{"title":"Effects of biochar and irrigation strategy optimization on soil water distribution and potato yield in arid and semi-arid regions: A simulation study based on HYDRUS-2D and AquaCrop","authors":"Jiawei Guo, Hui Zhou, Yongqiang Wang, Mingshou Fan, Meirong Wang, Peng Liu, Zhihui Shang, Liguo Jia","doi":"10.1016/j.eja.2026.128095","DOIUrl":"https://doi.org/10.1016/j.eja.2026.128095","url":null,"abstract":"","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"146 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502336","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 : 2026-03-21DOI: 10.1016/j.eja.2026.128084
Yu Jia, Kui Liu, Hiroshi Kubota, Prabhath Lokuruge, Gary Peng
{"title":"Benefits of pulse crops on subsequent crop yields are constrained by precipitation deficits","authors":"Yu Jia, Kui Liu, Hiroshi Kubota, Prabhath Lokuruge, Gary Peng","doi":"10.1016/j.eja.2026.128084","DOIUrl":"https://doi.org/10.1016/j.eja.2026.128084","url":null,"abstract":"","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"17 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496088","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 : 2026-03-20DOI: 10.1016/j.eja.2026.128093
Rodolphe Aziz, Antonio Pulina, Margherita Rizzu, Deodatus Stanley Kiriba, Mamadou Traoré, Noel Nekesa Makete, James Mantent Kombiok, Mohamed Joseph Sesay, Roula Khadra, Giovanna Seddaiu, Davide Cammarano
{"title":"Impacts of climate variability and multiple fertilization strategies on rainfed maize production in Sub-Saharan Africa","authors":"Rodolphe Aziz, Antonio Pulina, Margherita Rizzu, Deodatus Stanley Kiriba, Mamadou Traoré, Noel Nekesa Makete, James Mantent Kombiok, Mohamed Joseph Sesay, Roula Khadra, Giovanna Seddaiu, Davide Cammarano","doi":"10.1016/j.eja.2026.128093","DOIUrl":"https://doi.org/10.1016/j.eja.2026.128093","url":null,"abstract":"","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"31 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496095","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}
The decrease of rice yield due to global dimming has been extensively studied, but less attention has been paid to its effects on nitrogen (N) metabolism, including N uptake and utilization, activities of key N metabolic enzymes in leaves, and canopy N distribution. Field experiments were performed from 2021 to 2023, two hybrid varieties, Y-liangyou900 (YLY900) and Chuanyou6203 (CY6203), were grown under no shading (CK), 40% shading at booting (S) and 40% shading at grain-filling stage (SS). Shading significantly increased SPAD values in both varieties at anthesis and 15 days after anthesis (15DAA) compared with CK. Although shading improved straw N concentration, accumulation and allocation at maturity, it reduced grain N accumulation and allocation, total N uptake (TN) and N use efficiency for grain production (NUEg). Canopy N distribution responded variably: S reduced the N extinction coefficient (KN) of YLY900 by 20.9% but increased that of CY6203 by 31.7% on average across 2021–2022, explaining the superior NUEg of CY6203 under shading. Shading also decreased glutamine synthetase (GS) and nitrate reductase (NR) activities, while increasing glutamate synthase (GOGAT) activity in leaves at anthesis and 15DAA. A significant positive correlation was observed between TN and GS activity at anthesis, as well as with NR activity at both stages. Similarly, correlation analysis detected significant positive associations between NUEg and NR activity at anthesis, and GS activity at 15DAA. These results suggest that reduced NR and GS activities under shading contribute to lower TN and NUEg, and that CY6203 maintains higher NUE than YLY900 under shading.
{"title":"Changes of canopy nitrogen distribution and nitrogen metabolism in hybrid rice under global dimming: A three-year field study","authors":"Liying Huang, Fangfang Hou, Wanting Li, Liyan Shang, Shuaijun Dai, Chunhu Wang, Ke Liu, Zhaoqiang Jin, Shijie Shi, Xiaohai Tian, Yunbo Zhang","doi":"10.1016/j.eja.2026.128076","DOIUrl":"https://doi.org/10.1016/j.eja.2026.128076","url":null,"abstract":"The decrease of rice yield due to global dimming has been extensively studied, but less attention has been paid to its effects on nitrogen (N) metabolism, including N uptake and utilization, activities of key N metabolic enzymes in leaves, and canopy N distribution. Field experiments were performed from 2021 to 2023, two hybrid varieties, Y-liangyou900 (YLY900) and Chuanyou6203 (CY6203), were grown under no shading (CK), 40% shading at booting (S) and 40% shading at grain-filling stage (SS). Shading significantly increased SPAD values in both varieties at anthesis and 15 days after anthesis (15DAA) compared with CK. Although shading improved straw N concentration, accumulation and allocation at maturity, it reduced grain N accumulation and allocation, total N uptake (TN) and N use efficiency for grain production (NUEg). Canopy N distribution responded variably: S reduced the N extinction coefficient (K<ce:inf loc=\"post\">N</ce:inf>) of YLY900 by 20.9% but increased that of CY6203 by 31.7% on average across 2021–2022, explaining the superior NUEg of CY6203 under shading. Shading also decreased glutamine synthetase (GS) and nitrate reductase (NR) activities, while increasing glutamate synthase (GOGAT) activity in leaves at anthesis and 15DAA. A significant positive correlation was observed between TN and GS activity at anthesis, as well as with NR activity at both stages. Similarly, correlation analysis detected significant positive associations between NUEg and NR activity at anthesis, and GS activity at 15DAA. These results suggest that reduced NR and GS activities under shading contribute to lower TN and NUEg, and that CY6203 maintains higher NUE than YLY900 under shading.","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"44 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465425","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: 2025-11-21DOI: 10.1016/j.eja.2025.127926
Ruiqi Du , Wenbo Shi , Xianghui Lu , Youzhen Xiang , Yue Zhang , Xiaoying Feng , Yu Ma
Rapid and accurate acquisition of field crop yield is of great significance for agriculture management optimization, food security and crop productivity. By the non-destructive and high-throughput data acquisition, the unmanned aerial vehicle (UAV) remote sensing has become a key tool for crop growth monitoring. However, the scarcity of in-situ samples poses technical barriers and efficiency challenges to yield model training. This study has developed a new yield estimation framework that integrates process models, optical remote sensing, and transfer learning to improve the stability and accuracy of crop yield estimation under small sample conditions. The DSSAT was calibrated with hyperspectral UAV derived crop growth variables, to describe the spatial-temporal variation of small-scale field winter canola leaf nitrogen content during growing season. Firstly, a process-interpretative crop yield estimation framework, TrSC2Y, was pre-trained using the PROSAIL radiative transfer model and the DSSAT crop growth model. Secondly, TrSC2Y was fine-tuned using field observations and UAV hyper-spectra images from three-years canola experiment. Finally, the actual performance and application potential of fine-tuned TrSC2Y in canola yield estimation were evaluated with machine learning as a benchmark test. The results show that: (1) Pre-trained by the crop spectra dataset (from PROSAIL) and yield dataset (from DSSAT), TrSC2Y can accurately extract crop phenotype parameters from theoretical canopy spectra. The joint use of phenotype parameters from multiple growth stages can achieve the best yield estimation (R2= 0.98;RMSE= 33.07 kg/ha;MAE= 1.26 %);(2) Fine-tuned TrSC2Y can be transferred to the field winter canola yield estimation task and shows stable performance (R2= 0.86;RMSE=224.42 kg/ha;MAE=6.5 %). Compared with the machine learning benchmark test, the demand of modeling samples for TrSC2Y is reduced by 50 %; (3) TrSC2Y supports the visualization of field-scale winter canola yield and captures the spatial variability of winter canola yield caused by irrigation-fertilizer treatments.The above results provide a lightweight, cost-effective, and innovative method for field crop yield estimation, promoting the development of precision agriculture management and intelligent applications.
{"title":"TrSC2Y: A transfer-learning-based model from UAV hyper-spectra imagery for field-scale canola yield prediction by integrating DSSAT with PROSAIL","authors":"Ruiqi Du , Wenbo Shi , Xianghui Lu , Youzhen Xiang , Yue Zhang , Xiaoying Feng , Yu Ma","doi":"10.1016/j.eja.2025.127926","DOIUrl":"10.1016/j.eja.2025.127926","url":null,"abstract":"<div><div>Rapid and accurate acquisition of field crop yield is of great significance for agriculture management optimization, food security and crop productivity. By the non-destructive and high-throughput data acquisition, the unmanned aerial vehicle (UAV) remote sensing has become a key tool for crop growth monitoring. However, the scarcity of in-situ samples poses technical barriers and efficiency challenges to yield model training. This study has developed a new yield estimation framework that integrates process models, optical remote sensing, and transfer learning to improve the stability and accuracy of crop yield estimation under small sample conditions. The DSSAT was calibrated with hyperspectral UAV derived crop growth variables, to describe the spatial-temporal variation of small-scale field winter canola leaf nitrogen content during growing season. Firstly, a process-interpretative crop yield estimation framework, TrSC2Y, was pre-trained using the PROSAIL radiative transfer model and the DSSAT crop growth model. Secondly, TrSC2Y was fine-tuned using field observations and UAV hyper-spectra images from three-years canola experiment. Finally, the actual performance and application potential of fine-tuned TrSC2Y in canola yield estimation were evaluated with machine learning as a benchmark test. The results show that: (1) Pre-trained by the crop spectra dataset (from PROSAIL) and yield dataset (from DSSAT), TrSC2Y can accurately extract crop phenotype parameters from theoretical canopy spectra. The joint use of phenotype parameters from multiple growth stages can achieve the best yield estimation (R<sup>2</sup>= 0.98;RMSE= 33.07 kg/ha;MAE= 1.26 %);(2) Fine-tuned TrSC2Y can be transferred to the field winter canola yield estimation task and shows stable performance (R<sup>2</sup>= 0.86;RMSE=224.42 kg/ha;MAE=6.5 %). Compared with the machine learning benchmark test, the demand of modeling samples for TrSC2Y is reduced by 50 %; (3) TrSC2Y supports the visualization of field-scale winter canola yield and captures the spatial variability of winter canola yield caused by irrigation-fertilizer treatments.The above results provide a lightweight, cost-effective, and innovative method for field crop yield estimation, promoting the development of precision agriculture management and intelligent applications.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127926"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567431","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.eja.2025.127966
Lisma Safitri , Marcelo V. Galdos , Iput Pradiko , Alexis Comber , Andrew Challinor
Assessing the climate-smartness of oil palm (OP) agronomic practices is critical for ensuring sustainable, resilient, and low-emission production that meets growing demand and complies with international climate-friendly regulations. This study aims to assess the climate-smartness, defined as improved productivity, enhanced resilience and reduced GHG emissions, of OP agronomic practices under a changing climate. Climate-smartness of irrigation and empty fruit bunch (EFB) application with standard and reduced N fertiliser was assessed using yield change, carbon balance change and two climate-smart indices. The Agricultural Production SIMulator (APSIM) model was used to simulate yield, carbon balance components and water use over a 25-year plantation cycle. Uncertainty analysis included ten different sites, five GCMs (IPSL, GFDL, MPI, MRI, UKESM1), two emission scenarios (SSP1–2.6 and SSP5–8.5) and three periods (baseline: 1998–2022; mid-century: 2041–2065; end-century: 2071–2095). Irrigation emerges as the most climate-smart practice for OP production under climate change, showing strong synergy among mitigation, adaptation, and sustainable production. While gains in yield and soil organic carbon (SOC) are modest (median yield increase: 4.48 %; IQR: −12.10–10.79), emissions remain low, maintaining OP systems as carbon sinks (lowest carbon balance change, median: 0.21 tCeq ha⁻¹ yr⁻¹; IQR: 0.08–0.43). Irrigation also shows highest synergy in water productivity and GHG intensity (median index score: 0.36; IQR: 0.25–0.48). All EFB application scenarios improve productivity and adaptation through higher yields and SOC, though gains are offset by higher emissions from EFB decomposition in warmer conditions. Elevated temperature, higher N fertiliser and reduced plant density lower the climate-smartness of OP productions. This study improves understanding of balanced climate-smart practices. Choosing the climate-smart practices and maintaining optimised N fertiliser and plant density enhance synergy in sustainable production, mitigation and adaptation of OP under climate change.
{"title":"Assessing climate-smartness of agronomic practices in oil palm production under changing climate conditions","authors":"Lisma Safitri , Marcelo V. Galdos , Iput Pradiko , Alexis Comber , Andrew Challinor","doi":"10.1016/j.eja.2025.127966","DOIUrl":"10.1016/j.eja.2025.127966","url":null,"abstract":"<div><div>Assessing the climate-smartness of oil palm (OP) agronomic practices is critical for ensuring sustainable, resilient, and low-emission production that meets growing demand and complies with international climate-friendly regulations. This study aims to assess the climate-smartness, defined as improved productivity, enhanced resilience and reduced GHG emissions, of OP agronomic practices under a changing climate. Climate-smartness of irrigation and empty fruit bunch (EFB) application with standard and reduced N fertiliser was assessed using yield change, carbon balance change and two climate-smart indices. The Agricultural Production SIMulator (APSIM) model was used to simulate yield, carbon balance components and water use over a 25-year plantation cycle. Uncertainty analysis included ten different sites, five GCMs (IPSL, GFDL, MPI, MRI, UKESM1), two emission scenarios (SSP1–2.6 and SSP5–8.5) and three periods (baseline: 1998–2022; mid-century: 2041–2065; end-century: 2071–2095). Irrigation emerges as the most climate-smart practice for OP production under climate change, showing strong synergy among mitigation, adaptation, and sustainable production. While gains in yield and soil organic carbon (SOC) are modest (median yield increase: 4.48 %; IQR: −12.10–10.79), emissions remain low, maintaining OP systems as carbon sinks (lowest carbon balance change, median: 0.21 tC<sub>eq</sub> ha⁻¹ yr⁻¹; IQR: 0.08–0.43). Irrigation also shows highest synergy in water productivity and GHG intensity (median index score: 0.36; IQR: 0.25–0.48). All EFB application scenarios improve productivity and adaptation through higher yields and SOC, though gains are offset by higher emissions from EFB decomposition in warmer conditions. Elevated temperature, higher N fertiliser and reduced plant density lower the climate-smartness of OP productions. This study improves understanding of balanced climate-smart practices. Choosing the climate-smart practices and maintaining optimised N fertiliser and plant density enhance synergy in sustainable production, mitigation and adaptation of OP under climate change.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127966"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813858","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: 2025-12-01DOI: 10.1016/j.eja.2025.127942
Wei Wang , Bao-Zhong Wang , Wei Zhang , Meng-Ying Li , Jian-Ming Li , Sheng-Jun Ji , Muhammad Abrar , Muhammad Maqsood Ur Rehman , Wasim Khan , Hong-Yan Tao , Mohamed S. Sheteiwy , Wen-Ying Wang , You-Cai Xiong
Cereal-legume intercropping is widely recognized for enhancing crop productivity in semiarid rainfed systems. However, the mechanisms underlying its yield advantages and stability under variable rainfall conditions remain unclear, limiting its adoption as a climate-resilient strategy. This study evaluated the stability of crop yield and economic benefits across inter-annual rainfall fluctuations (418 mm in 2019, 362 mm in 2020, and 253 mm in 2021) in a three-year field experiment. We assessed yield–economic performance of maize-soybean and wheat-soybean intercropping systems and their impacts on key soil functional parameters to elucidate the mechanisms underlying climate resilience. Both maize-soybean and wheat-soybean intercropping were observed to harvest 17–26 % higher yields (per plant) and 1.04–1.26 land equivalent ratios, therefore enhancing land-use efficiency. Economically, maize-based systems were the most profitable, while wheat-soybean intercropping turned to improve net returns by 1654 USD ha⁻¹ . Climate-resilience analysis showed that intercropping reduced yield volatility by 10–61 % when precipitation declined (418–253 mm), highlighting its role in stabilizing agroecosystem productivity and economic benefits. Also, intercropping systems were found to significantly improve total nitrogen (13.7 %–20.6 %) and phosphorus (16.3 %–19.8 %). Mechanistically, the above indicators were resulted from improving soil microbial biomass (20.8 %–23.0 %), enhancing extracellular enzyme activities (9.3 %–15.8 % for C- and P-hydrolases) and promoting soil moisture retention (11.0 %–12.9 %). The data confirmed that intercropping can greatly enhance soil multifunctionality and thus contribute to yield and economic stability. Therefore, cereal-legume intercropping can act as a scalable strategy to enhance productivity, soil quality, and climate resilience in semiarid rainfed environment. The findings offer policymakers and smallholders a sustainable solution to balance land-use efficiency and climate adaptation.
{"title":"Cereal-legume intercropping stabilizes yield and economic advantages under variable rainfall in semiarid rainfed environment","authors":"Wei Wang , Bao-Zhong Wang , Wei Zhang , Meng-Ying Li , Jian-Ming Li , Sheng-Jun Ji , Muhammad Abrar , Muhammad Maqsood Ur Rehman , Wasim Khan , Hong-Yan Tao , Mohamed S. Sheteiwy , Wen-Ying Wang , You-Cai Xiong","doi":"10.1016/j.eja.2025.127942","DOIUrl":"10.1016/j.eja.2025.127942","url":null,"abstract":"<div><div>Cereal-legume intercropping is widely recognized for enhancing crop productivity in semiarid rainfed systems. However, the mechanisms underlying its yield advantages and stability under variable rainfall conditions remain unclear, limiting its adoption as a climate-resilient strategy. This study evaluated the stability of crop yield and economic benefits across inter-annual rainfall fluctuations (418 mm in 2019, 362 mm in 2020, and 253 mm in 2021) in a three-year field experiment. We assessed yield–economic performance of maize-soybean and wheat-soybean intercropping systems and their impacts on key soil functional parameters to elucidate the mechanisms underlying climate resilience. Both maize-soybean and wheat-soybean intercropping were observed to harvest 17–26 % higher yields (per plant) and 1.04–1.26 land equivalent ratios, therefore enhancing land-use efficiency. Economically, maize-based systems were the most profitable, while wheat-soybean intercropping turned to improve net returns by 1654 USD ha⁻¹ . Climate-resilience analysis showed that intercropping reduced yield volatility by 10–61 % when precipitation declined (418–253 mm), highlighting its role in stabilizing agroecosystem productivity and economic benefits. Also, intercropping systems were found to significantly improve total nitrogen (13.7 %–20.6 %) and phosphorus (16.3 %–19.8 %). Mechanistically, the above indicators were resulted from improving soil microbial biomass (20.8 %–23.0 %), enhancing extracellular enzyme activities (9.3 %–15.8 % for C- and P-hydrolases) and promoting soil moisture retention (11.0 %–12.9 %). The data confirmed that intercropping can greatly enhance soil multifunctionality and thus contribute to yield and economic stability. Therefore, cereal-legume intercropping can act as a scalable strategy to enhance productivity, soil quality, and climate resilience in semiarid rainfed environment. The findings offer policymakers and smallholders a sustainable solution to balance land-use efficiency and climate adaptation.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127942"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650848","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: 2026-01-02DOI: 10.1016/j.eja.2025.127975
Peng Zhang , Shuo Wang , Yongjiang Zhang , Hongchun Sun , Ke Zhang , Zhiying Bai , Lingxiao Zhu , Zhanbiao Wang , Hezhong Dong , Liantao Liu , Cundong Li
Conventional cotton production system in China’s Yellow River Valley (mid-April sowing, 45,000 plants ha⁻¹, and 240 kg N ha⁻¹) (MLH) achieve high yield but incurs excessive resource use and environmental costs. Sustainable intensification requires strategies that reconcile productivity, efficiency, and ecological outcomes. Through a three-year (2021–2023) field experiment, we assessed integrated management strategies combining sowing date (normal: mid-April; late: early May), planting density (typical: 45,000; high: 90,000 plants ha⁻¹), and nitrogen rate (conventional: 240 kg N ha⁻¹; reduced: 180 kg N ha⁻¹). Seed cotton yield, nitrogen use efficiency (NUE), energy flows, carbon/nitrogen footprints, and economic returns were quantified, and a sustainable performance index (SPI) was calculated for integrated assessment. Results showed that the late sowing + high density + reduced N rate (LHR) strategy maintained seed cotton yield while significantly increasing NUE by 34.8 % and energy productivity by 15.1 %, compared to the conventional MLH system. This strategy also reduced direct and indirect emissions (fertilizer production, labor), lowering the carbon footprint per unit yield by 34.5 % and nitrogen footprint by 31.9 %. The resultant decrease in environmental costs enhanced net ecosystem economic benefit by 27.1–38.3 %. Consistently, the SPI, integrating productivity, resource efficiency, environmental impact, and economics, confirmed late sowing + high density + reduced N as the optimal strategy for synergistic improvement in economic and ecological outcomes. These findings demonstrate that coordinated optimization of sowing date, density, and nitrogen management enables climate-resilient cotton production with lower emissions and higher resource efficiency—a transferable model for similar agroecosystems. Future integration with precision nitrogen technologies (e.g., deep placement, controlled-release fertilizers) could further amplify sustainability gains.
中国黄河流域的传统棉花生产体系（4月中旬播种，4.5万株（⁻¹），240 kg N（⁻¹））虽然产量高，但资源消耗和环境成本过高。可持续集约化需要协调生产力、效率和生态结果的战略。通过一项为期三年（2021-2023）的田间试验，我们评估了综合管理策略，包括播种日期（正常：4月中旬；晚：5月初）、种植密度（典型：4.5万株；高：9万株-⁻¹）和施氮量（常规：240 kg N ha⁻¹；减少：180 kg N ha⁻¹）。对籽棉产量、氮素利用效率（NUE）、能量流、碳/氮足迹和经济回报进行量化，并计算可持续绩效指数（SPI）进行综合评价。结果表明：晚播+ 高密度+ 降氮（LHR）策略在保持籽棉产量的同时，显著提高了氮肥利用效率34.8% %，能量生产力15.1% %。该策略还减少了直接和间接排放（化肥生产、劳动力），将单位产量的碳足迹降低了34.5% %，氮足迹降低了31.9% %。由此产生的环境成本降低使生态系统净经济效益提高27.1-38.3 %。综合生产力、资源效率、环境影响和经济效益的SPI一致证实，晚播+ 高密度+ 减氮是经济和生态效益协同改善的最佳策略。这些发现表明，播期、密度和氮肥管理的协调优化使气候适应型棉花生产具有更低的排放和更高的资源效率，这是类似农业生态系统的可转移模式。未来与精密氮肥技术的整合（如深埋、控释肥料）可以进一步扩大可持续性收益。
{"title":"Integrated management of sowing date, density and nitrogen reduces environmental footprints while sustaining cotton yield in the Yellow River Valley","authors":"Peng Zhang , Shuo Wang , Yongjiang Zhang , Hongchun Sun , Ke Zhang , Zhiying Bai , Lingxiao Zhu , Zhanbiao Wang , Hezhong Dong , Liantao Liu , Cundong Li","doi":"10.1016/j.eja.2025.127975","DOIUrl":"10.1016/j.eja.2025.127975","url":null,"abstract":"<div><div>Conventional cotton production system in China’s Yellow River Valley (mid-April sowing, 45,000 plants ha⁻¹, and 240 kg N ha⁻¹) (MLH) achieve high yield but incurs excessive resource use and environmental costs. Sustainable intensification requires strategies that reconcile productivity, efficiency, and ecological outcomes. Through a three-year (2021–2023) field experiment, we assessed integrated management strategies combining sowing date (normal: mid-April; late: early May), planting density (typical: 45,000; high: 90,000 plants ha⁻¹), and nitrogen rate (conventional: 240 kg N ha⁻¹; reduced: 180 kg N ha⁻¹). Seed cotton yield, nitrogen use efficiency (NUE), energy flows, carbon/nitrogen footprints, and economic returns were quantified, and a sustainable performance index (SPI) was calculated for integrated assessment. Results showed that the late sowing + high density + reduced N rate (LHR) strategy maintained seed cotton yield while significantly increasing NUE by 34.8 % and energy productivity by 15.1 %, compared to the conventional MLH system. This strategy also reduced direct and indirect emissions (fertilizer production, labor), lowering the carbon footprint per unit yield by 34.5 % and nitrogen footprint by 31.9 %. The resultant decrease in environmental costs enhanced net ecosystem economic benefit by 27.1–38.3 %. Consistently, the SPI, integrating productivity, resource efficiency, environmental impact, and economics, confirmed late sowing + high density + reduced N as the optimal strategy for synergistic improvement in economic and ecological outcomes. These findings demonstrate that coordinated optimization of sowing date, density, and nitrogen management enables climate-resilient cotton production with lower emissions and higher resource efficiency—a transferable model for similar agroecosystems. Future integration with precision nitrogen technologies (e.g., deep placement, controlled-release fertilizers) could further amplify sustainability gains.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127975"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883348","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: 2025-12-17DOI: 10.1016/j.eja.2025.127963
Wenhao Ren , Xianyue Li , Tingxi Liu , Ning Chen , Maoxin Xin , Qian Qi , Bin Liu , Hongxing Liu
As environmental concerns related to agricultural practices intensify, the excessive use of nitrogen fertilizers has become a major global challenge. Controlled-release fertilizers (CRF) offer a promising strategy to improve agricultural productivity, enhance nitrogen utilization, mitigate ecological impacts, and increase economic returns. This study examined the effectiveness of CRF in enhancing sunflower production and nitrogen use efficiency, as well as in reducing nitrogen losses (including ammonia (NH3) and nitrous oxide (N2O) emissions and nitrogen leaching) across multiple nitrogen treatments during a three-year field experiment. The results showed that CRF significantly increased nitrogen use efficiency by 14.33 % and reduced nitrogen volatilization by 26.56 %, particularly within the first 20 d after fertilization, during which NH3 and N2O emissions were markedly lower than those under traditional nitrogen fertilizer treatments. Furthermore, life cycle assessment (LCA) indicated that CRF substantially decreased environmental impacts, with reductions of 13.03 % in greenhouse gas emissions, 29.05 % in acidification potential, and 29.02 % in eutrophication potential. Under low to medium nitrogen application rates (e.g., N225), delayed nitrogen release further reduced nitrogen loss and alleviated environmental pressure. By integrating LCA with the ecological-economic benefits (BETA) model, this study quantified the ecological and economic value of CRF. The findings demonstrated that CRF delivered high economic returns and considerable environmental benefits within the optimal nitrogen application range, making it an effective approach for sustainable agricultural development. These evaluation methods offer a systematic framework for assessing the environmental and economic outcomes of CRF, providing theoretical support for its broader adoption in agricultural production.
{"title":"Impact of controlled-release fertilizer on nitrogen use efficiency, greenhouse gas emissions, and environmental sustainability in sunflower cropping systems","authors":"Wenhao Ren , Xianyue Li , Tingxi Liu , Ning Chen , Maoxin Xin , Qian Qi , Bin Liu , Hongxing Liu","doi":"10.1016/j.eja.2025.127963","DOIUrl":"10.1016/j.eja.2025.127963","url":null,"abstract":"<div><div>As environmental concerns related to agricultural practices intensify, the excessive use of nitrogen fertilizers has become a major global challenge. Controlled-release fertilizers (CRF) offer a promising strategy to improve agricultural productivity, enhance nitrogen utilization, mitigate ecological impacts, and increase economic returns. This study examined the effectiveness of CRF in enhancing sunflower production and nitrogen use efficiency, as well as in reducing nitrogen losses (including ammonia (NH<sub>3</sub>) and nitrous oxide (N<sub>2</sub>O) emissions and nitrogen leaching) across multiple nitrogen treatments during a three-year field experiment. The results showed that CRF significantly increased nitrogen use efficiency by 14.33 % and reduced nitrogen volatilization by 26.56 %, particularly within the first 20 d after fertilization, during which NH<sub>3</sub> and N<sub>2</sub>O emissions were markedly lower than those under traditional nitrogen fertilizer treatments. Furthermore, life cycle assessment (LCA) indicated that CRF substantially decreased environmental impacts, with reductions of 13.03 % in greenhouse gas emissions, 29.05 % in acidification potential, and 29.02 % in eutrophication potential. Under low to medium nitrogen application rates (e.g., N225), delayed nitrogen release further reduced nitrogen loss and alleviated environmental pressure. By integrating LCA with the ecological-economic benefits (BETA) model, this study quantified the ecological and economic value of CRF. The findings demonstrated that CRF delivered high economic returns and considerable environmental benefits within the optimal nitrogen application range, making it an effective approach for sustainable agricultural development. These evaluation methods offer a systematic framework for assessing the environmental and economic outcomes of CRF, providing theoretical support for its broader adoption in agricultural production.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127963"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784870","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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