European Journal of Agronomy最新文献

{"title":"Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions","authors":"Yiguang Fan ,&nbsp;Haikuan Feng ,&nbsp;Yang Liu ,&nbsp;Hao Feng ,&nbsp;Jibo Yue ,&nbsp;Xiuliang Jin ,&nbsp;Riqiang Chen ,&nbsp;Mingbo Bian ,&nbsp;Yanpeng Ma ,&nbsp;Guijun Yang","doi":"10.1016/j.eja.2024.127388","DOIUrl":"10.1016/j.eja.2024.127388","url":null,"abstract":"<div><div>The use of remote sensing technologies to monitor the nitrogen nutrient status of crops is gradually becoming a more sensible choice, as traditional methods are time-consuming, labor-intensive, and destructive. However, most predictive models utilizing remote sensing data are statistical rather than mechanistic, making them difficult to extend at interannual and regional scales. This study explored the interannual and regional transferability of the potato plant nitrogen content (PNC) prediction models, which combined environmental variables (EVs, e.g. temperature, precipitation, etc.) with proximal hyperspectral vegetation indices (VIs). Two methodologies were implemented to fuse EVs and VIs. The first involved a multiple regression analysis utilizing a multivariate linear model and a random forest approach, with VIs and EVs treated as independent variables, respectively. The second, a hierarchical linear model (HLM), employed EVs to dynamically adjust the relationship between VIs and PNC for different experimental sites. The predictive outcomes demonstrated that (i) the conventional method relying solely on optical VIs exhibited limited accuracy and stability in interannual and regional PNC forecasting; (ii) albeit the multivariate regression approach significantly enhanced model accuracy within the calibration set, its scalability across years and regions remained suboptimal; (iii) the HLM method exhibited high precision and scalability across years and regions, with <em>R</em>², RMSE, and NRMSE values of 0.68, 0.50 %, and 19.68 % in the validation set, respectively. Those findings corroborate that using a two-tier HLM method can automatically adjust for discrepancies in VIs response to PNC through EVs, thereby enhancing the model's stability. Provided that remote sensing data and EVs are sustainably acquired over the potato growth cycle, it will provide a particularly promising approach to potato nitrogen diagnostics as a decision-making tool for regional application of nitrogen fertilizer.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127388"},"PeriodicalIF":4.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446183","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 impact of long-term organic horticultural systems on energy outputs and carbon storages in relation to extreme rainfall events","authors":"Alessandro Persiani,&nbsp;Mariangela Diacono,&nbsp;Francesco Montemurro","doi":"10.1016/j.eja.2024.127398","DOIUrl":"10.1016/j.eja.2024.127398","url":null,"abstract":"<div><div>Enhancing resilience of agroecosystems of Mediterranean area is a challenge that involves both researchers and different stakeholders and, in this context, increasing crop diversity by redesigning agricultural systems can be considered among the most important tools. Therefore, the response of agroecological practices to climate change effects was tested in a long-term experiment on organic horticultural crops (MITIORG), which is characterized by a soil hydraulic arrangement in ridges, strips and the use (with different management options) of cover crops within cash crops rotations. The main objective of this study was to show how powerful is the sustainability assessment of agroecological practices by converting crops yield and biomass into energy outputs and carbon storages, in diversified horticultural systems. The obtained outputs (expressed in energy and carbon equivalents) were evaluated and analyzed considering the site-specific meteorological data in more than 10 horticultural cropping cycles, from autumn-winter 2014–15 to autumn-winter 2020–21. The Ridge and Strips (RS) system 1 (RS1 - cover crops as living mulch on ridges and break crops in strips, both with no-till termination) showed an enhancement of about 18 % of energy output and carbon (C) storages compared to RS2 (ridges and strips with green manured cover) when extreme precipitation events occurred. Moreover, RS3 (ridges and strips without cover crops) recorded a reduction of about 5 and 9 % of energy output and C storage, respectively, compared to the mean of RS1 and RS2 in periods with extreme events. Our results highlighted that using more diversified agroecological systems improved their overall average outputs, ensuring greater resilience during extreme weather events, since at least part of crop productions was safeguarded. Therefore, it is important to combine techniques that allow long-term resilience, such as choosing and well managing cover crops (agroecological service crops), according to site and systems specific conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127398"},"PeriodicalIF":4.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446184","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":"Enhancing soybean yield stability and soil health through long-term mulching strategies: Insights from a 13-year study","authors":"Jiajie Song ,&nbsp;Dingding Zhang ,&nbsp;Chenyu Wang ,&nbsp;Jianheng Song ,&nbsp;Shahzad Haider ,&nbsp;Sen Chang ,&nbsp;Xiaolong Shi ,&nbsp;Jinze Bai ,&nbsp;Jiaqi Hao ,&nbsp;Gaihe Yang ,&nbsp;Guangxin Ren ,&nbsp;Yongzhong Feng ,&nbsp;Xing Wang","doi":"10.1016/j.eja.2024.127383","DOIUrl":"10.1016/j.eja.2024.127383","url":null,"abstract":"<div><div>Sustainable agriculture systems incorporate important stabilizing mechanisms, such as mulching, for increasing yield and improving soil health. However, the synergistic effects of different long-term mulching practices on soybean yield stability and soil health remain unexplored. In this study, we conducted a 13-year long-term investigation to evaluate the impacts of various mulching methods—no mulching (CK), straw mulching (SM), plastic mulching (PM), and ridged and plastic mulching (RM)—over a 13-year period on soil nutrients, and soybean yield, stability, and sustainability. Our findings revealed that SM, PM, and RM treatments significantly increased the average yields by 28.02 %, 20.49 %, and 51.56 %, respectively. Moreover, SM and RM treatments significantly enhanced yield stability (SM +107.90 %, RM +98.82 %) and sustainability (SM +47.85 %, RM +37.14 %). Additionally, compared to CK, the SM treatment significantly increased the average soil organic carbon (SOC) and total nitrogen (STN) content by 16.78 % and 16.23 %, respectively. Meanwhile, mulching practices also improved soil reactive carbon and nitrogen pools. Compared to CK, plastic mulch reduced microbial biomass carbon (MBC) content (PM −8.85 %, RM −0.73 %) and soil ammonium nitrogen (AN) content (PM −8.19 %, RM −1.20 %), while increasing microbial biomass nitrogen (MBN) content (PM +8.73 %, RM +9.47 %). The SM treatment increased MBC, AN, and MBN contents by 0.24 %, 7.23 %, and 8.94 %, respectively. Additionally, SM and RM treatments significantly increased β-1,4-glucosidase (BG) activity (SM +98.74 %, RM +128.25 %) and decreased and β-1,4- n -acetamido-glucosidase (NAG) + 1-leucine aminopeptidase (LAP) (NAG + LAP) activity (SM −28.74 %, RM −25.33 %) compared to CK. Furthermore, SM, PM, and RM treatments significantly increased the Chao1 index by 35.30 %, 68.08 %, and 52.23 %, respectively, compared to CK. Finally, results of the Mantel test and random forest model indicated that the increases in yield and stability were due to improved soil temperature (ST), active carbon and nitrogen pools, enzyme activity, and diazotrophic bacterial diversity. In summary, our findings suggest that ridged and plastic mulching enhances soil nutrient effectiveness by maintaining soil moisture and regulating diazotrophic bacterial community structure, thereby increasing soybean yields. Conversely, straw mulching continuously supplies nutrients to the soil, enhancing soil quality and diazotrophic bacterial community structure, thus improving yield stability. Over all, our findings provides new insights into global long-term agricultural sustainability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127383"},"PeriodicalIF":4.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446182","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":"Implications of soil waterlogging for crop quality: A meta-analysis","authors":"Rui Yang ,&nbsp;Chunhu Wang ,&nbsp;Yinmiao Yang ,&nbsp;Matthew Tom Harrison ,&nbsp;Meixue Zhou ,&nbsp;Ke Liu","doi":"10.1016/j.eja.2024.127395","DOIUrl":"10.1016/j.eja.2024.127395","url":null,"abstract":"<div><div>Soil waterlogging in many arable regions of the world challenge the quantum and quality of crop production. While previous studies have assessed the impact of waterlogging on crop yields, understanding of how waterlogging implicates with crop quality remains in its infancy. Here, we conduct a systematic literature review and meta-analysis to assess how waterlogging influences grain quality. We also explore the role of engineering and agronomic strategies for alleviating adverse effects of waterlogging. We reveal that soil waterlogging has less impacts on grain quality than on yield; the latter decreasing by an average of 23 %, while average grain protein and starch content of waterlogged crops reduced by 6.7 % and 7.3 %, respectively. We attribute these differences to underlying mechanics of yield and grain quality formation, as well as biological processes conferring adaptation, plasticity and recovery. Reduced grain quality under waterlogging is associated with decreased activity of enzymes involved in leaf nitrogen and carbon metabolism. Unlike yields however, grain quality suffers less deterioration with prolonged waterlogging, with ultimate effects realized being a function of species-specific tolerance, timing and duration of waterlogging relative to crop stage, soil type and growing season weather. We underscore the potential in engineering and/or agronomic interventions for alleviating detrimental effects of waterlogging.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127395"},"PeriodicalIF":4.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil-climate interactions enhance understanding of long-term crop yield stability","authors":"Wanxue Zhu ,&nbsp;Ehsan Eyshi Rezaei ,&nbsp;Zhigang Sun ,&nbsp;Jundong Wang ,&nbsp;Stefan Siebert","doi":"10.1016/j.eja.2024.127386","DOIUrl":"10.1016/j.eja.2024.127386","url":null,"abstract":"<div><div>Improving crop yield and stability is crucial for sustainable food production, which is predominantly influenced by climate. Nutrient management mitigates the negative impacts of climate change on yield stability, but little is known about the explanatory capability of climate variables (especially canopy, soil, and nighttime temperatures) and soil nutrient interactions for yield anomalies. This study evaluated the long-term (1992–2020) responses of wheat and maize yields and yield anomalies to various climatic variables under distinct combinations of nitrogen (N), phosphorus (P), and potassium (K) nutrient supplies in the North China Plain. Results showed that NPK treatment improved the stability of relative yield anomalies (RYA) for wheat and maize by up to 65 % compared to the unfertilized control, while negatively affecting the stability of absolute yield anomalies (AYA). Nutrient supply affected the yield stability of maize more than that of wheat. Ground and soil temperatures contributed the most to the yield and yield anomalies, while air temperature was less associated. Models relying solely on climate data explained 34 % and 28 % of the wheat RYA and AYA, respectively, and 44 % and 49 % of the maize RYA and AYA, respectively. Incorporating nutrient-climate interactions improved the model explanatory power to 67 % for wheat RYA and to 62 % for maize RYA. Additionally, annual random effects were less critical in explaining maize yield and yield anomalies but significant for wheat RYA. The nutrient-climate interactions greatly improved the explanatory capability of models to crop yield anomalies, thereby supporting strategies for sustainable food production amidst changing climate.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127386"},"PeriodicalIF":4.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437656","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":"Advancing lettuce physiological state recognition in IoT aeroponic systems: A meta-learning-driven data fusion approach","authors":"Osama Elsherbiny ,&nbsp;Jianmin Gao ,&nbsp;Ming Ma ,&nbsp;Yinan Guo ,&nbsp;Mazhar H. Tunio ,&nbsp;Abdallah H. Mosha","doi":"10.1016/j.eja.2024.127387","DOIUrl":"10.1016/j.eja.2024.127387","url":null,"abstract":"<div><div>Automatically identifying key physiological factors in plants, such as leaf relative humidity (LRH), chlorophyll content (Chl), and nitrogen levels (N), is vital for effective aeroponic management and improving growth, yield, quality, and sustainability. Meta-learning (MetaL) solutions utilize data fusion and intelligent processing, ensuring fast and consistent outcomes. This paper aims to develop a novel MetaL framework that leverages multimodal data sources—including spectral, thermal, and IoT environmental data—to enable real-time, non-invasive identification of LRH, Chl, and N content in aeroponically grown lettuce. The research examined various spectral reflectance indices (SRIs) and thermal indicators from plant characteristics. Model-based feature selection was implemented using back-propagation neural networks (BPNN), decision trees (DT), and gradient boosting machines (GBM) to identify key attributes and optimize hyperparameters. The experimental findings indicated that deploying GBM-based top variables as the foundational model, combined with BPNN as the meta-model, significantly improved the accuracy of analyzing the assigned factors. The prediction scores (R²) for LRH, Chl, and N increased to 0.875 (RMSE=0.879), 0.886 (RMSE=0.694), and 0.930 (RMSE=0.184), respectively, compared to applying BPNN-based features alone as a standalone model. Overall, the designed methodology contributes to more accurate predictions of plant physiological states, enabling proactive steps toward sustainable aeroponic agriculture.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127387"},"PeriodicalIF":4.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437657","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":"Ratooning response of rice to preharvest nitrogen application under different availabilities of stem reserves","authors":"Weiyi Xie ,&nbsp;Syed Tahir Ata-Ul-Karim ,&nbsp;Yuji Yamasaki ,&nbsp;Fumitaka Shiotsu ,&nbsp;Yoichiro Kato","doi":"10.1016/j.eja.2024.127373","DOIUrl":"10.1016/j.eja.2024.127373","url":null,"abstract":"<div><div>Plant N nutrition and preharvest stem nonstructural carbohydrates (NSCs) greatly influence ratoon crop yield in a rice<img>ratoon-rice system. However, their physiological relationships haven’t been unraveled. We designed this study to test whether greater rice regeneration ability due to preharvest N application is accompanied by increased stem reserves, or whether plant N nutrition and stem reserves independently influence regeneration. First, we evaluated ratooning of crops that receive N after the main crop’s late reproductive stage. Second, we imposed shade to decrease light intensity by 64 %<img>69 % during grain filling of the main crop, and measured the effects of N application on NSCs of the main crop and ratoon crop growths in 2 years. N applied at 5 days after heading of the main crop consistently increased the regeneration ability and ratoon crop yield under non-shaded condition. It did not increase the regeneration ability under heavy shade, when only small amounts of stem NSCs accumulated. Without shade, N application at 5 days after heading increased the concentration of stem NSCs in only one of the two years, whereas the regeneration ability and ratoon crop yield increased in both years. Our results suggest that the increase in ratoon crop yield with preharvest N application requires more than a threshold amount of stem NSCs before the main crop is harvested. However, the preharvest N application can also promote tiller regeneration without further accumulation of stem reserves. N management for ratoon crops therefore depends on light conditions and the main crop’s stem reserves. The relationships between plant N, stem reserves, and regeneration ability revealed here will support improved N management for ratoon rice cultivation.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127373"},"PeriodicalIF":4.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419238","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":"Corrigendum to “Climate change impact and adaptation of rainfed cereal crops in sub-Saharan Africa” [Eur. J. Agron. 155 (2024) 127137]","authors":"Seyyedmajid Alimagham ,&nbsp;Marloes P. van Loon ,&nbsp;Julian Ramirez-Villegas ,&nbsp;Samuel Adjei-Nsiah ,&nbsp;Freddy Baijukya ,&nbsp;Abdullahi Bala ,&nbsp;Regis Chikowo ,&nbsp;João Vasco Silva ,&nbsp;Abdelkader Mahamane Soulé ,&nbsp;Godfrey Taulya ,&nbsp;Fatima Amor Tenorio ,&nbsp;Kindie Tesfaye ,&nbsp;Martin K. van Ittersum","doi":"10.1016/j.eja.2024.127390","DOIUrl":"10.1016/j.eja.2024.127390","url":null,"abstract":"","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127390"},"PeriodicalIF":4.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops","authors":"Rohit Anand ,&nbsp;Roaf Ahmad Parray ,&nbsp;Indra Mani ,&nbsp;Tapan Kumar Khura ,&nbsp;Harilal Kushwaha ,&nbsp;Brij Bihari Sharma ,&nbsp;Susheel Sarkar ,&nbsp;Samarth Godara","doi":"10.1016/j.eja.2024.127384","DOIUrl":"10.1016/j.eja.2024.127384","url":null,"abstract":"<div><div>This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127384"},"PeriodicalIF":4.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419327","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":"Climate, altitude, yield, and varieties drive lodging in sugarcane: A random forest approach to predict risk levels on a tropical island","authors":"Mathias Christina ,&nbsp;Benjamin Heuclin ,&nbsp;Raphaël Pilloni ,&nbsp;Mathilde Mellin ,&nbsp;Laurent Barau ,&nbsp;Jean-Yves Hoarau ,&nbsp;Thomas Dumont","doi":"10.1016/j.eja.2024.127381","DOIUrl":"10.1016/j.eja.2024.127381","url":null,"abstract":"<div><div>Lodging is a critical factor in reducing sugarcane yields worldwide, mainly due to the selection of highly productive varieties. Understanding the response of yield and lodging to the combined effects of climate, sugarcane traits, and varieties has become a priority under climate change. The aim of this study was to better understand the influence of plant characteristics, climate, and soil conditions on the trade-off between sugarcane yield and lodging on the tropical Reunion Island. Data from a 14-year experimental network run by the eRcane breeding institute were used to build random-forest models to predict sugarcane yield and lodging classes, i.e. &lt;10 %, 10–50 %, &gt;50 % of lodging. Yield and lodging probability were then predicted across the island using climate change projections from 2015 to 2035. Both yield and lodging were highly influenced by the variety and characteristics (height and tillering) and climatic conditions. Areas on the island at high altitudes were subject to high probability of lodging (&gt;50 %), while in areas with high wind speed, the risk of moderate lodging (10–50 %) increased. Overall, conditions or plant characteristics that favor higher yields increased lodging probability. Nevertheless, the correlation between yield and lodging probability varied considerably depending on the variety, highlighting the importance of sugarcane characteristics in resistance to lodging. This study highlights the fact that promoting more productive varieties in recent decades has led to an increase in lodging and identified critical environments on the island prone to increased risk of lodging.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"161 ","pages":"Article 127381"},"PeriodicalIF":4.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}