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.
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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.
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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.
{"title":"The effects of activated brackish water and nitrogen regulation on cotton habitat","authors":"Xue Zhao , Quanjiu Wang , Yi Guo , Zongyu Li , Wanghai Tao , Xiaoxian Duan","doi":"10.1016/j.fcr.2025.110294","DOIUrl":"10.1016/j.fcr.2025.110294","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110294"},"PeriodicalIF":6.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784874","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.2025.110295
Ying Meng , Xueqin Liu , Yue Li , Linghan Huang , Xu Tian , Syed Tahir Ata-Ul-Karim , Kang Yu , Hainie Zha , Xiaojun Liu , Yongchao Tian , Yan Zhu , Weixing Cao , Qiang Cao
Effective nitrogen (N) management is crucial for the sustainability of wheat production in China, yet achieving a balance between high yields, economic profitability, and environmental sustainability remains a major challenge. While optimal N application rates vary spatiotemporally, the interactions between N fertilization and environmental factors further complicate crop productivity predictions. To address this, a multilevel meta-regression framework was developed. Integrating 4961 observations from 571 publications, this framework enables a comprehensive assessment of optimal N rates for yield, economic, and environmental goals across China's seven major wheat-growing regions. These regions were delineated based on soil, climate, and varietal characteristics. The results revealed significant spatiotemporal variations in optimal N application rates, driven by regional differences in soil and climate conditions. Temporally, the yield-optimal N rate (YON) followed an inverted U-shaped trend, peaking in the 2000s (268 kg ha−1), whereas economic-optimal N rate (ECON) and environment-optimal N rate (ENON) showed a gradual increase. Region-specific N management thresholds optimize wheat production, boosting it by 15 % while simultaneously improving economic returns and reducing environmental pollution. Notably, yield responses to N optimization were influenced by soil-climate interactions, with harsher growing environments exhibiting greater marginal benefits from N fertilization compared to more favorable conditions. Crucially, N optimization effects varied even among similar production areas, underscoring the importance of localized agroecological adaptation. This study provides a data-driven framework for tailoring N management strategies to regional conditions, offering actionable insights for optimizing productivity, profitability, and sustainability in China's diverse wheat systems. The findings of this study not only refine N fertilizer recommendations but also equip policymakers and stakeholders with a holistic perspective on sustainable N management.
有效的氮素管理对中国小麦生产的可持续性至关重要，但实现高产、经济效益和环境可持续性之间的平衡仍然是一个重大挑战。虽然最佳施氮量存在时空差异，但氮肥与环境因子之间的相互作用进一步使作物生产力预测复杂化。为了解决这个问题，我们开发了一个多层次元回归框架。该框架整合了571份出版物的4961份观测数据，能够对中国七大小麦产区的产量、经济和环境目标的最佳施氮量进行综合评估。这些区域是根据土壤、气候和品种特征划定的。结果表明，受区域土壤和气候条件差异的影响，最佳施氮量存在显著的时空差异。从时间上看，产量最优施氮量（YON）呈倒u型趋势，在2000年代达到峰值（268 kg ha−1），而经济最优施氮量（ECON）和环境最优施氮量（ENON）呈逐渐上升趋势。区域氮管理阈值优化小麦产量，在提高经济效益和减少环境污染的同时，使小麦产量提高15% %。值得注意的是，对氮肥优化的产量响应受到土壤-气候相互作用的影响，与较有利的生长环境相比，较恶劣的生长环境显示出更大的氮肥边际效益。至关重要的是，即使在相似的生产区域，氮素优化效果也不尽相同，这强调了局部农业生态适应的重要性。本研究提供了一个数据驱动的框架，可根据区域情况定制氮素管理策略，为优化中国多种小麦系统的生产率、盈利能力和可持续性提供可操作的见解。本研究结果不仅完善了氮肥建议，而且为政策制定者和利益相关者提供了可持续氮肥管理的整体视角。
{"title":"Meta-analysis of environmental-induced changes in optimal nitrogen rate of wheat at regional scale in China","authors":"Ying Meng , Xueqin Liu , Yue Li , Linghan Huang , Xu Tian , Syed Tahir Ata-Ul-Karim , Kang Yu , Hainie Zha , Xiaojun Liu , Yongchao Tian , Yan Zhu , Weixing Cao , Qiang Cao","doi":"10.1016/j.fcr.2025.110295","DOIUrl":"10.1016/j.fcr.2025.110295","url":null,"abstract":"<div><div>Effective nitrogen (N) management is crucial for the sustainability of wheat production in China, yet achieving a balance between high yields, economic profitability, and environmental sustainability remains a major challenge. While optimal N application rates vary spatiotemporally, the interactions between N fertilization and environmental factors further complicate crop productivity predictions. To address this, a multilevel meta-regression framework was developed. Integrating 4961 observations from 571 publications, this framework enables a comprehensive assessment of optimal N rates for yield, economic, and environmental goals across China's seven major wheat-growing regions. These regions were delineated based on soil, climate, and varietal characteristics. The results revealed significant spatiotemporal variations in optimal N application rates, driven by regional differences in soil and climate conditions. Temporally, the yield-optimal N rate (YON) followed an inverted U-shaped trend, peaking in the 2000s (268 kg ha<sup>−1</sup>), whereas economic-optimal N rate (ECON) and environment-optimal N rate (ENON) showed a gradual increase. Region-specific N management thresholds optimize wheat production, boosting it by 15 % while simultaneously improving economic returns and reducing environmental pollution. Notably, yield responses to N optimization were influenced by soil-climate interactions, with harsher growing environments exhibiting greater marginal benefits from N fertilization compared to more favorable conditions. Crucially, N optimization effects varied even among similar production areas, underscoring the importance of localized agroecological adaptation. This study provides a data-driven framework for tailoring N management strategies to regional conditions, offering actionable insights for optimizing productivity, profitability, and sustainability in China's diverse wheat systems. The findings of this study not only refine N fertilizer recommendations but also equip policymakers and stakeholders with a holistic perspective on sustainable N management.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110295"},"PeriodicalIF":6.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785741","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 : 2025-12-15DOI: 10.1016/j.fcr.2025.110292
Finn Großmann , Henning Kage , Dieter Hackenberg , Till Rose
Context
Improving sugar beet yield under variable environmental conditions requires a detailed understanding of the physiological mechanisms that drive yield formation. In sugar beet, canopy development determines resource capture, while radiation use efficiency (RUE) regulates the transformation efficiency of primary resources, and assimilate partitioning regulates the allocation of dry matter to the storage root. High-throughput phenotyping offers opportunities to quantify these physiological processes across diverse environments and genetic backgrounds, thereby identifying key traits for yield improvement.
Methods
A scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (LAI), radiation interception efficiency (RIE), RUE, and harvest index (HI). Unmanned Aerial Vehicle (UAV)-derived multispectral imagery, combined with environmental records and harvest measurements, was used across more than 1300 field plots in Germany and Italy (2023–2024), covering three contrasting environments, two irrigation managements, and up to 171 genotypes. LAI estimation was calibrated and validated under different water regimes in northern Germany (mean absolute error, MAE = 0.30 m² m⁻²).
Results
Dynamic UAV-based LAI enabled continuous estimation of radiation interception and biomass accumulation. Total dry matter correlated strongly with cumulative effective (temperature-dependent) radiation interception (R² = 0.81), indicating a comparatively stable RUE across diverse conditions. Genotypic variation in yield formation was mainly driven by canopy-level processes: RIE accounted for 65 % of variation under water-limited conditions, while RUE accounted for 46 % under irrigation. Partitioning traits (HI and Sugar HI) contributed minimally in both irrigation managements.
Conclusions
The results highlight the dominant role of canopy development and radiation use in sugar beet yield formation under contrasting environmental conditions. The proposed UAV-based framework provides a transferable, high-throughput approach to quantify physiological yield drivers in field settings. This enables targeted trait selection for breeding and facilitates integration of functional yield components into crop improvement strategies.
{"title":"What drives yield formation in sugar beet? Quantifying functional components across genotypes and irrigation managements","authors":"Finn Großmann , Henning Kage , Dieter Hackenberg , Till Rose","doi":"10.1016/j.fcr.2025.110292","DOIUrl":"10.1016/j.fcr.2025.110292","url":null,"abstract":"<div><h3>Context</h3><div>Improving sugar beet yield under variable environmental conditions requires a detailed understanding of the physiological mechanisms that drive yield formation. In sugar beet, canopy development determines resource capture, while radiation use efficiency (<em>RUE</em>) regulates the transformation efficiency of primary resources, and assimilate partitioning regulates the allocation of dry matter to the storage root. High-throughput phenotyping offers opportunities to quantify these physiological processes across diverse environments and genetic backgrounds, thereby identifying key traits for yield improvement.</div></div><div><h3>Methods</h3><div>A scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (<em>LAI</em>), radiation interception efficiency (<em>RIE</em>), <em>RUE</em>, and harvest index (<em>HI</em>). Unmanned Aerial Vehicle (UAV)-derived multispectral imagery, combined with environmental records and harvest measurements, was used across more than 1300 field plots in Germany and Italy (2023–2024), covering three contrasting environments, two irrigation managements, and up to 171 genotypes. <em>LAI</em> estimation was calibrated and validated under different water regimes in northern Germany (mean absolute error, MAE = 0.30 m² m⁻²).</div></div><div><h3>Results</h3><div>Dynamic UAV-based <em>LAI</em> enabled continuous estimation of radiation interception and biomass accumulation. Total dry matter correlated strongly with cumulative effective (temperature-dependent) radiation interception (<em>R²</em> = 0.81), indicating a comparatively stable <em>RUE</em> across diverse conditions. Genotypic variation in yield formation was mainly driven by canopy-level processes: <em>RIE</em> accounted for 65 % of variation under water-limited conditions, while <em>RUE</em> accounted for 46 % under irrigation. Partitioning traits (<em>HI</em> and <em>Sugar HI</em>) contributed minimally in both irrigation managements.</div></div><div><h3>Conclusions</h3><div>The results highlight the dominant role of canopy development and radiation use in sugar beet yield formation under contrasting environmental conditions. The proposed UAV-based framework provides a transferable, high-throughput approach to quantify physiological yield drivers in field settings. This enables targeted trait selection for breeding and facilitates integration of functional yield components into crop improvement strategies.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"338 ","pages":"Article 110292"},"PeriodicalIF":6.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753853","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}
Maize-soybean intercropping, recognized as a sustainable agricultural practice, improves land productivity and resource use efficiency. However, the patterns of dry matter and nitrogen redistribution, which are critical for yield advantage, remain inadequately quantified under film-mulched drip fertigation.
Objective
This study aimed to quantify the post-flowering translocation and accumulation of dry matter and nitrogen of intercropped maize and soybean under film-mulched drip-fertigation, and identify the optimal configuration that maximizes the intercropping advantage.
Methods
A two-year field experiment was carried out in 2022 and 2023, including eight maize-soybean intercropping row configurations, with monocultures of maize and soybean as controls. Key plant organs were sampled at critical growth stages to determine their dry matter and nitrogen content, which facilitated the subsequent calculation of translocation parameters. The land equivalent ratio, actual yield loss index, and nitrogen equivalent ratio were utilized to evaluate the advantages of the intercropping systems.
Results
Intercropping significantly enhanced post-flowering dry matter translocation in maize by 23.5 % but reduced it in soybean by 34.5 %. Conversely, post-flowering nitrogen translocation was reduced in both maize by 39.9 % and soybean by 29.4 %. Dry matter accumulation after flowering was the primary source of grain yield, contributing 94.2 % in maize and 85.0 % in soybean, significantly outweighing the contribution from translocation (5.8 % for maize and 15.0 % for soybean). A similar trend was observed for nitrogen source. Among the configurations, two rows of maize alternating with four rows of soybean (M2S4) achieved the highest land equivalent ratio (1.52), actual yield loss index (1.29), intercropping advantage index (3.24) and nitrogen equivalent ratio (1.70).
Conclusion
The M2S4 configuration effectively coordinated dry matter and nitrogen translocation and accumulation, leading to enhanced resource complementarity and yield advantage in drip-fertigated maize-soybean intercropping. This finding provides effective strategy for improving productivity and nitrogen use efficiency in maize-soybean strip intercropping systems.
{"title":"Optimized row configuration enhances dry matter and nitrogen accumulation and translocation in drip-fertigated maize-soybean strip intercropping","authors":"Hongtai Kou, Zhenqi Liao, Zhenlin Lai, Yiyao Liu, Zhijun Li, Junliang Fan","doi":"10.1016/j.fcr.2025.110290","DOIUrl":"10.1016/j.fcr.2025.110290","url":null,"abstract":"<div><h3>Context</h3><div>Maize-soybean intercropping, recognized as a sustainable agricultural practice, improves land productivity and resource use efficiency. However, the patterns of dry matter and nitrogen redistribution, which are critical for yield advantage, remain inadequately quantified under film-mulched drip fertigation.</div></div><div><h3>Objective</h3><div>This study aimed to quantify the post-flowering translocation and accumulation of dry matter and nitrogen of intercropped maize and soybean under film-mulched drip-fertigation, and identify the optimal configuration that maximizes the intercropping advantage.</div></div><div><h3>Methods</h3><div>A two-year field experiment was carried out in 2022 and 2023, including eight maize-soybean intercropping row configurations, with monocultures of maize and soybean as controls. Key plant organs were sampled at critical growth stages to determine their dry matter and nitrogen content, which facilitated the subsequent calculation of translocation parameters. The land equivalent ratio, actual yield loss index, and nitrogen equivalent ratio were utilized to evaluate the advantages of the intercropping systems.</div></div><div><h3>Results</h3><div>Intercropping significantly enhanced post-flowering dry matter translocation in maize by 23.5 % but reduced it in soybean by 34.5 %. Conversely, post-flowering nitrogen translocation was reduced in both maize by 39.9 % and soybean by 29.4 %. Dry matter accumulation after flowering was the primary source of grain yield, contributing 94.2 % in maize and 85.0 % in soybean, significantly outweighing the contribution from translocation (5.8 % for maize and 15.0 % for soybean). A similar trend was observed for nitrogen source. Among the configurations, two rows of maize alternating with four rows of soybean (M2S4) achieved the highest land equivalent ratio (1.52), actual yield loss index (1.29), intercropping advantage index (3.24) and nitrogen equivalent ratio (1.70).</div></div><div><h3>Conclusion</h3><div>The M2S4 configuration effectively coordinated dry matter and nitrogen translocation and accumulation, leading to enhanced resource complementarity and yield advantage in drip-fertigated maize-soybean intercropping. This finding provides effective strategy for improving productivity and nitrogen use efficiency in maize-soybean strip intercropping systems.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"337 ","pages":"Article 110290"},"PeriodicalIF":6.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732423","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}
Maize is a key staple crop in Ghana, yet yields remain low (20–40 % of potential). Although fertilizer is promoted to enhance productivity, adoption is limited by highly variable yield responses.
Objective
This study analyzed spatial and environmental drivers of fertilizer effect heterogeneity using 2854 yield observations from randomized controlled trials and 10,916 pairwise absolute yield response to fertilizer (AR) estimates.
Methods
Causal forest (CF) and boosted random forest (BRF) models to estimated fertilizer effects, with BRF performance evaluated via a 10 × 10 nested cross-validation and grid search. SHapley Additive exPlanations and Accumulated Local Effects analyses identified key drivers of fertilizer effect heterogeneity and quantified the magnitude of their influence on fertilizer yield effect.
Results and conclusions
Fertilizer effect varied widely (–4.7–8.9 t ha–1), with the Sudan Savannah showing the highest median AR (2.8 t ha–1) and the Forest-Savannah Transition the lowest (0.9 t ha–1). BRF outperformed CF in predicting fertilizer effects (ME: –0.06–0.05 t ha–1 vs. –0.19 t ha–1, RMSE: 1.17–1.23 t ha–1 vs. 1.3 t ha– 1, MEC: 0.32–0.38 vs. 0.24 and CCC: 0.46–0.54 vs. 0.34). Key determinants of fertilizer effect heterogeneity included both climatic variables (Palmer Drought Severity Index [PDSI], vapor pressure deficit, rainfall) and soil properties (silt content, exchangeable aluminum). PDSI emerged as the dominant driver of fertilizer effect heterogeneity in the entire data set. However, the relative importance of soil versus climate varied spatially: soil properties were the main drivers of fertilizer effect in the Semi-Deciduous Forest and the Forest-Savannah Transition, whereas climatic variables played a stronger role in northern zones. Fertilizer yield effect increased by 0.4–1.6 t ha–1 with increasing PDSI, indicating that improved moisture availability enhances fertilizer use efficiency. Overall, optimal moisture conditions (PDSI > –2.0), the use of hybrid seeds, and the application of briquette fertilizer all contributed to higher fertilizer effects, whereas drought conditions substantially reduced them. Furthermore, fertilizer effect decreased by 0.2–1.4 t ha–1 as silt increased from 9 % to 30 %, and by 0.3–0.6 t ha–1 as exchangeable aluminum increased from 36 to 221 mg kg–1.
Significance
This study presents the first large-scale, data-driven assessment of fertilizer yield effects heterogeneity in Ghana, integrating causal and predictive machine learning with explainable AI. Findings support tailored fertilizer strategies by agro-ecological zones to reduce farmer risk and promote sustainable intensification.
玉米是加纳的主要主粮作物，但产量仍然很低（占潜力的20 - 40% %）。虽然肥料是为了提高生产力而推广的，但由于产量反应的高度变化，采用受到限制。目的利用随机对照试验的2854个产量观测值和10916个绝对产量对肥料（AR）估计的成对响应，分析肥料效应异质性的空间和环境驱动因素。方法因果森林（CF）和增强随机森林（BRF）模型用于估算肥料效应，BRF性能通过10 × 10嵌套交叉验证和网格搜索进行评估。SHapley加性解释和累积局部效应分析确定了肥料效应异质性的关键驱动因素，并量化了它们对肥料产量效应的影响程度。结果与结论施肥效应差异较大（- 4.7 ~ 8.9 t ha-1），其中苏丹大草原的AR中值最高（2.8 t ha-1），森林-大草原过渡的AR中值最低（0.9 t ha-1）。BRF在预测肥料效应方面优于CF （ME: - 0.06 - 0.05 t ha - 1 vs - 0.19 t ha - 1, RMSE: 1.17-1.23 t ha - 1 vs. 1.3 t ha - 1, MEC: 0.32-0.38 vs. 0.24, CCC: 0.46-0.54 vs. 0.34）。肥料效应异质性的关键决定因素包括气候变量（Palmer Drought Severity Index [PDSI]、蒸汽压亏缺、降雨量）和土壤性质（淤泥含量、交换性铝）。在整个数据集中，PDSI成为肥料效应异质性的主要驱动因素。然而，土壤与气候的相对重要性在空间上存在差异：土壤性质是半落叶林和森林-草原过渡区肥料效应的主要驱动因素，而气候变量在北部地区发挥更大的作用。随着PDSI的增加，肥料产量效应增加0.4 ~ 1.6 t ha-1，说明水分有效性的提高提高了肥料利用效率。总体而言，最佳水分条件（PDSI > -2.0）、杂交种子的使用和型煤肥的施用都有助于提高肥效，而干旱条件则大大降低了肥效。淤泥从9 %增加到30 %，肥效降低0.2 ~ 1.4 t ha-1；交换性铝从36 mg kg-1增加到221 mg kg-1，肥效降低0.3 ~ 0.6 t ha-1。本研究首次对加纳肥料产量效应异质性进行了大规模、数据驱动的评估，将因果和预测机器学习与可解释的人工智能相结合。研究结果支持农业生态区定制肥料策略，以降低农民风险并促进可持续集约化。
{"title":"Modeling and explaining fertilizer effect heterogeneity on maize yield in Ghana using causal and predictive machine learning","authors":"Anselme K.K. Kouame , Gerard B.M. Heuvelink , Prem S. Bindraban","doi":"10.1016/j.fcr.2025.110287","DOIUrl":"10.1016/j.fcr.2025.110287","url":null,"abstract":"<div><h3>Context</h3><div>Maize is a key staple crop in Ghana, yet yields remain low (20–40 % of potential). Although fertilizer is promoted to enhance productivity, adoption is limited by highly variable yield responses.</div></div><div><h3>Objective</h3><div>This study analyzed spatial and environmental drivers of fertilizer effect heterogeneity using 2854 yield observations from randomized controlled trials and 10,916 pairwise absolute yield response to fertilizer (AR) estimates.</div></div><div><h3>Methods</h3><div>Causal forest (CF) and boosted random forest (BRF) models to estimated fertilizer effects, with BRF performance evaluated via a 10 × 10 nested cross-validation and grid search. SHapley Additive exPlanations and Accumulated Local Effects analyses identified key drivers of fertilizer effect heterogeneity and quantified the magnitude of their influence on fertilizer yield effect.</div></div><div><h3>Results and conclusions</h3><div>Fertilizer effect varied widely (–4.7–8.9 t ha<sup>–1</sup>), with the Sudan Savannah showing the highest median AR (2.8 t ha<sup>–1</sup>) and the Forest-Savannah Transition the lowest (0.9 t ha<sup>–1</sup>). BRF outperformed CF in predicting fertilizer effects (ME: –0.06–0.05 t ha<sup>–1</sup> vs. –0.19 t ha<sup>–1</sup>, RMSE: 1.17–1.23 t ha<sup>–1</sup> vs. 1.3 t ha<sup>– 1</sup>, MEC: 0.32–0.38 vs. 0.24 and CCC: 0.46–0.54 vs. 0.34). Key determinants of fertilizer effect heterogeneity included both climatic variables (Palmer Drought Severity Index [PDSI], vapor pressure deficit, rainfall) and soil properties (silt content, exchangeable aluminum). PDSI emerged as the dominant driver of fertilizer effect heterogeneity in the entire data set. However, the relative importance of soil versus climate varied spatially: soil properties were the main drivers of fertilizer effect in the Semi-Deciduous Forest and the Forest-Savannah Transition, whereas climatic variables played a stronger role in northern zones. Fertilizer yield effect increased by 0.4–1.6 t ha<sup>–1</sup> with increasing PDSI, indicating that improved moisture availability enhances fertilizer use efficiency. Overall, optimal moisture conditions (PDSI > –2.0), the use of hybrid seeds, and the application of briquette fertilizer all contributed to higher fertilizer effects, whereas drought conditions substantially reduced them. Furthermore, fertilizer effect decreased by 0.2–1.4 t ha<sup>–1</sup> as silt increased from 9 % to 30 %, and by 0.3–0.6 t ha<sup>–1</sup> as exchangeable aluminum increased from 36 to 221 mg kg<sup>–1</sup>.</div></div><div><h3>Significance</h3><div>This study presents the first large-scale, data-driven assessment of fertilizer yield effects heterogeneity in Ghana, integrating causal and predictive machine learning with explainable AI. Findings support tailored fertilizer strategies by agro-ecological zones to reduce farmer risk and promote sustainable intensification.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"337 ","pages":"Article 110287"},"PeriodicalIF":6.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731169","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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