Pub Date : 2026-02-10DOI: 10.1016/j.fcr.2026.110391
John G. Howieson, Robert J. Harrison, Ron J. Yates, Belinda F. Hackney
The ley farming system of southern Australia emerged in the early 20th century as a flexible, resilient, sustainable and profitable form of agriculture. Ley farming relied upon a self-regenerating annual legume pasture (ley) to emerge without the intervention of machinery after a cereal crop phase. One key to the success of this system was that the pasture legume produced seed with physical dormancy (PY) to form a “seed bank” that would persist in the soil for several seasons. When the field was “rested” from cropping, the legume would germinate, feed grazing animals, and fix biological nitrogen.
{"title":"Fifty years of change: The decline of the Australian ley farming system as agriculture intensified, and pathways to its sustainable renewal","authors":"John G. Howieson, Robert J. Harrison, Ron J. Yates, Belinda F. Hackney","doi":"10.1016/j.fcr.2026.110391","DOIUrl":"https://doi.org/10.1016/j.fcr.2026.110391","url":null,"abstract":"The ley farming system of southern Australia emerged in the early 20th century as a flexible, resilient, sustainable and profitable form of agriculture. Ley farming relied upon a self-regenerating annual legume pasture (ley) to emerge without the intervention of machinery after a cereal crop phase. One key to the success of this system was that the pasture legume produced seed with physical dormancy (PY) to form a “seed bank” that would persist in the soil for several seasons. When the field was “rested” from cropping, the legume would germinate, feed grazing animals, and fix biological nitrogen.","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"91 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146756","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 : 2026-02-04DOI: 10.1016/j.fcr.2025.110291
Yannik Schlup, Lukas Graz, Andreas Kägi, Johan Six, Susanne Vogelgsang
{"title":"Mixed cropping of narrow-leaved lupin and oat enhances plant health and yield buffering while maintaining protein yield","authors":"Yannik Schlup, Lukas Graz, Andreas Kägi, Johan Six, Susanne Vogelgsang","doi":"10.1016/j.fcr.2025.110291","DOIUrl":"https://doi.org/10.1016/j.fcr.2025.110291","url":null,"abstract":"","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"9 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134141","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}
{"title":"Substituting straw with biochar enhances rice production and net economic benefit while reducing the carbon footprint in a double-cropping rice system","authors":"Shiqi Yang, Yanghaojun Xu, Wenyang Kuang, Wenhao Fan, Xueming Tan, Xiaohua Pan, Yanhua Zeng","doi":"10.1016/j.fcr.2026.110385","DOIUrl":"https://doi.org/10.1016/j.fcr.2026.110385","url":null,"abstract":"","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"18 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110601","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 : 2026-01-31DOI: 10.1016/j.fcr.2026.110371
Louis-Marie Raboin , Eric Gozé , Oumarou Diallo , Guelika Kafando , Benoit Joseph Batieno , Julie Dusserre
Context
In Sudano-Sahelian West Africa, declining soil fertility due to land degradation, low fertilizer use, and shortened fallows threatens productivity. Legume-based rotations offer a sustainable solution by improving nitrogen availability and soil health, especially under low-input conditions.
Objectives
This study aimed to enhance sorghum-based cropping systems through one-year legume rotations. It sought to characterize a diversity of legume crops and evaluate the effect of returning their biomass (excluding grain) to the soil on subsequent sorghum crop’s growth and yield.
Methods
Twenty crop precedents, comprising 17 legumes and 3 grasses, were evaluated across three randomized complete block design (RCBD) field trials under varying soil fertility conditions. Biological nitrogen fixation was quantified using the natural abundance (δ¹⁵N) method along with measurements of nitrogen content and total nitrogen accumulation in aboveground biomass. In the following season, sorghum was grown to assess rotational effects on yield and growth.
Results
The study revealed substantial and significant variability among legume species in biomass and nitrogen accumulation, with Crotalaria juncea and Centrosema pascuorum showing the highest values. However, these differences did not translate into significant yield gains for subsequent sorghum crops. While legumes outperformed grasses in improving sorghum yields, variation among legume species was minimal, suggesting nitrogen recycling inefficiencies under Sudano-Sahelian conditions.
Conclusions
Under Sudano-Sahelian conditions, non-nitrogen (non-N) effects appear to play a crucial role in the overall rotational benefits of legumes in cereal-based systems. Therefore, grain and fodder legumes should be preferred over green manure legumes because they provide high-protein food for humans and feed for animals, while enhancing the overall performance of crop rotations. Further diversification of legume crops is needed to optimize legume-nonlegume balance and manage trade-offs between food security and sustainable soil management.
{"title":"Limited variation in sorghum yield responses to diverse legume rotations under Sudano-Sahelian conditions","authors":"Louis-Marie Raboin , Eric Gozé , Oumarou Diallo , Guelika Kafando , Benoit Joseph Batieno , Julie Dusserre","doi":"10.1016/j.fcr.2026.110371","DOIUrl":"10.1016/j.fcr.2026.110371","url":null,"abstract":"<div><h3>Context</h3><div>In Sudano-Sahelian West Africa, declining soil fertility due to land degradation, low fertilizer use, and shortened fallows threatens productivity. Legume-based rotations offer a sustainable solution by improving nitrogen availability and soil health, especially under low-input conditions.</div></div><div><h3>Objectives</h3><div>This study aimed to enhance sorghum-based cropping systems through one-year legume rotations. It sought to characterize a diversity of legume crops and evaluate the effect of returning their biomass (excluding grain) to the soil on subsequent sorghum crop’s growth and yield.</div></div><div><h3>Methods</h3><div>Twenty crop precedents, comprising 17 legumes and 3 grasses, were evaluated across three randomized complete block design (RCBD) field trials under varying soil fertility conditions. Biological nitrogen fixation was quantified using the natural abundance (δ¹⁵N) method along with measurements of nitrogen content and total nitrogen accumulation in aboveground biomass. In the following season, sorghum was grown to assess rotational effects on yield and growth.</div></div><div><h3>Results</h3><div>The study revealed substantial and significant variability among legume species in biomass and nitrogen accumulation, with <em>Crotalaria juncea</em> and <em>Centrosema pascuorum</em> showing the highest values. However, these differences did not translate into significant yield gains for subsequent sorghum crops. While legumes outperformed grasses in improving sorghum yields, variation among legume species was minimal, suggesting nitrogen recycling inefficiencies under Sudano-Sahelian conditions.</div></div><div><h3>Conclusions</h3><div>Under Sudano-Sahelian conditions, non-nitrogen (non-N) effects appear to play a crucial role in the overall rotational benefits of legumes in cereal-based systems. Therefore, grain and fodder legumes should be preferred over green manure legumes because they provide high-protein food for humans and feed for animals, while enhancing the overall performance of crop rotations. Further diversification of legume crops is needed to optimize legume-nonlegume balance and manage trade-offs between food security and sustainable soil management.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"340 ","pages":"Article 110371"},"PeriodicalIF":6.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076930","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}
Small landholding agricultural landscapes face heightened risks due to adverse climatic conditions, threatening sustainable management practices and agri-food and nutritional security. The Eastern Indo-Gangetic Plains (EIGP), particularly Bihar, India, are vulnerable to these challenges, necessitating the optimization of cropping systems for enhanced productivity, profitability, and climate resilience.
Objective
This study investigated suitable cropping systems and the impacts of climate change on agri-food production systems in Bihar, India, to optimize the farm-level productivity, profitability, and sustainability.
Methods
Field demonstrations of climate-resilient agricultural (CRA) practices were conducted across 70 project locations in seven hubs from 2019 to 2024. Data from agro-climatic zones (ACZs) were analyzed to evaluate productivity, profitability, and sustainability of optimized cropping systems.
Results
The Rice–Potato + Maize (RPM) system showed the highest productivity across zones (34.10, 42.23, and 23.69 t ha−1 in ACZ I, IIIa, and IIIb, respectively). Soybean–Wheat–Mung bean (SWM) demonstrated higher profitability in ACZ I ($2400 ha−1) and IIIb ($211.43 ha−1), highlighting the economic potential of legume-based systems. In ACZ III, rice-based systems incorporating mustard ($2030.4 ha−1) and lentil ($1936.30 ha−1) were more profitable, emphasizing crop diversification and rotation strategies. Adverse climatic conditions significantly impacted agro-ecosystems, exacerbating threats to agri-food production systems.
Conclusions
Cropping system optimization enhances system productivity and profitability while mitigating climate risks. Legume- and rice-based systems demonstrate significant potential for economic and environmental sustainability in Bihar.
Significance
Policymakers should prioritize climate-resilient cropping systems as adaptive strategies to ensure sustainable agro-ecosystem management, enhance farm-level profitability, and improve agri-food and nutritional security in vulnerable regions.
由于不利的气候条件，小土地农业景观面临更大的风险，威胁到可持续管理做法以及农业粮食和营养安全。东印度-恒河平原（EIGP），特别是印度比哈尔邦，容易受到这些挑战的影响，因此有必要优化种植制度，以提高生产力、盈利能力和气候适应能力。目的研究印度比哈尔邦适宜的种植制度和气候变化对农业粮食生产系统的影响，以优化农场层面的生产力、盈利能力和可持续性。方法2019年至2024年，在7个中心的70个项目地点进行了气候适应型农业（CRA）实践的现场示范。分析了来自农业气候带（ACZs）的数据，以评估优化种植制度的生产力、盈利能力和可持续性。结果水稻-马铃薯+ 玉米（RPM）体系在ACZ I、IIIa和IIIb区表现出最高的生产力（分别为34.10、42.23和23.69 t ha−1）。大豆-小麦-绿豆（SWM）在ACZ I（2400美元 ha - 1）和IIIb（211.43美元 ha - 1）表现出更高的盈利能力，突出了豆类系统的经济潜力。在ACZ III，结合芥菜（2030.4 ha - 1美元）和扁豆（1936.30 ha - 1美元）的稻基系统更有利可图，强调作物多样化和轮作策略。不利的气候条件严重影响了农业生态系统，加剧了对农业粮食生产系统的威胁。结论种植系统优化在降低气候风险的同时提高了系统生产力和盈利能力。豆类和水稻系统在比哈尔邦的经济和环境可持续性方面显示出巨大的潜力。政策制定者应优先考虑气候适应型种植系统，将其作为适应性战略，以确保可持续的农业生态系统管理，提高农场层面的盈利能力，并改善脆弱地区的农业粮食和营养安全。
{"title":"Climate-resilient agriculture strategies to address the challenges of agri-food security and climate change","authors":"Raj Kumar Jat , Vijay Singh Meena , RK Sohane , RK Jha , Abhay Kumar , Ujjwal Kumar , Anjani Kumar , RN Singh , Shubham Durgude , Suneel Kumar , Illathur R. Reddy , S. Pazhanisamy , Rakesh Kumar , Sunita Kumari Meena , Ved Prakash , Sanjay Kumar , Brijendu Kumar , Umesh Narayan Umesh , Ranjan Kumar Singh , Ravikant Chaubey , Swati Sagar","doi":"10.1016/j.fcr.2026.110370","DOIUrl":"10.1016/j.fcr.2026.110370","url":null,"abstract":"<div><h3>Context</h3><div>Small landholding agricultural landscapes face heightened risks due to adverse climatic conditions, threatening sustainable management practices and agri-food and nutritional security. The Eastern Indo-Gangetic Plains (EIGP), particularly Bihar, India, are vulnerable to these challenges, necessitating the optimization of cropping systems for enhanced productivity, profitability, and climate resilience.</div></div><div><h3>Objective</h3><div>This study investigated suitable cropping systems and the impacts of climate change on agri-food production systems in Bihar, India, to optimize the farm-level productivity, profitability, and sustainability.</div></div><div><h3>Methods</h3><div>Field demonstrations of climate-resilient agricultural (CRA) practices were conducted across 70 project locations in seven hubs from 2019 to 2024. Data from agro-climatic zones (ACZs) were analyzed to evaluate productivity, profitability, and sustainability of optimized cropping systems.</div></div><div><h3>Results</h3><div>The Rice–Potato + Maize (RPM) system showed the highest productivity across zones (34.10, 42.23, and 23.69 t ha<sup>−1</sup> in ACZ I, IIIa, and IIIb, respectively). Soybean–Wheat–Mung bean (SWM) demonstrated higher profitability in ACZ I ($2400 ha<sup>−1</sup>) and IIIb ($211.43 ha<sup>−1</sup>), highlighting the economic potential of legume-based systems. In ACZ III, rice-based systems incorporating mustard ($2030.4 ha<sup>−1</sup>) and lentil ($1936.30 ha<sup>−1</sup>) were more profitable, emphasizing crop diversification and rotation strategies. Adverse climatic conditions significantly impacted agro-ecosystems, exacerbating threats to agri-food production systems.</div></div><div><h3>Conclusions</h3><div>Cropping system optimization enhances system productivity and profitability while mitigating climate risks. Legume- and rice-based systems demonstrate significant potential for economic and environmental sustainability in Bihar.</div></div><div><h3>Significance</h3><div>Policymakers should prioritize climate-resilient cropping systems as adaptive strategies to ensure sustainable agro-ecosystem management, enhance farm-level profitability, and improve agri-food and nutritional security in vulnerable regions.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"340 ","pages":"Article 110370"},"PeriodicalIF":6.4,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076540","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 : 2026-01-30DOI: 10.1016/j.fcr.2026.110372
Zhikai Cheng, Xiaobo Gu, Yuanling Zhang, Xiaohai Fang, Yang Xu, Shikun Sun, Yadan Du, Huanjie Cai
Context
Early forecasts of high-resolution (e.g., 1 km × 1 km) crop yields are crucial for ensuring agricultural sustainability, particularly under climate change. Conventional process-based (e.g., crop models) and data-driven (e.g., machine learning) approaches face limitations due to high uncertainty in complex scenarios and insufficient training samples, respectively.
Objective
To address these challenges, we developed an improved knowledge-guided deep learning (IKGDL) framework.
Methods
The IKGDL considered biophysical knowledge from multiple crop models (AquaCrop, crop-water productivity model; APSIM, Agricultural Production Systems sIMulator; and WOFOST, World Food Studies model) by the pre-training process and introduced additional constraints from remote sensing data (RS) and extreme climatic events (ECE) by the fine-tuning process.
Results and conclusions
The results showed that single crop model had high uncertainty caused by the model structure. The application of multiple crop models and active learning provided enough available samples for guiding the IKGDL framework to learn general knowledge about meteorological variables (maximum temperature, minimum temperature, and precipitation; MV) and yields. IKGDL achieved satisfactory yield forecasts approximately two months before crop harvest with low spatial and temporal uncertainties (coefficient of determination of 0.78 and 0.76, the normalized root mean square error of 16.24 % and 18.44 % for wheat and maize, respectively). Interpretive analyses quantified the contribution of multi-source data to yield prediction through the SHapley Additive exPlanation tool, with importance ranked as MV > RS > ECE. Although the contribution of ECE was lower, it could not be ignored due to its catastrophic damage to yields. The IKGDL provided a novel insight into regional crop yield prediction, and its good extensibility offered significant potential for continuous improvement in the future.
{"title":"Integrating multiple crop models and multi-source data in a knowledge-guided deep learning framework for wheat and maize yield forecasting in the Huang-Huai-Hai Plain, China","authors":"Zhikai Cheng, Xiaobo Gu, Yuanling Zhang, Xiaohai Fang, Yang Xu, Shikun Sun, Yadan Du, Huanjie Cai","doi":"10.1016/j.fcr.2026.110372","DOIUrl":"10.1016/j.fcr.2026.110372","url":null,"abstract":"<div><h3>Context</h3><div>Early forecasts of high-resolution (e.g., 1 km × 1 km) crop yields are crucial for ensuring agricultural sustainability, particularly under climate change. Conventional process-based (e.g., crop models) and data-driven (e.g., machine learning) approaches face limitations due to high uncertainty in complex scenarios and insufficient training samples, respectively.</div></div><div><h3>Objective</h3><div>To address these challenges, we developed an improved knowledge-guided deep learning (IKGDL) framework.</div></div><div><h3>Methods</h3><div>The IKGDL considered biophysical knowledge from multiple crop models (AquaCrop, crop-water productivity model; APSIM, Agricultural Production Systems sIMulator; and WOFOST, World Food Studies model) by the pre-training process and introduced additional constraints from remote sensing data (RS) and extreme climatic events (ECE) by the fine-tuning process.</div></div><div><h3>Results and conclusions</h3><div>The results showed that single crop model had high uncertainty caused by the model structure. The application of multiple crop models and active learning provided enough available samples for guiding the IKGDL framework to learn general knowledge about meteorological variables (maximum temperature, minimum temperature, and precipitation; MV) and yields. IKGDL achieved satisfactory yield forecasts approximately two months before crop harvest with low spatial and temporal uncertainties (coefficient of determination of 0.78 and 0.76, the normalized root mean square error of 16.24 % and 18.44 % for wheat and maize, respectively). Interpretive analyses quantified the contribution of multi-source data to yield prediction through the SHapley Additive exPlanation tool, with importance ranked as MV > RS > ECE. Although the contribution of ECE was lower, it could not be ignored due to its catastrophic damage to yields. The IKGDL provided a novel insight into regional crop yield prediction, and its good extensibility offered significant potential for continuous improvement in the future.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"340 ","pages":"Article 110372"},"PeriodicalIF":6.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076539","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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