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":"2026-03-01","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 : 2026-03-01Epub Date: 2025-12-12DOI: 10.1016/j.eja.2025.127952
Anna Siczek , Marcin Becher , Stanisław Kalembasa , Dorota Kalembasa
In sustainable plant production, the cultivation of legumes is crucial as it reduces the need for mineral fertilizers by fixing nitrogen (N), which is then available to succeeding crops, mainly cereals. While the effects of legumes as precrops on the first following crops have been relatively well documented, their longer-term impact on the productivity of subsequent crops in the rotation remains poorly understood. This research aimed to determine the amount of N fixed (% Ndfa, 15N isotope dilution method) by faba bean (Vicia faba L.) and taken up by the grains and crop residues (roots and straw) of two succeeding plants. Two three-year cycles were carried out (2017–2019, 2018–2020) with the following crop sequence: faba bean/wheat-wheat-triticale. The % Ndfa in faba bean residues was found to be 78.1–79.4 %, depending on the year, which gave 29–43 kg ha−1 of N introduced into the soil from the total N amount of 38–55 kg ha−1. With wheat residues, 28–35 kg ha−1 of N was incorporated. The year of the experiment significantly affected the yield and N parameters of cereals at maturity, mainly for the first crop after precrop. Significant changes were observed between precrop treatments. N uptake by cereals from faba bean residues (N recovery) was significantly higher than that from wheat residues, both in grains and crop residues of the succeeding crops. Cereal yields, N and macronutrient (P, K, Ca, Mg) yields were improved or unchanged by using legumes in the crop sequence, depending on the plant part examined (grains or straw), year and crop. N derived from legumes is important for the productivity of subsequent cereals, mainly for grains, but also for crop residue yields: higher yields can be achieved as compared to non-legume treatment. The findings may be useful with regard to optimizing crop rotation strategies and sustainable agricultural practices.
在可持续植物生产中，豆科植物的种植至关重要，因为它通过固定氮(N)减少了对矿物肥料的需求，这些氮随后可用于后续作物，主要是谷物。虽然豆科作物作为前茬作物对后继作物的影响已经有了较好的记录，但它们对轮作中后继作物生产力的长期影响仍然知之甚少。本研究旨在测定蚕豆（Vicia faba L.）的固定氮量（% Ndfa， 15N同位素稀释法）以及后续两株作物的籽粒和作物残茬（根和秸秆）对N的吸收。采用两个三年周期（2017-2019、2018-2020），作物顺序如下：蚕豆/小麦-小麦-小黑麦。不同年份蚕豆渣中Ndfa含量为78.1 ~ 79.4% %，从38 ~ 55 kg ha−1总施氮量中，可获得29 ~ 43 kg ha−1。小麦秸秆施氮量为28-35 kg ha−1。试验年份对谷物成熟期产量和氮素参数影响显著，主要是对预茬后第一季影响显著。在作物前处理之间观察到显著的变化。在后续作物的籽粒和作物残茬中，谷物对蚕豆残氮的吸收（氮恢复）显著高于小麦残氮。根据所研究的植物部分（谷物或秸秆）、年份和作物，在作物序列中使用豆类可提高谷物产量、氮和常量营养素（P、K、Ca、Mg）产量或保持不变。从豆科植物中提取的氮对后续谷物（主要是谷物）的生产力很重要，但对作物残茬产量也很重要：与非豆科植物处理相比，可以获得更高的产量。研究结果可能有助于优化作物轮作策略和可持续农业实践。
{"title":"The uptake of nitrogen biologically fixed by faba bean by cereals grown as succeeding crops","authors":"Anna Siczek , Marcin Becher , Stanisław Kalembasa , Dorota Kalembasa","doi":"10.1016/j.eja.2025.127952","DOIUrl":"10.1016/j.eja.2025.127952","url":null,"abstract":"<div><div>In sustainable plant production, the cultivation of legumes is crucial as it reduces the need for mineral fertilizers by fixing nitrogen (N), which is then available to succeeding crops, mainly cereals. While the effects of legumes as precrops on the first following crops have been relatively well documented, their longer-term impact on the productivity of subsequent crops in the rotation remains poorly understood. This research aimed to determine the amount of N fixed (% Ndfa, <sup>15</sup>N isotope dilution method) by faba bean (<em>Vicia faba</em> L.) and taken up by the grains and crop residues (roots and straw) of two succeeding plants. Two three-year cycles were carried out (2017–2019, 2018–2020) with the following crop sequence: faba bean/wheat-wheat-triticale. The % Ndfa in faba bean residues was found to be 78.1–79.4 %, depending on the year, which gave 29–43 kg ha<sup>−1</sup> of N introduced into the soil from the total N amount of 38–55 kg ha<sup>−1</sup>. With wheat residues, 28–35 kg ha<sup>−1</sup> of N was incorporated. The year of the experiment significantly affected the yield and N parameters of cereals at maturity, mainly for the first crop after precrop. Significant changes were observed between precrop treatments. N uptake by cereals from faba bean residues (N recovery) was significantly higher than that from wheat residues, both in grains and crop residues of the succeeding crops. Cereal yields, N and macronutrient (P, K, Ca, Mg) yields were improved or unchanged by using legumes in the crop sequence, depending on the plant part examined (grains or straw), year and crop. N derived from legumes is important for the productivity of subsequent cereals, mainly for grains, but also for crop residue yields: higher yields can be achieved as compared to non-legume treatment. The findings may be useful with regard to optimizing crop rotation strategies and sustainable agricultural practices.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127952"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-10DOI: 10.1016/j.eja.2025.127951
Hao Ren , Qingfeng Dong , Siting Li , Dezheng Liu , Xubin Zhang , Xue Wang , Liang Chen , Yin-Gang Hu
Lodging is a complex trait that limits wheat (Triticum aestivum L.) yield potential, and no single trait can fully capture lodging resistance. Identifying key traits and developing reliable, field-applicable indicators are crucial for breeding lodging-resistant cultivars. In this study, lodging resistance was systematically assessed in 274 wheat varieties across three consecutive growing seasons (2022–2024). Genotype, growing season, growth stage, and their interactions significantly affected lodging-associated traits, with a clear temporal alignment between meteorological conditions and lodging events. Comparative analysis between lodged and non-lodged plants revealed that lodging negatively influenced spike and kernel traits. Multivariate analyses indicated that height-related traits accounted for nearly 50 % of the phenotypic variance related to lodging resistance and showed negative correlations, while traits related to stem weight and fullness explained 24 % and 8 %, respectively. Among these, stem wall thickness (SWT), second basal internode fullness (SBF), single stem elasticity (SSE), and stem strength (SS) emerged as key positive contributors, whereas plant height (PH), center of gravity height (CGH), and basal internode lengths were negatively associated. Stepwise regression and path analyses further identified SWT and SBF as primary determinants of SS, while CGH was the key factor influencing SSE. Structural equation modeling demonstrated that height-related traits exerted significant negative effects on stem anatomical structure, mechanical traits, and lodging index. Furthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed. It exhibited a strong correlation with the comprehensive lodging score (D value) and high consistency with clustering results, providing a practical assessment tool. These findings provide valuable insights for assessing lodging resistance and guiding strong-stem breeding strategies in wheat.
{"title":"Multi-trait analysis to identify key factors influencing wheat lodging resistance and validation of an integrative lodging index","authors":"Hao Ren , Qingfeng Dong , Siting Li , Dezheng Liu , Xubin Zhang , Xue Wang , Liang Chen , Yin-Gang Hu","doi":"10.1016/j.eja.2025.127951","DOIUrl":"10.1016/j.eja.2025.127951","url":null,"abstract":"<div><div>Lodging is a complex trait that limits wheat (<em>Triticum aestivum</em> L<em>.</em>) yield potential, and no single trait can fully capture lodging resistance. Identifying key traits and developing reliable, field-applicable indicators are crucial for breeding lodging-resistant cultivars. In this study, lodging resistance was systematically assessed in 274 wheat varieties across three consecutive growing seasons (2022–2024). Genotype, growing season, growth stage, and their interactions significantly affected lodging-associated traits, with a clear temporal alignment between meteorological conditions and lodging events. Comparative analysis between lodged and non-lodged plants revealed that lodging negatively influenced spike and kernel traits. Multivariate analyses indicated that height-related traits accounted for nearly 50 % of the phenotypic variance related to lodging resistance and showed negative correlations, while traits related to stem weight and fullness explained 24 % and 8 %, respectively. Among these, stem wall thickness (SWT), second basal internode fullness (SBF), single stem elasticity (SSE), and stem strength (SS) emerged as key positive contributors, whereas plant height (PH), center of gravity height (CGH), and basal internode lengths were negatively associated. Stepwise regression and path analyses further identified SWT and SBF as primary determinants of SS, while CGH was the key factor influencing SSE. Structural equation modeling demonstrated that height-related traits exerted significant negative effects on stem anatomical structure, mechanical traits, and lodging index. Furthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed. It exhibited a strong correlation with the comprehensive lodging score (D value) and high consistency with clustering results, providing a practical assessment tool. These findings provide valuable insights for assessing lodging resistance and guiding strong-stem breeding strategies in wheat.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127951"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731128","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}
Unmanned aerial vehicles (UAVs) offer potential for precise, sustainable herbicide application in wheat, but their efficacy under variable field conditions requires robust evaluation. This study aims to rectify these research gaps and introduces a novel multi-parameter field evaluation of UAV-based herbicide applications for sustainable wheat weed management, explicitly evaluating the critical impact of real-time meteorological variability and application parameters. Field experiments over two growing seasons (2021/2022 and 2022/2023) compared UAV applications (30 L ha−1, fine-to-medium droplets) at 1.5 m and 2.5 m altitudes with conventional spraying (200 L ha−1, medium-coarse droplets), using constant doses of two commercial herbicide formulations (1. tritosulfuron + florasulam; 2. iodosulfuron-methyl-sodium + amidosulfuron + mefenpyr-diethyl). Efficacy was measured via species-specific weed density and fresh mass reduction. UAV treatments achieved significantly higher or equivalent suppression of Capsella bursa-pastoris, Lactuca serriola, Sinapis arvensis, and Veronica persica compared to conventional spraying. Despite more challenging conditions (high temperature, lower humidity, stronger wind) in the second season, overall efficacy increased, attributed to using a more effective herbicide formulation, underscoring the critical role of herbicide selection for UAV systems. Lamium purpureum exhibited significant weather sensitivity, with lower flight altitude enhancing fresh mass reduction. Correlation analysis suggested temperature positively (r = 0.586, p = 0.045) and wind velocity negatively (r = ̵ 0.588, p = 0.045) influenced treatment efficacy. UAV applications achieved up to 90 % efficacy in the second year by 31 DAHA (i.e., herbicide application) while using 85 % less water. This research provides the first multi-parameter field validation of UAV herbicide application under variable environmental conditions, demonstrating its viability and significant water-saving potential. The findings offer crucial, actionable inferences for optimizing UAV parameters (altitude, droplet size) with herbicide selection and real-time weather data, benefiting global precision agriculture efforts towards resource-efficient and environmentally responsible weed management.
无人机（uav）提供了在小麦上精确、可持续施用除草剂的潜力，但其在可变田间条件下的有效性需要可靠的评估。本研究旨在弥补这些研究空白，并引入一种新的多参数田间评估方法，明确评估实时气象变率和应用参数对小麦杂草可持续管理的关键影响。在两个生长季节（2021/2022和2022/2023）的现场实验中，比较了无人机在1.5 m和2.5 m海拔的应用（30 L ha−1，细至中等滴）与传统喷洒（200 L ha−1，中至粗滴），使用恒定剂量的两种商业除草剂配方(1。三磺隆+ florasulam；2. 碘磺隆-甲基钠+氨基磺隆+甲芬吡酯-二乙基)。通过种特异性杂草密度和新鲜质量减少来衡量效果。与常规喷洒相比，无人机处理对法氏囊荠菜、serriola、Sinapis arvensis和Veronica persica的抑制效果明显更高或相当。尽管第二季的条件更具挑战性（高温、低湿度、强风），但由于使用了更有效的除草剂配方，总体效果有所提高，这强调了除草剂选择对无人机系统的关键作用。紫叶Lamium purpureum表现出显著的天气敏感性，较低的飞行高度增强了鲜质量的减少。相关分析表明，温度对治疗效果有正影响（r = 0.586, p = 0.045），风速对治疗效果有负影响（r = 0.588, p = 0.045）。无人机应用在第二年通过31 DAHA（即除草剂应用）达到高达90% %的效率，同时使用85% %的水。本研究首次对不同环境条件下的无人机除草剂应用进行了多参数现场验证，证明了其可行性和显著的节水潜力。该研究结果为优化无人机参数（高度、液滴大小）、除草剂选择和实时天气数据提供了关键的、可操作的推断，有利于全球精准农业努力实现资源高效和对环境负责的杂草管理。
{"title":"Optimizing UAV-based herbicide applications for sustainable wheat weed management by a novel multi-parameter field evaluation under variable environmental conditions","authors":"Biljana Boskovic , Dragana Bozic , Milan Drazic , Kosta Gligorevic , Velibor Spalevic , Shuraik Kader , Milos Pajic","doi":"10.1016/j.eja.2025.127945","DOIUrl":"10.1016/j.eja.2025.127945","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) offer potential for precise, sustainable herbicide application in wheat, but their efficacy under variable field conditions requires robust evaluation. This study aims to rectify these research gaps and introduces a novel multi-parameter field evaluation of UAV-based herbicide applications for sustainable wheat weed management, explicitly evaluating the critical impact of real-time meteorological variability and application parameters. Field experiments over two growing seasons (2021/2022 and 2022/2023) compared UAV applications (30 L ha<sup>−1</sup>, fine-to-medium droplets) at 1.5 m and 2.5 m altitudes with conventional spraying (200 L ha<sup>−1</sup>, medium-coarse droplets), using constant doses of two commercial herbicide formulations (1. <em>tritosulfuron</em> + <em>florasulam</em>; 2. <em>iodosulfuron-methyl-sodium</em> + <em>amidosulfuron</em> + <em>mefenpyr-diethyl</em>). Efficacy was measured via species-specific weed density and fresh mass reduction. UAV treatments achieved significantly higher or equivalent suppression of <em>Capsella bursa-pastoris</em>, <em>Lactuca serriola</em>, <em>Sinapis arvensis</em>, and <em>Veronica persica</em> compared to conventional spraying. Despite more challenging conditions (high temperature, lower humidity, stronger wind) in the second season, overall efficacy increased, attributed to using a more effective herbicide formulation, underscoring the critical role of herbicide selection for UAV systems. <em>Lamium purpureum</em> exhibited significant weather sensitivity, with lower flight altitude enhancing fresh mass reduction. Correlation analysis suggested temperature positively (r = 0.586, p = 0.045) and wind velocity negatively (r = ̵ 0.588, p = 0.045) influenced treatment efficacy. UAV applications achieved up to 90 % efficacy in the second year by 31 DAHA (i.e., herbicide application) while using 85 % less water. This research provides the first multi-parameter field validation of UAV herbicide application under variable environmental conditions, demonstrating its viability and significant water-saving potential. The findings offer crucial, actionable inferences for optimizing UAV parameters (altitude, droplet size) with herbicide selection and real-time weather data, benefiting global precision agriculture efforts towards resource-efficient and environmentally responsible weed management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127945"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-10DOI: 10.1016/j.eja.2025.127958
Liguang Cheng , Younggu Her , Chul Soo Park , Dong-Hyeon Kim , Taeil Jang
Effective water management is critical for enhancing winter wheat yield, grain quality, and resilience in rice-wheat double-cropping systems of South and East Asia. However, production in paddy soils remains constrained by spring droughts, post-anthesis waterlogging, and the absence of region-specific irrigation guidelines. This study evaluated precision irrigation strategies integrating real-time soil moisture monitoring to improve water use efficiency (WUE) and crop performance under variable climates. Field experiments conducted over three seasons (2021–2024) in South Korea compared three treatments: conventional rainfed (CRF), soil moisture-based irrigation at 55 % available soil water (SIA), and at 55 % saturation water content (SIS). SIA consistently outperformed CRF and SIS, increasing grain yield by 20–27 %, WUE by 10–22 %, and leaf area index by up to 16 %. Maintaining soil moisture within the 0–40 cm available water range between jointing and grain filling optimized growth and resource use, whereas SIS induced oversaturation and CRF suffered from moisture deficits. This study offers an integrated framework linking agronomic performance to sensor-trigger logic and on-farm constraints. By converting real-time soil-moisture readings into stage-specific irrigation rules, this work shows that the available soil water-based threshold (SIA) increases yield and WUE over rainfed and saturation-based approaches while revealing how soils and seasonal climate shape outcomes.
{"title":"Improving winter wheat yield and water use efficiency using soil moisture sensor-driven precision furrow irrigation","authors":"Liguang Cheng , Younggu Her , Chul Soo Park , Dong-Hyeon Kim , Taeil Jang","doi":"10.1016/j.eja.2025.127958","DOIUrl":"10.1016/j.eja.2025.127958","url":null,"abstract":"<div><div>Effective water management is critical for enhancing winter wheat yield, grain quality, and resilience in rice-wheat double-cropping systems of South and East Asia. However, production in paddy soils remains constrained by spring droughts, post-anthesis waterlogging, and the absence of region-specific irrigation guidelines. This study evaluated precision irrigation strategies integrating real-time soil moisture monitoring to improve water use efficiency (WUE) and crop performance under variable climates. Field experiments conducted over three seasons (2021–2024) in South Korea compared three treatments: conventional rainfed (CRF), soil moisture-based irrigation at 55 % available soil water (SIA), and at 55 % saturation water content (SIS). SIA consistently outperformed CRF and SIS, increasing grain yield by 20–27 %, WUE by 10–22 %, and leaf area index by up to 16 %. Maintaining soil moisture within the 0–40 cm available water range between jointing and grain filling optimized growth and resource use, whereas SIS induced oversaturation and CRF suffered from moisture deficits. This study offers an integrated framework linking agronomic performance to sensor-trigger logic and on-farm constraints. By converting real-time soil-moisture readings into stage-specific irrigation rules, this work shows that the available soil water-based threshold (SIA) increases yield and WUE over rainfed and saturation-based approaches while revealing how soils and seasonal climate shape outcomes.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127958"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-18DOI: 10.1016/j.eja.2025.127960
Wenchao Qi , Le Yu , Tao Liu , Hui Wu , Qiang Zhao , Linsheng Wu , Xiaoyan Kang , Yibo Wang , Lifu Zhang
Reliable and intelligent retrieval of leaf traits from hyperspectral reflectance is crucial for assessing ecosystem functions, yet conventional approaches struggle with spectral complexity and nonlinearities. To address these challenges, we developed the Leaf Trait Retrieval Network (LTRN), a novel deep learning framework that integrates Kolmogorov–Arnold Network (KAN), Transformer, and Temporal Convolutional Networks (TCN) for end-to-end trait estimation. Model validation was carried out using a large spectral–trait database covering hundreds of plant species and four functional traits. Experimental results demonstrated that LTRN model outperforms state-of-the-art deep learning models, achieving R2 values greater than 0.78 for estimating chlorophyll content (Chla+b), equivalent water thickness (EWT), carotenoid content (Ccar), and leaf mass per area (LMA). Further analyses indicated that the LTRN model delivers stable estimation performance across spectral resolutions of 10–25 nm. Moreover, the model demonstrates strong stability across varying proportions of training samples. These findings underscore the robustness and stability of LTRN for large-scale vegetation trait retrieval, offering a valuable framework for advancing the intelligent estimation of other ecological parameters.
{"title":"Intelligent retrieval of leaf traits using hyperspectral reflectance and deep learning","authors":"Wenchao Qi , Le Yu , Tao Liu , Hui Wu , Qiang Zhao , Linsheng Wu , Xiaoyan Kang , Yibo Wang , Lifu Zhang","doi":"10.1016/j.eja.2025.127960","DOIUrl":"10.1016/j.eja.2025.127960","url":null,"abstract":"<div><div>Reliable and intelligent retrieval of leaf traits from hyperspectral reflectance is crucial for assessing ecosystem functions, yet conventional approaches struggle with spectral complexity and nonlinearities. To address these challenges, we developed the Leaf Trait Retrieval Network (LTRN), a novel deep learning framework that integrates Kolmogorov–Arnold Network (KAN), Transformer, and Temporal Convolutional Networks (TCN) for end-to-end trait estimation. Model validation was carried out using a large spectral–trait database covering hundreds of plant species and four functional traits. Experimental results demonstrated that LTRN model outperforms state-of-the-art deep learning models, achieving R<sup>2</sup> values greater than 0.78 for estimating chlorophyll content (Chl<sub>a+b</sub>), equivalent water thickness (EWT), carotenoid content (C<sub>car</sub>), and leaf mass per area (LMA). Further analyses indicated that the LTRN model delivers stable estimation performance across spectral resolutions of 10–25 nm. Moreover, the model demonstrates strong stability across varying proportions of training samples. These findings underscore the robustness and stability of LTRN for large-scale vegetation trait retrieval, offering a valuable framework for advancing the intelligent estimation of other ecological parameters.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127960"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-22DOI: 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":"2026-03-01","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 : 2026-03-01Epub Date: 2025-12-11DOI: 10.1016/j.eja.2025.127957
Pengfei Shen , Feiyang He , Quan Wang , Yuanbo Zhang , Miao Li , Fei Chen , Xiaoxia Wen , Weiyan Wang , Yuncheng Liao
How to improve tillage practices and nitrogen management for enhancing nitrogen absorption and redistribution, thereby increasing crop yields and resource utilization efficiency remains a current challenge in agriculture. However, these effects exhibit marked heterogeneity across agroecological regions and warrant further scrutiny, particularly in dryland farming systems. To address this issue, a two-factor split-plot field experiment, including two tillage practices in the main plots (i.e., rotary tillage and plow tillage, represented by RT and PT, respectively) and three nitrogen fertilizer gradients in the subplots (i.e., 180, 240, and 300 kg N ha⁻¹, represented by N1, N2, and N3, respectively) was carried out over three consecutive winter wheat seasons in the dryland wheat-maize intercropping agricultural area of Northwest China. Results demonstrated that RT effectively enhanced the sustained supply capacity of soil moisture and nutrients, increased dry matter accumulation and promoted plant photosynthetic performance of the winter wheat plants. When integrated with the N2 treatment, this practice further elevates grain yield and substantially enhances resource-use efficiency. Over the 3-year growing seasons, compared with the PT–N2, the RT–N2 treatment significantly increased winter wheat yield, net income, and water-use efficiency by 13.12–15.02 %, 45.12–58.02 %, and 10.72–19.97 %, respectively. In addition, the RT–N2 treatment optimised pre-anthesis nitrogen accumulation and post-anthesis nitrogen uptake in the aboveground tissues of winter wheat, thereby increasing nitrogen uptake efficiency, nitrogen agronomic efficiency, nitrogen recovery efficiency, and nitrogen partial factor productivity by averages of 20.53 %, 37.3 %, 59.07 %, and 11.63 %, respectively. Meanwhile, the RT-N2 also reduced nitrate nitrogen leaching within the 0–200 cm soil profile after winter wheat harvest by an average of 17.89 % compared with the PT-N2 treatment. The partial least squares path model revealed that soil moisture and nutrient availability are the primary drivers of winter wheat productivity, whereas nitrate-N leaching from the 60–140 cm soil layer emerges as the dominant limiting factor. Overall, the management strategy integrating RT with N2 delivers synergistic gains in winter wheat productivity while simultaneously advancing environmental sustainability. This finding provides a robust basis for optimising tillage and nitrogen management in wheat–maize double-cropping systems, thereby facilitating the concurrent realisation of high yields and ecological stability.
{"title":"Optimization of tillage and nitrogen fertilization improves yield and resource utilization efficiency of rainfed winter wheat","authors":"Pengfei Shen , Feiyang He , Quan Wang , Yuanbo Zhang , Miao Li , Fei Chen , Xiaoxia Wen , Weiyan Wang , Yuncheng Liao","doi":"10.1016/j.eja.2025.127957","DOIUrl":"10.1016/j.eja.2025.127957","url":null,"abstract":"<div><div>How to improve tillage practices and nitrogen management for enhancing nitrogen absorption and redistribution, thereby increasing crop yields and resource utilization efficiency remains a current challenge in agriculture. However, these effects exhibit marked heterogeneity across agroecological regions and warrant further scrutiny, particularly in dryland farming systems. To address this issue, a two-factor split-plot field experiment, including two tillage practices in the main plots (i.e., rotary tillage and plow tillage, represented by RT and PT, respectively) and three nitrogen fertilizer gradients in the subplots (i.e., 180, 240, and 300 kg N ha⁻¹, represented by N1, N2, and N3, respectively) was carried out over three consecutive winter wheat seasons in the dryland wheat-maize intercropping agricultural area of Northwest China. Results demonstrated that RT effectively enhanced the sustained supply capacity of soil moisture and nutrients, increased dry matter accumulation and promoted plant photosynthetic performance of the winter wheat plants. When integrated with the N2 treatment, this practice further elevates grain yield and substantially enhances resource-use efficiency. Over the 3-year growing seasons, compared with the PT–N2, the RT–N2 treatment significantly increased winter wheat yield, net income, and water-use efficiency by 13.12–15.02 %, 45.12–58.02 %, and 10.72–19.97 %, respectively. In addition, the RT–N2 treatment optimised pre-anthesis nitrogen accumulation and post-anthesis nitrogen uptake in the aboveground tissues of winter wheat, thereby increasing nitrogen uptake efficiency, nitrogen agronomic efficiency, nitrogen recovery efficiency, and nitrogen partial factor productivity by averages of 20.53 %, 37.3 %, 59.07 %, and 11.63 %, respectively. Meanwhile, the RT-N2 also reduced nitrate nitrogen leaching within the 0–200 cm soil profile after winter wheat harvest by an average of 17.89 % compared with the PT-N2 treatment. The partial least squares path model revealed that soil moisture and nutrient availability are the primary drivers of winter wheat productivity, whereas nitrate-N leaching from the 60–140 cm soil layer emerges as the dominant limiting factor. Overall, the management strategy integrating RT with N2 delivers synergistic gains in winter wheat productivity while simultaneously advancing environmental sustainability. This finding provides a robust basis for optimising tillage and nitrogen management in wheat–maize double-cropping systems, thereby facilitating the concurrent realisation of high yields and ecological stability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127957"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-08DOI: 10.1016/j.eja.2025.127950
Lei Sun , Quanzhong Huang , Dongyang Ren , Min Li , Xu Xu , Yunwu Xiong , Guanhua Huang
Agricultural water demand in the Yellow River Basin (YRB) is increasingly shaped by climate change and human activities, posing challenges to sustainable water management. This study analyzed the temporal evolution and driving factors of crop water requirement (CWR) for four major crops—spring wheat, winter wheat, spring maize, and summer maize—using a dynamic crop coefficient model based on MODIS NDVI (2000–2020), combined with the FAO Penman–Monteith method. Pearson correlation and random forest model were employed to identify dominant climatic and anthropogenic influences. Future CWR trends (2021–2100) were projected under nine combined scenarios, integrating three CMIP6 climate pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5) with three cropping area strategies (baseline, +15 %, –15 %). Finally, an assessment framework was established to evaluate agricultural water stress under future scenarios. Results show that the NDVI-based model effectively captures intra-seasonal crop variation and improves CWR estimation accuracy. From 2000–2020, per-unit CWR showed a significant increase for spring wheat and spring maize, whereas a decreasing trend was observed for summer maize. Human activities, especially the irrigated area, were the main driver of CWR change, surpassing climatic factors. Future projections indicate a significant upward trend in per-unit CWR (p < 0.001), with wheat being more sensitive to emission scenarios. Spatially, high-CWR zones are expected to shift from the arid northwest to the central and lower plains. Scenario combined high emission with planting expansion exhibit an approximately 16 % increase in mean annual CWR and result in the highest projected water stress. These findings provide a scientific basis for adaptive water governance and climate-resilient agricultural planning in large river basins.
{"title":"From climate knowledge to adaptive action: Crop water requirement and agricultural water risk in the Yellow River Basin","authors":"Lei Sun , Quanzhong Huang , Dongyang Ren , Min Li , Xu Xu , Yunwu Xiong , Guanhua Huang","doi":"10.1016/j.eja.2025.127950","DOIUrl":"10.1016/j.eja.2025.127950","url":null,"abstract":"<div><div>Agricultural water demand in the Yellow River Basin (YRB) is increasingly shaped by climate change and human activities, posing challenges to sustainable water management. This study analyzed the temporal evolution and driving factors of crop water requirement (CWR) for four major crops—spring wheat, winter wheat, spring maize, and summer maize—using a dynamic crop coefficient model based on MODIS NDVI (2000–2020), combined with the FAO Penman–Monteith method. Pearson correlation and random forest model were employed to identify dominant climatic and anthropogenic influences. Future CWR trends (2021–2100) were projected under nine combined scenarios, integrating three CMIP6 climate pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5) with three cropping area strategies (baseline, +15 %, –15 %). Finally, an assessment framework was established to evaluate agricultural water stress under future scenarios. Results show that the NDVI-based model effectively captures intra-seasonal crop variation and improves CWR estimation accuracy. From 2000–2020, per-unit CWR showed a significant increase for spring wheat and spring maize, whereas a decreasing trend was observed for summer maize. Human activities, especially the irrigated area, were the main driver of CWR change, surpassing climatic factors. Future projections indicate a significant upward trend in per-unit CWR (<em>p</em> < 0.001), with wheat being more sensitive to emission scenarios. Spatially, high-CWR zones are expected to shift from the arid northwest to the central and lower plains. Scenario combined high emission with planting expansion exhibit an approximately 16 % increase in mean annual CWR and result in the highest projected water stress. These findings provide a scientific basis for adaptive water governance and climate-resilient agricultural planning in large river basins.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127950"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-02DOI: 10.1016/j.eja.2025.127980
Chengming Yan , Dongsheng An , Yanan Liu , Baoshan Zhao , Qiufang Zhao , Zhiling Ma , Haiyang Ma
Long-term pineapple monoculture severely degrades soil in tropical regions, necessitating sustainable remediation strategies. This study evaluated the effectiveness of crop rotation and organic fertilization in improving soil health and productivity through a field experiment in a decade-long continuously cropped pineapple orchard, with four treatments: continuous pineapple (PP, control); PP with 30 % organic fertilizer substitution (OP); Alpinia officinarum–pineapple rotation (GP); banana–pineapple rotation (BP). Soil health was evaluated using three metrics: the soil quality index (SQI) derived from conventional biochemical indicators, and soil multifunctionality (SMF) based on seven enzymatic activities, and microbial community structure via phospholipid fatty acid (PLFA) profiling. Microbial metabolic limitation was determined via extracellular enzyme stoichiometry. Compared to PP, both remediation strategies significantly alleviated microbial phosphorus (P) limitation, increasing EEAN:P by 12.416.6 % and reducing the vector angle by 20.127.7 %. Consequently, they enhanced microbial abundance (PLFA increased 30.9–84.1 %) and diversity, with increases in SQI (21.247.4 %), SMF (26.845.7 %), and pineapple yield (9.325.5 %). Crop rotation, particularly BP, consistently outperformed organic fertilization across these metrics—e.g., BP rotation increased total PLFA by 84.1 %, markedly exceeding the 30.9 % increase under OP. Structural equation modeling (SEM) analysis confirmed a critical pathway whereby P limitation alleviation enhanced microbial abundance and SMF, directly boosting yield. Thus, while both strategies were beneficial, crop rotation was superior, with BP most improving soil health and productivity. These findings inform sustainable tropical agriculture and soil health indicator selection.
{"title":"Rotation and organic fertilization alleviate soil microbial phosphorus limitation and improve soil health and productivity in a continuous cropping pineapple orchard","authors":"Chengming Yan , Dongsheng An , Yanan Liu , Baoshan Zhao , Qiufang Zhao , Zhiling Ma , Haiyang Ma","doi":"10.1016/j.eja.2025.127980","DOIUrl":"10.1016/j.eja.2025.127980","url":null,"abstract":"<div><div>Long-term pineapple monoculture severely degrades soil in tropical regions, necessitating sustainable remediation strategies. This study evaluated the effectiveness of crop rotation and organic fertilization in improving soil health and productivity through a field experiment in a decade-long continuously cropped pineapple orchard, with four treatments: continuous pineapple (PP, control); PP with 30 % organic fertilizer substitution (OP); <em>Alpinia officinarum–</em>pineapple rotation (GP); banana–pineapple rotation (BP). Soil health was evaluated using three metrics: the soil quality index (SQI) derived from conventional biochemical indicators, and soil multifunctionality (SMF) based on seven enzymatic activities, and microbial community structure via phospholipid fatty acid (PLFA) profiling. Microbial metabolic limitation was determined via extracellular enzyme stoichiometry. Compared to PP, both remediation strategies significantly alleviated microbial phosphorus (P) limitation, increasing EEA<sub>N:P</sub> by 12.4<img>16.6 % and reducing the vector angle by 20.1<img>27.7 %. Consequently, they enhanced microbial abundance (PLFA increased 30.9–84.1 %) and diversity, with increases in SQI (21.2<img>47.4 %), SMF (26.8<img>45.7 %), and pineapple yield (9.3<img>25.5 %). Crop rotation, particularly BP, consistently outperformed organic fertilization across these metrics—e.g., BP rotation increased total PLFA by 84.1 %, markedly exceeding the 30.9 % increase under OP. Structural equation modeling (SEM) analysis confirmed a critical pathway whereby P limitation alleviation enhanced microbial abundance and SMF, directly boosting yield. Thus, while both strategies were beneficial, crop rotation was superior, with BP most improving soil health and productivity. These findings inform sustainable tropical agriculture and soil health indicator selection.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127980"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883347","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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