Pub Date : 2026-04-01Epub Date: 2026-02-07DOI: 10.1016/j.eja.2026.128029
Qi Wang, Xiaoyi Han, Qixuan Wang, Minlong Du, Xinyue Lei, Jiahao Ge, Rong Zhong, Chenxi Wan, Xiaoli Gao, Pu Yang, Jinfeng Gao
<div><div>Continuous cropping disrupts farmland ecosystems, leading to aggravated soil-borne diseases and substantial crop yield losses. Soil amendments are considered promising strategies to alleviate continuous cropping obstacles. However, the effects of chemical fertilizers combined with organic manure and biochar amendments on soil water content (SWC), soil nitrogen pool levels, plant physiological traits, yield, and resource utilization efficiency in continuous common buckwheat cropping systems remain poorly understood. To address this knowledge gap, we conducted a four-year (2022–2025) field positioning experiment on the Loess Plateau with a completely randomized design including five treatments with four replicates: (a) no fertilizer (CK), (b) chemical fertilizers (NPK: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, and 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O), (c) chemical fertilizers combined with organic manure (NPKM: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O, and 22500 kg ha<sup>−1</sup> organic manure), (d) chemical fertilizers combined with biochar (NPKB: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O, and 10000 kg ha<sup>−1</sup> biochar), and (e) chemical fertilizers combined with organic manure and biochar (NPKMB: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O, 11250 kg ha<sup>−1</sup> organic manure, and 5000 kg ha<sup>−1</sup> biochar). Results showed that compared to other treatments, NPKMB elevated SWC in the 0–100 cm soil layer (9.66–67.16 %) and increased total nitrogen (TN) (7.95–209.13 %) and alkali-hydrolyzable nitrogen (AN) (4.84–187.41 %) contents, thus creating a suitable soil environment for common buckwheat growth under continuous cropping stress. Meanwhile, NPKMB significantly enhanced the activities of root superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) by 3.47–45.31 %, 1.04–63.29 %, and 2.15–78.84 %, respectively, while increasing the contents of root proline, soluble sugar, and soluble protein by 6.90–83.67 %, 7.13–75.04 %, and 4.04–110.93 %, delaying root senescence and facilitating water and nitrogen absorption. Additionally, NPKMB improved leaf net photosynthetic rate (Pn, 9.78–95.52 %), stomatal conductance (Gs, 5.90–112.36 %), transpiration rate (Tr, 6.66–62.28 %), and chlorophyll content (SPAD value, 6.51–25.76 %), thereby promoting crop growth. Consequently, after three years of continuous cropping, NPKMB effectively alleviated growth constraints, achieving the highest dry matter weight (29.25 g plant<sup>−1</sup>), grain yield (1082.65 kg ha<sup>−1</sup>), N uptake (99.49 kg ha<sup>−1</sup>), and water use efficiency (WUE, 5.77 kg ha<sup>−1</sup> mm<sup>−1</sup>). Overall, NPKMB fertilization strategy alleviated continuous cropping growth constraints of commo
{"title":"Soil amendments improved physiological characteristics, grain yield, and water use efficiency of common buckwheat under multi-year continuous cropping","authors":"Qi Wang, Xiaoyi Han, Qixuan Wang, Minlong Du, Xinyue Lei, Jiahao Ge, Rong Zhong, Chenxi Wan, Xiaoli Gao, Pu Yang, Jinfeng Gao","doi":"10.1016/j.eja.2026.128029","DOIUrl":"10.1016/j.eja.2026.128029","url":null,"abstract":"<div><div>Continuous cropping disrupts farmland ecosystems, leading to aggravated soil-borne diseases and substantial crop yield losses. Soil amendments are considered promising strategies to alleviate continuous cropping obstacles. However, the effects of chemical fertilizers combined with organic manure and biochar amendments on soil water content (SWC), soil nitrogen pool levels, plant physiological traits, yield, and resource utilization efficiency in continuous common buckwheat cropping systems remain poorly understood. To address this knowledge gap, we conducted a four-year (2022–2025) field positioning experiment on the Loess Plateau with a completely randomized design including five treatments with four replicates: (a) no fertilizer (CK), (b) chemical fertilizers (NPK: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, and 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O), (c) chemical fertilizers combined with organic manure (NPKM: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O, and 22500 kg ha<sup>−1</sup> organic manure), (d) chemical fertilizers combined with biochar (NPKB: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O, and 10000 kg ha<sup>−1</sup> biochar), and (e) chemical fertilizers combined with organic manure and biochar (NPKMB: 180 kg ha<sup>−1</sup> N, 75 kg ha<sup>−1</sup> P<sub>2</sub>O<sub>5</sub>, 37.5 kg ha<sup>−1</sup> K<sub>2</sub>O, 11250 kg ha<sup>−1</sup> organic manure, and 5000 kg ha<sup>−1</sup> biochar). Results showed that compared to other treatments, NPKMB elevated SWC in the 0–100 cm soil layer (9.66–67.16 %) and increased total nitrogen (TN) (7.95–209.13 %) and alkali-hydrolyzable nitrogen (AN) (4.84–187.41 %) contents, thus creating a suitable soil environment for common buckwheat growth under continuous cropping stress. Meanwhile, NPKMB significantly enhanced the activities of root superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) by 3.47–45.31 %, 1.04–63.29 %, and 2.15–78.84 %, respectively, while increasing the contents of root proline, soluble sugar, and soluble protein by 6.90–83.67 %, 7.13–75.04 %, and 4.04–110.93 %, delaying root senescence and facilitating water and nitrogen absorption. Additionally, NPKMB improved leaf net photosynthetic rate (Pn, 9.78–95.52 %), stomatal conductance (Gs, 5.90–112.36 %), transpiration rate (Tr, 6.66–62.28 %), and chlorophyll content (SPAD value, 6.51–25.76 %), thereby promoting crop growth. Consequently, after three years of continuous cropping, NPKMB effectively alleviated growth constraints, achieving the highest dry matter weight (29.25 g plant<sup>−1</sup>), grain yield (1082.65 kg ha<sup>−1</sup>), N uptake (99.49 kg ha<sup>−1</sup>), and water use efficiency (WUE, 5.77 kg ha<sup>−1</sup> mm<sup>−1</sup>). Overall, NPKMB fertilization strategy alleviated continuous cropping growth constraints of commo","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128029"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134092","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 : 2026-04-01Epub Date: 2026-01-13DOI: 10.1016/j.eja.2026.128000
Ferdaous Rezgui , Louise Blanc , Daniel Plaza-Bonilla , Jorge Lampurlanés , Christos Dordas , Paschalis Papakaloudis , Andreas Michalitsis , Laure Hossard , Fatima Lambarraa-Lehnhardt , Sonoko D. Bellingrath-Kimura , Carsten Paul , Moritz Reckling
Agriculture has long been at the core of Mediterranean culture, resulting in multifunctional landscapes and diverse ecosystem services. In Mediterranean Europe, policy favored specialized agriculture, and reversing this trend has proven difficult. Diversification of crop rotations holds ecological benefits, yet adoption remains low. The objective of this study was to accompany Spanish and Greek stakeholders in a structured learning process beginning with the co-design of available diversification options. It continued with an ex-ante assessment of agri-environmental, social, and economic performance of these options, followed by a co-evaluation step where stakeholders rated both the assessed performances and the indicators used. These ratings were analyzed using an importance-performance matrix. Finally, the adoption likelihood of diversification was predicted using the Adoption and Diffusion Outcome Prediction (ADOPT) tool. The ex-ante assessment revealed that legumes, rapeseed, and intercropping systems generally outperformed continuous cereal cropping in the agri-environmental and social dimensions but not economically, with a profit reduction of up to 12 %. From the stakeholders’ ratings, we learned that they placed the greatest importance on the economic indicators. In contrast, the agri-environmental dimension was given little importance even when energy use indicators increased by 5–42 %. Likewise, diversified systems offered notable social benefits, such as reduced workload by up to 29 %, but social aspects were ranked as less important. This divergent performance of the diversified options was translated into low adoption rates. Legume systems reached a 23–28 % adoption rate in 8–10 years, while intercropping reached 14 % in 17 years, and rapeseed systems reached only 4–5 % in 9–11 years. Economic performance emerged as the main barrier to the adoption of diversification. This study evaluated the impacts of different diversification options available to local farmers from both scientific and a local stakeholder perspective. This process can be adapted to other regions to create shared knowledge, thus enabling a wide range of actors to better understand diversification impacts. This knowledge gain affects the stakeholder’s capacity to adopt diversification options and, beforehand, their willingness to do so.
{"title":"Stakeholders' critical perception of diversification strategies in cereal-based rotations","authors":"Ferdaous Rezgui , Louise Blanc , Daniel Plaza-Bonilla , Jorge Lampurlanés , Christos Dordas , Paschalis Papakaloudis , Andreas Michalitsis , Laure Hossard , Fatima Lambarraa-Lehnhardt , Sonoko D. Bellingrath-Kimura , Carsten Paul , Moritz Reckling","doi":"10.1016/j.eja.2026.128000","DOIUrl":"10.1016/j.eja.2026.128000","url":null,"abstract":"<div><div>Agriculture has long been at the core of Mediterranean culture, resulting in multifunctional landscapes and diverse ecosystem services. In Mediterranean Europe, policy favored specialized agriculture, and reversing this trend has proven difficult. Diversification of crop rotations holds ecological benefits, yet adoption remains low. The objective of this study was to accompany Spanish and Greek stakeholders in a structured learning process beginning with the co-design of available diversification options. It continued with an ex-ante assessment of agri-environmental, social, and economic performance of these options, followed by a co-evaluation step where stakeholders rated both the assessed performances and the indicators used. These ratings were analyzed using an importance-performance matrix. Finally, the adoption likelihood of diversification was predicted using the Adoption and Diffusion Outcome Prediction (ADOPT) tool. The ex-ante assessment revealed that legumes, rapeseed, and intercropping systems generally outperformed continuous cereal cropping in the agri-environmental and social dimensions but not economically, with a profit reduction of up to 12 %. From the stakeholders’ ratings, we learned that they placed the greatest importance on the economic indicators. In contrast, the agri-environmental dimension was given little importance even when energy use indicators increased by 5–42 %. Likewise, diversified systems offered notable social benefits, such as reduced workload by up to 29 %, but social aspects were ranked as less important. This divergent performance of the diversified options was translated into low adoption rates. Legume systems reached a 23–28 % adoption rate in 8–10 years, while intercropping reached 14 % in 17 years, and rapeseed systems reached only 4–5 % in 9–11 years. Economic performance emerged as the main barrier to the adoption of diversification. This study evaluated the impacts of different diversification options available to local farmers from both scientific and a local stakeholder perspective. This process can be adapted to other regions to create shared knowledge, thus enabling a wide range of actors to better understand diversification impacts. This knowledge gain affects the stakeholder’s capacity to adopt diversification options and, beforehand, their willingness to do so.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128000"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962439","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}
Zinc (Zn) deficiency is a major constraint to maize yield and grain quality globally, especially in alkaline soils. The efficacy of conventional broadcast Zn fertilization is often limited by soil fixation and high spatial variability. This study evaluates when localized Zn placement surpasses broadcast application, and establishes recommended application rates. Furthermore, it develops spatially explicit, soil-Zn-stratified management strategies to boost maize productivity, providing a quantitative basis for nutrient management across diverse agroecosystems. By integrating a meta-analysis with Random Forest (RF) modeling to evaluate the efficacy of localized versus broadcast application of Zn fertilizer and predict yield responses across diverse agroecosystems. Localized Zn application significantly outperformed broadcast methods in terms of grain yield, increasing it by 8.8 % compared to 5.2 %. The advantage was particularly notable in alkaline soils pH > 7, soil organic matter (SOM) levels 10–20 g kg−1, and elevated soil total nitrogen (N) > 1 g kg−1. We identified annual precipitation and soil DTPA-Zn as the primary predictors of yield response. Recommend Zn application rates depended on soil Zn status: 8 kg ha−1 for 0.5–1.0 mg kg−1 soil DTPA-Zn, 4 kg ha−1 for 1.0–1.5 mg kg−1 soil DTPA-Zn, and 3 kg ha−1 for > 1.5 mg kg−1 soil DTPA-Zn. A scenario analysis projected that implementing a recommended national Zn application rate of 6.8 kg ha−1 could increase China's maize yield by an average of 3.9 %, with regional gains ranging from 2.3 % to 4.4 %. This study provides a unified framework for recommending zinc fertilization in maize by clarifying when localized application offers yield advantages and defining soil Zn thresholds for rate adjustment. The guidance developed here supports more efficient Zn use and provides actionable strategies to improve maize productivity across diverse agroecosystems.
锌（Zn）缺乏是全球玉米产量和粮食品质的主要制约因素，特别是在碱性土壤中。传统撒播施锌的效果往往受到土壤固结性和空间变异性的限制。本研究评估局部施锌何时优于广播施锌，并建立推荐施锌率。此外，它还制定了空间明确的土壤锌分层管理策略，以提高玉米生产力，为不同农业生态系统的养分管理提供定量基础。通过整合随机森林（RF）模型的荟萃分析，评估局部施用与撒播施用锌肥的效果，并预测不同农业生态系统的产量响应。在籽粒产量方面，局部施锌显著优于撒播方法，增产8.8 %，比5.2% %。在pH >； 7、土壤有机质（SOM）水平10-20 g kg−1和土壤全氮(N)水平升高>； 1 g kg−1的碱性土壤中，这种优势尤为显著。我们确定年降水量和土壤DTPA-Zn是产量响应的主要预测因子。推荐锌应用利率取决于土壤锌状态:8 公斤 公顷−1 0.5 -1.0 毫克公斤−1土壤DTPA-Zn 4 公斤 公顷−1 1.0 -1.5 毫克公斤−1土壤DTPA-Zn和3 公斤 公顷−1祝辞 1.5 毫克公斤−1土壤DTPA-Zn。情景分析预测，实施全国推荐的6.8 kg ha - 1锌施用量可使中国玉米产量平均提高3.9 %，区域增产幅度在2.3 %至4.4 %之间。本研究阐明了局部施锌何时具有产量优势，并确定了调整施锌量的土壤锌阈值，为推荐玉米施锌提供了统一的框架。这里制定的指南支持更有效地利用锌，并提供可操作的战略，以提高不同农业生态系统的玉米生产力。
{"title":"From application methods to rate recommendations: Integrated strategies for improving maize response to zinc fertilization","authors":"Fucheng Gao , Shan Chen , Chengxiang Zhou , Baogang Yu , Chunqin Zou","doi":"10.1016/j.eja.2026.128007","DOIUrl":"10.1016/j.eja.2026.128007","url":null,"abstract":"<div><div>Zinc (Zn) deficiency is a major constraint to maize yield and grain quality globally, especially in alkaline soils. The efficacy of conventional broadcast Zn fertilization is often limited by soil fixation and high spatial variability. This study evaluates when localized Zn placement surpasses broadcast application, and establishes recommended application rates. Furthermore, it develops spatially explicit, soil-Zn-stratified management strategies to boost maize productivity, providing a quantitative basis for nutrient management across diverse agroecosystems. By integrating a meta-analysis with Random Forest (RF) modeling to evaluate the efficacy of localized versus broadcast application of Zn fertilizer and predict yield responses across diverse agroecosystems. Localized Zn application significantly outperformed broadcast methods in terms of grain yield, increasing it by 8.8 % compared to 5.2 %. The advantage was particularly notable in alkaline soils pH > 7, soil organic matter (SOM) levels 10–20 g kg<sup>−1</sup>, and elevated soil total nitrogen (N) > 1 g kg<sup>−1</sup>. We identified annual precipitation and soil DTPA-Zn as the primary predictors of yield response. Recommend Zn application rates depended on soil Zn status: 8 kg ha<sup>−1</sup> for 0.5–1.0 mg kg<sup>−1</sup> soil DTPA-Zn, 4 kg ha<sup>−1</sup> for 1.0–1.5 mg kg<sup>−1</sup> soil DTPA-Zn, and 3 kg ha<sup>−1</sup> for > 1.5 mg kg<sup>−1</sup> soil DTPA-Zn. A scenario analysis projected that implementing a recommended national Zn application rate of 6.8 kg ha<sup>−1</sup> could increase China's maize yield by an average of 3.9 %, with regional gains ranging from 2.3 % to 4.4 %. This study provides a unified framework for recommending zinc fertilization in maize by clarifying when localized application offers yield advantages and defining soil Zn thresholds for rate adjustment. The guidance developed here supports more efficient Zn use and provides actionable strategies to improve maize productivity across diverse agroecosystems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128007"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014816","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 : 2026-04-01Epub Date: 2026-01-27DOI: 10.1016/j.eja.2026.128016
M. Córdoba , P. Paccioretti , C. Bozzer , M. Balzarini
Accurate field-scale mapping of soil organic matter (SOM) is essential for implementing precision agriculture strategies that enhance productivity and sustainability by supporting site-specific management. This study assessed three quantile-based machine learning (ML) algorithms—Quantile Regression Forest (QRF), Stochastic Gradient Boosting (SGB), and Deep Learning (DL)—in terms of predictive accuracy, uncertainty quantification, and spatial coherence. The models were trained using 7807 georeferenced SOM samples collected from 2052 fields, together with remote sensing and topographic covariates. To explicitly account for spatial autocorrelation, an additional covariate was derived from ordinary block kriging of SOM. Model performance was evaluated using root mean squared error (RMSE), mean error (ME), prediction interval coverage probability (PICP), and local standard deviation (LSD) as an indicator of spatial smoothness. Spatial validation was used to reduce potential bias arising from spatial autocorrelation. QRF consistently achieved the best balance among accuracy, uncertainty representation, and spatial coherence. Although SGB reached slightly higher accuracy, it underestimated uncertainty and produced noisier spatial patterns. DL generated the smoothest maps but tended to underestimate SOM and provided less reliable uncertainty estimates. Notably, QRF performance remained stable across fields with different sampling intensities, highlighting its robustness and practical relevance in data-limited scenarios. Overall, QRF models enhanced with spatially informed covariates provide a reliable framework for field-scale SOM prediction and uncertainty quantification—critical inputs for optimizing agricultural practices, guiding nutrient management, and supporting sustainable land management.
{"title":"Field-scale digital mapping of soil organic matter using spatially enhanced quantile machine-learning models","authors":"M. Córdoba , P. Paccioretti , C. Bozzer , M. Balzarini","doi":"10.1016/j.eja.2026.128016","DOIUrl":"10.1016/j.eja.2026.128016","url":null,"abstract":"<div><div>Accurate field-scale mapping of soil organic matter (SOM) is essential for implementing precision agriculture strategies that enhance productivity and sustainability by supporting site-specific management. This study assessed three quantile-based machine learning (ML) algorithms—Quantile Regression Forest (QRF), Stochastic Gradient Boosting (SGB), and Deep Learning (DL)—in terms of predictive accuracy, uncertainty quantification, and spatial coherence. The models were trained using 7807 georeferenced SOM samples collected from 2052 fields, together with remote sensing and topographic covariates. To explicitly account for spatial autocorrelation, an additional covariate was derived from ordinary block kriging of SOM. Model performance was evaluated using root mean squared error (RMSE), mean error (ME), prediction interval coverage probability (PICP), and local standard deviation (LSD) as an indicator of spatial smoothness. Spatial validation was used to reduce potential bias arising from spatial autocorrelation. QRF consistently achieved the best balance among accuracy, uncertainty representation, and spatial coherence. Although SGB reached slightly higher accuracy, it underestimated uncertainty and produced noisier spatial patterns. DL generated the smoothest maps but tended to underestimate SOM and provided less reliable uncertainty estimates. Notably, QRF performance remained stable across fields with different sampling intensities, highlighting its robustness and practical relevance in data-limited scenarios. Overall, QRF models enhanced with spatially informed covariates provide a reliable framework for field-scale SOM prediction and uncertainty quantification—critical inputs for optimizing agricultural practices, guiding nutrient management, and supporting sustainable land management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128016"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072118","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 : 2026-04-01Epub Date: 2026-02-02DOI: 10.1016/j.eja.2026.128019
Farooq Shah , Qiansi Liao , Yuxuan Gao , Dandan Wang , Zhaojie Li , Wei Wu
Given sugarcane’s key role in global sugar and bioethanol production, a substantial increase in yield is essential to meet escalating demands. However, due to its unique stem-harvesting nature, efforts to further boost yield could render it highly susceptible to lodging. Hence, agronomic interventions are urgently needed to balance the tradeoff between sugarcane yield and lodging resistance. The current research investigates the dynamic changes in sugarcane stem and root lodging resistance. It also evaluates the potential of two key agronomic practices, planting depth and bud density, to mitigate the tradeoff between sugarcane yield and lodging resistance, along with the underlying mechanisms. This three-year study employs the safety factor technique to evaluate sugarcane’s resistance to both stem and root lodging throughout its growing season, examining two planting depths (30 cm and 40 cm) and four bud densities (3.0, 4.5, 6.0, and 7.5 buds m–2). The highest susceptibility to lodging in sugarcane occurred between 180–210 DAP (days after planting). Deeper planting enhanced the lodging resistance of sugarcane without compromising yield. On the other hand, higher bud density improved sugar yield while maintaining or improving lodging resistance. Sugarcane exhibited greater susceptibility towards root lodging than stem lodging, whereas root system size was the key trait associated with enhanced lodging resistance under deeper planting. The enhanced lodging resistance with deeper planting and yield improvement with higher bud density implies that combining these agronomic practices can mitigate the tradeoff between sugarcane yield and lodging resistance. Given sugarcane’s high susceptibility to root lodging and the critical role of anchorage in resistance, agronomic and breeding strategies should prioritize expanding the root system size to improve stability and boost lodging resistance.
{"title":"Planting deeper with optimum bud density improves lodging resistance and sugar yield in sugarcane (Saccharum officinarum)","authors":"Farooq Shah , Qiansi Liao , Yuxuan Gao , Dandan Wang , Zhaojie Li , Wei Wu","doi":"10.1016/j.eja.2026.128019","DOIUrl":"10.1016/j.eja.2026.128019","url":null,"abstract":"<div><div>Given sugarcane’s key role in global sugar and bioethanol production, a substantial increase in yield is essential to meet escalating demands. However, due to its unique stem-harvesting nature, efforts to further boost yield could render it highly susceptible to lodging. Hence, agronomic interventions are urgently needed to balance the tradeoff between sugarcane yield and lodging resistance. The current research investigates the dynamic changes in sugarcane stem and root lodging resistance. It also evaluates the potential of two key agronomic practices, planting depth and bud density, to mitigate the tradeoff between sugarcane yield and lodging resistance, along with the underlying mechanisms. This three-year study employs the safety factor technique to evaluate sugarcane’s resistance to both stem and root lodging throughout its growing season, examining two planting depths (30 cm and 40 cm) and four bud densities (3.0, 4.5, 6.0, and 7.5 buds m<sup>–2</sup>). The highest susceptibility to lodging in sugarcane occurred between 180–210 DAP (days after planting). Deeper planting enhanced the lodging resistance of sugarcane without compromising yield. On the other hand, higher bud density improved sugar yield while maintaining or improving lodging resistance. Sugarcane exhibited greater susceptibility towards root lodging than stem lodging, whereas root system size was the key trait associated with enhanced lodging resistance under deeper planting. The enhanced lodging resistance with deeper planting and yield improvement with higher bud density implies that combining these agronomic practices can mitigate the tradeoff between sugarcane yield and lodging resistance. Given sugarcane’s high susceptibility to root lodging and the critical role of anchorage in resistance, agronomic and breeding strategies should prioritize expanding the root system size to improve stability and boost lodging resistance.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128019"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110582","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 : 2026-04-01Epub Date: 2026-01-09DOI: 10.1016/j.eja.2026.127993
Shun Wang , Bowen Zhang , Yinchao Che , Guang Zheng , Yanna Ren , Lei Xi , Xinming Ma , Shuping Xiong
Accurate detection of wheat seedlings is crucial for monitoring early population establishment and evaluating sowing quality. However, detection in real field environments remains challenging due to diverse seedling morphology, varying planting densities, occlusion, and complex background interference. Although deep learning has promoted the development of agricultural vision systems, existing wheat seedling detection methods still suffer from two key limitations: (1) insufficient modeling of spatial contextual relationships, leading to degraded accuracy under dense planting and complex field conditions; and (2) difficulty in balancing detection performance and computational efficiency, restricting real-time deployment on resource-limited agricultural devices. To address these issues, this study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules: (1) the Depthwise-Transformer-Vision (DTV) module integrates Depthwise Separable Convolutions (DSC), Vision Transformer, and multi-scale spatial pooling to efficiently represent local structures, spatial context, and global patterns of wheat seedlings; (2) the Feature Enhancement Module(FEM) incorporates coordinate attention to enhance seedling-related features while suppressing background interference; and (3) the Feature Coordination Module (FCM) performs multi-scale feature interaction with reduced computational cost. These components jointly improve robustness under dense planting and complex field conditions while maintaining lightweight deployment characteristics. Furthermore, we construct the Wheat Seedling Dataset (WSD), covering multiple planting densities, varieties, and field environments across two growing seasons. Experimental results show that TCE-YOLO outperforms mainstream detectors while maintaining high efficiency, providing a deployable solution for wheat seedling detection under real field conditions.
{"title":"A wheat seedling detection model based on efficient feature extraction and coordinate attention mechanism","authors":"Shun Wang , Bowen Zhang , Yinchao Che , Guang Zheng , Yanna Ren , Lei Xi , Xinming Ma , Shuping Xiong","doi":"10.1016/j.eja.2026.127993","DOIUrl":"10.1016/j.eja.2026.127993","url":null,"abstract":"<div><div>Accurate detection of wheat seedlings is crucial for monitoring early population establishment and evaluating sowing quality. However, detection in real field environments remains challenging due to diverse seedling morphology, varying planting densities, occlusion, and complex background interference. Although deep learning has promoted the development of agricultural vision systems, existing wheat seedling detection methods still suffer from two key limitations: (1) insufficient modeling of spatial contextual relationships, leading to degraded accuracy under dense planting and complex field conditions; and (2) difficulty in balancing detection performance and computational efficiency, restricting real-time deployment on resource-limited agricultural devices. To address these issues, this study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules: (1) the Depthwise-Transformer-Vision (DTV) module integrates Depthwise Separable Convolutions (DSC), Vision Transformer, and multi-scale spatial pooling to efficiently represent local structures, spatial context, and global patterns of wheat seedlings; (2) the Feature Enhancement Module(FEM) incorporates coordinate attention to enhance seedling-related features while suppressing background interference; and (3) the Feature Coordination Module (FCM) performs multi-scale feature interaction with reduced computational cost. These components jointly improve robustness under dense planting and complex field conditions while maintaining lightweight deployment characteristics. Furthermore, we construct the Wheat Seedling Dataset (WSD), covering multiple planting densities, varieties, and field environments across two growing seasons. Experimental results show that TCE-YOLO outperforms mainstream detectors while maintaining high efficiency, providing a deployable solution for wheat seedling detection under real field conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 127993"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928664","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 : 2026-04-01Epub Date: 2026-01-13DOI: 10.1016/j.eja.2026.128003
Godspower Oke Omokaro
Herbicides remain the dominant tools for weed control because of their cost effectiveness and selectivity, yet prolonged and intensive use has raised concern regarding soil degradation, disruption of microbial communities, non-target effects, and the rapid emergence of herbicide resistance. This research synthesizes evidence on the ecological impacts of herbicides and evaluates biological control strategies as sustainable and complementary alternatives within integrated weed management. A PRISMA-ScR guided literature review identified 108 peer reviewed studies published between 2000 and 2025 from Scopus, PubMed, ScienceDirect and SpringerLink, with selective inclusion of foundational literature capturing early biological weed control research. Evidence indicates that herbicides alter soil microbial biomass, enzyme activity, and community composition, with outcomes dependent on herbicide class, application rate, soil properties, and environmental context. Glyphosate and atrazine suppress sensitive microbial taxa while enriching specialized degraders, reflecting ecological disruption and microbial adaptation. Fungal communities, particularly arbuscular mycorrhizal fungi, are consistently vulnerable, leading to reduced nutrient acquisition and weakened plant resilience. Herbicide resistance continues to expand globally, undermining long term chemical efficacy. Biological control strategies, including microbial agents such as Trichoderma and Bacillus, insect herbivores, grazing animals, allelopathic crops, bioherbicides, compost and biochar, demonstrate diverse mechanisms of weed suppression and soil restoration across agroecosystems. These approaches enhance crop competitiveness and stimulate beneficial microbial functions, although field performance is constrained by environmental variability, formulation stability, regulatory barriers, and limited extension support. The findings emphasize the need for integrative and sound weed management.
{"title":"Biological control strategies as sustainable alternatives to herbicides in weed management","authors":"Godspower Oke Omokaro","doi":"10.1016/j.eja.2026.128003","DOIUrl":"10.1016/j.eja.2026.128003","url":null,"abstract":"<div><div>Herbicides remain the dominant tools for weed control because of their cost effectiveness and selectivity, yet prolonged and intensive use has raised concern regarding soil degradation, disruption of microbial communities, non-target effects, and the rapid emergence of herbicide resistance. This research synthesizes evidence on the ecological impacts of herbicides and evaluates biological control strategies as sustainable and complementary alternatives within integrated weed management. A PRISMA-ScR guided literature review identified 108 peer reviewed studies published between 2000 and 2025 from Scopus, PubMed, ScienceDirect and SpringerLink, with selective inclusion of foundational literature capturing early biological weed control research. Evidence indicates that herbicides alter soil microbial biomass, enzyme activity, and community composition, with outcomes dependent on herbicide class, application rate, soil properties, and environmental context. Glyphosate and atrazine suppress sensitive microbial taxa while enriching specialized degraders, reflecting ecological disruption and microbial adaptation. Fungal communities, particularly arbuscular mycorrhizal fungi, are consistently vulnerable, leading to reduced nutrient acquisition and weakened plant resilience. Herbicide resistance continues to expand globally, undermining long term chemical efficacy. Biological control strategies, including microbial agents such as <em>Trichoderma</em> and <em>Bacillus</em>, insect herbivores, grazing animals, allelopathic crops, bioherbicides, compost and biochar, demonstrate diverse mechanisms of weed suppression and soil restoration across agroecosystems. These approaches enhance crop competitiveness and stimulate beneficial microbial functions, although field performance is constrained by environmental variability, formulation stability, regulatory barriers, and limited extension support. The findings emphasize the need for integrative and sound weed management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128003"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961720","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 : 2026-04-01Epub Date: 2026-02-10DOI: 10.1016/j.eja.2026.128033
Jinhui Zheng , Le Yu
Accurately predicting the grain protein content (GPC) and yield of winter wheat is of significant strategic importance amid rising food demand and intensifying global market competition. However, traditional single-model approaches struggle to achieve high simulation accuracy in complex agricultural ecosystems. This study proposes a novel multi-model ensemble (MME) framework that integrates the APSIM-NG (Agricultural Production Systems Simulator-Next Generation) process-based crop model, four machine learning algorithms (Random Forest, Extreme Gradient Boosting, Multiple Linear Regression, and Long Short-Term Memory), and two ensemble methods (AIC-weighted model averaging and simple model averaging) to enhance the predictive accuracy of GPC and yield in North China Plain. The MME framework incorporates remote sensing data, extreme weather indices, and crop growth observations from 2008 to 2020 for a comprehensive performance evaluation. Validation results for the period 2015–2020 indicate that the MME framework outperforms both the baseline APSIM-NG model and the best-performing machine-learning method, achieving a Pearson’s r of 0.89 (RMSE = 0.32 %, R² = 0.76) for GPC prediction and reducing the yield RMSE to 316.96 kg/ha (Pearson’s r = 0.94, R² = 0.91). Furthermore, importance analysis indicates that within this framework, photosynthesis-related and extreme stress factors are the most influential predictors, contributing 8–12 % to model importance, highlighting the substantial impact of including extreme weather factors on model accuracy. By effectively combining process-based modeling with data-driven methods, the MME framework significantly enhances predictive accuracy and model robustness. These findings offer a more reliable technical foundation for forecasting winter wheat yield and grain quality under variable and extreme climatic conditions.
在粮食需求不断增长和全球市场竞争日益激烈的背景下，准确预测冬小麦籽粒蛋白质含量和产量具有重要的战略意义。然而，在复杂的农业生态系统中，传统的单模型方法难以达到较高的模拟精度。本文提出了一种基于APSIM-NG （Agricultural Production Systems Simulator-Next Generation）过程的作物模型、4种机器学习算法（随机森林、极端梯度增强、多元线性回归和长短期记忆）和2种集成方法（aic加权模型平均和简单模型平均）的多模型集成框架，以提高华北平原GPC和产量的预测精度。MME框架结合了2008年至2020年的遥感数据、极端天气指数和作物生长观测数据，以进行综合绩效评估。2015-2020年期间的验证结果表明，MME框架优于基准apsm - ng模型和性能最佳的机器学习方法，实现了0.89的Pearson’s r （RMSE = 0.32 %,r²= 0.76）的GPC预测，并将产量RMSE降至316.96 kg/ha （Pearson’s r = 0.94,r²= 0.91）。此外，重要性分析表明，在此框架内，光合作用相关因子和极端胁迫因子是影响最大的预测因子，对模型重要性的贡献为8-12 %，这突出了纳入极端天气因子对模型精度的重大影响。通过将基于过程的建模与数据驱动的方法有效结合，MME框架显著提高了预测精度和模型鲁棒性。这些发现为预测多变和极端气候条件下冬小麦产量和籽粒品质提供了更可靠的技术基础。
{"title":"Multi-model ensemble of process-based and data-driven approaches improves modeling of wheat grain protein content and yield","authors":"Jinhui Zheng , Le Yu","doi":"10.1016/j.eja.2026.128033","DOIUrl":"10.1016/j.eja.2026.128033","url":null,"abstract":"<div><div>Accurately predicting the grain protein content (GPC) and yield of winter wheat is of significant strategic importance amid rising food demand and intensifying global market competition. However, traditional single-model approaches struggle to achieve high simulation accuracy in complex agricultural ecosystems. This study proposes a novel multi-model ensemble (MME) framework that integrates the APSIM-NG (Agricultural Production Systems Simulator-Next Generation) process-based crop model, four machine learning algorithms (Random Forest, Extreme Gradient Boosting, Multiple Linear Regression, and Long Short-Term Memory), and two ensemble methods (AIC-weighted model averaging and simple model averaging) to enhance the predictive accuracy of GPC and yield in North China Plain. The MME framework incorporates remote sensing data, extreme weather indices, and crop growth observations from 2008 to 2020 for a comprehensive performance evaluation. Validation results for the period 2015–2020 indicate that the MME framework outperforms both the baseline APSIM-NG model and the best-performing machine-learning method, achieving a Pearson’s r of 0.89 (RMSE = 0.32 %, R² = 0.76) for GPC prediction and reducing the yield RMSE to 316.96 kg/ha (Pearson’s r = 0.94, R² = 0.91). Furthermore, importance analysis indicates that within this framework, photosynthesis-related and extreme stress factors are the most influential predictors, contributing 8–12 % to model importance, highlighting the substantial impact of including extreme weather factors on model accuracy. By effectively combining process-based modeling with data-driven methods, the MME framework significantly enhances predictive accuracy and model robustness. These findings offer a more reliable technical foundation for forecasting winter wheat yield and grain quality under variable and extreme climatic conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128033"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153274","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 : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.eja.2025.127979
Haifang Pang , Shangying Ma , Fengni Xue , Zongzhen Li , Junjie Hu , Zeyu Xin , Yongzhe Ren , Yanhao Lian , Tongbao Lin , Zhiqiang Wang
Global warming-induced drought threatens the micronutrient quality of agricultural products, as water availability governs nutrient translocation and partitioning within plants. However, the effects of supplemental irrigation on wheat micronutrient dynamics under warming conditions remain poorly characterised. Here, a 3-year field experiment was conducted to investigate the effects of warming treatments (CK: ambient temperature; WS: winter-spring warming; FF: flowering-grain filling warming) and irrigation regimes (CI: conventional irrigation; SI: supplemental irrigation) on metal micronutrients (Fe, Mn, Cu, and Zn) in winter wheat and their driving factors. Results showed that warming slightly increased grain Fe, Mn, and Zn concentrations (0.3–5.8 %) but reduced their total accumulations (17.1–34.6 %). Compared to CK, FF decreased soil available micronutrients (9–35.7 %), with Fe and Zn availability strongly correlated with microbial taxa (Chloroflexi, Proteobacteria, and Firmicutes). SI increased grain Fe (3.1 %) and Zn (7.1 %) concentrations, enhanced total micronutrient uptake (6.8–22.6 %), and elevated soil-available micronutrients (6.2–25.8 %) relative to CI. Structural equation modelling revealed that soil temperature, moisture, pH, nutrients, available micronutrients, and microbiota jointly regulated grain micronutrients through direct pathways, with microbes being a key driver (total direct effect value = 0.619, p < 0.001). We conclude that supplemental irrigation effectively mitigates warming-induced micronutrient depletion, whereas the soil microbiota plays a pivotal role in mediating wheat micronutrient acquisition. These findings advance adaptive strategies to safeguard crop nutritional security under changing climate conditions.
{"title":"Supplementary irrigation alleviates the inhibition effect of warming on metallic micronutrients absorption","authors":"Haifang Pang , Shangying Ma , Fengni Xue , Zongzhen Li , Junjie Hu , Zeyu Xin , Yongzhe Ren , Yanhao Lian , Tongbao Lin , Zhiqiang Wang","doi":"10.1016/j.eja.2025.127979","DOIUrl":"10.1016/j.eja.2025.127979","url":null,"abstract":"<div><div>Global warming-induced drought threatens the micronutrient quality of agricultural products, as water availability governs nutrient translocation and partitioning within plants. However, the effects of supplemental irrigation on wheat micronutrient dynamics under warming conditions remain poorly characterised. Here, a 3-year field experiment was conducted to investigate the effects of warming treatments (CK: ambient temperature; WS: winter-spring warming; FF: flowering-grain filling warming) and irrigation regimes (CI: conventional irrigation; SI: supplemental irrigation) on metal micronutrients (Fe, Mn, Cu, and Zn) in winter wheat and their driving factors. Results showed that warming slightly increased grain Fe, Mn, and Zn concentrations (0.3–5.8 %) but reduced their total accumulations (17.1–34.6 %). Compared to CK, FF decreased soil available micronutrients (9–35.7 %), with Fe and Zn availability strongly correlated with microbial taxa (Chloroflexi, Proteobacteria, and Firmicutes). SI increased grain Fe (3.1 %) and Zn (7.1 %) concentrations, enhanced total micronutrient uptake (6.8–22.6 %), and elevated soil-available micronutrients (6.2–25.8 %) relative to CI. Structural equation modelling revealed that soil temperature, moisture, pH, nutrients, available micronutrients, and microbiota jointly regulated grain micronutrients through direct pathways, with microbes being a key driver (total direct effect value = 0.619, p < 0.001). We conclude that supplemental irrigation effectively mitigates warming-induced micronutrient depletion, whereas the soil microbiota plays a pivotal role in mediating wheat micronutrient acquisition. These findings advance adaptive strategies to safeguard crop nutritional security under changing climate conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 127979"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897842","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 : 2026-04-01Epub Date: 2026-01-17DOI: 10.1016/j.eja.2026.128008
Liang Fang , Dongping Shen , Zhen Wang , Linli Zhou , Tingting Zhang , Guoqiang Zhang , Jun Xue , Ruizhi Xie , Peng Hou , Keru Wang , Bo Ming , Ling Gou , Shaokun Li
Confronted with the dual imperatives of ensuring food security and reducing environmental pollution in China’s intensive agricultural systems, this study proposes and validates an innovative crop management paradigm: a High-Density Production System enabled by Precision Stage-Specific Regulation (HD-PSR). Based on a three-year field experiment spanning a wide nitrogen (N) application gradient (0–765 kg N ha⁻¹), we assessed the effects of N rate on grain yield, nitrogen partial factor productivity (PFPN), plant N dynamics (uptake, distribution, and remobilization), soil residual N, and nitrous oxide (N₂O) emissions. The results show that the system achieved a clear yield plateau of 14.7–16.5 t ha⁻¹ at 243.8–306.4 kg N ha⁻¹ , while sustaining efficient internal N uptake and remobilization, providing a strong physiological basis for high yield. Simultaneously, the system markedly reduced the direct N₂O emission factor to a consistently low range of 0.3 %–0.9 %, well below the IPCC default. Notably, both cumulative N₂O emissions and the emission factor exhibited a strictly linear relationship with N application rate, in contrast to the exponential increases widely reported under conventional fertilization. This linearity is attributed to split application, which prevents the accumulation of soil mineral N that typically triggers microbial N₂O emission pulses. A comprehensive benefit index identified approximately 289 kg N ha⁻¹ as the synergistic optimum for high yield and low emissions. Collectively, these findings demonstrate that HD-PSR—through deep integration of high-density planting with whole-season, physiology-oriented precision regulation—can simultaneously enhance grain yield and nitrogen-use sustainability, offering a practical systemic pathway for the sustainable intensification of cereal production.
面对中国集约化农业系统中确保粮食安全和减少环境污染的双重需求，本研究提出并验证了一种创新的作物管理模式：由精确阶段特定调控（HD-PSR）实现的高密度生产系统。通过一项为期3年的大田试验，研究了不同施氮量（0-765 kg N ha⁻¹）对粮食产量、氮素偏因子生产力（PFPN）、植株氮素动态（吸收、分配和再动员）、土壤残氮和氧化亚氮（N₂O）排放的影响。结果表明，该体系达到了14.7-16.5 tha⁻¹ （243.8-306.4 kg N ha⁻¹ ）的明显产量平台，同时保持了体内氮的有效吸收和再迁移，为高产提供了强有力的生理基础。同时，该系统显著降低了直接的N₂O排放因子，持续降低到0.3 % -0.9 %的较低范围，远低于IPCC的默认值。值得注意的是，累积N₂O排放量和排放因子与施氮量呈严格的线性关系，而常规施肥则呈指数增长。这种线性归因于拆分应用，这可以防止土壤矿物N的积累，而土壤矿物N通常会触发微生物N₂O发射脉冲。综合效益指数确定约289 kg N ha⁻¹ 为高产低排放的协同最优。综上所述，通过高密度种植与全季、以生理为导向的精准调控的深度融合，hd - psr可以同时提高粮食产量和氮素利用的可持续性，为谷物生产的可持续集约化提供了切实可行的系统途径。
{"title":"Achieving synergistic improvements in maize yield and nitrogen use sustainability through a novel high-density production system enabled by precision stage-specific regulation","authors":"Liang Fang , Dongping Shen , Zhen Wang , Linli Zhou , Tingting Zhang , Guoqiang Zhang , Jun Xue , Ruizhi Xie , Peng Hou , Keru Wang , Bo Ming , Ling Gou , Shaokun Li","doi":"10.1016/j.eja.2026.128008","DOIUrl":"10.1016/j.eja.2026.128008","url":null,"abstract":"<div><div>Confronted with the dual imperatives of ensuring food security and reducing environmental pollution in China’s intensive agricultural systems, this study proposes and validates an innovative crop management paradigm: a High-Density Production System enabled by Precision Stage-Specific Regulation (HD-PSR). Based on a three-year field experiment spanning a wide nitrogen (N) application gradient (0–765 kg N ha⁻¹), we assessed the effects of N rate on grain yield, nitrogen partial factor productivity (PFP<sub>N</sub>), plant N dynamics (uptake, distribution, and remobilization), soil residual N, and nitrous oxide (N₂O) emissions. The results show that the system achieved a clear yield plateau of 14.7–16.5 t ha⁻¹ at 243.8–306.4 kg N ha⁻¹ , while sustaining efficient internal N uptake and remobilization, providing a strong physiological basis for high yield. Simultaneously, the system markedly reduced the direct N₂O emission factor to a consistently low range of 0.3 %–0.9 %, well below the IPCC default. Notably, both cumulative N₂O emissions and the emission factor exhibited a strictly linear relationship with N application rate, in contrast to the exponential increases widely reported under conventional fertilization. This linearity is attributed to split application, which prevents the accumulation of soil mineral N that typically triggers microbial N₂O emission pulses. A comprehensive benefit index identified approximately 289 kg N ha⁻¹ as the synergistic optimum for high yield and low emissions. Collectively, these findings demonstrate that HD-PSR—through deep integration of high-density planting with whole-season, physiology-oriented precision regulation—can simultaneously enhance grain yield and nitrogen-use sustainability, offering a practical systemic pathway for the sustainable intensification of cereal production.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128008"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979827","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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