF":6.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><h3>CONTEXT</h3><div>Reducing greenhouse gas (GHG) emissions from rice fields involves complex decision-making processes that require evaluating multiple conflicting criteria. Multi-criteria decision making (MCDM) provides a structured approach for comparing mitigation strategies, considering diverse parameters and criteria. Quantifying these parameters gives the result in the form of rankings to provide the best-suited mitigation strategy.</div></div><div><h3>OBJECTIVE</h3><div>This study applied different MCDM techniques to rank the best GHG mitigation strategy for methane (CH<sub>4</sub>), and nitrous oxide (N<sub>2</sub>O), based on site-specific soil and plant parameters (criteria) and field emission data from two consecutive seasons from flooded rice paddy systems of Assam, India. It was hypothesized that the MCDM approach could provide a ranking-based, detailed analysis of different site-specific management practices (agrotechnologies) based on select criteria in the study area. This ranking will have the dual objectives of maximizing yield and reducing emissions for sustainable rice cultivation.</div></div><div><h3>METHODS</h3><div>Field experiment on CH<sub>4</sub> and N<sub>2</sub>O emissions was studied under the impact of six climate smart agro-technological treatments, namely farmer's practices (FP), recommended dose of fertilisers (RDF), direct-seeded rice (DSR), intermittent wetting and drying (IWD), methanotroph application (MTH), and ammonium sulphate (AS) management, for two seasonal cropping cycles (Boro and Sali seasons). In our study, six criteria were considered among the six treatments, and a specific weightage was given to them by using six different weight criteria methods (CRITIC, Entropy, MEREC, CILOS, IDOCRIW, and equal weight). These obtained weights were recalculated by IDOCRIW to improve accuracy. The weighted values so obtained were then subjected to rank determination by TOPSIS, EC-TOPSIS, COPRAS, and WSM.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The study found that IWD was ranked the highest (1<sup>st</sup> rank) among the six treatments in terms of overall GHG mitigation and yield efficiency. And this was observed for both the Boro and Sali seasons. The MCDM analysis also validated the experimental data, which also showed IWD having the least CH<sub>4</sub> efflux and maximum yield in both seasons. MCDM took into consideration a variety of causes or factors (criteria) that can affect the outcome. It not only validated the experimental design and work but also provided an understanding of the associated parameters within the treatments and among the treatments.</div></div><div><h3>SIGNIFICANCE</h3><div>The research highlights how MCDM could tackle unique challenges in rice farming, such as balancing yield security with emission cuts and adapting region-specific solutions, ultimately paving the way for sustainable agriculture. Although certain methodological limitations, such as sensitivity to no
{"title":"Seasonal comparison of the impacts of climate-smart agrotechnologies on greenhouse gas mitigation in flooded rice fields: Application of multi-criteria decision making (MCDM) technique","authors":"Manas Protim Rajbonshi , Debaditya Gupta , Sudip Mitra","doi":"10.1016/j.agsy.2025.104550","DOIUrl":"10.1016/j.agsy.2025.104550","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Reducing greenhouse gas (GHG) emissions from rice fields involves complex decision-making processes that require evaluating multiple conflicting criteria. Multi-criteria decision making (MCDM) provides a structured approach for comparing mitigation strategies, considering diverse parameters and criteria. Quantifying these parameters gives the result in the form of rankings to provide the best-suited mitigation strategy.</div></div><div><h3>OBJECTIVE</h3><div>This study applied different MCDM techniques to rank the best GHG mitigation strategy for methane (CH<sub>4</sub>), and nitrous oxide (N<sub>2</sub>O), based on site-specific soil and plant parameters (criteria) and field emission data from two consecutive seasons from flooded rice paddy systems of Assam, India. It was hypothesized that the MCDM approach could provide a ranking-based, detailed analysis of different site-specific management practices (agrotechnologies) based on select criteria in the study area. This ranking will have the dual objectives of maximizing yield and reducing emissions for sustainable rice cultivation.</div></div><div><h3>METHODS</h3><div>Field experiment on CH<sub>4</sub> and N<sub>2</sub>O emissions was studied under the impact of six climate smart agro-technological treatments, namely farmer's practices (FP), recommended dose of fertilisers (RDF), direct-seeded rice (DSR), intermittent wetting and drying (IWD), methanotroph application (MTH), and ammonium sulphate (AS) management, for two seasonal cropping cycles (Boro and Sali seasons). In our study, six criteria were considered among the six treatments, and a specific weightage was given to them by using six different weight criteria methods (CRITIC, Entropy, MEREC, CILOS, IDOCRIW, and equal weight). These obtained weights were recalculated by IDOCRIW to improve accuracy. The weighted values so obtained were then subjected to rank determination by TOPSIS, EC-TOPSIS, COPRAS, and WSM.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The study found that IWD was ranked the highest (1<sup>st</sup> rank) among the six treatments in terms of overall GHG mitigation and yield efficiency. And this was observed for both the Boro and Sali seasons. The MCDM analysis also validated the experimental data, which also showed IWD having the least CH<sub>4</sub> efflux and maximum yield in both seasons. MCDM took into consideration a variety of causes or factors (criteria) that can affect the outcome. It not only validated the experimental design and work but also provided an understanding of the associated parameters within the treatments and among the treatments.</div></div><div><h3>SIGNIFICANCE</h3><div>The research highlights how MCDM could tackle unique challenges in rice farming, such as balancing yield security with emission cuts and adapting region-specific solutions, ultimately paving the way for sustainable agriculture. Although certain methodological limitations, such as sensitivity to no","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104550"},"PeriodicalIF":6.1,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1016/j.agsy.2025.104548
Min Kang , Dongzheng Zhang , Yuan Cao , Liujun Xiao , Liang Tang , Leilei Liu , Weixing Cao , Yan Zhu , Bing Liu
CONTEXT
Global wheat production faces growing threats from climate change, particularly rising temperatures, necessitating region-specific adaptive strategies. In China, a key wheat producer and consumer, these challenges vary by region due to differences in climate, soil, and management practices.
OBJECTIVE
This study aims to evaluate how adaptive strategies—adjusting sowing dates, anthesis dates, and enhancing heat tolerance—can mitigate the adverse impacts of warming on wheat yields across China's diverse wheat-producing subregions.
METHODS
The improved WheatGrow model, incorporating heat stress effects, was used to simulate wheat yield responses under future warming scenarios. Strategies assessed include advancing sowing and anthesis dates and improving heat tolerance, tailored to subregions like Southwestern Winter Wheat Subregion (SWS), Yangtze River Winter Wheat Subregion (MYS), Northern Winter Wheat Subregion (NS), and Huang-Huai Winter Wheat Subregion (HHS).
RESULTS AND CONCLUSIONS
Advancing sowing dates can better mitigate the negative effects of warming in the SWS and MYS. Advancing anthesis date can increase yields in the NS, HHS and MYS, significantly reducing yield losses caused by heat stress. Additionally, improving heat tolerance in wheat cultivars can lead to higher yield improvements in the NS and HHS. Under the three warming scenarios, comprehensive adaptation strategies significantly reduced yield losses in all four subregions. Under the 1.5 °C HAPPI scenario, the total wheat production in China increased by 0.67 % with the optimal comprehensive adaptation strategy.
SIGNIFICANCE
These findings highlight the importance of region-specific adaptations to sustain wheat productivity in China amid climate change, offering actionable insights for policymakers and farmers to enhance food security.
{"title":"Integrative adaptation strategies for stabilizing wheat productivity with rising temperatures in China","authors":"Min Kang , Dongzheng Zhang , Yuan Cao , Liujun Xiao , Liang Tang , Leilei Liu , Weixing Cao , Yan Zhu , Bing Liu","doi":"10.1016/j.agsy.2025.104548","DOIUrl":"10.1016/j.agsy.2025.104548","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Global wheat production faces growing threats from climate change, particularly rising temperatures, necessitating region-specific adaptive strategies. In China, a key wheat producer and consumer, these challenges vary by region due to differences in climate, soil, and management practices.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to evaluate how adaptive strategies—adjusting sowing dates, anthesis dates, and enhancing heat tolerance—can mitigate the adverse impacts of warming on wheat yields across China's diverse wheat-producing subregions.</div></div><div><h3>METHODS</h3><div>The improved WheatGrow model, incorporating heat stress effects, was used to simulate wheat yield responses under future warming scenarios. Strategies assessed include advancing sowing and anthesis dates and improving heat tolerance, tailored to subregions like Southwestern Winter Wheat Subregion (SWS), Yangtze River Winter Wheat Subregion (MYS), Northern Winter Wheat Subregion (NS), and Huang-Huai Winter Wheat Subregion (HHS).</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Advancing sowing dates can better mitigate the negative effects of warming in the SWS and MYS. Advancing anthesis date can increase yields in the NS, HHS and MYS, significantly reducing yield losses caused by heat stress. Additionally, improving heat tolerance in wheat cultivars can lead to higher yield improvements in the NS and HHS. Under the three warming scenarios, comprehensive adaptation strategies significantly reduced yield losses in all four subregions. Under the 1.5 °C HAPPI scenario, the total wheat production in China increased by 0.67 % with the optimal comprehensive adaptation strategy.</div></div><div><h3>SIGNIFICANCE</h3><div>These findings highlight the importance of region-specific adaptations to sustain wheat productivity in China amid climate change, offering actionable insights for policymakers and farmers to enhance food security.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"231 ","pages":"Article 104548"},"PeriodicalIF":6.1,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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