Pub Date : 2026-03-15DOI: 10.1016/j.agsy.2026.104719
Gaëtan Seimandi-Corda, Eric Justes, Benoit Gleizes, Eric Lecloux, Eric Bazerthe, Lionel Alletto
The agroecological transition offer opportunities to reduce agriculture's environmental impacts by reducing reliance on synthetic fertilisers and pesticides. Crop diversification, in both time and space, is a key strategy including extended crop rotations, intercropping, and cover crops. Yet, relationships between reduced input use and associated environmental impacts remain insufficiently quantified.
{"title":"Cover crops and intercropping help reduce nitrate and pesticide leaching in low-input systems","authors":"Gaëtan Seimandi-Corda, Eric Justes, Benoit Gleizes, Eric Lecloux, Eric Bazerthe, Lionel Alletto","doi":"10.1016/j.agsy.2026.104719","DOIUrl":"https://doi.org/10.1016/j.agsy.2026.104719","url":null,"abstract":"The agroecological transition offer opportunities to reduce agriculture's environmental impacts by reducing reliance on synthetic fertilisers and pesticides. Crop diversification, in both time and space, is a key strategy including extended crop rotations, intercropping, and cover crops. Yet, relationships between reduced input use and associated environmental impacts remain insufficiently quantified.","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"213 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465289","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 : 2026-03-15DOI: 10.1016/j.agsy.2026.104702
Youchao Shi, Xiaobin Jin, Xinyuan Liang, Bo Han, Yinkang Zhou
Rapid urbanization and dietary transitions are continuously intensifying food demand in emerging economies, raising urgent concerns about the long-term sustainability of agricultural systems. If land degradation risks are overlooked, future national food security targets may become difficult to achieve due to biophysical constraints.
{"title":"Future food demand and agricultural land degradation in emerging economies: A spatial systems diagnosis from BRICS-plus","authors":"Youchao Shi, Xiaobin Jin, Xinyuan Liang, Bo Han, Yinkang Zhou","doi":"10.1016/j.agsy.2026.104702","DOIUrl":"https://doi.org/10.1016/j.agsy.2026.104702","url":null,"abstract":"Rapid urbanization and dietary transitions are continuously intensifying food demand in emerging economies, raising urgent concerns about the long-term sustainability of agricultural systems. If land degradation risks are overlooked, future national food security targets may become difficult to achieve due to biophysical constraints.","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"24 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465337","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 : 2026-03-01Epub Date: 2026-01-15DOI: 10.1016/j.agsy.2026.104640
Dan Liu , Jianjun Jin , Zanlu Zou , Jie Yang , Chenyang Zhang
CONTEXT
The decoupling of crop and livestock production has intensified resource inefficiencies and environmental pressures in smallholder farming systems. Opposing this trend, crop–livestock cooperation at farm, community, and regional levels is increasingly promoted as a pathway toward sustainable agriculture. However, despite its expansion, the strength of such integration remains difficult to measure due to the absence of reliable and scalable indicators.
OBJECTIVE
This study aims to develop a spatially weighted Crop–Livestock Integration Index (CLII) and evaluate its green transition implications in herbivorous livestock systems in northwest China.
METHODS
CLII was put in relation with a composite green transition index resulting from the aggregation of normalized greenhouse gas (GHG) emission and feed cost efficiency. Life cycle assessment (LCA) was employed to calculate GHG emissions from six key production stages. Feed cost efficiency and the composite green transition performance index were also evaluated.
RESULTS AND CONCLUSIONS
The results for beef cattle households in Yuanzhou, Ningxia showed that CLII values ranged from 0 to 1, reflecting high variability across households. The average CLII was only 0.36, indicating a generally low level of integration, particularly in Yanglang Village where the mean value was 0.09. Higher CLII was associated with lower purchased feed costs and reduced GHG emissions from enteric fermentation, manure management, composting and transport. A regression analysis confirmed that CLII significantly improved green transition performance at the household level.
SIGNIFICANCE
The CLII method offers a spatially sensitive, data-efficient tool for measuring integration intensity and sustainability outcomes. It enables evidence-based policy design for promoting regionally adapted, low-carbon livestock development pathways.
{"title":"Quantifying crop-livestock integration for the green transition of herbivorous livestock farmers: Development of a novel index and its application in Northwest China","authors":"Dan Liu , Jianjun Jin , Zanlu Zou , Jie Yang , Chenyang Zhang","doi":"10.1016/j.agsy.2026.104640","DOIUrl":"10.1016/j.agsy.2026.104640","url":null,"abstract":"<div><h3>CONTEXT</h3><div>The decoupling of crop and livestock production has intensified resource inefficiencies and environmental pressures in smallholder farming systems. Opposing this trend, crop–livestock cooperation at farm, community, and regional levels is increasingly promoted as a pathway toward sustainable agriculture. However, despite its expansion, the strength of such integration remains difficult to measure due to the absence of reliable and scalable indicators.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to develop a spatially weighted Crop–Livestock Integration Index (CLII) and evaluate its green transition implications in herbivorous livestock systems in northwest China.</div></div><div><h3>METHODS</h3><div>CLII was put in relation with a composite green transition index resulting from the aggregation of normalized greenhouse gas (GHG) emission and feed cost efficiency. Life cycle assessment (LCA) was employed to calculate GHG emissions from six key production stages. Feed cost efficiency and the composite green transition performance index were also evaluated.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The results for beef cattle households in Yuanzhou, Ningxia showed that CLII values ranged from 0 to 1, reflecting high variability across households. The average CLII was only 0.36, indicating a generally low level of integration, particularly in Yanglang Village where the mean value was 0.09. Higher CLII was associated with lower purchased feed costs and reduced GHG emissions from enteric fermentation, manure management, composting and transport. A regression analysis confirmed that CLII significantly improved green transition performance at the household level.</div></div><div><h3>SIGNIFICANCE</h3><div>The CLII method offers a spatially sensitive, data-efficient tool for measuring integration intensity and sustainability outcomes. It enables evidence-based policy design for promoting regionally adapted, low-carbon livestock development pathways.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104640"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972977","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}
Land degradation in red and lateritic soils of India, particularly in the northeast, poses a serious threat to agroecological stability, agricultural productivity, soil health, and rural livelihoods. Agroforestry is increasingly recognized as a sustainable approach for restoring degraded ecosystems, rejuvenating soil health, and improving farmers' livelihoods, yet region-specific empirical evidence remains limited.
OBJECTIVE
This study aimed to assess the long-term ecological and economic viability of various agroforestry systems for rehabilitating degraded land and enhancing the delivery of multiple ecosystem services in red and lateritic soils of Northeast India.
METHODS
A decade-long agroforestry field experiment (2014–2024) with silvi species Gmelina (Gmelina arborea Roxb), fruit plant sweet orange (Citrus sinensis L. Osbeck), and grain legume pigeon pea (Cajanus cajan L. Millsp) under monoculture and integrated agroforestry system was conducted in West Bengal in eastern India. Seven systems (monoculture and agroforestry-based) were evaluated using eleven biophysical and economic indicators, including biomass recycling, soil organic carbon, enzyme activity, microbial resilience, net margin, and greenhouse gas (GHG) emissions.
RESULTS AND CONCLUSION
The tri-component agroforestry system (Gmelina–sweet orange–pigeon pea) showed the highest multifunctionality index, producing 7.26 t ha−1 yr−1 of recyclable biomass, and significantly improving soil carbon, dehydrogenase activity, water-holding capacity, and biodiversity. Economically, this system outperformed monocultures with 2–3 times higher net margin and energy efficiency. Although associated with higher GHG emission, this system offered net environmental benefits through enhanced carbon sequestration and resilience.
SIGNIFICANCE
This study demonstrates that the locally adapted agroforestry systems have potential to restore degraded red and lateritic soils while delivering broad ecosystem services and improving farmers' livelihoods. These results support the scaling of such systems across similar agroecological zones in India and globally.
背景印度红土和红土的土地退化，特别是在东北部，对农业生态稳定性、农业生产力、土壤健康和农村生计构成严重威胁。农林业越来越被认为是恢复退化生态系统、恢复土壤健康和改善农民生计的可持续方法，但具体区域的经验证据仍然有限。本研究旨在评估各种农林业系统在恢复印度东北部红土和红土退化土地和增强多种生态系统服务提供方面的长期生态和经济可行性。方法2014-2024年，在印度东部西孟加拉邦进行了为期10年的农林业田间试验，试验采用单栽培-复合农林业系统，采用银银种Gmelina （Gmelina arborea Roxb）、果实植物甜橙（Citrus sinensis L. Osbeck）和籽粒豆科木豆（Cajanus cajan L. Millsp）。采用11个生物物理和经济指标对7个系统（以单一栽培和农林业为基础）进行了评估，包括生物质循环、土壤有机碳、酶活性、微生物恢复力、净边际和温室气体（GHG）排放。结果与结论三组分复合农林业系统（绿麦草-甜橙-鸽豆）的多功能性指数最高，可回收生物量为7.26 t ha - 1 yr - 1，显著提高了土壤碳、脱氢酶活性、持水能力和生物多样性。从经济上讲，该系统的净利润率和能源效率比单一栽培高2-3倍。虽然与较高的温室气体排放有关，但该系统通过增强碳固存和恢复力提供了净环境效益。本研究表明，适应当地的农林业系统具有恢复退化的红土和红土的潜力，同时提供广泛的生态系统服务并改善农民的生计。这些结果支持在印度和全球类似的农业生态区扩大这种系统的规模。
{"title":"Rehabilitating fragile ecosystems through agroforestry in red and lateritic soils: A multi-criteria systems perspective","authors":"Benukar Biswas , Debashis Chakraborty , Jagadish Timsina , Anandkumar Naorem , Mousumi Mondal , Sahely Kanthal , Saju Adhikary , Udayan Rudra Bhowmick , Pushpendu Sardar , Mallika Koley , Sk Moinuddin , Ashutosh Kumar , Kiranmay Patra , Trisha Manna , Arindam Sarkar , Kalyan Jana , Sanjib Kumar Das , Bikash Ranjan Ray","doi":"10.1016/j.agsy.2025.104597","DOIUrl":"10.1016/j.agsy.2025.104597","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Land degradation in red and lateritic soils of India, particularly in the northeast, poses a serious threat to agroecological stability, agricultural productivity, soil health, and rural livelihoods. Agroforestry is increasingly recognized as a sustainable approach for restoring degraded ecosystems, rejuvenating soil health, and improving farmers' livelihoods, yet region-specific empirical evidence remains limited.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to assess the long-term ecological and economic viability of various agroforestry systems for rehabilitating degraded land and enhancing the delivery of multiple ecosystem services in red and lateritic soils of Northeast India.</div></div><div><h3>METHODS</h3><div>A decade-long agroforestry field experiment (2014–2024) with silvi species Gmelina (<em>Gmelina arborea</em> Roxb), fruit plant sweet orange (<em>Citrus sinensis</em> L. Osbeck), and grain legume pigeon pea (<em>Cajanus cajan</em> L. Millsp) under monoculture and integrated agroforestry system was conducted in West Bengal in eastern India. Seven systems (monoculture and agroforestry-based) were evaluated using eleven biophysical and economic indicators, including biomass recycling, soil organic carbon, enzyme activity, microbial resilience, net margin, and greenhouse gas (GHG) emissions.</div></div><div><h3>RESULTS AND CONCLUSION</h3><div>The tri-component agroforestry system (Gmelina–sweet orange–pigeon pea) showed the highest multifunctionality index, producing 7.26 t ha<sup>−1</sup> yr<sup>−1</sup> of recyclable biomass, and significantly improving soil carbon, dehydrogenase activity, water-holding capacity, and biodiversity. Economically, this system outperformed monocultures with 2–3 times higher net margin and energy efficiency. Although associated with higher GHG emission, this system offered net environmental benefits through enhanced carbon sequestration and resilience.</div></div><div><h3>SIGNIFICANCE</h3><div>This study demonstrates that the locally adapted agroforestry systems have potential to restore degraded red and lateritic soils while delivering broad ecosystem services and improving farmers' livelihoods. These results support the scaling of such systems across similar agroecological zones in India and globally.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104597"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734210","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 : 2026-03-01Epub Date: 2026-01-10DOI: 10.1016/j.agsy.2026.104633
Jaap Sok , Tom Kisters , Argyris Kanellopoulos
CONTEXT
Although nature-inclusive agriculture (NIA) has gained attention in Dutch policy, its adoption in dairy systems remains limited due to economic trade-offs. This challenge arises against a backdrop of farm intensification and increasingly stringent environmental regulations that restrict further expansion.
OBJECTIVE
This paper quantifies the economic trade-offs of adopting NIA practices by estimating their opportunity costs. Assessing the variability in opportunity costs among farms is also policy-relevant, as it highlights which farms face higher economic barriers and informs where targeted support or compensation may be necessary.
METHODS
We employed an empirical bio-economic model with 407 predefined farm plans from the 2018 Dutch Farm Accountancy Data Network (FADN). For each farm, we determined the income-maximizing farm plan and then used the shadow price from a re-optimized farm plan as a proxy for the opportunity cost of each NIA practice. To assess how structural and policy-related factors shape these costs, we examined four scenarios representing different levels of managerial flexibility.
RESULTS AND CONCLUSION
Opportunity costs of NIA adoption vary substantially across farms and practices. More extensive strategies can achieve higher levels of adoption but often come with higher costs. Sunk costs and capital lock-in significantly limit farmers' flexibility to adapt their systems. Long-term adoption of NIA practices requires not only financial compensation but also attention to structural, institutional, and behavioural factors.
SIGNIFICANCE
This study provides a basis for designing more efficient, differentiated, and farm-specific compensation schemes. It also highlights the need for broader public support to fairly reward farmers for providing both agricultural goods and ecosystem services. The findings offers new insights to inform policy design for balancing agricultural productivity and environmental sustainability in European farming systems.
{"title":"Quantifying the opportunity costs of nature-inclusive agriculture in the Netherlands","authors":"Jaap Sok , Tom Kisters , Argyris Kanellopoulos","doi":"10.1016/j.agsy.2026.104633","DOIUrl":"10.1016/j.agsy.2026.104633","url":null,"abstract":"<div><h3><em>CONTEXT</em></h3><div>Although nature-inclusive agriculture (NIA) has gained attention in Dutch policy, its adoption in dairy systems remains limited due to economic trade-offs. This challenge arises against a backdrop of farm intensification and increasingly stringent environmental regulations that restrict further expansion.</div></div><div><h3><em>OBJECTIVE</em></h3><div>This paper quantifies the economic trade-offs of adopting NIA practices by estimating their opportunity costs. Assessing the variability in opportunity costs among farms is also policy-relevant, as it highlights which farms face higher economic barriers and informs where targeted support or compensation may be necessary.</div></div><div><h3><em>METHODS</em></h3><div>We employed an empirical bio-economic model with 407 predefined farm plans from the 2018 Dutch Farm Accountancy Data Network (FADN). For each farm, we determined the income-maximizing farm plan and then used the shadow price from a re-optimized farm plan as a proxy for the opportunity cost of each NIA practice. To assess how structural and policy-related factors shape these costs, we examined four scenarios representing different levels of managerial flexibility.</div></div><div><h3><em>RESULTS AND CONCLUSION</em></h3><div>Opportunity costs of NIA adoption vary substantially across farms and practices. More extensive strategies can achieve higher levels of adoption but often come with higher costs. Sunk costs and capital lock-in significantly limit farmers' flexibility to adapt their systems. Long-term adoption of NIA practices requires not only financial compensation but also attention to structural, institutional, and behavioural factors.</div></div><div><h3><em>SIGNIFICANCE</em></h3><div>This study provides a basis for designing more efficient, differentiated, and farm-specific compensation schemes. It also highlights the need for broader public support to fairly reward farmers for providing both agricultural goods and ecosystem services. The findings offers new insights to inform policy design for balancing agricultural productivity and environmental sustainability in European farming systems.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104633"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956819","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 : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.agsy.2026.104658
Dakuan Qiao , Yitian Jin , Yi Luo , Junfeng Zhu
Imbalanced fertilization not only affects yield but is a critical, yet overlooked, factor contributing to grain losses. This study utilizes microdata from 2541 farm households across 24 provinces in China to empirically analyze the nonlinear, threshold, and moderation effects, as well as the heterogeneity, of balanced fertilization (BF) on the combine harvesting loss rate of maize (CHLRM). Research findings demonstrate that the average CHLRM in China is 2.575%. The impact of BF on CHLRM is not linear; rather, it exhibits a significant and robust “U-shaped” nonlinear effect, which remains significant after addressing endogeneity. Marginal effect analysis indicates that the average marginal effects of BFI and BFI2 on CHLRM are −0.402 and 0.131, respectively. However, this U-shaped effect is contingent upon planting scale and agricultural income. The effect is significant for households with planting scale below 24.34 mu and agricultural income shares below 40%. Additional analysis reveals that the adoption of agricultural digital technology significantly amplifies BF's U-shaped loss-reduction effect. Heterogeneity analysis indicates that the loss-reduction effect of BF is more stable in major grain-producing regions. However, this effect exhibits structural variation: BF demonstrates stronger loss reduction at high loss quantiles but becomes insignificant for farmers already at the lowest loss quantile (Q0.10). Therefore, policies should shift from singular fertilizer reduction targets to promoting precision balancing. This requires implementing tailored guidance: BFI technology should be prioritized for small-to-medium-scale and low-income farm households, while large scale and professional farmers should be supported in adopting capital substitution pathways such as advanced machinery.
{"title":"Does balanced fertilization reduce combine harvesting loss of maize: Evidence from 24 provinces in China","authors":"Dakuan Qiao , Yitian Jin , Yi Luo , Junfeng Zhu","doi":"10.1016/j.agsy.2026.104658","DOIUrl":"10.1016/j.agsy.2026.104658","url":null,"abstract":"<div><div>Imbalanced fertilization not only affects yield but is a critical, yet overlooked, factor contributing to grain losses. This study utilizes microdata from 2541 farm households across 24 provinces in China to empirically analyze the nonlinear, threshold, and moderation effects, as well as the heterogeneity, of balanced fertilization (BF) on the combine harvesting loss rate of maize (CHLRM). Research findings demonstrate that the average CHLRM in China is 2.575%. The impact of BF on CHLRM is not linear; rather, it exhibits a significant and robust “U-shaped” nonlinear effect, which remains significant after addressing endogeneity. Marginal effect analysis indicates that the average marginal effects of BFI and BFI<sup>2</sup> on CHLRM are −0.402 and 0.131, respectively. However, this U-shaped effect is contingent upon planting scale and agricultural income. The effect is significant for households with planting scale below 24.34 mu and agricultural income shares below 40%. Additional analysis reveals that the adoption of agricultural digital technology significantly amplifies BF's U-shaped loss-reduction effect. Heterogeneity analysis indicates that the loss-reduction effect of BF is more stable in major grain-producing regions. However, this effect exhibits structural variation: BF demonstrates stronger loss reduction at high loss quantiles but becomes insignificant for farmers already at the lowest loss quantile (Q0.10). Therefore, policies should shift from singular fertilizer reduction targets to promoting precision balancing. This requires implementing tailored guidance: BFI technology should be prioritized for small-to-medium-scale and low-income farm households, while large scale and professional farmers should be supported in adopting capital substitution pathways such as advanced machinery.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104658"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110560","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 : 2026-03-01Epub Date: 2026-01-13DOI: 10.1016/j.agsy.2026.104636
Junjie Qiu , Yuekai Hu , Dailiang Peng , Haijian Liu , Weichun Tao , Bin Xie , Tangao Hu , Jiake Wang , Xiao Liang , Tao Chen , Junfeng Xu
Sunflower is a critical oilseed crop that underpins global food security. As the world's largest exporter of sunflower oil, Ukraine produced 6.89 million tons in 2021, accounting for approximately one-third of global production. However, the ongoing Russia–Ukraine conflict has severely disrupted the local agricultural system, and the specific impacts on sunflower production dynamics remain unclear. To address this, we constructed a comprehensive monitoring framework by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. First, we mapped annual sunflower cultivation distributions from 2019 to 2023 using an automated sample extraction method coupled with a Random Forest model, achieving an overall classification accuracy of 94.35%. Second, we implemented grid-based production prediction to capture fine-scale agricultural productivity heterogeneity. The results reveal a 49.5% decline in sunflower cultivation area between 2021 and 2022, accompanied by severe landscape fragmentation. Notably, the loss pattern exhibited a distinct “strip-like” distribution along the conflict frontline. Regarding production, while total output collapsed, the trend in unit yields diverged from the drastic reduction in cultivated areas, suggesting a potential shift in the agricultural production mode toward concentration. Finally, analysis based on multi-source indicators confirmed that farmland destruction, personnel loss, and water source damage were the drivers of agricultural decline in the region. These findings highlight the high vulnerability of agricultural systems under armed conflict and provide critical insights for post-conflict agricultural recovery and sustainable land use policymaking.
{"title":"Analyzing the disruption of agricultural systems by conflict: A case study of sunflower production in eastern Ukraine","authors":"Junjie Qiu , Yuekai Hu , Dailiang Peng , Haijian Liu , Weichun Tao , Bin Xie , Tangao Hu , Jiake Wang , Xiao Liang , Tao Chen , Junfeng Xu","doi":"10.1016/j.agsy.2026.104636","DOIUrl":"10.1016/j.agsy.2026.104636","url":null,"abstract":"<div><div>Sunflower is a critical oilseed crop that underpins global food security. As the world's largest exporter of sunflower oil, Ukraine produced 6.89 million tons in 2021, accounting for approximately one-third of global production. However, the ongoing Russia–Ukraine conflict has severely disrupted the local agricultural system, and the specific impacts on sunflower production dynamics remain unclear. To address this, we constructed a comprehensive monitoring framework by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. First, we mapped annual sunflower cultivation distributions from 2019 to 2023 using an automated sample extraction method coupled with a Random Forest model, achieving an overall classification accuracy of 94.35%. Second, we implemented grid-based production prediction to capture fine-scale agricultural productivity heterogeneity. The results reveal a 49.5% decline in sunflower cultivation area between 2021 and 2022, accompanied by severe landscape fragmentation. Notably, the loss pattern exhibited a distinct “strip-like” distribution along the conflict frontline. Regarding production, while total output collapsed, the trend in unit yields diverged from the drastic reduction in cultivated areas, suggesting a potential shift in the agricultural production mode toward concentration. Finally, analysis based on multi-source indicators confirmed that farmland destruction, personnel loss, and water source damage were the drivers of agricultural decline in the region. These findings highlight the high vulnerability of agricultural systems under armed conflict and provide critical insights for post-conflict agricultural recovery and sustainable land use policymaking.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104636"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962098","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 : 2026-03-01Epub Date: 2026-01-22DOI: 10.1016/j.agsy.2026.104639
Lanie A. Alejo , Orlando F. Balderama , Elmer A. Rosete , Juan M. Pulhin
CONTEXT
Climate change is altering temperature and rainfall patterns, threatening agricultural productivity in tropical countries like the Philippines. Isabela Province, a major rice and corn producing region, is highly exposed to these risks. Estimating future yield responses and identifying adaptation options are essential for ensuring food security.
OBJECTIVE
This study aimed to assess the impacts of climate change on rice and corn yields in Isabela by 2050 under three Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5). It also evaluated whether adjusting planting dates and applying supplemental irrigation could reduce potential yield losses.
METHODS
Simulations were conducted using the DSSAT crop simulation model for upland rice, lowland rice, and rainfed corn. Mid-century climate data were sourced from DOST-PAGASA under CMIP6 scenarios. Simulations covered dry, normal, and wet years across planting seasons. Weekly planting runs identified optimal sowing dates, while additional runs evaluated irrigation effects. Crop genetic coefficients were based on previously calibrated and validated Philippine crop simulation studies using the DSSAT model.
RESULTS AND CONCLUSIONS
Yield reductions were observed under all climate scenarios, particularly in lowland rice and rainfed corn during the dry season. CO₂ fertilization helped mitigate losses in upland systems. Retrofitting planting calendars improved yields by up to 150% in upland rice, 42% in lowland rice, and 82% in rainfed corn. When irrigation was added, yield gains increased further by up to 229%, 118%, and 120%, respectively. Dry years showed the highest improvements. Adjusting planting schedules and adding irrigation are effective, climate-smart strategies to boost yield and resilience. These measures can help reduce yield losses and support food security planning in climate-vulnerable regions. The findings provide practical insights for local adaptation and agricultural policy development.
SIGNIFICANCE
This study highlights the potential of climate-informed planting calendars and targeted irrigation as low-cost, high-impact adaptation strategies. These approaches can enhance the resilience of rice and corn systems and support climate-smart agricultural planning.
{"title":"Adaptive Crop Management Strategies to Mitigate Climate Change Impacts on Rice and Maize Production in the Philippines","authors":"Lanie A. Alejo , Orlando F. Balderama , Elmer A. Rosete , Juan M. Pulhin","doi":"10.1016/j.agsy.2026.104639","DOIUrl":"10.1016/j.agsy.2026.104639","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Climate change is altering temperature and rainfall patterns, threatening agricultural productivity in tropical countries like the Philippines. Isabela Province, a major rice and corn producing region, is highly exposed to these risks. Estimating future yield responses and identifying adaptation options are essential for ensuring food security.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to assess the impacts of climate change on rice and corn yields in Isabela by 2050 under three Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5). It also evaluated whether adjusting planting dates and applying supplemental irrigation could reduce potential yield losses.</div></div><div><h3>METHODS</h3><div>Simulations were conducted using the DSSAT crop simulation model for upland rice, lowland rice, and rainfed corn. Mid-century climate data were sourced from DOST-PAGASA under CMIP6 scenarios. Simulations covered dry, normal, and wet years across planting seasons. Weekly planting runs identified optimal sowing dates, while additional runs evaluated irrigation effects. Crop genetic coefficients were based on previously calibrated and validated Philippine crop simulation studies using the DSSAT model.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Yield reductions were observed under all climate scenarios, particularly in lowland rice and rainfed corn during the dry season. CO₂ fertilization helped mitigate losses in upland systems. Retrofitting planting calendars improved yields by up to 150% in upland rice, 42% in lowland rice, and 82% in rainfed corn. When irrigation was added, yield gains increased further by up to 229%, 118%, and 120%, respectively. Dry years showed the highest improvements. Adjusting planting schedules and adding irrigation are effective, climate-smart strategies to boost yield and resilience. These measures can help reduce yield losses and support food security planning in climate-vulnerable regions. The findings provide practical insights for local adaptation and agricultural policy development.</div></div><div><h3>SIGNIFICANCE</h3><div>This study highlights the potential of climate-informed planting calendars and targeted irrigation as low-cost, high-impact adaptation strategies. These approaches can enhance the resilience of rice and corn systems and support climate-smart agricultural planning.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104639"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033223","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 : 2026-03-01Epub Date: 2025-12-22DOI: 10.1016/j.agsy.2025.104624
Kalyan Roy , Marta Monjardino , Mohammed Mainuddin , Sukamal Sarkar , Krishnendu Ray , Poulami Sen , Srijan Samanta , Akash Panda , Sanchayeeta Misra , Argha Ghosh , Esmat Ara Begum , Rupak Goswami
Context
Sustainable Intensification (SI) aims to boost smallholder productivity while conserving natural resources. However, farm-level heterogeneity often limits equitable access to SI benefits. While most typology studies rely on quantitative methods, few use integrated mixed methods to develop scalable typologies from small samples, especially in fragile agroecosystems.
Objectives
This study aimed to: a) classify heterogenous farms using participatory and statistical methods; b) construct a flexible decision support tree to scale out farm typologies to locations beyond initial study area; and c) examine the validity and usefulness of the scalable farm types through stakeholder engagement.
Methods
A mixed-methods design was used. First, Focus Group Discussions in four villages used a participatory card-sorting exercise where farmers classified 202 beneficiary households by eight visualized parameters (cropping pattern, landholding, off-farm income, etc.). The resulting farmer-defined groups formed the basis for respondent sampling in the questionnaire survey. Quantitative data from survey were subjected to Principal Component Analysis and Cluster Analysis to identify statistical farm types. Farm types were characterized using the household data and five-year recall data on changing livelihoods trends. For wider application, a Classification and Regression Tree (CRT) analysis was performed to generate a decision tree, using the identified farm types as target variable The tree was refined and successfully applied in new locations, with validation from local experts. Yield differences of SI technology (Zero-Tillage Potato) across farm types were compared between expert and empirical classifications.
Results and conclusions
Statistical analysis identified five dynamic farm types, including a distinct landless group, ranging from resource-rich to resource-poor. The CRT classified seven types with 86.6 % accuracy using binary splits on landholding, livestock, income, irrigation, and crop diversity, with additional branches for unique configurations. Expert validation showed strong concordance. Field testing revealed yield differences across farm types, aligning with expert classifications. The typology illustrates a progression from low-input systems to diversified, resource-rich farms integrating crops, livestock, fisheries, and innovations in water, nutrients, mechanization, and markets.
Significance
The study demonstrates the utility of typologies not only for classification but also for effectively targeting SI interventions. This scalable, context-sensitive framework supports innovation upscaling in heterogeneous agroecosystems, especially where longitudinal data are unavailable.
{"title":"Developing scalable farm typologies to guide sustainable intensification in the fragile agroecosystems of the Indian Sundarbans","authors":"Kalyan Roy , Marta Monjardino , Mohammed Mainuddin , Sukamal Sarkar , Krishnendu Ray , Poulami Sen , Srijan Samanta , Akash Panda , Sanchayeeta Misra , Argha Ghosh , Esmat Ara Begum , Rupak Goswami","doi":"10.1016/j.agsy.2025.104624","DOIUrl":"10.1016/j.agsy.2025.104624","url":null,"abstract":"<div><h3>Context</h3><div>Sustainable Intensification (SI) aims to boost smallholder productivity while conserving natural resources. However, farm-level heterogeneity often limits equitable access to SI benefits. While most typology studies rely on quantitative methods, few use integrated mixed methods to develop scalable typologies from small samples, especially in fragile agroecosystems.</div></div><div><h3>Objectives</h3><div>This study aimed to: a) classify heterogenous farms using participatory and statistical methods; b) construct a flexible decision support tree to scale out farm typologies to locations beyond initial study area; and c) examine the validity and usefulness of the scalable farm types through stakeholder engagement.</div></div><div><h3>Methods</h3><div>A mixed-methods design was used. First, Focus Group Discussions in four villages used a participatory card-sorting exercise where farmers classified 202 beneficiary households by eight visualized parameters (cropping pattern, landholding, off-farm income, etc.). The resulting farmer-defined groups formed the basis for respondent sampling in the questionnaire survey. Quantitative data from survey were subjected to Principal Component Analysis and Cluster Analysis to identify statistical farm types. Farm types were characterized using the household data and five-year recall data on changing livelihoods trends. For wider application, a Classification and Regression Tree (CRT) analysis was performed to generate a decision tree, using the identified farm types as target variable The tree was refined and successfully applied in new locations, with validation from local experts. Yield differences of SI technology (Zero-Tillage Potato) across farm types were compared between expert and empirical classifications.</div></div><div><h3>Results and conclusions</h3><div>Statistical analysis identified five dynamic farm types, including a distinct landless group, ranging from resource-rich to resource-poor. The CRT classified seven types with 86.6 % accuracy using binary splits on landholding, livestock, income, irrigation, and crop diversity, with additional branches for unique configurations. Expert validation showed strong concordance. Field testing revealed yield differences across farm types, aligning with expert classifications. The typology illustrates a progression from low-input systems to diversified, resource-rich farms integrating crops, livestock, fisheries, and innovations in water, nutrients, mechanization, and markets.</div></div><div><h3>Significance</h3><div>The study demonstrates the utility of typologies not only for classification but also for effectively targeting SI interventions. This scalable, context-sensitive framework supports innovation upscaling in heterogeneous agroecosystems, especially where longitudinal data are unavailable.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104624"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837423","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 : 2026-03-01Epub Date: 2025-12-02DOI: 10.1016/j.agsy.2025.104592
Lennart Kokemohr , Klaus Mittenzwei , Till Kuhn
Context
The Norwegian Government agreed with the leading farmers' unions to include the agricultural sector in the national effort to mitigate greenhouse gas (GHG) emissions. The parties agreed to abate 5 million t CO2eq in 2021–2030. Emissions shall be mitigated by reducing food waste, dietary change, and farm-level abatement measures. Among these, the farmers' unions agreed to pursue mitigation efforts at the farm level.
Objective
This paper contributes to the current debate by calculating marginal farm-level abatement cost curves of seven typical Norwegian dairy farms.
Methods
Dairy farms are chosen due to their contribution to the sectors' emissions. The farm-level optimization model FarmDyn is adapted to Norwegian conditions to represent local prices, yields, endowments, policies, regulations, emission calculation, and abatement technology. The model is applied to seven representative dairy farms, identified by K-medoid clustering from the Farm Accountancy Data Network.
Results and Conclusions
The results show that up to 14 % of farm-level emissions can be mitigated at costs below the carbon tax level proposed by the Norwegian government (2000 NOK per t CO2eq). Further mitigation efforts are bound to high costs. The preferred abatement measures include optimizing feeding to increase the share of concentrates, thereby reducing enteric fermentation emissions by up to 21 %. Replacing regular diesel with biodiesel and utilizing advanced manure application technology can reduce total emissions by up to 3 %. Ultimately, farms reduce their herds to mitigate further emissions, resulting in a decrease of up to 11 % and 68 % in revenues from sold milk and bull beef, respectively. This begins on farms with high stocking densities, due to their limited ability to optimize feeding.
Due to high farm-level abatement costs, mitigation targets conflict with other policy goals, namely, securing farm income and maintaining production. Compensation could address the loss in income, but the reduction in production requires further action. Given the limited reduction potential for farm-level abatement at competitive costs, we suggest that dietary change and food waste reduction must achieve a significant share of the envisioned abatement target.
Significance
This study provides insights into the economic feasibility of farm-level GHG mitigation by quantifying marginal abatement cost curves for Norwegian dairy farms. We highlight the financial constraints farmers could face in meeting national targets and showcase promising mitigation measures. Finally, we contribute to the current debate by demonstrating that achieving emission reductions at farm-level may compromise other policy objectives, underscoring the importance of balancing sustainability and economic viability.
{"title":"Greenhouse gas abatement costs of Norwegian dairy farms","authors":"Lennart Kokemohr , Klaus Mittenzwei , Till Kuhn","doi":"10.1016/j.agsy.2025.104592","DOIUrl":"10.1016/j.agsy.2025.104592","url":null,"abstract":"<div><h3>Context</h3><div>The Norwegian Government agreed with the leading farmers' unions to include the agricultural sector in the national effort to mitigate greenhouse gas (GHG) emissions. The parties agreed to abate 5 million t CO<sub>2</sub>eq in 2021–2030. Emissions shall be mitigated by reducing food waste, dietary change, and farm-level abatement measures. Among these, the farmers' unions agreed to pursue mitigation efforts at the farm level.</div></div><div><h3>Objective</h3><div>This paper contributes to the current debate by calculating marginal farm-level abatement cost curves of seven typical Norwegian dairy farms.</div></div><div><h3>Methods</h3><div>Dairy farms are chosen due to their contribution to the sectors' emissions. The farm-level optimization model FarmDyn is adapted to Norwegian conditions to represent local prices, yields, endowments, policies, regulations, emission calculation, and abatement technology. The model is applied to seven representative dairy farms, identified by K-medoid clustering from the Farm Accountancy Data Network.</div></div><div><h3>Results and Conclusions</h3><div>The results show that up to 14 % of farm-level emissions can be mitigated at costs below the carbon tax level proposed by the Norwegian government (2000 NOK per t CO<sub>2</sub>eq). Further mitigation efforts are bound to high costs. The preferred abatement measures include optimizing feeding to increase the share of concentrates, thereby reducing enteric fermentation emissions by up to 21 %. Replacing regular diesel with biodiesel and utilizing advanced manure application technology can reduce total emissions by up to 3 %. Ultimately, farms reduce their herds to mitigate further emissions, resulting in a decrease of up to 11 % and 68 % in revenues from sold milk and bull beef, respectively. This begins on farms with high stocking densities, due to their limited ability to optimize feeding.</div><div>Due to high farm-level abatement costs, mitigation targets conflict with other policy goals, namely, securing farm income and maintaining production. Compensation could address the loss in income, but the reduction in production requires further action. Given the limited reduction potential for farm-level abatement at competitive costs, we suggest that dietary change and food waste reduction must achieve a significant share of the envisioned abatement target.</div></div><div><h3>Significance</h3><div>This study provides insights into the economic feasibility of farm-level GHG mitigation by quantifying marginal abatement cost curves for Norwegian dairy farms. We highlight the financial constraints farmers could face in meeting national targets and showcase promising mitigation measures. Finally, we contribute to the current debate by demonstrating that achieving emission reductions at farm-level may compromise other policy objectives, underscoring the importance of balancing sustainability and economic viability.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"233 ","pages":"Article 104592"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645544","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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