Pub Date : 2025-12-24DOI: 10.1016/j.fcr.2025.110313
Yalin Yu , Menghan Dong , Yuanchang Xia , Bin Sun , Yanling Li , Ahmad Latif Virk , Haishui Yang , Feng-Min Li , Shiping Liu , Zheng-Rong Kan
Context
Conservation tillage (no-tillage straw mulch, NTS) is widely adopted to improve soil quality and soil organic carbon (SOC) sequestration, yet its yield effects in rice-wheat systems are inconsistent.
Objective
A 20-year field experiment was designed to quantify the impacts of NTS on rice and wheat yields and identify underlying mechanisms from soil quality.
Methods
Field experiment included three treatments: NTS, plow-tillage straw removal (PT0), and plow-tillage straw return (PTS). Crop yield and its components were measured annually. Soil samples for soil quality index (SQI), enzyme stoichiometry, and soil bulk density analysis were collected at rice harvest in October 2021 from 0 to 5 cm and 5 to 15 cm layers.
Results
NTS significantly increased wheat yield by 9.19 % and 7.88 %, whereas decreased rice yield by 3.73 % and 13.78 % compared with PT0 and PTS from 2021 to 2023 (20–22 years after establishment), respectively. The increase in wheat yield was attributed to the improvement of SQI in topsoil (0–5 cm). Due to the higher SOC and total nitrogen, NTS improved SQI by 102 % and 87.1 % compared with PT0 and PTS in topsoil, respectively. In contrast, rice yield was mainly affected by soil bulk density, with the highest yield observed under PTS. NTS caused greater bulk density in subsoil (5–15 cm) in flooded paddy soils, consequently inhibiting rice root development. In topsoil, enzyme stoichiometric analysis indicated that NTS shifted the soil microbial nutrient acquisition strategy toward a greater demand for nitrogen (N). Given high N demands for rice, this shift may significantly constrain nutrient uptake and crop productivity.
Conclusions
Long-term NTS improves topsoil quality and boosts wheat yield, but compaction and insufficient N supply in subsoil reduce rice yield in flooded paddies.
Implications
Our findings reveal a trade-off in the effects of long-term conservation tillage on crop yields with distinct mechanisms in rice-wheat cropping systems. This insight provides critical empirical basis for future targeted optimization of tillage practices to balance yield sustainability in the lower Yangtze River region.
{"title":"Twenty-year field evidence reveals crop-specific impacts of conservation tillage on yield in a rice-wheat system","authors":"Yalin Yu , Menghan Dong , Yuanchang Xia , Bin Sun , Yanling Li , Ahmad Latif Virk , Haishui Yang , Feng-Min Li , Shiping Liu , Zheng-Rong Kan","doi":"10.1016/j.fcr.2025.110313","DOIUrl":"10.1016/j.fcr.2025.110313","url":null,"abstract":"<div><h3>Context</h3><div>Conservation tillage (no-tillage straw mulch, NTS) is widely adopted to improve soil quality and soil organic carbon (SOC) sequestration, yet its yield effects in rice-wheat systems are inconsistent.</div></div><div><h3>Objective</h3><div>A 20-year field experiment was designed to quantify the impacts of NTS on rice and wheat yields and identify underlying mechanisms from soil quality.</div></div><div><h3>Methods</h3><div>Field experiment included three treatments: NTS, plow-tillage straw removal (PT0), and plow-tillage straw return (PTS). Crop yield and its components were measured annually. Soil samples for soil quality index (SQI), enzyme stoichiometry, and soil bulk density analysis were collected at rice harvest in October 2021 from 0 to 5 cm and 5 to 15 cm layers.</div></div><div><h3>Results</h3><div>NTS significantly increased wheat yield by 9.19 % and 7.88 %, whereas decreased rice yield by 3.73 % and 13.78 % compared with PT0 and PTS from 2021 to 2023 (20–22 years after establishment), respectively. The increase in wheat yield was attributed to the improvement of SQI in topsoil (0–5 cm). Due to the higher SOC and total nitrogen, NTS improved SQI by 102 % and 87.1 % compared with PT0 and PTS in topsoil, respectively. In contrast, rice yield was mainly affected by soil bulk density, with the highest yield observed under PTS. NTS caused greater bulk density in subsoil (5–15 cm) in flooded paddy soils, consequently inhibiting rice root development. In topsoil, enzyme stoichiometric analysis indicated that NTS shifted the soil microbial nutrient acquisition strategy toward a greater demand for nitrogen (N). Given high N demands for rice, this shift may significantly constrain nutrient uptake and crop productivity.</div></div><div><h3>Conclusions</h3><div>Long-term NTS improves topsoil quality and boosts wheat yield, but compaction and insufficient N supply in subsoil reduce rice yield in flooded paddies.</div></div><div><h3>Implications</h3><div>Our findings reveal a trade-off in the effects of long-term conservation tillage on crop yields with distinct mechanisms in rice-wheat cropping systems. This insight provides critical empirical basis for future targeted optimization of tillage practices to balance yield sustainability in the lower Yangtze River region.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110313"},"PeriodicalIF":6.4,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.fcr.2025.110311
Masoud Karbasi , Gurjit S. Randhawa , Aitazaz A. Farooque , Mumtaz Ali , Mehdi Jamei , Khabat Khosravi , Hassan Afzal , Anurag Malik , Qamar U. Zaman
Context
Yield forecasting is crucial to the agricultural planning enterprise, such as input control, farm logistics and reduction of economic risks. The soils in the Maritime provinces of Canada have a great difference in their properties which affect the productivity of crops. Such variability requires a strong prediction model that could address the different characteristics of soil.
Objective
This research proposal is expected to establish a stable potato yield prediction model based on the soil property data of New Brunswick and Prince Edward Island and determine whether the application of optimization techniques with deep learning can enhance the prediction accuracy over the conventional machine learning approach.
Methods
Soil samples were taken at eight experimental sites in the 2017 and 2018 growing seasons, with 18 soil properties being captured. The feature selection techniques were used to create three input scenarios (Comb1, Comb2, Comb3). To optimize the selection of input variables, a hybrid prediction model, DNN-SBO (Deep Neural Network -Satin Bowerbird Optimization), was suggested and refined with the Boruta feature selection and Best Subset Regression-WASPAS. The performance of the model was tested in comparison with Kernel Ridge Regression (KRR), Elastic Net, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR), on the evaluation metrics of Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The model interpretability was done using SHAP (Shapley Additive exPlanation) analysis.
Results and Conclusions
Comb2 was the best input scenario that consisted of Total Base Saturation, Sulfur, Magnesium, Potash, Aluminum, Zinc, Phosphate, Manganese, Organic Matter, Iron, and Copper. DNN-SBO model had the best predictive power with R= 0.903 (train) and RMSE= 4.165 t/ha and MAPE= 6.766 % and R= 0.853(test) and RMSE= 5.522 t/ha and MAPE= 9.707 %. The SHAP analysis has shown that Iron was the most significant predictor (mean SHAP = +5.49), next was Copper, Zinc, Phosphorus, and Organic Matter.
Significance
The paper sheds light on the promise of deep learning that is based on bio-inspired optimization and feature selection techniques in order to achieve a significant increase in crop yield prediction. The findings can lead to the wider use of the similar methods in precision agriculture, which will result in smarter and data-driven farming in variably soiled areas.
{"title":"Utilizing soil characteristics and hybrid machine learning for interpretable potato yield prediction: A study with satin-bowerbird optimization and deep neural network","authors":"Masoud Karbasi , Gurjit S. Randhawa , Aitazaz A. Farooque , Mumtaz Ali , Mehdi Jamei , Khabat Khosravi , Hassan Afzal , Anurag Malik , Qamar U. Zaman","doi":"10.1016/j.fcr.2025.110311","DOIUrl":"10.1016/j.fcr.2025.110311","url":null,"abstract":"<div><h3>Context</h3><div>Yield forecasting is crucial to the agricultural planning enterprise, such as input control, farm logistics and reduction of economic risks. The soils in the Maritime provinces of Canada have a great difference in their properties which affect the productivity of crops. Such variability requires a strong prediction model that could address the different characteristics of soil.</div></div><div><h3>Objective</h3><div>This research proposal is expected to establish a stable potato yield prediction model based on the soil property data of New Brunswick and Prince Edward Island and determine whether the application of optimization techniques with deep learning can enhance the prediction accuracy over the conventional machine learning approach.</div></div><div><h3>Methods</h3><div>Soil samples were taken at eight experimental sites in the 2017 and 2018 growing seasons, with 18 soil properties being captured. The feature selection techniques were used to create three input scenarios (Comb1, Comb2, Comb3). To optimize the selection of input variables, a hybrid prediction model, DNN-SBO (Deep Neural Network -Satin Bowerbird Optimization), was suggested and refined with the Boruta feature selection and Best Subset Regression-WASPAS. The performance of the model was tested in comparison with Kernel Ridge Regression (KRR), Elastic Net, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR), on the evaluation metrics of Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The model interpretability was done using SHAP (Shapley Additive exPlanation) analysis.</div></div><div><h3>Results and Conclusions</h3><div>Comb2 was the best input scenario that consisted of Total Base Saturation, Sulfur, Magnesium, Potash, Aluminum, Zinc, Phosphate, Manganese, Organic Matter, Iron, and Copper. DNN-SBO model had the best predictive power with R= 0.903 (train) and RMSE= 4.165 t/ha and MAPE= 6.766 % and R= 0.853(test) and RMSE= 5.522 t/ha and MAPE= 9.707 %. The SHAP analysis has shown that Iron was the most significant predictor (mean SHAP = +5.49), next was Copper, Zinc, Phosphorus, and Organic Matter.</div></div><div><h3>Significance</h3><div>The paper sheds light on the promise of deep learning that is based on bio-inspired optimization and feature selection techniques in order to achieve a significant increase in crop yield prediction. The findings can lead to the wider use of the similar methods in precision agriculture, which will result in smarter and data-driven farming in variably soiled areas.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110311"},"PeriodicalIF":6.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.fcr.2025.110312
Tao Yushan , Wang Jie , Ma Xiaohan , Zhang Shuxiang , Guo Yanjun
Context
Efficient management of crop straw and soil phosphorus (P) is vital for maintaining productivity and sustainability in China’s intensive farmlands. While straw return is widely practiced, the mechanistic effects on soil P fractions across diverse soils and climates remain unclear, limiting development of optimized, site-specific strategies.
Problem
Despite widespread straw return, the pathways by which it enhances P availability are poorly quantified. Key uncertainties remain regarding: (1) differential responses of total P (TP), available P (AP), and Olsen-P to straw return; (2) interactions between soil properties, climate, and management duration; and (3) the relative contributions of organic matter accumulation versus chemical solubilization in P mobilization. This gap hinders mechanistic understanding and the design of precision nutrient management strategies.
Objectives and Methods
This meta-analysis synthesized 263 datasets from 55 studies (up to December 31, 2024) to: (1) quantify straw return effects on TP, AP, and Olsen-P; (2) identify key environmental and management drivers of variability; and (3) elucidate underlying mechanistic pathways linking straw return, soil organic matter (SOM), and P bioavailability. Analyses included random-effects models (LnRR effect sizes), meta-regression, random forest algorithms, and structural equation modeling (SEM).
Results
Straw return significantly increased SOM (+4.1 %), TP (+7.1 %), AP (+8.3 %), and Olsen-P (+11.2 %). TP accumulation was mainly driven by the duration of straw incorporation, while Olsen-P increases were strongly influenced by initial soil pH, with greater enhancement in acidic and neutral soils than in alkaline soils. Random forest models identified initial soil properties, especially total nitrogen and pH, as dominant predictors of P responses. SEM indicated that SOM serves as a central hub, directly enhancing P fractions and indirectly increasing bioavailability via soil acidification. Straw return enhances P through two complementary mechanisms: long-term physical TP buildup and rapid biochemical activation of bioavailable P.
Conclusions
Straw return systematically improves soil P supply in Chinese croplands via dual SOM-mediated pathways.
Implications
These findings support transitioning from uniform straw return practices to precision strategies tailored to local soil conditions and target P fractions, advancing both nutrient use efficiency and soil health.
{"title":"Mechanisms linking straw return to soil phosphorus cycling in Chinese farmland: A meta-analysis","authors":"Tao Yushan , Wang Jie , Ma Xiaohan , Zhang Shuxiang , Guo Yanjun","doi":"10.1016/j.fcr.2025.110312","DOIUrl":"10.1016/j.fcr.2025.110312","url":null,"abstract":"<div><h3>Context</h3><div>Efficient management of crop straw and soil phosphorus (P) is vital for maintaining productivity and sustainability in China’s intensive farmlands. While straw return is widely practiced, the mechanistic effects on soil P fractions across diverse soils and climates remain unclear, limiting development of optimized, site-specific strategies.</div></div><div><h3>Problem</h3><div>Despite widespread straw return, the pathways by which it enhances P availability are poorly quantified. Key uncertainties remain regarding: (1) differential responses of total P (TP), available P (AP), and Olsen-P to straw return; (2) interactions between soil properties, climate, and management duration; and (3) the relative contributions of organic matter accumulation versus chemical solubilization in P mobilization. This gap hinders mechanistic understanding and the design of precision nutrient management strategies.</div></div><div><h3>Objectives and Methods</h3><div>This meta-analysis synthesized 263 datasets from 55 studies (up to December 31, 2024) to: (1) quantify straw return effects on TP, AP, and Olsen-P; (2) identify key environmental and management drivers of variability; and (3) elucidate underlying mechanistic pathways linking straw return, soil organic matter (SOM), and P bioavailability. Analyses included random-effects models (LnRR effect sizes), meta-regression, random forest algorithms, and structural equation modeling (SEM).</div></div><div><h3>Results</h3><div>Straw return significantly increased SOM (+4.1 %), TP (+7.1 %), AP (+8.3 %), and Olsen-P (+11.2 %). TP accumulation was mainly driven by the duration of straw incorporation, while Olsen-P increases were strongly influenced by initial soil pH, with greater enhancement in acidic and neutral soils than in alkaline soils. Random forest models identified initial soil properties, especially total nitrogen and pH, as dominant predictors of P responses. SEM indicated that SOM serves as a central hub, directly enhancing P fractions and indirectly increasing bioavailability via soil acidification. Straw return enhances P through two complementary mechanisms: long-term physical TP buildup and rapid biochemical activation of bioavailable P.</div></div><div><h3>Conclusions</h3><div>Straw return systematically improves soil P supply in Chinese croplands via dual SOM-mediated pathways.</div></div><div><h3>Implications</h3><div>These findings support transitioning from uniform straw return practices to precision strategies tailored to local soil conditions and target P fractions, advancing both nutrient use efficiency and soil health.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110312"},"PeriodicalIF":6.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.fcr.2025.110296
Yanze Ma , Yansen Xu , Xintong Hu , Evgenios Agathokleous , Kazuhiko Kobayashi , Xinyou Yin , Rong Cao , Zhaozhong Feng
Context
Elevated tropospheric ozone (O3) negatively affects both assimilate production (source) and accumulation (sink), leading to reductions in wheat yield. However, the effects of elevated O3 on source-sink relationships during grain filling in wheat remain unknown.
Objective
The objectives of this study were to investigate the impacts of elevated O3 on source supply, sink growth and their relationship, and to identify the key factors underlying differences in O3 sensitivity among wheat cultivars.
Methods
A two-year field experiment was conducted at a Free-Air O3–Concentration Enrichment (O3–FACE) facility in China to investigate the effects of elevated O3 on 12 wheat cultivars. A model-based analysis of grain weight and aboveground biomass dynamics was used to evaluate the effects of elevated O3 on the source-sink relationship.
Results and conclusions
Elevated O3 significantly reduced grain yield by 12.7 % across cultivars, with yield losses ranging from 2.4 % to 24.7 %. A significant trade-off between grain yield and O3 sensitivity indicated that high-yielding cultivars tend to be more sensitive to O3. Elevated O3 significantly decreased the rate and duration of post-anthesis source supply and sink growth, but the source was more sensitive to O3 than the sink. The remobilization of pre-anthesis carbon reserves was increased by elevated O3, partially mitigating the negative effects of O3 on sink growth in the O3-sensitive cultivars. The sensitivity of cultivars to O3 is primarily attributable to the response of source supply to elevated O3.
Implications
In summary, reducing O3-induced impairment of source capacity and increasing remobilization of carbon reserves under high O3 level are critical strategies for breeding high-yielding and O3-tolerant wheat cultivars.
{"title":"Response of source capacity to elevated ozone during grain filling determines ozone sensitivity of wheat cultivars","authors":"Yanze Ma , Yansen Xu , Xintong Hu , Evgenios Agathokleous , Kazuhiko Kobayashi , Xinyou Yin , Rong Cao , Zhaozhong Feng","doi":"10.1016/j.fcr.2025.110296","DOIUrl":"10.1016/j.fcr.2025.110296","url":null,"abstract":"<div><h3>Context</h3><div>Elevated tropospheric ozone (O<sub>3</sub>) negatively affects both assimilate production (source) and accumulation (sink), leading to reductions in wheat yield. However, the effects of elevated O<sub>3</sub> on source-sink relationships during grain filling in wheat remain unknown.</div></div><div><h3>Objective</h3><div>The objectives of this study were to investigate the impacts of elevated O<sub>3</sub> on source supply, sink growth and their relationship, and to identify the key factors underlying differences in O<sub>3</sub> sensitivity among wheat cultivars.</div></div><div><h3>Methods</h3><div>A two-year field experiment was conducted at a Free-Air O<sub>3</sub>–Concentration Enrichment (O<sub>3</sub>–FACE) facility in China to investigate the effects of elevated O<sub>3</sub> on 12 wheat cultivars. A model-based analysis of grain weight and aboveground biomass dynamics was used to evaluate the effects of elevated O<sub>3</sub> on the source-sink relationship.</div></div><div><h3>Results and conclusions</h3><div>Elevated O<sub>3</sub> significantly reduced grain yield by 12.7 % across cultivars, with yield losses ranging from 2.4 % to 24.7 %. A significant trade-off between grain yield and O<sub>3</sub> sensitivity indicated that high-yielding cultivars tend to be more sensitive to O<sub>3</sub>. Elevated O<sub>3</sub> significantly decreased the rate and duration of post-anthesis source supply and sink growth, but the source was more sensitive to O<sub>3</sub> than the sink. The remobilization of pre-anthesis carbon reserves was increased by elevated O<sub>3</sub>, partially mitigating the negative effects of O<sub>3</sub> on sink growth in the O<sub>3</sub>-sensitive cultivars. The sensitivity of cultivars to O<sub>3</sub> is primarily attributable to the response of source supply to elevated O<sub>3</sub>.</div></div><div><h3>Implications</h3><div>In summary, reducing O<sub>3</sub>-induced impairment of source capacity and increasing remobilization of carbon reserves under high O<sub>3</sub> level are critical strategies for breeding high-yielding and O<sub>3</sub>-tolerant wheat cultivars.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110296"},"PeriodicalIF":6.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.fcr.2025.110308
Wei Wu , Yang Wang , Huasen Xu , Boyang Fu , Zhengping Peng , Christoph-Martin Geilfus , Cheng Xue
Context
Balancing high grain yield and superior quality in wheat production is challenging due to their inherent trade-off. Split nitrogen (N) application at the booting stage has shown potential for simultaneously improving both yield and quality; however, the underlying mechanisms driving this synergy remain insufficiently understood.
Objectives
To determine how booting-stage split N improves grain yield while maintaining or enhancing grain protein concentration and strengthening gluten protein composition.
Methods
Three seasons of field experiments (2017–2020) at one Hebei site with the strong-gluten winter wheat ‘Gaoyou 2018’ compared four N strategies and quantified N uptake, utilization, translocation and distribution at whole-plant, organ and protein levels. A complementary 2022–2023 pot experiment used 15N labeling to trace N uptake and distribution across growth stages.
Results
Split N application at the booting stage significantly enhanced dry matter and N accumulation, particularly during the post-anthesis period of wheat, and improved N distribution. Booting-stage split N increased grain yield by 12.6 % and strengthened protein quality without diluting grain protein concentration. Mechanistically, it boosted post-anthesis growth and N supply: post-anthesis dry matter rose by 36.1 % and post-anthesis N uptake nearly doubled (+99.2 %), elevating the contribution of post-anthesis sources to grain N. Mixed-model regressions showed post-anthesis biomass was strongly associated with post-anthesis N uptake (R² = 0.53), and a model combining post-anthesis N uptake with pre-anthesis N remobilization explained 92 % of its variation. The pot study corroborated this pathway: booting increased total 15N uptake and its partitioning to grain (86.7 % of absorbed 15N), with 24.8 % and 40.6 % incorporated into gliadin and glutenin, respectively. Canopy traits supported this pathway: greater flag-leaf area at anthesis tracked grain number and early post-anthesis flag-leaf duration aligned with thousand grain weight.
Conclusion
Booting-stage split N aligns N supply with stem elongation and early grain filling, increasing post-anthesis N uptake and dry-matter accumulation and directing more absorbed N to grain and gluten fractions, thereby improving yield and quality simultaneously.
Implications
These results support efficient N timing to achieve high yield and superior quality without increasing total N input, advancing more sustainable wheat production.
{"title":"Optimizing the yield-quality balance in wheat by enhancing N uptake and allocation through split N application at the booting stage","authors":"Wei Wu , Yang Wang , Huasen Xu , Boyang Fu , Zhengping Peng , Christoph-Martin Geilfus , Cheng Xue","doi":"10.1016/j.fcr.2025.110308","DOIUrl":"10.1016/j.fcr.2025.110308","url":null,"abstract":"<div><h3>Context</h3><div>Balancing high grain yield and superior quality in wheat production is challenging due to their inherent trade-off. Split nitrogen (N) application at the booting stage has shown potential for simultaneously improving both yield and quality; however, the underlying mechanisms driving this synergy remain insufficiently understood.</div></div><div><h3>Objectives</h3><div>To determine how booting-stage split N improves grain yield while maintaining or enhancing grain protein concentration and strengthening gluten protein composition.</div></div><div><h3>Methods</h3><div>Three seasons of field experiments (2017–2020) at one Hebei site with the strong-gluten winter wheat ‘Gaoyou 2018’ compared four N strategies and quantified N uptake, utilization, translocation and distribution at whole-plant, organ and protein levels. A complementary 2022–2023 pot experiment used <sup>15</sup>N labeling to trace N uptake and distribution across growth stages.</div></div><div><h3>Results</h3><div>Split N application at the booting stage significantly enhanced dry matter and N accumulation, particularly during the post-anthesis period of wheat, and improved N distribution. Booting-stage split N increased grain yield by 12.6 % and strengthened protein quality without diluting grain protein concentration. Mechanistically, it boosted post-anthesis growth and N supply: post-anthesis dry matter rose by 36.1 % and post-anthesis N uptake nearly doubled (+99.2 %), elevating the contribution of post-anthesis sources to grain N. Mixed-model regressions showed post-anthesis biomass was strongly associated with post-anthesis N uptake (R² = 0.53), and a model combining post-anthesis N uptake with pre-anthesis N remobilization explained 92 % of its variation. The pot study corroborated this pathway: booting increased total <sup>15</sup>N uptake and its partitioning to grain (86.7 % of absorbed <sup>15</sup>N), with 24.8 % and 40.6 % incorporated into gliadin and glutenin, respectively. Canopy traits supported this pathway: greater flag-leaf area at anthesis tracked grain number and early post-anthesis flag-leaf duration aligned with thousand grain weight.</div></div><div><h3>Conclusion</h3><div>Booting-stage split N aligns N supply with stem elongation and early grain filling, increasing post-anthesis N uptake and dry-matter accumulation and directing more absorbed N to grain and gluten fractions, thereby improving yield and quality simultaneously.</div></div><div><h3>Implications</h3><div>These results support efficient N timing to achieve high yield and superior quality without increasing total N input, advancing more sustainable wheat production.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110308"},"PeriodicalIF":6.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.fcr.2025.110307
Thomas Awio , Louis Kouadio , Ali Ibrahim , Aina Andriatsiorimanana , Kazuki Saito , Kalimuthu Senthilkumar
Context
Increasing rice productivity is key to achieve rice self-sufficiency in sub-Saharan Africa (SSA) where current consumption surpasses local production, mainly due to low yield associated with sub-optimal management practices. Good agricultural practices (GAPs) – considered as an integrated practices including soil, water, weed, pest, and disease management are critical in increasing farmers’ yields. However, there is a lack of comprehensive assessment of on-farm yield variation with GAPs across production systems and agroecological zones (AEZs) at the continental level.
Objectives
The objectives of the study were to (i) quantify yield variation with GAPs in three production systems and (ii) identify major production factors causing yield variation.
Methods
From 2013 – 2022, GAPs were tested on-farm in 987 fields across 34 sites in 20 SSA countries. Yield data from GAPs plots were compared with farmers’ yields obtained from an independent yield gap survey.
Results
Yield with GAPs varied significantly (p < 0.001) across production systems and AEZs. Mean yields were 5.1, 3.9, and 2.5 t ha–1 in irrigated lowland (IL), rainfed lowland (RL), and rainfed upland (RU), respectively. Yield gain with GAPs averaged 0.7, 1.1 and 0.8 t ha–1 in IL, RL and RU; and was smaller in sites having higher farmers’ yields. Overall, 78, 87 and 88 % of the GAPs plots in IL, RL and RU, respectively, had higher yields compared with farmers’ yields. GAPs significantly (p = 0.01) reduced yield variation across production systems by 25, 29 and 20 % in IL, RL and RU, respectively. N, P and K use efficiencies, defined as partial factor productivity (kg grain/kg nutrient applied), were significantly (p < 0.001) higher in IL (59, 153 and 151 kg grain/kg N, P and K, respectively), followed by RL (47, 123 and 129 kg grain/kg N, P and K) and lowest in RU (31, 81 and 80 kg grain/kg N, P and K), with positive correlations between yield and N, P and K use efficiencies. Across production systems and AEZs, bunding, levelling, basal N, P and K and total N rates were among the top ranked management practices influencing yield, where high yielding plots were associated with good levelling and bunding.
Conclusion
There is substantial potential to further increase productivity by improving on-farm management practices—particularly to enhance nutrient use efficiency—to close rice yield gaps across diverse production systems in SSA.
Significance
The study contributes to better understanding of the effect of GAPs on yield and yield variation, and production factors that influence yield variation at a large geographical area of SSA.
背景提高水稻生产力是撒哈拉以南非洲实现水稻自给自足的关键，该地区目前的消费量超过了当地产量，主要原因是与次优管理做法相关的低产量。良好农业规范（gap）——被视为包括土壤、水、杂草、病虫害管理在内的综合做法，对提高农民产量至关重要。然而，在大陆层面上，缺乏对生产系统和农业生态区（aez）之间存在差距的农场产量变化的综合评估。本研究的目的是(i)量化三个生产系统中gap的产量变化，（ii）确定导致产量变化的主要生产因素。方法从2013年到2022年，在20个SSA国家34个地点的987个农田中对gap进行了测试。将gap地块的产量数据与独立产量缺口调查获得的农民产量进行比较。结果不同生产系统和aez的gap产量差异显著（p <； 0.001）。灌溉低地（IL）、雨养低地（RL）和旱地（RU）的平均产量分别为5.1、3.9和2.5 t ha-1。IL、RL和RU的gap平均为0.7、1.1和0.8 t ha-1；在农民产量高的地方，面积更小。总体而言，与农民产量相比，白区、RL区和RU区分别有78%、87%和88%（ %）的gap地块产量较高。gap显著（p = 0.01）降低了不同生产系统中IL、RL和RU的产量差异，分别降低了25%、29%和20% %。氮、磷、钾利用效率，即部分要素生产率（kg粒/kg施养分），IL（分别为59、153和151 kg粒/kg N、P和K）显著（P <； 0.001）高，RL（分别为47、123和129 kg粒/kg N、P和K）次之，RU（31、81和80 kg粒/kg N、P和K）最低，产量与N、P和K利用效率呈正相关。在整个生产系统和经济专用区，捆绑、平整、基础氮、磷、钾和全氮水平是影响产量的最高管理措施，其中高产地块与良好的平整和捆绑相关。结论通过改进农场管理实践，特别是提高养分利用效率，进一步提高生产力，缩小SSA不同生产系统之间的水稻产量差距，具有巨大的潜力。意义本研究有助于更好地了解SSA大地理区域gap对产量和产量变化的影响，以及影响产量变化的生产因素。
{"title":"Improving rice yield, its stability, and nutrient use efficiency in sub-Saharan Africa using good agricultural practices","authors":"Thomas Awio , Louis Kouadio , Ali Ibrahim , Aina Andriatsiorimanana , Kazuki Saito , Kalimuthu Senthilkumar","doi":"10.1016/j.fcr.2025.110307","DOIUrl":"10.1016/j.fcr.2025.110307","url":null,"abstract":"<div><h3>Context</h3><div>Increasing rice productivity is key to achieve rice self-sufficiency in sub-Saharan Africa (SSA) where current consumption surpasses local production, mainly due to low yield associated with sub-optimal management practices. Good agricultural practices (GAPs) – considered as an integrated practices including soil, water, weed, pest, and disease management are critical in increasing farmers’ yields. However, there is a lack of comprehensive assessment of on-farm yield variation with GAPs across production systems and agroecological zones (AEZs) at the continental level.</div></div><div><h3>Objectives</h3><div>The objectives of the study were to (i) quantify yield variation with GAPs in three production systems and (ii) identify major production factors causing yield variation.</div></div><div><h3>Methods</h3><div>From 2013 – 2022, GAPs were tested on-farm in 987 fields across 34 sites in 20 SSA countries. Yield data from GAPs plots were compared with farmers’ yields obtained from an independent yield gap survey.</div></div><div><h3>Results</h3><div>Yield with GAPs varied significantly (p < 0.001) across production systems and AEZs. Mean yields were 5.1, 3.9, and 2.5 t ha<sup>–1</sup> in irrigated lowland (IL), rainfed lowland (RL), and rainfed upland (RU), respectively. Yield gain with GAPs averaged 0.7, 1.1 and 0.8 t ha<sup>–1</sup> in IL, RL and RU; and was smaller in sites having higher farmers’ yields. Overall, 78, 87 and 88 % of the GAPs plots in IL, RL and RU, respectively, had higher yields compared with farmers’ yields. GAPs significantly (p = 0.01) reduced yield variation across production systems by 25, 29 and 20 % in IL, RL and RU, respectively. N, P and K use efficiencies, defined as partial factor productivity (kg grain/kg nutrient applied), were significantly (p < 0.001) higher in IL (59, 153 and 151 kg grain/kg N, P and K, respectively), followed by RL (47, 123 and 129 kg grain/kg N, P and K) and lowest in RU (31, 81 and 80 kg grain/kg N, P and K), with positive correlations between yield and N, P and K use efficiencies. Across production systems and AEZs, bunding, levelling, basal N, P and K and total N rates were among the top ranked management practices influencing yield, where high yielding plots were associated with good levelling and bunding.</div></div><div><h3>Conclusion</h3><div>There is substantial potential to further increase productivity by improving on-farm management practices—particularly to enhance nutrient use efficiency—to close rice yield gaps across diverse production systems in SSA.</div></div><div><h3>Significance</h3><div>The study contributes to better understanding of the effect of GAPs on yield and yield variation, and production factors that influence yield variation at a large geographical area of SSA.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110307"},"PeriodicalIF":6.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.fcr.2025.110298
Haiyan Wang , Tingyao Cai , Zhong Chen , Qingsong Zhang , Yingcheng Wang , Zhengyuan Liang , Junhao Wang , Qi Miao , Huifang Zheng , Zihan Wang , Yulong Yin , Zhenling Cui
Context
Navigating the trade-offs between food production and environmental sustainability has become increasingly challenging in the context of accelerating climate change in China. A key question is whether the highly heterogeneous spatial patterns of greenhouse gas emissions (GHG) and nitrogen (N) and phosphorus (P) surpluses across croplands can be mitigated to remain within county-level planetary boundaries while maintaining sustainable food production.
Objective
This study aims to minimize cropland GHG emissions in China through an integrated strategy that combines improved management, optimized cropland redistribution, and dietary shifts, while keeping N and P surpluses within county-level planetary boundaries and ensuring sustained food production.
Methods
We downscaled the planetary boundaries for GHG emissions and nutrient surpluses to county level based on population and cropland, and freshwater resources allocations. Then, we evaluated the mitigation potential of improved management (informed by the national farm survey), crop redistribution (using linear programming), and dietary shifts (50 % Dietary Guidelines for Chinese Residents (2016)), both individually and in combination.
Results and conclusions
Our results demonstrate that the integrated strategy could reduce GHG emissions, N surplus, P surplus, and arable land area by 55 %, 62 %, 67 %, and 54 %, respectively, compared with the current status. Furthermore, this strategy would enable approximately 53 % of China’s counties to remain within the planetary boundaries for GHG emissions as well as N and P surpluses, which accounts for around 60 % of the total adjusted sowing area. The mitigation potential exhibits pronounced spatial heterogeneity, with Southeast China and the Yangtze River Basin experiencing the greatest reductions. However, even under the integrated strategy, 932 counties still exceed the planetary boundaries for P surplus, underscoring persistent challenges. Despite socio-economic and cultural constraints, achieving synergistic reductions in environmental impacts and remaining within multiple planetary boundaries at the county scale holds significant promise.
Significance
This study provides a practical and scalable pathway for mitigating agricultural environmental pressures while supporting sustainable production, particularly in smallholder-dominated systems. The insights offer valuable guidance for other developing countries, such as India and African nations, seeking to reconcile rising food demand with the need to remain within Earth’s safe operating space.
{"title":"Minimizing cropland GHG emissions while maintaining nutrient surplus within planetary boundaries in China","authors":"Haiyan Wang , Tingyao Cai , Zhong Chen , Qingsong Zhang , Yingcheng Wang , Zhengyuan Liang , Junhao Wang , Qi Miao , Huifang Zheng , Zihan Wang , Yulong Yin , Zhenling Cui","doi":"10.1016/j.fcr.2025.110298","DOIUrl":"10.1016/j.fcr.2025.110298","url":null,"abstract":"<div><h3>Context</h3><div>Navigating the trade-offs between food production and environmental sustainability has become increasingly challenging in the context of accelerating climate change in China. A key question is whether the highly heterogeneous spatial patterns of greenhouse gas emissions (GHG) and nitrogen (N) and phosphorus (P) surpluses across croplands can be mitigated to remain within county-level planetary boundaries while maintaining sustainable food production.</div></div><div><h3>Objective</h3><div>This study aims to minimize cropland GHG emissions in China through an integrated strategy that combines improved management, optimized cropland redistribution, and dietary shifts, while keeping N and P surpluses within county-level planetary boundaries and ensuring sustained food production.</div></div><div><h3>Methods</h3><div>We downscaled the planetary boundaries for GHG emissions and nutrient surpluses to county level based on population and cropland, and freshwater resources allocations. Then, we evaluated the mitigation potential of improved management (informed by the national farm survey), crop redistribution (using linear programming), and dietary shifts (50 % Dietary Guidelines for Chinese Residents (2016)), both individually and in combination.</div></div><div><h3>Results and conclusions</h3><div>Our results demonstrate that the integrated strategy could reduce GHG emissions, N surplus, P surplus, and arable land area by 55 %, 62 %, 67 %, and 54 %, respectively, compared with the current status. Furthermore, this strategy would enable approximately 53 % of China’s counties to remain within the planetary boundaries for GHG emissions as well as N and P surpluses, which accounts for around 60 % of the total adjusted sowing area. The mitigation potential exhibits pronounced spatial heterogeneity, with Southeast China and the Yangtze River Basin experiencing the greatest reductions. However, even under the integrated strategy, 932 counties still exceed the planetary boundaries for P surplus, underscoring persistent challenges. Despite socio-economic and cultural constraints, achieving synergistic reductions in environmental impacts and remaining within multiple planetary boundaries at the county scale holds significant promise.</div></div><div><h3>Significance</h3><div>This study provides a practical and scalable pathway for mitigating agricultural environmental pressures while supporting sustainable production, particularly in smallholder-dominated systems. The insights offer valuable guidance for other developing countries, such as India and African nations, seeking to reconcile rising food demand with the need to remain within Earth’s safe operating space.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110298"},"PeriodicalIF":6.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.fcr.2025.110297
Lijun Chi, Yatong Chu, Han Zeng, Xinran Guo, Xiuzhi Zang, Tianxiao Cao, Jin Chen, Kun Zhang, Dongqing Yang
<div><h3>Context</h3><div>Integrating cover crops with optimized nitrogen (N) management is a promising approach for sustainable peanut (<em>Arachis hypogaea</em> L.) production. However, the mechanistic linkages linking soil microbial communities, soil C:N stoichiometry, plant physiological traits, and yield formation remain poorly understood.</div></div><div><h3>Objective</h3><div>This study aimed to clarify how ryegrass cover crop incorporation combined with reduced N fertilization modulates soil C:N stoichiometry, microbial community structure, enzyme activities, and peanut productivity, with the goal of identifying an optimal strategy to balance yield and nitrogen use efficiency (NUE).</div></div><div><h3>Methods</h3><div>A two-year field experiment was conducted employing two residue management strategies—cover crop incorporation (H) and biomass removal (N)—under four N rates: 0 (N0), 60 (N60), 90 (N90), and 120 kg N ha<sup>−1</sup>(N120). This resulted in eight treatment combinations (HN0, HN60, HN90, HN120, N0, N60, N90, and N120). Key measurements included soil C:N stoichiometry (SOC, TN, AN, NO<sub>3</sub>⁻–N, and SOC:TN ratio), extracellular enzyme activities (urease, cellulase, invertase), microbial richness and diversity, community composition, leaf physiological traits (SPAD, ΦPSII, SPS, and NR), pod yield, and NUE.</div></div><div><h3>Results</h3><div>Ryegrass incorporation significantly enhanced peanut pod yield by 19.95 %–22.50 % compared with biomass removal. Notably, under incorporation, a 25 % N reduction (HN90) achieved yields statistically equivalent to the full N rate (HN120) but increased agronomic N efficiency (AEN) by 46.84 % relative to N90. Incorporation increased SOC, TN, AN, NO<sub>3</sub>⁻–N, and the SOC:TN ratio while maintaining high enzyme activities comparable to HN120. Microbial richness and diversity were also improved; specifically, HN90 selectively enriched beneficial taxa, including <em>Lysobacter</em>, <em>Bacillus</em>, <em>Brevibacillus</em>, and <em>Gemmatimonas</em>, while suppressing pathogenic genera such as <em>Fusicolla</em> and <em>Fusarium</em>. Although N reduction generally decreased SPAD and ΦPSII, the 25 % N reduction under incorporation caused only minor declines compared with N120. SPS and NR activities followed similar trends. Structural equation modeling confirmed that microbial community structure and enzyme activities directly optimized soil C:N stoichiometry, which in turn positively regulated plant physiological traits and yield formation.</div></div><div><h3>Conclusions</h3><div>Integrating ryegrass cover crop incorporation with moderate N reduction (25 %) enhances microbial community function, promotes nutrient cycling, sustains photosynthetic performance, and synergistically improves both yield and NUE in peanut systems.</div></div><div><h3>Implications</h3><div>This management strategy offers an effective pathway to achieve coordinated improvements in soil health, nitrogen efficie
覆盖作物与优化氮素管理相结合是花生可持续生产的有效途径。然而，土壤微生物群落、土壤碳氮化学计量、植物生理性状和产量形成之间的机制联系仍然知之甚少。目的研究黑麦草覆盖与低施氮对土壤C:N化学计量、微生物群落结构、酶活性和花生生产力的调节作用，以期找到平衡产量和氮素利用效率（NUE）的最佳策略。方法在0 （N0）、60 （N60）、90 （N90）和120 kg N ha−1(N120) 4种氮肥水平下，采用覆盖还田(H)和生物量去除(N)两种秸秆管理策略进行为期2年的田间试验。结果有8种治疗组合（HN0、HN60、HN90、HN120、N0、N60、N90和N120）。关键测量包括土壤C:N化学计量（SOC， TN， AN， NO3 -N和SOC:TN比），细胞外酶活性（脲酶，纤维素酶，转化酶），微生物丰富度和多样性，群落组成，叶片生理性状（SPAD， ΦPSII， SPS和NR），豆荚产量和氮肥利用。结果与生物质去除相比，黑麦草添加显著提高花生豆荚产量19.95 % ~ 22.50 %。值得注意的是，在混作条件下，减少25 % N （HN90）的产量在统计上与全施氮（HN120）相当，但农艺N效率（AEN）相对于N90提高了46.84 %。掺入增加了SOC， TN， AN, NO3 -N，和SOC:TN的比率，同时保持与HN120相当的高酶活性。微生物丰富度和多样性也有所提高；具体来说，HN90选择性地富集有益菌群，包括溶菌属、芽孢杆菌属、短芽孢杆菌属和双胞菌属，同时抑制致病菌属，如镰刀菌属和镰刀菌属。虽然氮素减量总体上降低了SPAD和ΦPSII，但与N120相比，掺入后的25 %氮素减量只引起了轻微的下降。SPS和NR活动也有类似的趋势。结构方程模型证实，微生物群落结构和酶活性直接优化土壤C:N化学计量，进而正向调节植物生理性状和产量形成。结论黑麦草覆盖作物配施适度减氮（25% %）可增强花生系统微生物群落功能，促进养分循环，维持光合性能，协同提高产量和氮肥利用效率。该管理策略为实现土壤健康、氮素效率和作物生产力的协调改善提供了有效途径，为减少肥料投入下花生的可持续生产提供了机制基础。
{"title":"Cover crop incorporation with moderate nitrogen reduction regulates soil microbial communities to drive C:N stoichiometry for enhanced peanut yield and efficiency","authors":"Lijun Chi, Yatong Chu, Han Zeng, Xinran Guo, Xiuzhi Zang, Tianxiao Cao, Jin Chen, Kun Zhang, Dongqing Yang","doi":"10.1016/j.fcr.2025.110297","DOIUrl":"10.1016/j.fcr.2025.110297","url":null,"abstract":"<div><h3>Context</h3><div>Integrating cover crops with optimized nitrogen (N) management is a promising approach for sustainable peanut (<em>Arachis hypogaea</em> L.) production. However, the mechanistic linkages linking soil microbial communities, soil C:N stoichiometry, plant physiological traits, and yield formation remain poorly understood.</div></div><div><h3>Objective</h3><div>This study aimed to clarify how ryegrass cover crop incorporation combined with reduced N fertilization modulates soil C:N stoichiometry, microbial community structure, enzyme activities, and peanut productivity, with the goal of identifying an optimal strategy to balance yield and nitrogen use efficiency (NUE).</div></div><div><h3>Methods</h3><div>A two-year field experiment was conducted employing two residue management strategies—cover crop incorporation (H) and biomass removal (N)—under four N rates: 0 (N0), 60 (N60), 90 (N90), and 120 kg N ha<sup>−1</sup>(N120). This resulted in eight treatment combinations (HN0, HN60, HN90, HN120, N0, N60, N90, and N120). Key measurements included soil C:N stoichiometry (SOC, TN, AN, NO<sub>3</sub>⁻–N, and SOC:TN ratio), extracellular enzyme activities (urease, cellulase, invertase), microbial richness and diversity, community composition, leaf physiological traits (SPAD, ΦPSII, SPS, and NR), pod yield, and NUE.</div></div><div><h3>Results</h3><div>Ryegrass incorporation significantly enhanced peanut pod yield by 19.95 %–22.50 % compared with biomass removal. Notably, under incorporation, a 25 % N reduction (HN90) achieved yields statistically equivalent to the full N rate (HN120) but increased agronomic N efficiency (AEN) by 46.84 % relative to N90. Incorporation increased SOC, TN, AN, NO<sub>3</sub>⁻–N, and the SOC:TN ratio while maintaining high enzyme activities comparable to HN120. Microbial richness and diversity were also improved; specifically, HN90 selectively enriched beneficial taxa, including <em>Lysobacter</em>, <em>Bacillus</em>, <em>Brevibacillus</em>, and <em>Gemmatimonas</em>, while suppressing pathogenic genera such as <em>Fusicolla</em> and <em>Fusarium</em>. Although N reduction generally decreased SPAD and ΦPSII, the 25 % N reduction under incorporation caused only minor declines compared with N120. SPS and NR activities followed similar trends. Structural equation modeling confirmed that microbial community structure and enzyme activities directly optimized soil C:N stoichiometry, which in turn positively regulated plant physiological traits and yield formation.</div></div><div><h3>Conclusions</h3><div>Integrating ryegrass cover crop incorporation with moderate N reduction (25 %) enhances microbial community function, promotes nutrient cycling, sustains photosynthetic performance, and synergistically improves both yield and NUE in peanut systems.</div></div><div><h3>Implications</h3><div>This management strategy offers an effective pathway to achieve coordinated improvements in soil health, nitrogen efficie","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110297"},"PeriodicalIF":6.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.fcr.2025.110281
Chang Ye , Yi Tao , Deshun Xiao , Yanan Xu , Chunmei Xu , Yuanhui Liu , Kai Yu , Danying Wang
<div><h3>Context</h3><div>Soil texture is a pivotal factor influencing soil structure and nutrient cycling. Nevertheless, the disparities in nitrogen (N) supply capacity among paddy soils with varying textures and their impacts on rice N uptake remain poorly understood.</div></div><div><h3>Objective</h3><div>This study aimed to compare N mineralization parameters across paddy soils with varying textures, and explore their effects on the plant N uptake and utilization, thereby providing a theoretical foundation for implementing scientific fertilization practices based on soil type.</div></div><div><h3>Methods</h3><div>This study employed a flooded incubation experiment to investigate soil N mineralization parameters across three soil textures: loam (L), silty loam (SL), and silty clay loam (SCL). Using two rice varieties (YY1540 and YD6) with different N uptake capacities as materials, field and pot experiments were conducted across the three distinct soil textures under two N rates (a no-N control and an N treatment) to analyze the influence of soil texture on N mineralization and plant N uptake. Additionally, the study employed the <sup>15</sup>N tracer method to track the fate of fertilizer-N in both rice plants and soil. Key parameters were measured, including soil N mineralization parameters, plant N accumulation, grain yield, Calculations were performed for N use efficiency,<sup>15</sup>N recovery efficiency, and <sup>15</sup> N residue percentage in the soil after rice harvest.</div></div><div><h3>Results</h3><div>The results showed that the soil N supply capacity in the CK was highest in silt clay loam (SCL), followed by silty loam (SL), and the lowest in loam (L). However, upon fertilizer-N application, the net N mineralization rate of L was significantly increased, with its N supply capacity exceeding that of SL. Correlation analysis showed that in the CK, soil N mineralization was influenced by soil carbon (C) and nitrogen (N) contents as well as soil texture, whereas soil texture emerged as the predominant factor after the application of fertilizer-N. Both rice varieties YY1540 and YD6 exhibited the highest yield, dry matter accumulation, and N accumulation in SCL soil, regardless of fertilization. Nevertheless, the response to N fertilization varied among soil types, with L showing the highest increase ratio in grain yield and total N recovery efficiency (NRE), followed by SL and SCL. Conversely, the <sup>15</sup>N fertilizer recovery efficiency (<sup>15</sup>NRE) demonstrated an opposite trend, increasing from L to SL to SCL. The field experiment revealed that YY1540, characterized by strong N uptake capacity, displayed greater sensitivity to soil texture variations compared to YD6, which had a weaker N uptake capacity. The N accumulation of YY1540 was significantly correlated with the soil N mineralization rate constant <em>k</em>, while the correlation was not significant for YD6.</div></div><div><h3>Conclusions</h3><div>These find
{"title":"Effects of soil textures on N mineralization, uptake and utilization in paddy rice","authors":"Chang Ye , Yi Tao , Deshun Xiao , Yanan Xu , Chunmei Xu , Yuanhui Liu , Kai Yu , Danying Wang","doi":"10.1016/j.fcr.2025.110281","DOIUrl":"10.1016/j.fcr.2025.110281","url":null,"abstract":"<div><h3>Context</h3><div>Soil texture is a pivotal factor influencing soil structure and nutrient cycling. Nevertheless, the disparities in nitrogen (N) supply capacity among paddy soils with varying textures and their impacts on rice N uptake remain poorly understood.</div></div><div><h3>Objective</h3><div>This study aimed to compare N mineralization parameters across paddy soils with varying textures, and explore their effects on the plant N uptake and utilization, thereby providing a theoretical foundation for implementing scientific fertilization practices based on soil type.</div></div><div><h3>Methods</h3><div>This study employed a flooded incubation experiment to investigate soil N mineralization parameters across three soil textures: loam (L), silty loam (SL), and silty clay loam (SCL). Using two rice varieties (YY1540 and YD6) with different N uptake capacities as materials, field and pot experiments were conducted across the three distinct soil textures under two N rates (a no-N control and an N treatment) to analyze the influence of soil texture on N mineralization and plant N uptake. Additionally, the study employed the <sup>15</sup>N tracer method to track the fate of fertilizer-N in both rice plants and soil. Key parameters were measured, including soil N mineralization parameters, plant N accumulation, grain yield, Calculations were performed for N use efficiency,<sup>15</sup>N recovery efficiency, and <sup>15</sup> N residue percentage in the soil after rice harvest.</div></div><div><h3>Results</h3><div>The results showed that the soil N supply capacity in the CK was highest in silt clay loam (SCL), followed by silty loam (SL), and the lowest in loam (L). However, upon fertilizer-N application, the net N mineralization rate of L was significantly increased, with its N supply capacity exceeding that of SL. Correlation analysis showed that in the CK, soil N mineralization was influenced by soil carbon (C) and nitrogen (N) contents as well as soil texture, whereas soil texture emerged as the predominant factor after the application of fertilizer-N. Both rice varieties YY1540 and YD6 exhibited the highest yield, dry matter accumulation, and N accumulation in SCL soil, regardless of fertilization. Nevertheless, the response to N fertilization varied among soil types, with L showing the highest increase ratio in grain yield and total N recovery efficiency (NRE), followed by SL and SCL. Conversely, the <sup>15</sup>N fertilizer recovery efficiency (<sup>15</sup>NRE) demonstrated an opposite trend, increasing from L to SL to SCL. The field experiment revealed that YY1540, characterized by strong N uptake capacity, displayed greater sensitivity to soil texture variations compared to YD6, which had a weaker N uptake capacity. The N accumulation of YY1540 was significantly correlated with the soil N mineralization rate constant <em>k</em>, while the correlation was not significant for YD6.</div></div><div><h3>Conclusions</h3><div>These find","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110281"},"PeriodicalIF":6.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.fcr.2025.110294
Xue Zhao , Quanjiu Wang , Yi Guo , Zongyu Li , Wanghai Tao , Xiaoxian Duan
Soil salinization and freshwater scarcity are among the primary constraints limiting sustainable agricultural development in arid and semi-arid regions. To improve irrigation water use efficiency and promote the rational utilization of brackish water resources, this study integrated field experiments with theoretical analysis in typical cotton fields in southern Xinjiang. It systematically investigated the effects of magnetic-electric activated brackish water applied via mulched drip irrigation on soil salinity distribution, cotton physiological growth parameters, yield and quality, and water-nitrogen use efficiency. The results demonstrated that: (1) Activated brackish water significantly reduced soil salinity in the cotton root zone, with decreases in root-zone salt content and total salt accumulation ranging from 9.46 % to 23.60 % and 3.42–50.91 %, respectively; (2) It markedly enhanced cotton growth and physiological performance, with improvements ranging from –4.35–55.15 % and 0.92–29.51 %, respectively; (3) Compared to untreated brackish water, the activated treatment increased seed cotton yield and water-nitrogen use efficiency by 1.52 %–58.91 % and 74.79 %–96.60 %, respectively; (4) Considering the synergistic effects of activated water and nitrogen application, the optimal management regime was identified as an irrigation quota of 4875 m³ /ha combined with a nitrogen application rate of 350 kg/ha. These findings provide a scientific basis for mitigating freshwater scarcity and controlling secondary soil salinization in saline-prone regions.
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