Pub Date : 2025-11-29DOI: 10.1016/j.eja.2025.127946
Mona Schatke , Johanna Bensch , Lena Ulber , Bärbel Gerowitt , Christoph von Redwitz
Four decision concepts were tested for ex-ante chemical weed control decisions to be integrated into site-specific weed management (SSWM). Two concepts assess the economic profitability of a weed control treatment: one considers the abundance of weed taxonomic groups, and the other takes into account individual weed species. The third and fourth concepts utilize weed functional traits to quantify a species’ ability to provide ecosystem services (service potential) and its competitive ability (disservice potential), thereby informing management decisions. Based on grid-based manual weed assessments in five winter cereal fields in Germany, species-specific weed distribution maps were created using an interpolation approach. For each square meter of the fields, a weed control recommendation was generated using each of the four decision concepts, followed by the creation of weed control maps. Control recommendations by the two economic decision concepts showed the highest similarity across all fields, recommending weed control for 23 %–100 % (weed groups) and 6 %–100 % (species-specific) of the total field area. The concepts based on functional weed traits recommended weed control for 2 %–50 % of the area. Both economic decision concepts recommended a weed control treatment in areas recommended to be left untreated when functional traits are considered. The analysis revealed strengths and weaknesses in all concepts and recommends combining functional weed traits and economic profitability.
{"title":"Trait-based weed control decisions compared to economic thresholds for site-specific weed management","authors":"Mona Schatke , Johanna Bensch , Lena Ulber , Bärbel Gerowitt , Christoph von Redwitz","doi":"10.1016/j.eja.2025.127946","DOIUrl":"10.1016/j.eja.2025.127946","url":null,"abstract":"<div><div>Four decision concepts were tested for ex-ante chemical weed control decisions to be integrated into site-specific weed management (SSWM). Two concepts assess the economic profitability of a weed control treatment: one considers the abundance of weed taxonomic groups, and the other takes into account individual weed species. The third and fourth concepts utilize weed functional traits to quantify a species’ ability to provide ecosystem services (service potential) and its competitive ability (disservice potential), thereby informing management decisions. Based on grid-based manual weed assessments in five winter cereal fields in Germany, species-specific weed distribution maps were created using an interpolation approach. For each square meter of the fields, a weed control recommendation was generated using each of the four decision concepts, followed by the creation of weed control maps. Control recommendations by the two economic decision concepts showed the highest similarity across all fields, recommending weed control for 23 %–100 % (weed groups) and 6 %–100 % (species-specific) of the total field area. The concepts based on functional weed traits recommended weed control for 2 %–50 % of the area. Both economic decision concepts recommended a weed control treatment in areas recommended to be left untreated when functional traits are considered. The analysis revealed strengths and weaknesses in all concepts and recommends combining functional weed traits and economic profitability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127946"},"PeriodicalIF":5.5,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614020","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}
Pest and disease control is critical for agricultural productivity, as infestations reduce crop yields, compromise quality, and threaten food security. Although chemical control remains prevalent, pesticide overuse causes ecological disruption. Physical plant protection technologies offer sustainable alternatives by leveraging acoustic, optical, electrical, and thermal energy to disrupt pest physiology. This review systematically analyzes these technologies including steam, flame, microwave, laser, and acoustic treatments detailing their mechanisms, efficiencies, and limitations. While effective for pesticide-free production in protected crops, challenges include high equipment costs, operational complexity, and ecological trade-offs. We compare 16 physical control mXethods and identify unresolved issues in weed management, soil disinfection, and ecological regulation, concluding with recommendations for future research.
{"title":"The application and challenges of physical technology in modern agricultural plant protection","authors":"Shaobo Li, Qingyang Feng, Shaomeng Yu, Qianfeng Liu, Yang Cao, Guangzhao Tian, Yunfu Chen, Wei Qiu","doi":"10.1016/j.eja.2025.127944","DOIUrl":"10.1016/j.eja.2025.127944","url":null,"abstract":"<div><div>Pest and disease control is critical for agricultural productivity, as infestations reduce crop yields, compromise quality, and threaten food security. Although chemical control remains prevalent, pesticide overuse causes ecological disruption. Physical plant protection technologies offer sustainable alternatives by leveraging acoustic, optical, electrical, and thermal energy to disrupt pest physiology. This review systematically analyzes these technologies including steam, flame, microwave, laser, and acoustic treatments detailing their mechanisms, efficiencies, and limitations. While effective for pesticide-free production in protected crops, challenges include high equipment costs, operational complexity, and ecological trade-offs. We compare 16 physical control mXethods and identify unresolved issues in weed management, soil disinfection, and ecological regulation, concluding with recommendations for future research.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127944"},"PeriodicalIF":5.5,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611862","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 : 2025-11-27DOI: 10.1016/j.eja.2025.127925
Eric Asamoah , Gerard B.M. Heuvelink , Vincent Logah , Johan G.B. Leenaars , Prem S. Bindraban
Efficient fertilizer application is vital for enhancing maize production and profitability in Sub-Saharan Africa, where soil fertility varies widely across regions. This study aimed to develop a machine learning approach for generating site-specific fertilizer recommendations for maize production in Ghana and to evaluate its performance against conventional and semi-mechanistic approaches. A random forest machine learning model was trained on 482 maize yield experiments, consisting of 3136 yield observations collected from 1991 to 2020, to predict maize yield response to different fertilizer rates. The model incorporated multiple explanatory variables, including soil properties, climate conditions, and management practices, to generate fertilizer response curves from which fertilizer recommendations were derived for 14 sites across three agro-ecological zones in Ghana where field validation experiments were conducted. On these sites, the recommendations were compared with recommendations derived from the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), Conventional Fertilizer Dose Response (CFDR), and Updated Conventional Fertilizer Dose Response (UCFDR) approaches and validated through field experiments. The machine learning approach generally recommended lower rates of phosphorus and potassium than the other approaches, while nitrogen recommendations were comparable. In the Guinea Savanna zone, the recommendations from the machine learning approach outperformed those from the other approaches, producing higher mean yields for three out of the four sites in the zone. In the Forest-Savanna Transition (FST) zone, the machine learning model recommendations led to higher mean yields at four sites, while the approaches based on QUEFTS and UCFDR performed best at two other sites. In the Semi-deciduous Forest zone, the recommendations of the QUEFTS approach resulted in the highest mean yields at three sites, and CFDR at one site. Despite high input prices during the period of experimentation, the machine learning approach-based recommendations demonstrated higher net profit margins in the FST zone, suggesting cost-effectiveness in this zone. These findings indicate that site-specific fertilizer recommendations are more efficient than blanket recommendations and that machine learning approaches offer a promising and innovative approach for generating cost-effective, site-specific fertilizer recommendations in tropical climates.
{"title":"Fertilizer recommendations for maize production in Ghana: Comparison of machine learning, semi-mechanistic and conventional approaches","authors":"Eric Asamoah , Gerard B.M. Heuvelink , Vincent Logah , Johan G.B. Leenaars , Prem S. Bindraban","doi":"10.1016/j.eja.2025.127925","DOIUrl":"10.1016/j.eja.2025.127925","url":null,"abstract":"<div><div>Efficient fertilizer application is vital for enhancing maize production and profitability in Sub-Saharan Africa, where soil fertility varies widely across regions. This study aimed to develop a machine learning approach for generating site-specific fertilizer recommendations for maize production in Ghana and to evaluate its performance against conventional and semi-mechanistic approaches. A random forest machine learning model was trained on 482 maize yield experiments, consisting of 3136 yield observations collected from 1991 to 2020, to predict maize yield response to different fertilizer rates. The model incorporated multiple explanatory variables, including soil properties, climate conditions, and management practices, to generate fertilizer response curves from which fertilizer recommendations were derived for 14 sites across three agro-ecological zones in Ghana where field validation experiments were conducted. On these sites, the recommendations were compared with recommendations derived from the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), Conventional Fertilizer Dose Response (CFDR), and Updated Conventional Fertilizer Dose Response (UCFDR) approaches and validated through field experiments. The machine learning approach generally recommended lower rates of phosphorus and potassium than the other approaches, while nitrogen recommendations were comparable. In the Guinea Savanna zone, the recommendations from the machine learning approach outperformed those from the other approaches, producing higher mean yields for three out of the four sites in the zone. In the Forest-Savanna Transition (FST) zone, the machine learning model recommendations led to higher mean yields at four sites, while the approaches based on QUEFTS and UCFDR performed best at two other sites. In the Semi-deciduous Forest zone, the recommendations of the QUEFTS approach resulted in the highest mean yields at three sites, and CFDR at one site. Despite high input prices during the period of experimentation, the machine learning approach-based recommendations demonstrated higher net profit margins in the FST zone, suggesting cost-effectiveness in this zone. These findings indicate that site-specific fertilizer recommendations are more efficient than blanket recommendations and that machine learning approaches offer a promising and innovative approach for generating cost-effective, site-specific fertilizer recommendations in tropical climates.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127925"},"PeriodicalIF":5.5,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611856","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 : 2025-11-27DOI: 10.1016/j.eja.2025.127878
Sibylle Lustenberger , Bassirou Bonfoh , Bognan Valentin Koné , Johan Six , Günther Fink
<div><h3>Context</h3><div>In Côte d’Ivoire, cocoa is primarily produced on small-scale monoculture plantations as the main source of income for much of the rural population. Fertilization of cocoa farms remains uncommon, and long-term production without fertilization contributes to soil degradation. The ongoing decrease in productivity on small-scale cocoa farms undermines producers’ livelihoods and aggravates poverty. Poultry litter compost from the emerging poultry industry bares potential as a sustainable alternative to mineral fertilizers, but its effectiveness and profitability for cocoa production remain unknown.</div></div><div><h3>Objective</h3><div>Our study aimed to compare productivity and profitability effects of mineral-, compost-, and mixed fertilizers on a representative sample of established small-scale, age-diverse cocoa fields.</div></div><div><h3>Methods</h3><div>Our randomized controlled on-farm experiment included 120 farmers’ cocoa fields in central Côte d’Ivoire to assess productivity and profitability of three fertilizer options over one production cycle: Organic- (composted poultry litter, 71 kg N ha<sup>−1</sup>y<sup>−1</sup>), mineral- (marketed NPK+, 15 kg N ha<sup>−1</sup>y<sup>−1</sup>), and 50:50 combined organic and mineral fertilization (43 kg N ha<sup>−1</sup>y<sup>−1</sup>). Experimental plots comprised three cocoa trees per treatment and trees were fertilized twice before trees’ main harvest yields were measured. We estimated bean dry weights, annual yields and financial incomes per hectare. Treatment differences in yield and market value per hectare were tested using linear mixed-effects models, and report value-to-cost ratio (VCR = additional cocoa market value divided by total fertilization cost) of treatments’ projected annual harvests. We predicted compost fertilization VCR under both low-end and high-end price scenarios to account for regional variation in commercialization of poultry litter sale and resulting price variance.</div></div><div><h3>Results and conclusions</h3><div>Organic fertilization led to the highest increase of main harvest productivity (+ 190 kg dryweight per ha (dw), 38 %) followed by mixed fertilization (+ 145 kg ha<sup>−1</sup> dw, 31 %) and mineral fertilization (+ 118 kg ha<sup>−1</sup> dw, 22 %). Organic fertilization showed a high positive return on investment (VCR<sub>l</sub> = 3.08, CI = 1.94, 4.22) in the low cost scenario of USD 104 ha<sup>−1</sup> y<sup>−1</sup>, but not when high costs were assumed (VCR<sub>h</sub> = 0.94, CI = 0.59, 1.29, USD 342 ha<sup>−1</sup> y<sup>−1</sup>). The value-to-cost ratio was below one for both the mixed (VCR<sub>l</sub> = 0.88, CI = 0.47, 1.29, USD 290 and VCR<sub>h</sub> = 0.62, CI = 0.33, 0.91, USD 409 ha<sup>−1</sup> y<sup>−1</sup>) and the mineral fertilizer (VCR = 0.26, CI = 0.01, 0.51, USD 460 ha<sup>−1</sup> y<sup>−1</sup>).</div></div><div><h3>Significance</h3><div>This study provides first experimental evidence of the effectiveness
在Côte科特迪瓦，可可主要由小规模单一种植种植园生产，是大部分农村人口的主要收入来源。可可农场很少施肥，长期不施肥的生产会导致土壤退化。小规模可可农场的生产力持续下降，破坏了生产者的生计，加剧了贫困。新兴家禽业的家禽垃圾堆肥具有作为矿物肥料的可持续替代品的潜力，但其对可可生产的有效性和盈利能力尚不清楚。我们的研究旨在比较矿物肥料、堆肥肥料和混合肥料对已建立的小规模、年龄不同的可可田的代表性样本的生产力和盈利能力的影响。方法采用随机对照的农场试验方法，在Côte科特迪瓦中部120个农户的可可田进行试验，以评估三种肥料方案在一个生产周期内的生产力和盈利能力：有机肥料（堆肥家禽粪便，71 kg N ha−1y−1）、矿物肥料（市场销售的氮磷钾+，15 kg N ha−1y−1）和50:50有机和矿物联合施肥（43 kg N ha−1y−1）。试验田每处理三棵可可树，在测量树的主要收获产量之前，对树进行两次施肥。我们估计了每公顷豆子的干重、年产量和财政收入。使用线性混合效应模型测试了每公顷产量和市场价值的处理差异，并报告了处理的预计年收成的价值成本比（VCR =额外的可可市场价值除以总施肥成本）。我们预测了低端和高端价格情景下的堆肥施肥VCR，以解释家禽产仔销售商业化的区域差异和由此产生的价格差异。结果与结论有机肥对主要收获生产力的提高最大（+ 190 kg / hw, 38 %），其次是混肥（+ 145 kg ha−1 dw, 31 %）和矿肥（+ 118 kg ha−1 dw, 22 %）。在104美元 ha−1 y−1的低成本情况下，有机肥显示出较高的正投资回报率（VCRh = 3.08, CI = 1.94, 4.22），但在高成本情况下则不是这样（VCRh = 0.94, CI = 0.59, 1.29, 342美元 ha−1 y−1）。混合肥料（VCR = 0.88, CI = 0.47, 1.29, USD 290, VCRh = 0.62, CI = 0.33, 0.91, USD 409 ha−1 y−1）和矿物肥（VCR = 0.26, CI = 0.01, 0.51, USD 460 ha−1 y−1）的价值成本比均低于1。本研究首次为小规模可可种植中禽畜堆肥有机施肥的有效性和效益提供了实验证据。虽然施肥对提高生产力和收入至关重要，但普遍较低的VCR突出表明，可可豆的农场价格不足，这阻碍了大多数经过试验的施肥策略的有利可图的采用。关键的政策建议包括确保适当的农场收购价，为投入成本和物流提供有针对性的补贴，以及促进推广服务，鼓励农民在田间试用肥料。需要进一步的研究，包括长期的农场试验和对农民认为采用肥料的障碍的定性研究，为支持可可农业生态系统的肥力和恢复力的有效政策提供信息。
{"title":"On-farm fertilization experiment on small-scale cocoa farms in Côte d′Ivoire: Evaluation of poultry litter compost for sustainable yield and profitability","authors":"Sibylle Lustenberger , Bassirou Bonfoh , Bognan Valentin Koné , Johan Six , Günther Fink","doi":"10.1016/j.eja.2025.127878","DOIUrl":"10.1016/j.eja.2025.127878","url":null,"abstract":"<div><h3>Context</h3><div>In Côte d’Ivoire, cocoa is primarily produced on small-scale monoculture plantations as the main source of income for much of the rural population. Fertilization of cocoa farms remains uncommon, and long-term production without fertilization contributes to soil degradation. The ongoing decrease in productivity on small-scale cocoa farms undermines producers’ livelihoods and aggravates poverty. Poultry litter compost from the emerging poultry industry bares potential as a sustainable alternative to mineral fertilizers, but its effectiveness and profitability for cocoa production remain unknown.</div></div><div><h3>Objective</h3><div>Our study aimed to compare productivity and profitability effects of mineral-, compost-, and mixed fertilizers on a representative sample of established small-scale, age-diverse cocoa fields.</div></div><div><h3>Methods</h3><div>Our randomized controlled on-farm experiment included 120 farmers’ cocoa fields in central Côte d’Ivoire to assess productivity and profitability of three fertilizer options over one production cycle: Organic- (composted poultry litter, 71 kg N ha<sup>−1</sup>y<sup>−1</sup>), mineral- (marketed NPK+, 15 kg N ha<sup>−1</sup>y<sup>−1</sup>), and 50:50 combined organic and mineral fertilization (43 kg N ha<sup>−1</sup>y<sup>−1</sup>). Experimental plots comprised three cocoa trees per treatment and trees were fertilized twice before trees’ main harvest yields were measured. We estimated bean dry weights, annual yields and financial incomes per hectare. Treatment differences in yield and market value per hectare were tested using linear mixed-effects models, and report value-to-cost ratio (VCR = additional cocoa market value divided by total fertilization cost) of treatments’ projected annual harvests. We predicted compost fertilization VCR under both low-end and high-end price scenarios to account for regional variation in commercialization of poultry litter sale and resulting price variance.</div></div><div><h3>Results and conclusions</h3><div>Organic fertilization led to the highest increase of main harvest productivity (+ 190 kg dryweight per ha (dw), 38 %) followed by mixed fertilization (+ 145 kg ha<sup>−1</sup> dw, 31 %) and mineral fertilization (+ 118 kg ha<sup>−1</sup> dw, 22 %). Organic fertilization showed a high positive return on investment (VCR<sub>l</sub> = 3.08, CI = 1.94, 4.22) in the low cost scenario of USD 104 ha<sup>−1</sup> y<sup>−1</sup>, but not when high costs were assumed (VCR<sub>h</sub> = 0.94, CI = 0.59, 1.29, USD 342 ha<sup>−1</sup> y<sup>−1</sup>). The value-to-cost ratio was below one for both the mixed (VCR<sub>l</sub> = 0.88, CI = 0.47, 1.29, USD 290 and VCR<sub>h</sub> = 0.62, CI = 0.33, 0.91, USD 409 ha<sup>−1</sup> y<sup>−1</sup>) and the mineral fertilizer (VCR = 0.26, CI = 0.01, 0.51, USD 460 ha<sup>−1</sup> y<sup>−1</sup>).</div></div><div><h3>Significance</h3><div>This study provides first experimental evidence of the effectiveness","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127878"},"PeriodicalIF":5.5,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609497","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 : 2025-11-26DOI: 10.1016/j.eja.2025.127929
S. ABARNA , D. KESAVARAJA
Paddy yield prediction plays a crucial role in agriculture, enabling farmers to make informed decisions. This work proposes an innovative approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for accurate paddy yield forecasting. The hybrid model harnesses the spatial understanding capabilities of CNNs and the sequential learning ability of RNNs to capture both local and temporal dependencies in agricultural data. A key enhancement introduced in this method is the incorporation of a dynamic parameter calibration technique. Traditional regularization methods often rely on static values, which may not adapt effectively to varying complexities in the dataset. The proposed approach dynamically adjusts the regularization strength parameters during training, allowing the model to better converge to different patterns and fluctuations in paddy growth parameters. The dataset utilized for training and evaluation comprises comprehensive agricultural variables, including soil composition, climate conditions, and historical yield data. Experiments demonstrate the effectiveness of the hybrid CNN-RNN architecture with dynamic parameter calibration which improves prediction accuracy over conventional models. This research contributes to the advancement of precision agriculture by providing a more robust and adaptable framework for paddy yield prediction. The integration of spatial and temporal features, along with dynamic parameter calibration, showcases the potential for optimizing agricultural decision-making processes and mitigating the impact of unpredictable factors on paddy production.
{"title":"Dynamic parameter calibration based deep network for paddy yield prediction","authors":"S. ABARNA , D. KESAVARAJA","doi":"10.1016/j.eja.2025.127929","DOIUrl":"10.1016/j.eja.2025.127929","url":null,"abstract":"<div><div>Paddy yield prediction plays a crucial role in agriculture, enabling farmers to make informed decisions. This work proposes an innovative approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for accurate paddy yield forecasting. The hybrid model harnesses the spatial understanding capabilities of CNNs and the sequential learning ability of RNNs to capture both local and temporal dependencies in agricultural data. A key enhancement introduced in this method is the incorporation of a dynamic parameter calibration technique. Traditional regularization methods often rely on static values, which may not adapt effectively to varying complexities in the dataset. The proposed approach dynamically adjusts the regularization strength parameters during training, allowing the model to better converge to different patterns and fluctuations in paddy growth parameters. The dataset utilized for training and evaluation comprises comprehensive agricultural variables, including soil composition, climate conditions, and historical yield data. Experiments demonstrate the effectiveness of the hybrid CNN-RNN architecture with dynamic parameter calibration which improves prediction accuracy over conventional models. This research contributes to the advancement of precision agriculture by providing a more robust and adaptable framework for paddy yield prediction. The integration of spatial and temporal features, along with dynamic parameter calibration, showcases the potential for optimizing agricultural decision-making processes and mitigating the impact of unpredictable factors on paddy production.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127929"},"PeriodicalIF":5.5,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599014","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 : 2025-11-26DOI: 10.1016/j.eja.2025.127943
Ying Song , Zhijie Li , Xiaoling He , Jiaqiong Zhang , Jinxia Fu , Fenli Zheng , Zhi Li
Selecting an appropriate straw return method is crucial for enhancing crop productivity and promoting sustainable agriculture in the black soil region of Northeast China. However, few studies have evaluated the effectiveness of different straw return methods on crop yield, and their regional applicability has not yet been established. This study integrates machine learning approaches and meta-analysis to assess the impact of straw mulch (SM) and straw incorporation (SI) on crop yields under varying climate, soils, and agricultural management conditions in Northeast China’s drylands. Straw return overall increases crop yield by ∼5 %, among which SM and SI have similar mean contributions to yield improvements (5 % vs 4 %). The effects of two straw return methods vary with environmental conditions; specifically, SM outperforms SI under low temperatures (mean annual temperature MAT <6 ℃), drought (mean annual precipitation MAP <600 mm), and moderate erosion (mean annual soil erosion ASE 0.5–2 t/ha), but SI has better effects with high temperatures (MAT >6 ℃), high precipitation (MAP >600 mm), and severe erosion (ASE >2 t/ha). SM achieves the highest yield benefit (8 %) under moderate straw return amounts (6000–10,000 kg/ha), whereas SI performs the best (6 %) at low straw return amounts (< 6000 kg/ha). Furthermore, the yield-enhancing effects of both methods intensifies with increasing experimental duration, with SI's effect gradually and consistently surpassing that of SM. Spatial prediction results reveal that the overall extent of yield increase for SI is 9 %, with higher increasing yield potential observed in the southwest and southeast regions, while the extent of yield increase for SM is lower, at only 3 %. This study elucidates the differentiated yield-enhancing effects of different straw return methods in the black soil region, providing a scientific basis for precision agricultural management and sustainable utilization of black soil in Northeast China and other similar regions.
{"title":"Unraveling the regional dynamics of straw mulching and incorporation on crop yields in Northeast China","authors":"Ying Song , Zhijie Li , Xiaoling He , Jiaqiong Zhang , Jinxia Fu , Fenli Zheng , Zhi Li","doi":"10.1016/j.eja.2025.127943","DOIUrl":"10.1016/j.eja.2025.127943","url":null,"abstract":"<div><div>Selecting an appropriate straw return method is crucial for enhancing crop productivity and promoting sustainable agriculture in the black soil region of Northeast China. However, few studies have evaluated the effectiveness of different straw return methods on crop yield, and their regional applicability has not yet been established. This study integrates machine learning approaches and meta-analysis to assess the impact of straw mulch (SM) and straw incorporation (SI) on crop yields under varying climate, soils, and agricultural management conditions in Northeast China’s drylands. Straw return overall increases crop yield by ∼5 %, among which SM and SI have similar mean contributions to yield improvements (5 % vs 4 %). The effects of two straw return methods vary with environmental conditions; specifically, SM outperforms SI under low temperatures (mean annual temperature MAT <6 ℃), drought (mean annual precipitation MAP <600 mm), and moderate erosion (mean annual soil erosion ASE 0.5–2 t/ha), but SI has better effects with high temperatures (MAT >6 ℃), high precipitation (MAP >600 mm), and severe erosion (ASE >2 t/ha). SM achieves the highest yield benefit (8 %) under moderate straw return amounts (6000–10,000 kg/ha), whereas SI performs the best (6 %) at low straw return amounts (< 6000 kg/ha). Furthermore, the yield-enhancing effects of both methods intensifies with increasing experimental duration, with SI's effect gradually and consistently surpassing that of SM. Spatial prediction results reveal that the overall extent of yield increase for SI is 9 %, with higher increasing yield potential observed in the southwest and southeast regions, while the extent of yield increase for SM is lower, at only 3 %. This study elucidates the differentiated yield-enhancing effects of different straw return methods in the black soil region, providing a scientific basis for precision agricultural management and sustainable utilization of black soil in Northeast China and other similar regions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127943"},"PeriodicalIF":5.5,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609504","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 : 2025-11-24DOI: 10.1016/j.eja.2025.127928
Xiaodong Sun , Andong Cai , Chengjie Ren , Shuohong Zhang , Qiang Li , Shutang Liu , Shuiqing Zhang , Huimin Zhang , Yu Li , Kailou Liu , Minggang Xu
The balance of carbon (C), nitrogen (N), and phosphorus (P) stoichiometry fundamentally regulates nutrient cycling and microbial metabolism in terrestrial ecosystems. However, the mechanisms through which long-term fertilization and climate jointly shape multidimensional stoichiometric networks and microbial life history strategies remain unclear. In this study, six long-term (27–44 years) fertilization experiments across a 17° latitudinal gradient in China were examined under three treatments: no fertilizer (CK), mineral fertilizer (CF), and mineral plus manure fertilizer (CFM). By integrating ecological stoichiometry with metagenomic approaches, this study assessed how fertilization and climate affect soil, resource, microbial, and enzyme stoichiometry, and how these stoichiometric shifts influence microbial life history strategies. Results showed that long-term fertilization altered stoichiometric patterns, strengthening network connectivity among soil, resource, microbial, and enzymatic stoichiometry. CFM reduced soil and microbial C:P and N:P ratios by 35–70 % and decreased DOC:Olsen-P and DON:Olsen-P by up to 95 %. These shifts restructured microbial life history strategies, promoting a transition from resource acquisition (A) to growth yield (Y) strategies, with Y strategists increasing to 45–56 % under fertilization. Moreover, available resource and microbial stoichiometry, particularly DOC:Olsen-P and DON:Olsen-P ratios, were the primary predictors of microbial strategies, linking stoichiometric balance to microbial energetic allocation. Fertilization and climate jointly regulated microbial life history strategies by alleviating C:P and N:P imbalances and promoting stoichiometric homeostasis. Overall, these findings establish a mechanistic framework connecting nutrient supply, stoichiometric regulation, and microbial adaptation, thereby providing theoretical guidance for optimizing fertilization practices and maintaining soil nutrient sustainability across climatic regions.
{"title":"Long-term fertilization reshapes stoichiometric networks driving shifts in microbial life history strategies across China’s croplands","authors":"Xiaodong Sun , Andong Cai , Chengjie Ren , Shuohong Zhang , Qiang Li , Shutang Liu , Shuiqing Zhang , Huimin Zhang , Yu Li , Kailou Liu , Minggang Xu","doi":"10.1016/j.eja.2025.127928","DOIUrl":"10.1016/j.eja.2025.127928","url":null,"abstract":"<div><div>The balance of carbon (C), nitrogen (N), and phosphorus (P) stoichiometry fundamentally regulates nutrient cycling and microbial metabolism in terrestrial ecosystems. However, the mechanisms through which long-term fertilization and climate jointly shape multidimensional stoichiometric networks and microbial life history strategies remain unclear. In this study, six long-term (27–44 years) fertilization experiments across a 17° latitudinal gradient in China were examined under three treatments: no fertilizer (CK), mineral fertilizer (CF), and mineral plus manure fertilizer (CFM). By integrating ecological stoichiometry with metagenomic approaches, this study assessed how fertilization and climate affect soil, resource, microbial, and enzyme stoichiometry, and how these stoichiometric shifts influence microbial life history strategies. Results showed that long-term fertilization altered stoichiometric patterns, strengthening network connectivity among soil, resource, microbial, and enzymatic stoichiometry. CFM reduced soil and microbial C:P and N:P ratios by 35–70 % and decreased DOC:Olsen-P and DON:Olsen-P by up to 95 %. These shifts restructured microbial life history strategies, promoting a transition from resource acquisition (A) to growth yield (Y) strategies, with Y strategists increasing to 45–56 % under fertilization. Moreover, available resource and microbial stoichiometry, particularly DOC:Olsen-P and DON:Olsen-P ratios, were the primary predictors of microbial strategies, linking stoichiometric balance to microbial energetic allocation. Fertilization and climate jointly regulated microbial life history strategies by alleviating C:P and N:P imbalances and promoting stoichiometric homeostasis. Overall, these findings establish a mechanistic framework connecting nutrient supply, stoichiometric regulation, and microbial adaptation, thereby providing theoretical guidance for optimizing fertilization practices and maintaining soil nutrient sustainability across climatic regions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127928"},"PeriodicalIF":5.5,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145583818","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 : 2025-11-22DOI: 10.1016/j.eja.2025.127930
Shaofeng Huang, Qi Zhang, Siyuan Dai
Regional agricultural drought (RAD) can cause great losses and is a complex phenomenon with multiple attribution factors. Previous studies have rarely examined agricultural droughts from the perspective of regional events, overlooking the temporal and spatial synchronicity in their development processes. In this study, we enhanced the conventional three-dimensional (3D, latitude × longitude × time) connectivity approach by modifying the spatial connectivity criteria and objectively establishing two critical minimum area thresholds to identify RADs. The remote sensing-based Crop Water Stress Index (CWSI) was employed to characterize agricultural drought in the North China Plain (NCP). A total of 114 RADs were detected across the NCP from 2000 to 2023, and their occurrence characteristics and attribution factors were analyzed. The results suggested that setting the minimum area threshold for spatially contiguous agricultural drought clusters at 2.0 % of the total study area yielded more stable identification outcomes. The average duration of the 114 RADs was 52.24 days, with 23.68 % of the events lasting longer than three months and 31.58 % covering more than 90 % of the study area. In the NCP, spring and autumn were periods characterized by frequent and severe agricultural droughts, with spring droughts more intense than autumn droughts. From 2000, the severity and intensity of RADs exhibited a slight decreasing trend. RADs occurred much more frequently in the northwestern region, and the southwestward-moving events were the most common. Using the Geodetector method, precipitation, relative humidity, and evaporation were detected as the top three meteorological factors attributed the spatial distribution of RADs in the NCP. Potential evaporation and precipitation were the predominant meteorological factors influencing the interannual fluctuation of RADs. The Atlantic Multidecadal Oscillation and Western Pacific Subtropical High were identified as the primary teleconnection attributors of interannual variability of RADs. These findings provide novel insight into the characteristics and drivers of RADs, and can offer valuable references for agricultural planning and management from a regional perspective.
{"title":"Identification of regional agricultural drought in the North China Plain and its attribution factors","authors":"Shaofeng Huang, Qi Zhang, Siyuan Dai","doi":"10.1016/j.eja.2025.127930","DOIUrl":"10.1016/j.eja.2025.127930","url":null,"abstract":"<div><div>Regional agricultural drought (RAD) can cause great losses and is a complex phenomenon with multiple attribution factors. Previous studies have rarely examined agricultural droughts from the perspective of regional events, overlooking the temporal and spatial synchronicity in their development processes. In this study, we enhanced the conventional three-dimensional (3D, latitude × longitude × time) connectivity approach by modifying the spatial connectivity criteria and objectively establishing two critical minimum area thresholds to identify RADs. The remote sensing-based Crop Water Stress Index (CWSI) was employed to characterize agricultural drought in the North China Plain (NCP). A total of 114 RADs were detected across the NCP from 2000 to 2023, and their occurrence characteristics and attribution factors were analyzed. The results suggested that setting the minimum area threshold for spatially contiguous agricultural drought clusters at 2.0 % of the total study area yielded more stable identification outcomes. The average duration of the 114 RADs was 52.24 days, with 23.68 % of the events lasting longer than three months and 31.58 % covering more than 90 % of the study area. In the NCP, spring and autumn were periods characterized by frequent and severe agricultural droughts, with spring droughts more intense than autumn droughts. From 2000, the severity and intensity of RADs exhibited a slight decreasing trend. RADs occurred much more frequently in the northwestern region, and the southwestward-moving events were the most common. Using the Geodetector method, precipitation, relative humidity, and evaporation were detected as the top three meteorological factors attributed the spatial distribution of RADs in the NCP. Potential evaporation and precipitation were the predominant meteorological factors influencing the interannual fluctuation of RADs. The Atlantic Multidecadal Oscillation and Western Pacific Subtropical High were identified as the primary teleconnection attributors of interannual variability of RADs. These findings provide novel insight into the characteristics and drivers of RADs, and can offer valuable references for agricultural planning and management from a regional perspective.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127930"},"PeriodicalIF":5.5,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567790","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 : 2025-11-22DOI: 10.1016/j.eja.2025.127924
Qiaoling Liu , Jianyu Zhao , Fengxin Wang , Kaijing Yang , Jialu Dai , Bin Yang
Climate change threatens global agriculture through extreme weather and shifting growing conditions. Potatoes, a critical staple crop, face challenges like heat stress and water scarcity. Optimising agronomic practices, such as drip irrigation and film mulching, is critical to achieving climate-smart potato production and ensuring food security. During 2021–2100, the DeNitrification-DeComposition (DNDC) model and the Multiscale Geographically Weighted Regression (MGWR) model were comprehensively used to assess the effects of drip irrigation with and without film mulching on potato yield and global warming potential (GWP) under different future climate scenarios in the main potato producing areas of northern China. The results indicated that the DNDC model could effectively predict potato growth and emissions of nitrous oxide and methane (adjusted R2 > 0.81, normalized root mean square error < 0.20). Compared to without film mulching (NM), the aboveground biomass and tuber yield were increased under drip irrigation with film mulch (TM), with the mean annual tuber yield of potatoes being 6.2 %-7.4 % higher under multiple emission scenarios. The GWP of TM increased by 1.1–1.4 times, and the net GWP offset decreased by 9.4 %-16.3 %. The MGWR analysis showed that precipitation had a significant positive effect on tuber yield in Inner Mongolia, Gansu and Ningxia, while temperature was the main negative influence on yield in Shaanxi. The main drivers of GWP were temperature and precipitation, with significant differences between regions. The findings provide a scientific basis for developing management strategies to adapt to and mitigate the effects of climate change on potato production, emphasizing the need to strike a balance between increasing yields and reducing greenhouse gas emissions.
{"title":"Film-mulched drip irrigation in the main potato production areas of Northern China: Assessing future yield, greenhouse gas emissions and drivers under climate change","authors":"Qiaoling Liu , Jianyu Zhao , Fengxin Wang , Kaijing Yang , Jialu Dai , Bin Yang","doi":"10.1016/j.eja.2025.127924","DOIUrl":"10.1016/j.eja.2025.127924","url":null,"abstract":"<div><div>Climate change threatens global agriculture through extreme weather and shifting growing conditions. Potatoes, a critical staple crop, face challenges like heat stress and water scarcity. Optimising agronomic practices, such as drip irrigation and film mulching, is critical to achieving climate-smart potato production and ensuring food security. During 2021–2100, the DeNitrification-DeComposition (DNDC) model and the Multiscale Geographically Weighted Regression (MGWR) model were comprehensively used to assess the effects of drip irrigation with and without film mulching on potato yield and global warming potential (GWP) under different future climate scenarios in the main potato producing areas of northern China. The results indicated that the DNDC model could effectively predict potato growth and emissions of nitrous oxide and methane (adjusted R<sup>2</sup> > 0.81, normalized root mean square error < 0.20). Compared to without film mulching (NM), the aboveground biomass and tuber yield were increased under drip irrigation with film mulch (TM), with the mean annual tuber yield of potatoes being 6.2 %-7.4 % higher under multiple emission scenarios. The GWP of TM increased by 1.1–1.4 times, and the net GWP offset decreased by 9.4 %-16.3 %. The MGWR analysis showed that precipitation had a significant positive effect on tuber yield in Inner Mongolia, Gansu and Ningxia, while temperature was the main negative influence on yield in Shaanxi. The main drivers of GWP were temperature and precipitation, with significant differences between regions. The findings provide a scientific basis for developing management strategies to adapt to and mitigate the effects of climate change on potato production, emphasizing the need to strike a balance between increasing yields and reducing greenhouse gas emissions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127924"},"PeriodicalIF":5.5,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567428","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 : 2025-11-21DOI: 10.1016/j.eja.2025.127926
Ruiqi Du , Wenbo Shi , Xianghui Lu , Youzhen Xiang , Yue Zhang , Xiaoying Feng , Yu Ma
Rapid and accurate acquisition of field crop yield is of great significance for agriculture management optimization, food security and crop productivity. By the non-destructive and high-throughput data acquisition, the unmanned aerial vehicle (UAV) remote sensing has become a key tool for crop growth monitoring. However, the scarcity of in-situ samples poses technical barriers and efficiency challenges to yield model training. This study has developed a new yield estimation framework that integrates process models, optical remote sensing, and transfer learning to improve the stability and accuracy of crop yield estimation under small sample conditions. The DSSAT was calibrated with hyperspectral UAV derived crop growth variables, to describe the spatial-temporal variation of small-scale field winter canola leaf nitrogen content during growing season. Firstly, a process-interpretative crop yield estimation framework, TrSC2Y, was pre-trained using the PROSAIL radiative transfer model and the DSSAT crop growth model. Secondly, TrSC2Y was fine-tuned using field observations and UAV hyper-spectra images from three-years canola experiment. Finally, the actual performance and application potential of fine-tuned TrSC2Y in canola yield estimation were evaluated with machine learning as a benchmark test. The results show that: (1) Pre-trained by the crop spectra dataset (from PROSAIL) and yield dataset (from DSSAT), TrSC2Y can accurately extract crop phenotype parameters from theoretical canopy spectra. The joint use of phenotype parameters from multiple growth stages can achieve the best yield estimation (R2= 0.98;RMSE= 33.07 kg/ha;MAE= 1.26 %);(2) Fine-tuned TrSC2Y can be transferred to the field winter canola yield estimation task and shows stable performance (R2= 0.86;RMSE=224.42 kg/ha;MAE=6.5 %). Compared with the machine learning benchmark test, the demand of modeling samples for TrSC2Y is reduced by 50 %; (3) TrSC2Y supports the visualization of field-scale winter canola yield and captures the spatial variability of winter canola yield caused by irrigation-fertilizer treatments.The above results provide a lightweight, cost-effective, and innovative method for field crop yield estimation, promoting the development of precision agriculture management and intelligent applications.
{"title":"TrSC2Y: A transfer-learning-based model from UAV hyper-spectra imagery for field-scale canola yield prediction by integrating DSSAT with PROSAIL","authors":"Ruiqi Du , Wenbo Shi , Xianghui Lu , Youzhen Xiang , Yue Zhang , Xiaoying Feng , Yu Ma","doi":"10.1016/j.eja.2025.127926","DOIUrl":"10.1016/j.eja.2025.127926","url":null,"abstract":"<div><div>Rapid and accurate acquisition of field crop yield is of great significance for agriculture management optimization, food security and crop productivity. By the non-destructive and high-throughput data acquisition, the unmanned aerial vehicle (UAV) remote sensing has become a key tool for crop growth monitoring. However, the scarcity of in-situ samples poses technical barriers and efficiency challenges to yield model training. This study has developed a new yield estimation framework that integrates process models, optical remote sensing, and transfer learning to improve the stability and accuracy of crop yield estimation under small sample conditions. The DSSAT was calibrated with hyperspectral UAV derived crop growth variables, to describe the spatial-temporal variation of small-scale field winter canola leaf nitrogen content during growing season. Firstly, a process-interpretative crop yield estimation framework, TrSC2Y, was pre-trained using the PROSAIL radiative transfer model and the DSSAT crop growth model. Secondly, TrSC2Y was fine-tuned using field observations and UAV hyper-spectra images from three-years canola experiment. Finally, the actual performance and application potential of fine-tuned TrSC2Y in canola yield estimation were evaluated with machine learning as a benchmark test. The results show that: (1) Pre-trained by the crop spectra dataset (from PROSAIL) and yield dataset (from DSSAT), TrSC2Y can accurately extract crop phenotype parameters from theoretical canopy spectra. The joint use of phenotype parameters from multiple growth stages can achieve the best yield estimation (R<sup>2</sup>= 0.98;RMSE= 33.07 kg/ha;MAE= 1.26 %);(2) Fine-tuned TrSC2Y can be transferred to the field winter canola yield estimation task and shows stable performance (R<sup>2</sup>= 0.86;RMSE=224.42 kg/ha;MAE=6.5 %). Compared with the machine learning benchmark test, the demand of modeling samples for TrSC2Y is reduced by 50 %; (3) TrSC2Y supports the visualization of field-scale winter canola yield and captures the spatial variability of winter canola yield caused by irrigation-fertilizer treatments.The above results provide a lightweight, cost-effective, and innovative method for field crop yield estimation, promoting the development of precision agriculture management and intelligent applications.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127926"},"PeriodicalIF":5.5,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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