European Journal of Agronomy最新文献

{"title":"Soil compaction restricts tea growth through nutrient limitation and oxidative stress, associated with flavonoid metabolic changes: A four-year field trial","authors":"Wenyan Yang ,&nbsp;Wenbin Liu ,&nbsp;Jiawei Ma ,&nbsp;Huan Zhang ,&nbsp;Zhenyu Yang ,&nbsp;Yuchun Wang ,&nbsp;Mei Wang ,&nbsp;Dan Liu","doi":"10.1016/j.eja.2026.128012","DOIUrl":"10.1016/j.eja.2026.128012","url":null,"abstract":"<div><h3>Purpose</h3><div>The widespread adoption of mechanical harvesters in tea plantations has intensified soil compaction, posing a major abiotic constraint on tea plant growth. Elucidating the physiological mechanisms underlying such stress is critical for developing effective mitigation strategies.</div></div><div><h3>Methods</h3><div>A four-year field experiment comparing tillage (T), no-tillage (NT), and mechanical compaction (MC) treatments on plant physiological, metabolic, and transcriptional responses of tea root and leaf tissues were employed in this study.</div></div><div><h3>Results</h3><div>T treatment significantly enhanced soil nutrient availability, with available nitrogen, phosphorus, potassium, iron, and manganese concentrations 19.32–109.16 % higher than those under MC treatment. MC reduced plant height, suppressed root architecture development, and induced antioxidant defenses. Both roots and leaves showed increased malondialdehyde (MDA) levels and elevated activities of CAT and POD by 18.23–77.01 %, indicating oxidative stress mitigation. Metabolomic profiling identified flavonoid biosynthesis as the dominant response pathway under compaction, with key flavonoid metabolites including epicatechin, luteoforol, and phloretin, accumulating markedly in both tissues. Hormonal analysis showed increased levels of gibberellin A7 and brassinolide under MC, and organ-specific expression of regulatory genes (e.g., <em>CHS</em>, <em>DFR</em>, <em>IAA</em>, <em>PYL</em>) coordinated these metabolic adjustments.</div></div><div><h3>Conclusions</h3><div>This study demonstrates that soil compaction severely limits tea plant growth while triggering defense-related metabolic regulation. In contrast, tillage enhances nutrient availability and promotes plant development, whereas compaction induces oxidative stress and stimulates flavonoid-mediated defense responses. Overall, these findings provide mechanistic insights into tea plant adaptation to soil compaction and offer valuable guidance for sustainable management practices in tea cultivation.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128012"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048009","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}

{"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 ,&nbsp;Wenbo Shi ,&nbsp;Xianghui Lu ,&nbsp;Youzhen Xiang ,&nbsp;Yue Zhang ,&nbsp;Xiaoying Feng ,&nbsp;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 (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.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127926"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","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}

{"title":"Assessing climate-smartness of agronomic practices in oil palm production under changing climate conditions","authors":"Lisma Safitri ,&nbsp;Marcelo V. Galdos ,&nbsp;Iput Pradiko ,&nbsp;Alexis Comber ,&nbsp;Andrew Challinor","doi":"10.1016/j.eja.2025.127966","DOIUrl":"10.1016/j.eja.2025.127966","url":null,"abstract":"<div><div>Assessing the climate-smartness of oil palm (OP) agronomic practices is critical for ensuring sustainable, resilient, and low-emission production that meets growing demand and complies with international climate-friendly regulations. This study aims to assess the climate-smartness, defined as improved productivity, enhanced resilience and reduced GHG emissions, of OP agronomic practices under a changing climate. Climate-smartness of irrigation and empty fruit bunch (EFB) application with standard and reduced N fertiliser was assessed using yield change, carbon balance change and two climate-smart indices. The Agricultural Production SIMulator (APSIM) model was used to simulate yield, carbon balance components and water use over a 25-year plantation cycle. Uncertainty analysis included ten different sites, five GCMs (IPSL, GFDL, MPI, MRI, UKESM1), two emission scenarios (SSP1–2.6 and SSP5–8.5) and three periods (baseline: 1998–2022; mid-century: 2041–2065; end-century: 2071–2095). Irrigation emerges as the most climate-smart practice for OP production under climate change, showing strong synergy among mitigation, adaptation, and sustainable production. While gains in yield and soil organic carbon (SOC) are modest (median yield increase: 4.48 %; IQR: −12.10–10.79), emissions remain low, maintaining OP systems as carbon sinks (lowest carbon balance change, median: 0.21 tC_eq ha⁻¹ yr⁻¹; IQR: 0.08–0.43). Irrigation also shows highest synergy in water productivity and GHG intensity (median index score: 0.36; IQR: 0.25–0.48). All EFB application scenarios improve productivity and adaptation through higher yields and SOC, though gains are offset by higher emissions from EFB decomposition in warmer conditions. Elevated temperature, higher N fertiliser and reduced plant density lower the climate-smartness of OP productions. This study improves understanding of balanced climate-smart practices. Choosing the climate-smart practices and maintaining optimised N fertiliser and plant density enhance synergy in sustainable production, mitigation and adaptation of OP under climate change.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127966"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813858","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}

{"title":"Cereal-legume intercropping stabilizes yield and economic advantages under variable rainfall in semiarid rainfed environment","authors":"Wei Wang ,&nbsp;Bao-Zhong Wang ,&nbsp;Wei Zhang ,&nbsp;Meng-Ying Li ,&nbsp;Jian-Ming Li ,&nbsp;Sheng-Jun Ji ,&nbsp;Muhammad Abrar ,&nbsp;Muhammad Maqsood Ur Rehman ,&nbsp;Wasim Khan ,&nbsp;Hong-Yan Tao ,&nbsp;Mohamed S. Sheteiwy ,&nbsp;Wen-Ying Wang ,&nbsp;You-Cai Xiong","doi":"10.1016/j.eja.2025.127942","DOIUrl":"10.1016/j.eja.2025.127942","url":null,"abstract":"<div><div>Cereal-legume intercropping is widely recognized for enhancing crop productivity in semiarid rainfed systems. However, the mechanisms underlying its yield advantages and stability under variable rainfall conditions remain unclear, limiting its adoption as a climate-resilient strategy. This study evaluated the stability of crop yield and economic benefits across inter-annual rainfall fluctuations (418 mm in 2019, 362 mm in 2020, and 253 mm in 2021) in a three-year field experiment. We assessed yield–economic performance of maize-soybean and wheat-soybean intercropping systems and their impacts on key soil functional parameters to elucidate the mechanisms underlying climate resilience. Both maize-soybean and wheat-soybean intercropping were observed to harvest 17–26 % higher yields (per plant) and 1.04–1.26 land equivalent ratios, therefore enhancing land-use efficiency. Economically, maize-based systems were the most profitable, while wheat-soybean intercropping turned to improve net returns by 1654 USD ha⁻¹ . Climate-resilience analysis showed that intercropping reduced yield volatility by 10–61 % when precipitation declined (418–253 mm), highlighting its role in stabilizing agroecosystem productivity and economic benefits. Also, intercropping systems were found to significantly improve total nitrogen (13.7 %–20.6 %) and phosphorus (16.3 %–19.8 %). Mechanistically, the above indicators were resulted from improving soil microbial biomass (20.8 %–23.0 %), enhancing extracellular enzyme activities (9.3 %–15.8 % for C- and P-hydrolases) and promoting soil moisture retention (11.0 %–12.9 %). The data confirmed that intercropping can greatly enhance soil multifunctionality and thus contribute to yield and economic stability. Therefore, cereal-legume intercropping can act as a scalable strategy to enhance productivity, soil quality, and climate resilience in semiarid rainfed environment. The findings offer policymakers and smallholders a sustainable solution to balance land-use efficiency and climate adaptation.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127942"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650848","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}

{"title":"Integrated management of sowing date, density and nitrogen reduces environmental footprints while sustaining cotton yield in the Yellow River Valley","authors":"Peng Zhang ,&nbsp;Shuo Wang ,&nbsp;Yongjiang Zhang ,&nbsp;Hongchun Sun ,&nbsp;Ke Zhang ,&nbsp;Zhiying Bai ,&nbsp;Lingxiao Zhu ,&nbsp;Zhanbiao Wang ,&nbsp;Hezhong Dong ,&nbsp;Liantao Liu ,&nbsp;Cundong Li","doi":"10.1016/j.eja.2025.127975","DOIUrl":"10.1016/j.eja.2025.127975","url":null,"abstract":"<div><div>Conventional cotton production system in China’s Yellow River Valley (mid-April sowing, 45,000 plants ha⁻¹, and 240 kg N ha⁻¹) (MLH) achieve high yield but incurs excessive resource use and environmental costs. Sustainable intensification requires strategies that reconcile productivity, efficiency, and ecological outcomes. Through a three-year (2021–2023) field experiment, we assessed integrated management strategies combining sowing date (normal: mid-April; late: early May), planting density (typical: 45,000; high: 90,000 plants ha⁻¹), and nitrogen rate (conventional: 240 kg N ha⁻¹; reduced: 180 kg N ha⁻¹). Seed cotton yield, nitrogen use efficiency (NUE), energy flows, carbon/nitrogen footprints, and economic returns were quantified, and a sustainable performance index (SPI) was calculated for integrated assessment. Results showed that the late sowing + high density + reduced N rate (LHR) strategy maintained seed cotton yield while significantly increasing NUE by 34.8 % and energy productivity by 15.1 %, compared to the conventional MLH system. This strategy also reduced direct and indirect emissions (fertilizer production, labor), lowering the carbon footprint per unit yield by 34.5 % and nitrogen footprint by 31.9 %. The resultant decrease in environmental costs enhanced net ecosystem economic benefit by 27.1–38.3 %. Consistently, the SPI, integrating productivity, resource efficiency, environmental impact, and economics, confirmed late sowing + high density + reduced N as the optimal strategy for synergistic improvement in economic and ecological outcomes. These findings demonstrate that coordinated optimization of sowing date, density, and nitrogen management enables climate-resilient cotton production with lower emissions and higher resource efficiency—a transferable model for similar agroecosystems. Future integration with precision nitrogen technologies (e.g., deep placement, controlled-release fertilizers) could further amplify sustainability gains.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127975"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883348","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}

{"title":"Impact of controlled-release fertilizer on nitrogen use efficiency, greenhouse gas emissions, and environmental sustainability in sunflower cropping systems","authors":"Wenhao Ren ,&nbsp;Xianyue Li ,&nbsp;Tingxi Liu ,&nbsp;Ning Chen ,&nbsp;Maoxin Xin ,&nbsp;Qian Qi ,&nbsp;Bin Liu ,&nbsp;Hongxing Liu","doi":"10.1016/j.eja.2025.127963","DOIUrl":"10.1016/j.eja.2025.127963","url":null,"abstract":"<div><div>As environmental concerns related to agricultural practices intensify, the excessive use of nitrogen fertilizers has become a major global challenge. Controlled-release fertilizers (CRF) offer a promising strategy to improve agricultural productivity, enhance nitrogen utilization, mitigate ecological impacts, and increase economic returns. This study examined the effectiveness of CRF in enhancing sunflower production and nitrogen use efficiency, as well as in reducing nitrogen losses (including ammonia (NH₃) and nitrous oxide (N₂O) emissions and nitrogen leaching) across multiple nitrogen treatments during a three-year field experiment. The results showed that CRF significantly increased nitrogen use efficiency by 14.33 % and reduced nitrogen volatilization by 26.56 %, particularly within the first 20 d after fertilization, during which NH₃ and N₂O emissions were markedly lower than those under traditional nitrogen fertilizer treatments. Furthermore, life cycle assessment (LCA) indicated that CRF substantially decreased environmental impacts, with reductions of 13.03 % in greenhouse gas emissions, 29.05 % in acidification potential, and 29.02 % in eutrophication potential. Under low to medium nitrogen application rates (e.g., N225), delayed nitrogen release further reduced nitrogen loss and alleviated environmental pressure. By integrating LCA with the ecological-economic benefits (BETA) model, this study quantified the ecological and economic value of CRF. The findings demonstrated that CRF delivered high economic returns and considerable environmental benefits within the optimal nitrogen application range, making it an effective approach for sustainable agricultural development. These evaluation methods offer a systematic framework for assessing the environmental and economic outcomes of CRF, providing theoretical support for its broader adoption in agricultural production.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127963"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784870","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}

{"title":"The application and challenges of physical technology in modern agricultural plant protection","authors":"Shaobo Li,&nbsp;Qingyang Feng,&nbsp;Shaomeng Yu,&nbsp;Qianfeng Liu,&nbsp;Yang Cao,&nbsp;Guangzhao Tian,&nbsp;Yunfu Chen,&nbsp;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}

{"title":"The uptake of nitrogen biologically fixed by faba bean by cereals grown as succeeding crops","authors":"Anna Siczek ,&nbsp;Marcin Becher ,&nbsp;Stanisław Kalembasa ,&nbsp;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, ¹⁵N 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⁻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.</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}

{"title":"Multi-trait analysis to identify key factors influencing wheat lodging resistance and validation of an integrative lodging index","authors":"Hao Ren ,&nbsp;Qingfeng Dong ,&nbsp;Siting Li ,&nbsp;Dezheng Liu ,&nbsp;Xubin Zhang ,&nbsp;Xue Wang ,&nbsp;Liang Chen ,&nbsp;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}

{"title":"Intelligent retrieval of leaf traits using hyperspectral reflectance and deep learning","authors":"Wenchao Qi ,&nbsp;Le Yu ,&nbsp;Tao Liu ,&nbsp;Hui Wu ,&nbsp;Qiang Zhao ,&nbsp;Linsheng Wu ,&nbsp;Xiaoyan Kang ,&nbsp;Yibo Wang ,&nbsp;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 R² values greater than 0.78 for estimating chlorophyll content (Chl_a+b), equivalent water thickness (EWT), carotenoid content (C_car), 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}