European Journal of Agronomy最新文献

{"title":"Forecasting crop yield through a data-driven framework of remote sensing and biophysical knowledge: A case study for wheat and maize in the Guanzhong Plain, China","authors":"Zhikai Cheng,&nbsp;Xiaobo Gu,&nbsp;Yuanling Zhang,&nbsp;Tongtong Zhao,&nbsp;Shikun Sun,&nbsp;Yadan Du,&nbsp;Huanjie Cai","doi":"10.1016/j.eja.2026.128038","DOIUrl":"10.1016/j.eja.2026.128038","url":null,"abstract":"<div><div>Accurate early yield forecasts are essential for maximizing benefits and ensuring food security in the Guanzhong Plain, China. Process-based crop models are often constrained by uncertain input data, which limits their ability to forecast yield at the regional grid level (e.g., 1 km × 1 km). Statistical models, ignore the biophysical mechanisms underlying crop growth and development, and their performance is limited by the quantity and quality of available training data. Therefore, there is an urgent need for a more comprehensive and robust grid-level wheat and maize yield forecasting approach for the Guanzhong Plain. In this study, an interpretable data-driven framework was developed to forecast wheat and maize yields by combining remote sensing (solar-induced chlorophyll fluorescence, SIF; spectral indices, SIs) and biophysical knowledge (APSIM outputs and extreme climatic events) data. A Bayesian integration model (BIM) was trained on high-quality synthetic datasets (obtained by the synthetic minority oversampling technique for regression, SMOTER) to achieve accurate harvest-time yield forecasts at specific time windows. The results showed that the integration of multi-source data reduced the yield prediction error, with the overall normalized root mean square error (NRMSE) decreasing by 0.6 %–39.0 % compared to the single-source models. The data-driven model trained on the SMOTER -based synthetic dataset achieved the highest yield forecasting accuracy (wheat: NRMSE = 16.2 %; maize: NRMSE = 20.7 %). The SIF made the largest contribution to yield forecasts and showed strong interactions and synergies with other feature variables (e.g., aboveground biomass, drought, and low temperature stress), further enhancing model performance. Overall, the proposed data-driven framework demonstrates a promising way for improving grid-level yield forecasting and provides useful insights for the sustainable development of agricultural systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128038"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160808","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":"Advancements in weed mapping: A systematic review","authors":"Mohammad Jahanbakht ,&nbsp;Alex Olsen ,&nbsp;Ross Marchant ,&nbsp;Emilie Fillols ,&nbsp;Mostafa Rahimi Azghadi","doi":"10.1016/j.eja.2026.127992","DOIUrl":"10.1016/j.eja.2026.127992","url":null,"abstract":"<div><div>Weed mapping plays a critical role in precision management by providing accurate and timely data on weed distribution, enabling targeted control and reduced herbicide use. This minimizes environmental impacts, supports sustainable land management, and improves outcomes across agricultural and natural environments. Recent advances in weed mapping leverage ground-vehicle Red Green Blue (RGB) cameras, satellite and drone-based remote sensing combined with sensors such as spectral, Near Infra-Red (NIR), and thermal cameras. The resulting data are processed using advanced techniques including big data analytics and machine learning, significantly improving the spatial and temporal resolution of weed maps and enabling site-specific management decisions. Despite a growing body of research in this domain, there is a lack of comprehensive literature reviews specifically focused on weed mapping. In particular, the absence of a structured analysis spanning the entire mapping pipeline, from data acquisition to processing techniques and mapping tools, limits progress in the field. This review addresses these gaps by systematically examining state-of-the-art methods in data acquisition (sensor and platform technologies), data processing (including annotation and modelling), and mapping techniques (such as spatiotemporal analysis and decision support tools). In the data processing stage, weed detection was identified as a critical enabling component of the mapping pipeline; accordingly, dedicated sections were included to systematically review state-of-the-art methods. Following PRISMA guidelines, we critically evaluate and synthesize key findings from the literature to provide a holistic understanding of the weed mapping landscape. This review serves as a foundational reference to guide future research and support the development of efficient, scalable, and sustainable weed management systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 127992"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979743","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":"Soil amendments improved physiological characteristics, grain yield, and water use efficiency of common buckwheat under multi-year continuous cropping","authors":"Qi Wang,&nbsp;Xiaoyi Han,&nbsp;Qixuan Wang,&nbsp;Minlong Du,&nbsp;Xinyue Lei,&nbsp;Jiahao Ge,&nbsp;Rong Zhong,&nbsp;Chenxi Wan,&nbsp;Xiaoli Gao,&nbsp;Pu Yang,&nbsp;Jinfeng Gao","doi":"10.1016/j.eja.2026.128029","DOIUrl":"10.1016/j.eja.2026.128029","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Continuous cropping disrupts farmland ecosystems, leading to aggravated soil-borne diseases and substantial crop yield losses. Soil amendments are considered promising strategies to alleviate continuous cropping obstacles. However, the effects of chemical fertilizers combined with organic manure and biochar amendments on soil water content (SWC), soil nitrogen pool levels, plant physiological traits, yield, and resource utilization efficiency in continuous common buckwheat cropping systems remain poorly understood. To address this knowledge gap, we conducted a four-year (2022–2025) field positioning experiment on the Loess Plateau with a completely randomized design including five treatments with four replicates: (a) no fertilizer (CK), (b) chemical fertilizers (NPK: 180 kg ha&lt;sup&gt;−1&lt;/sup&gt; N, 75 kg ha&lt;sup&gt;−1&lt;/sup&gt; P&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt;, and 37.5 kg ha&lt;sup&gt;−1&lt;/sup&gt; K&lt;sub&gt;2&lt;/sub&gt;O), (c) chemical fertilizers combined with organic manure (NPKM: 180 kg ha&lt;sup&gt;−1&lt;/sup&gt; N, 75 kg ha&lt;sup&gt;−1&lt;/sup&gt; P&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt;, 37.5 kg ha&lt;sup&gt;−1&lt;/sup&gt; K&lt;sub&gt;2&lt;/sub&gt;O, and 22500 kg ha&lt;sup&gt;−1&lt;/sup&gt; organic manure), (d) chemical fertilizers combined with biochar (NPKB: 180 kg ha&lt;sup&gt;−1&lt;/sup&gt; N, 75 kg ha&lt;sup&gt;−1&lt;/sup&gt; P&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt;, 37.5 kg ha&lt;sup&gt;−1&lt;/sup&gt; K&lt;sub&gt;2&lt;/sub&gt;O, and 10000 kg ha&lt;sup&gt;−1&lt;/sup&gt; biochar), and (e) chemical fertilizers combined with organic manure and biochar (NPKMB: 180 kg ha&lt;sup&gt;−1&lt;/sup&gt; N, 75 kg ha&lt;sup&gt;−1&lt;/sup&gt; P&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;5&lt;/sub&gt;, 37.5 kg ha&lt;sup&gt;−1&lt;/sup&gt; K&lt;sub&gt;2&lt;/sub&gt;O, 11250 kg ha&lt;sup&gt;−1&lt;/sup&gt; organic manure, and 5000 kg ha&lt;sup&gt;−1&lt;/sup&gt; biochar). Results showed that compared to other treatments, NPKMB elevated SWC in the 0–100 cm soil layer (9.66–67.16 %) and increased total nitrogen (TN) (7.95–209.13 %) and alkali-hydrolyzable nitrogen (AN) (4.84–187.41 %) contents, thus creating a suitable soil environment for common buckwheat growth under continuous cropping stress. Meanwhile, NPKMB significantly enhanced the activities of root superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) by 3.47–45.31 %, 1.04–63.29 %, and 2.15–78.84 %, respectively, while increasing the contents of root proline, soluble sugar, and soluble protein by 6.90–83.67 %, 7.13–75.04 %, and 4.04–110.93 %, delaying root senescence and facilitating water and nitrogen absorption. Additionally, NPKMB improved leaf net photosynthetic rate (Pn, 9.78–95.52 %), stomatal conductance (Gs, 5.90–112.36 %), transpiration rate (Tr, 6.66–62.28 %), and chlorophyll content (SPAD value, 6.51–25.76 %), thereby promoting crop growth. Consequently, after three years of continuous cropping, NPKMB effectively alleviated growth constraints, achieving the highest dry matter weight (29.25 g plant&lt;sup&gt;−1&lt;/sup&gt;), grain yield (1082.65 kg ha&lt;sup&gt;−1&lt;/sup&gt;), N uptake (99.49 kg ha&lt;sup&gt;−1&lt;/sup&gt;), and water use efficiency (WUE, 5.77 kg ha&lt;sup&gt;−1&lt;/sup&gt; mm&lt;sup&gt;−1&lt;/sup&gt;). Overall, NPKMB fertilization strategy alleviated continuous cropping growth constraints of commo","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128029"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134092","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":"Stakeholders' critical perception of diversification strategies in cereal-based rotations","authors":"Ferdaous Rezgui ,&nbsp;Louise Blanc ,&nbsp;Daniel Plaza-Bonilla ,&nbsp;Jorge Lampurlanés ,&nbsp;Christos Dordas ,&nbsp;Paschalis Papakaloudis ,&nbsp;Andreas Michalitsis ,&nbsp;Laure Hossard ,&nbsp;Fatima Lambarraa-Lehnhardt ,&nbsp;Sonoko D. Bellingrath-Kimura ,&nbsp;Carsten Paul ,&nbsp;Moritz Reckling","doi":"10.1016/j.eja.2026.128000","DOIUrl":"10.1016/j.eja.2026.128000","url":null,"abstract":"<div><div>Agriculture has long been at the core of Mediterranean culture, resulting in multifunctional landscapes and diverse ecosystem services. In Mediterranean Europe, policy favored specialized agriculture, and reversing this trend has proven difficult. Diversification of crop rotations holds ecological benefits, yet adoption remains low. The objective of this study was to accompany Spanish and Greek stakeholders in a structured learning process beginning with the co-design of available diversification options. It continued with an ex-ante assessment of agri-environmental, social, and economic performance of these options, followed by a co-evaluation step where stakeholders rated both the assessed performances and the indicators used. These ratings were analyzed using an importance-performance matrix. Finally, the adoption likelihood of diversification was predicted using the Adoption and Diffusion Outcome Prediction (ADOPT) tool. The ex-ante assessment revealed that legumes, rapeseed, and intercropping systems generally outperformed continuous cereal cropping in the agri-environmental and social dimensions but not economically, with a profit reduction of up to 12 %. From the stakeholders’ ratings, we learned that they placed the greatest importance on the economic indicators. In contrast, the agri-environmental dimension was given little importance even when energy use indicators increased by 5–42 %. Likewise, diversified systems offered notable social benefits, such as reduced workload by up to 29 %, but social aspects were ranked as less important. This divergent performance of the diversified options was translated into low adoption rates. Legume systems reached a 23–28 % adoption rate in 8–10 years, while intercropping reached 14 % in 17 years, and rapeseed systems reached only 4–5 % in 9–11 years. Economic performance emerged as the main barrier to the adoption of diversification. This study evaluated the impacts of different diversification options available to local farmers from both scientific and a local stakeholder perspective. This process can be adapted to other regions to create shared knowledge, thus enabling a wide range of actors to better understand diversification impacts. This knowledge gain affects the stakeholder’s capacity to adopt diversification options and, beforehand, their willingness to do so.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128000"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962439","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":"From application methods to rate recommendations: Integrated strategies for improving maize response to zinc fertilization","authors":"Fucheng Gao ,&nbsp;Shan Chen ,&nbsp;Chengxiang Zhou ,&nbsp;Baogang Yu ,&nbsp;Chunqin Zou","doi":"10.1016/j.eja.2026.128007","DOIUrl":"10.1016/j.eja.2026.128007","url":null,"abstract":"<div><div>Zinc (Zn) deficiency is a major constraint to maize yield and grain quality globally, especially in alkaline soils. The efficacy of conventional broadcast Zn fertilization is often limited by soil fixation and high spatial variability. This study evaluates when localized Zn placement surpasses broadcast application, and establishes recommended application rates. Furthermore, it develops spatially explicit, soil-Zn-stratified management strategies to boost maize productivity, providing a quantitative basis for nutrient management across diverse agroecosystems. By integrating a meta-analysis with Random Forest (RF) modeling to evaluate the efficacy of localized versus broadcast application of Zn fertilizer and predict yield responses across diverse agroecosystems. Localized Zn application significantly outperformed broadcast methods in terms of grain yield, increasing it by 8.8 % compared to 5.2 %. The advantage was particularly notable in alkaline soils pH &gt; 7, soil organic matter (SOM) levels 10–20 g kg⁻¹, and elevated soil total nitrogen (N) &gt; 1 g kg⁻¹. We identified annual precipitation and soil DTPA-Zn as the primary predictors of yield response. Recommend Zn application rates depended on soil Zn status: 8 kg ha⁻¹ for 0.5–1.0 mg kg⁻¹ soil DTPA-Zn, 4 kg ha⁻¹ for 1.0–1.5 mg kg⁻¹ soil DTPA-Zn, and 3 kg ha⁻¹ for &gt; 1.5 mg kg⁻¹ soil DTPA-Zn. A scenario analysis projected that implementing a recommended national Zn application rate of 6.8 kg ha⁻¹ could increase China's maize yield by an average of 3.9 %, with regional gains ranging from 2.3 % to 4.4 %. This study provides a unified framework for recommending zinc fertilization in maize by clarifying when localized application offers yield advantages and defining soil Zn thresholds for rate adjustment. The guidance developed here supports more efficient Zn use and provides actionable strategies to improve maize productivity across diverse agroecosystems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128007"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014816","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":"A wheat seedling detection model based on efficient feature extraction and coordinate attention mechanism","authors":"Shun Wang ,&nbsp;Bowen Zhang ,&nbsp;Yinchao Che ,&nbsp;Guang Zheng ,&nbsp;Yanna Ren ,&nbsp;Lei Xi ,&nbsp;Xinming Ma ,&nbsp;Shuping Xiong","doi":"10.1016/j.eja.2026.127993","DOIUrl":"10.1016/j.eja.2026.127993","url":null,"abstract":"<div><div>Accurate detection of wheat seedlings is crucial for monitoring early population establishment and evaluating sowing quality. However, detection in real field environments remains challenging due to diverse seedling morphology, varying planting densities, occlusion, and complex background interference. Although deep learning has promoted the development of agricultural vision systems, existing wheat seedling detection methods still suffer from two key limitations: (1) insufficient modeling of spatial contextual relationships, leading to degraded accuracy under dense planting and complex field conditions; and (2) difficulty in balancing detection performance and computational efficiency, restricting real-time deployment on resource-limited agricultural devices. To address these issues, this study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules: (1) the Depthwise-Transformer-Vision (DTV) module integrates Depthwise Separable Convolutions (DSC), Vision Transformer, and multi-scale spatial pooling to efficiently represent local structures, spatial context, and global patterns of wheat seedlings; (2) the Feature Enhancement Module(FEM) incorporates coordinate attention to enhance seedling-related features while suppressing background interference; and (3) the Feature Coordination Module (FCM) performs multi-scale feature interaction with reduced computational cost. These components jointly improve robustness under dense planting and complex field conditions while maintaining lightweight deployment characteristics. Furthermore, we construct the Wheat Seedling Dataset (WSD), covering multiple planting densities, varieties, and field environments across two growing seasons. Experimental results show that TCE-YOLO outperforms mainstream detectors while maintaining high efficiency, providing a deployable solution for wheat seedling detection under real field conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 127993"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928664","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":"Biological control strategies as sustainable alternatives to herbicides in weed management","authors":"Godspower Oke Omokaro","doi":"10.1016/j.eja.2026.128003","DOIUrl":"10.1016/j.eja.2026.128003","url":null,"abstract":"<div><div>Herbicides remain the dominant tools for weed control because of their cost effectiveness and selectivity, yet prolonged and intensive use has raised concern regarding soil degradation, disruption of microbial communities, non-target effects, and the rapid emergence of herbicide resistance. This research synthesizes evidence on the ecological impacts of herbicides and evaluates biological control strategies as sustainable and complementary alternatives within integrated weed management. A PRISMA-ScR guided literature review identified 108 peer reviewed studies published between 2000 and 2025 from Scopus, PubMed, ScienceDirect and SpringerLink, with selective inclusion of foundational literature capturing early biological weed control research. Evidence indicates that herbicides alter soil microbial biomass, enzyme activity, and community composition, with outcomes dependent on herbicide class, application rate, soil properties, and environmental context. Glyphosate and atrazine suppress sensitive microbial taxa while enriching specialized degraders, reflecting ecological disruption and microbial adaptation. Fungal communities, particularly arbuscular mycorrhizal fungi, are consistently vulnerable, leading to reduced nutrient acquisition and weakened plant resilience. Herbicide resistance continues to expand globally, undermining long term chemical efficacy. Biological control strategies, including microbial agents such as <em>Trichoderma</em> and <em>Bacillus</em>, insect herbivores, grazing animals, allelopathic crops, bioherbicides, compost and biochar, demonstrate diverse mechanisms of weed suppression and soil restoration across agroecosystems. These approaches enhance crop competitiveness and stimulate beneficial microbial functions, although field performance is constrained by environmental variability, formulation stability, regulatory barriers, and limited extension support. The findings emphasize the need for integrative and sound weed management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128003"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961720","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":"Field-scale digital mapping of soil organic matter using spatially enhanced quantile machine-learning models","authors":"M. Córdoba ,&nbsp;P. Paccioretti ,&nbsp;C. Bozzer ,&nbsp;M. Balzarini","doi":"10.1016/j.eja.2026.128016","DOIUrl":"10.1016/j.eja.2026.128016","url":null,"abstract":"<div><div>Accurate field-scale mapping of soil organic matter (SOM) is essential for implementing precision agriculture strategies that enhance productivity and sustainability by supporting site-specific management. This study assessed three quantile-based machine learning (ML) algorithms—Quantile Regression Forest (QRF), Stochastic Gradient Boosting (SGB), and Deep Learning (DL)—in terms of predictive accuracy, uncertainty quantification, and spatial coherence. The models were trained using 7807 georeferenced SOM samples collected from 2052 fields, together with remote sensing and topographic covariates. To explicitly account for spatial autocorrelation, an additional covariate was derived from ordinary block kriging of SOM. Model performance was evaluated using root mean squared error (RMSE), mean error (ME), prediction interval coverage probability (PICP), and local standard deviation (LSD) as an indicator of spatial smoothness. Spatial validation was used to reduce potential bias arising from spatial autocorrelation. QRF consistently achieved the best balance among accuracy, uncertainty representation, and spatial coherence. Although SGB reached slightly higher accuracy, it underestimated uncertainty and produced noisier spatial patterns. DL generated the smoothest maps but tended to underestimate SOM and provided less reliable uncertainty estimates. Notably, QRF performance remained stable across fields with different sampling intensities, highlighting its robustness and practical relevance in data-limited scenarios. Overall, QRF models enhanced with spatially informed covariates provide a reliable framework for field-scale SOM prediction and uncertainty quantification—critical inputs for optimizing agricultural practices, guiding nutrient management, and supporting sustainable land management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128016"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072118","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":"Planting deeper with optimum bud density improves lodging resistance and sugar yield in sugarcane (Saccharum officinarum)","authors":"Farooq Shah ,&nbsp;Qiansi Liao ,&nbsp;Yuxuan Gao ,&nbsp;Dandan Wang ,&nbsp;Zhaojie Li ,&nbsp;Wei Wu","doi":"10.1016/j.eja.2026.128019","DOIUrl":"10.1016/j.eja.2026.128019","url":null,"abstract":"<div><div>Given sugarcane’s key role in global sugar and bioethanol production, a substantial increase in yield is essential to meet escalating demands. However, due to its unique stem-harvesting nature, efforts to further boost yield could render it highly susceptible to lodging. Hence, agronomic interventions are urgently needed to balance the tradeoff between sugarcane yield and lodging resistance. The current research investigates the dynamic changes in sugarcane stem and root lodging resistance. It also evaluates the potential of two key agronomic practices, planting depth and bud density, to mitigate the tradeoff between sugarcane yield and lodging resistance, along with the underlying mechanisms. This three-year study employs the safety factor technique to evaluate sugarcane’s resistance to both stem and root lodging throughout its growing season, examining two planting depths (30 cm and 40 cm) and four bud densities (3.0, 4.5, 6.0, and 7.5 buds m^–2). The highest susceptibility to lodging in sugarcane occurred between 180–210 DAP (days after planting). Deeper planting enhanced the lodging resistance of sugarcane without compromising yield. On the other hand, higher bud density improved sugar yield while maintaining or improving lodging resistance. Sugarcane exhibited greater susceptibility towards root lodging than stem lodging, whereas root system size was the key trait associated with enhanced lodging resistance under deeper planting. The enhanced lodging resistance with deeper planting and yield improvement with higher bud density implies that combining these agronomic practices can mitigate the tradeoff between sugarcane yield and lodging resistance. Given sugarcane’s high susceptibility to root lodging and the critical role of anchorage in resistance, agronomic and breeding strategies should prioritize expanding the root system size to improve stability and boost lodging resistance.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128019"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110582","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-model ensemble of process-based and data-driven approaches improves modeling of wheat grain protein content and yield","authors":"Jinhui Zheng ,&nbsp;Le Yu","doi":"10.1016/j.eja.2026.128033","DOIUrl":"10.1016/j.eja.2026.128033","url":null,"abstract":"<div><div>Accurately predicting the grain protein content (GPC) and yield of winter wheat is of significant strategic importance amid rising food demand and intensifying global market competition. However, traditional single-model approaches struggle to achieve high simulation accuracy in complex agricultural ecosystems. This study proposes a novel multi-model ensemble (MME) framework that integrates the APSIM-NG (Agricultural Production Systems Simulator-Next Generation) process-based crop model, four machine learning algorithms (Random Forest, Extreme Gradient Boosting, Multiple Linear Regression, and Long Short-Term Memory), and two ensemble methods (AIC-weighted model averaging and simple model averaging) to enhance the predictive accuracy of GPC and yield in North China Plain. The MME framework incorporates remote sensing data, extreme weather indices, and crop growth observations from 2008 to 2020 for a comprehensive performance evaluation. Validation results for the period 2015–2020 indicate that the MME framework outperforms both the baseline APSIM-NG model and the best-performing machine-learning method, achieving a Pearson’s r of 0.89 (RMSE = 0.32 %, R² = 0.76) for GPC prediction and reducing the yield RMSE to 316.96 kg/ha (Pearson’s r = 0.94, R² = 0.91). Furthermore, importance analysis indicates that within this framework, photosynthesis-related and extreme stress factors are the most influential predictors, contributing 8–12 % to model importance, highlighting the substantial impact of including extreme weather factors on model accuracy. By effectively combining process-based modeling with data-driven methods, the MME framework significantly enhances predictive accuracy and model robustness. These findings offer a more reliable technical foundation for forecasting winter wheat yield and grain quality under variable and extreme climatic conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"175 ","pages":"Article 128033"},"PeriodicalIF":5.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153274","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}