European Journal of Agronomy最新文献

{"title":"Evaluating the AgMIP calibration protocol for crop models; case study and new diagnostic tests","authors":"Daniel Wallach ,&nbsp;Kwang Soo Kim ,&nbsp;Shinwoo Hyun ,&nbsp;Samuel Buis ,&nbsp;Peter Thorburn ,&nbsp;Henrike Mielenz ,&nbsp;Sabine Julia Seidel ,&nbsp;Phillip D. Alderman ,&nbsp;Benjamin Dumont ,&nbsp;Mohammad Hassan Fallah ,&nbsp;Gerrit Hoogenboom ,&nbsp;Eric Justes ,&nbsp;Kurt-Christian Kersebaum ,&nbsp;Marie Launay ,&nbsp;Luisa Leolini ,&nbsp;Muhammad Zeeshan Mehmood ,&nbsp;Marco Moriondo ,&nbsp;Qi Jing ,&nbsp;Budong Qian ,&nbsp;Schulz Susanne ,&nbsp;Taru Palosuo","doi":"10.1016/j.eja.2025.127659","DOIUrl":"10.1016/j.eja.2025.127659","url":null,"abstract":"<div><div>Crop simulation models are important tools in agronomy. Typically, they need to be calibrated before being used for new environments or cultivars. However, there is a large variability in calibration approaches, which contributes to uncertainty in simulated values, so it is important to develop improved calibration procedures that are widely applicable. The AgMIP calibration group recently proposed a comprehensive, generic calibration protocol that is directly based on standard statistical parameter estimation in regression models. Weighted least squares (WLS) is used to handle multiple response variables and forward regression using the corrected Akaike Information Criterion (AICc) is used to select the parameters to be calibrated. The protocol includes two adaptations, which are specific to each model and data set. First, initial approximations to the WLS parameters are obtained by fitting variables one group at a time. Secondly, “major” parameters are identified that are intended to reduce bias, analogously to the constant in linear regression. In this study, new diagnostic tools to be included in the protocol are proposed and tested in a case study. The diagnostics test whether the protocol does indeed lead to good initial approximations to the WLS parameters, and whether the protocol does indeed substantially reduce bias. These diagnostics provide in-depth understanding of the calibration process, reveal problems and help suggest solutions. The diagnostics should increase confidence in the results of the protocol. Having a reliable, generic calibration approach, like the augmented AgMIP protocol, is essential to using crop models more effectively.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127659"},"PeriodicalIF":4.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904540","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":"Selecting crop variables and parameters for the calibration of a new cultivar in a crop model: A case study of winter wheat for STICS","authors":"Meije Gawinowski ,&nbsp;Maël Aubry ,&nbsp;Samuel Buis ,&nbsp;Cécile Garcia ,&nbsp;Jean-Charles Deswarte ,&nbsp;Marie-Odile Bancal ,&nbsp;Marie Launay","doi":"10.1016/j.eja.2025.127677","DOIUrl":"10.1016/j.eja.2025.127677","url":null,"abstract":"<div><div>Crop models need to be regularly updated with parameterizations for new cultivars, but this requires calibration, which is a major challenge. Using the winter wheat cultivar Rubisko as a case study, we applied for the first time on experimental data a new calibration protocol to estimate the parameters of the STICS crop model for this new cultivar with multi-trial experimental data. We tested the calibration protocol in different conditions, with or without LAI and/or biomass experimental data, and we found that the resulting LAI and biomass dynamics strongly diverged. This study contributes to provide guidance to modelers for the calibration of a new cultivar in a crop model by focusing on the selection of variables and parameters to estimate as well as criteria for evaluating calibration strategies. With an application to winter wheat for the STICS crop model, this study has shown that the choice of calibration steps has a major impact on simulated outputs, but with a strong dependence on the structure of the experimental dataset. Firstly, this paper provides a methodology for the selection of calibration variables and associated parameters based on three criteria: 1) the relevance of the values of the estimated parameters, 2) the bias part of the mean square error, and 3) the analysis of the residuals. Secondly, by applying this methodology, we have shown that calibration based on LAI measurements is the most robust in the case of sparse observed data at the end of the cycle. Based on these results, we recommend caution when including parameters related to radiation-use efficiency; in particular, they should not be calibrated together with parameters related to leaf growth on biomass data alone. This study has enabled an appropriate calibration strategy to be defined, which will allow more modern French wheat cultivars to be parameterized in the STICS crop model.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127677"},"PeriodicalIF":4.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907892","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":"Optimization of agronomic management positively affects soil GHG emission: Viable solutions of mitigation in moist and dry Mediterranean climate zones","authors":"Mara Gabbrielli ,&nbsp;Marco Perfetto ,&nbsp;Marco Botta ,&nbsp;Iride Volpi ,&nbsp;Alessia Castellucci ,&nbsp;Matteo Ruggeri ,&nbsp;Marina Allegrezza ,&nbsp;Nicola Alessi ,&nbsp;Leonardo Vario ,&nbsp;Marco Acutis ,&nbsp;Alessia Perego ,&nbsp;Giorgio Ragaglini","doi":"10.1016/j.eja.2025.127668","DOIUrl":"10.1016/j.eja.2025.127668","url":null,"abstract":"<div><div>This study assesses the greenhouse gases (GHG) mitigation potential of two cropping systems under the diverse pedoclimatic conditions of two sites in Northern and Southern Italy, belonging to moist and dry climate zones, respectively. The two cropping systems, implemented at field scale in silty-clay-loam soils, were an optimized cropping system (ECS), designed and managed to be more efficient in the use of nitrogen and in the conservation of soil organic carbon (SOC), and a conventional system (CCS). N₂O and CO₂ soil fluxes were measured daily over three to four years using automatic stations comprising eight non-steady-state chambers per site. The ARMOSA model, calibrated and validated with measured data, provided reliable simulations of GHG fluxes and crop yields, aiding environmental impact assessments. In the Northern moist site, the ECS showed a significant GHG mitigation effect, serving as a GHG sink due to reduced N₂O emissions (N input-scaled emission: 0.0030 kg N-N₂O kg N⁻¹). Conversely, the CCS, despite its higher productivity and SOC storage, emitted more N₂O (N input-scaled emissions 0.0051 kg N-N₂O kg N⁻¹), making it a GHG source. In the moist site ECS had an effective mitigation potential compared with CCS, while in the Southern dry site both systems had lower GHG emissions than at the moist site, due to the reduced N rates (-27 % in CCS, −33 % in ECS), thus resulting as GHG sinks. This study underscores the relevance of agronomic mitigation practices, like leguminous crops integration and optimized nitrogen management where GHG emission are fostered by site-specific pedoclimatic conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127668"},"PeriodicalIF":4.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904541","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":"Improved estimation of nitrogen use efficiency in maize from the fusion of UAV multispectral imagery and LiDAR point cloud","authors":"Bo Chen ,&nbsp;Shenghao Gu ,&nbsp;Guanmin Huang ,&nbsp;Xianju Lu ,&nbsp;Wushuai Chang ,&nbsp;Guangtao Wang ,&nbsp;Xinyu Guo ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.eja.2025.127666","DOIUrl":"10.1016/j.eja.2025.127666","url":null,"abstract":"<div><div>Nitrogen use efficiency (NUE) is a key indicator for selecting nitrogen-efficient crop cultivars and optimizing fertilization strategies. However, NUE is typically assessed using destructive and laborious sampling methods, hindering the advancement of sustainable agriculture. The objective is to test whether the fusion of phenotyping data simultaneously acquired by multi-source sensors that reflect more functional and structural traits can improve the estimation accuracy of the highly integrated trait NUE in maize. Multispectral (MS) and light detection and ranging (LiDAR) data were simultaneously acquired during critical growth stages across two years of maize cultivar and nitrogen fertilizer field experiments using a multi-sensor UAV platform. Three machine learning algorithms, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR) and Support Vector Machine Regression (SVR) were selected to construct NUE estimation models based on three data sources：MS, LiDAR, and MS+LiDAR. The results demonstrated distinct differences in nitrogen utilization efficiency (NU_tE) and nitrogen agronomy efficiency (NAE) among maize cultivars at critical growth stages. These differences were efficiently and accurately identified using multi-source data combined with the machine learning algorithms. The RFR method obtained the highest model validation accuracy with an average R_test² = 0.68 and RMSE_test = 6.66 kg kg⁻¹. The average accuracy of multi-source data fusion was improved by 20.21 % compared to a single data source, and the RFR+MS+LiDAR method for NU_tE estimation obtained the highest model accuracy in the two-year validation dataset with R_test² = 0. 86 and RMSE_test = 8.5 kg kg⁻¹. The method proposed in this study mitigates the impact of canopy spectral saturation during the late growth stages of maize, enhancing the accuracy of NUE estimation by improving the convergence between predicted and observed values. This multi-source data fusion approach, based on a UAV platform, enables effective monitoring of NUE at critical growth stages. Consequently, it advances rapid, non-destructive NUE assessment in maize, supporting efficient breeding and precision nitrogen management strategies.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127666"},"PeriodicalIF":4.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900138","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":"Vegetation indices for the detection and classification of leaf nitrogen deficiency in maize","authors":"Yang Zhang ,&nbsp;Zhichong Wang ,&nbsp;Klaus Spohrer ,&nbsp;Alice-Jacqueline Reineke ,&nbsp;Xiongkui He ,&nbsp;Joachim Müller","doi":"10.1016/j.eja.2025.127665","DOIUrl":"10.1016/j.eja.2025.127665","url":null,"abstract":"<div><div>Nitrogen is an important nutrient with respect to crop growth, development and yield. Hence, site specific and optimal nitrogen fertilization requires knowledge of spatial nitrogen distribution and deficiencies in the field. Optical methods to determine leaf nitrogen concentration (LNC) have advantages over laboratory methods because of lower costs and faster performance. Together with e.g. unmanned aerial vehicles (UAV), optical methods can also be used to acquire LNC information with high spatial resolution. The main goal of this research was therefore to determine the most suitable vegetation indices for the detection and classification of nitrogen differences and deficiencies in maize (<em>Zea mays</em> L.). Hyperspectral images from 450 nm to 998 nm of fully expanded maize leaves from four different nitrogen treatments (0.72–2.88 g N/plant) were acquired under controlled light conditions and the corresponding LNC were determined. Then optimal wavelength-pairs for two predefined vegetation index formulas, the normalized difference spectral index (NDSI) and the ratio spectral index (RSI), were identified. Finally, the performances of the identified vegetation indices and selected vegetation indices from the literature to predict and classify LNC were assessed by means of a simulated pattern map that reflects spatially varying LNC classes. It was found that a wavelength from the red edge region (718 nm) was the most significant for LNC (r = 0.92). The vegetation index formulas considered (NDSI and RSI) showed the best performances when wavelength-pairs from the red-edge and NIR region (722 nm, 950 nm) were combined. Both vegetation indices showed a strong relationship with LNC (R²=0.90 for NDSI, R²=0.86 for RSI) and performed best at predicting LNC classes and their distribution in the simulated pattern map (accuracy=91.7 %, kappa=0.87).</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127665"},"PeriodicalIF":4.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887746","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":"Integrating phenology knowledge and Bayesian networks to map cropping patterns in South China","authors":"Jianbin Tao,&nbsp;Qiyue Jiang,&nbsp;Jinyuan Wang","doi":"10.1016/j.eja.2025.127663","DOIUrl":"10.1016/j.eja.2025.127663","url":null,"abstract":"<div><div>Crop maps play a crucial role in agricultural remote sensing applications at both regional and national levels, particularly in monitoring cropland use, simulating cropping intensity, estimating crop yields, and assessing agricultural sustainability. Existing crop mapping methods primarily rely on machine learning algorithms, which often depend heavily on sample data and lack portability. Crops follow a relatively stable seasonal growth pattern, which can be captured through time-series remote sensing data. The similarity in phenological characteristics of crops with the same cropping pattern and the differences between those of different cropping patterns serve as the foundation for crop mapping. This research proposes a new method for crop mapping by integrating phenological knowledge into a Bayesian network framework. By extracting key phenological features and encoding crop phenology knowledge with a small number of samples, a Bayesian network model was developed for mapping cropping patterns on the Jianghan Plain. Several spectral and geophysical metrics from key phenological stages were used as feature nodes. The method was validated on the Jianghan Plain, which has a complex cropping pattern with diverse crop types, including winter wheat, winter rapeseed, paddy rice, soybean, and corn. The method demonstrates excellent performance, achieving an overall accuracy of 93 % and a Kappa coefficient of 0.92. The results demonstrate that: (1) Phenological knowledge allows model parameters to be derived without the need for samples (or using very few samples), with no significant reduction in accuracy. (2) The method exhibits strong robustness and portability. The proposed approach enables \"weak learning and strong inference,\" eliminating inaccurate fitting during the inference process and enhancing both the interpretability and portability of the model.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127663"},"PeriodicalIF":4.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887747","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":"Exploration of biochars for enhancing soil health, carbon sequestration, and greenhouse gas emission reductions under citrus cultivation","authors":"Muhammad Qasim ,&nbsp;Saba Babar ,&nbsp;Muhammad Usama Younas ,&nbsp;Nimra Rajput ,&nbsp;Xiaoyang Xia ,&nbsp;Jiyuan Wang ,&nbsp;Xiangling Wang ,&nbsp;Cuncang Jiang","doi":"10.1016/j.eja.2025.127661","DOIUrl":"10.1016/j.eja.2025.127661","url":null,"abstract":"<div><div>Exploration of biochar application for soil health and carbon (C) sequestration, and their impacts on greenhouse gas (GHG) emissions under citrus orchard cultivation is a less explored aspect. As citrus orchard soils are mainly acidic, soil organic matter (SOM) loss via acidity-induced decomposition and subsequent GHG emissions can be reduced through addition of biochar. Due to the alkaline nature of biochar, it improves soil fertility by increasing SOM, reduces GHG emissions, enhances nutrient storage and sequesters C through improved rates of nutrient cycling. In the context of citrus orchards, where soil health is of prime importance for sustained productivity, integration of biochar offers promising prospects. In this document, we have explored previous literature about biochar application on properties of acidic soils under citrus cultivation along with its prospects in improving physicochemical and biological properties. In return, soil-water-plant interactions produce exponential yield returns. Thus, authors have explored in-detail methods for biochar production, its application to citrus orchard soils for their health improvements, C sequestration and reduction of GHG emissions, thereby mitigating the climate change. Moreover, this review may serve as a document of support to promote the use of biochar for improving degraded soils under citrus orchard production to uplift their health and citrus yield as well as carbon sequestration and GHG reduction.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127661"},"PeriodicalIF":4.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887744","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":"Comparative analysis of the effects of different dimensionality reduction algorithms on hyperspectral estimation of total nitrogen content in wheat soils","authors":"Juan Bai ,&nbsp;Shiyou Zhu ,&nbsp;Yingchao Hao ,&nbsp;Xinzhe Li ,&nbsp;Chenbo Yang ,&nbsp;Chao Wang ,&nbsp;Xingxing Qiao ,&nbsp;Meichen Feng ,&nbsp;Lujie Xiao ,&nbsp;Xiaoyan Song ,&nbsp;Meijun Zhang ,&nbsp;Sha Yang ,&nbsp;Guangxin Li ,&nbsp;Wude Yang","doi":"10.1016/j.eja.2025.127660","DOIUrl":"10.1016/j.eja.2025.127660","url":null,"abstract":"<div><h3>Context</h3><div>The level of soil nitrogen supply profoundly impacts the growth, development, and yield formation capacity of winter wheat. Excessive use of nitrogen-based fertilizers in current agricultural practices has negative consequences on both the environment and crop growth. Therefore, real-time, non-destructive estimation of soil total nitrogen content using hyperspectral remote sensing technology is crucial for advancing crop fertilization strategies and precision agriculture.</div></div><div><h3>Objectives</h3><div>(1) Explores the effects of different dimensionality reduction algorithms on hyperspectral estimation of soil total nitrogen content in wheat fields. (2) Investigates the optimal model for hyperspectral detection of total nitrogen content in wheat field soils in the Jinzhong region of Shanxi Province.</div></div><div><h3>Methods</h3><div>This study integrates various preprocessing methods and applies four dimensionality reduction algorithms—principal component analysis (PCA), singular value decomposition (SVD), unrelated variable elimination (UVE) and random forest (RF)—to reduce the data dimensions. Support vector regression (SVR) and back propagation neural network (BPNN) models for estimating soil total nitrogen content were then constructed and compared with gradient boosted decision tree (GBDT).</div></div><div><h3>Results</h3><div>The feature extraction algorithms PCA and SVD produced the same principal components and cumulative contributions when reducing the dimensionality of hyperspectral data. The number of characteristic bands selected by UVE was much smaller than that selected by RF. The characteristic bands selected by RF spanned the visible, near-infrared, and mid-infrared wavelength ranges, while those selected by UVE were mostly located within the visible light wavelength range. The modelling results following PCA and SVD dimensionality reduction were relatively similar, while the models based on RF-selected bands showed little change compared to full-spectrum band modeling. The SVR model constructed using multiplicative scatter correction (MSC) preprocessing and SVD dimensionality reduction had the highest accuracy in estimating soil total nitrogen content. (R_c²=0.87, R_v²=0.85; RMSE_c=0.13, RMSE_v=0.14; RPD_c=2.82, RPD_v=2.55; MAE_c=0.10, MAE_v=0.10)</div></div><div><h3>Conclusions</h3><div>Dimensionality reduction algorithms significantly contribute to the development of hyperspectral estimation models for soil total nitrogen content. The feature extraction algorithm (PCA and SVD) shows more obvious effect in improving the spectral modeling accuracy compared to the feature selection algorithm (UVE and RF). The optimal estimation model combination for hyperspectral detection of total nitrogen content in wheat field soils is MSC+SVD+SVR.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127660"},"PeriodicalIF":4.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879215","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 phenological phases of early and mid-late budbreak olive cultivars in a changing future climate over the Euro-Mediterranean region","authors":"Ali Didevarasl ,&nbsp;Jose M. Costa-Saura ,&nbsp;Donatella Spano ,&nbsp;Pierfrancesco Deiana ,&nbsp;Richard L. Snyder ,&nbsp;Diana Rechid ,&nbsp;BulowKatharina Bülow ,&nbsp;Maurizio Mulas ,&nbsp;Giovanni Nieddu ,&nbsp;Antonio Trabucco","doi":"10.1016/j.eja.2025.127658","DOIUrl":"10.1016/j.eja.2025.127658","url":null,"abstract":"<div><div>Future climate changes will likely alter the length and timing of phenological phases of olive crop. The timing and management of agronomic practices (planting, irrigation, fertilization, crop protection, harvesting, etc.) are based on phenological phases and plant development. Consequently, accurate phenological assessments are essential to define climate risks and guide optimal management apt to mitigate climate change effects on olive development. This research highlights future changes in olive phenological phases (i.e., sprouting, blooming, and pit hardening) over the Euro-Mediterranean region for both early and mid-late budbreak cultivars. We apply a Chill, Anti-Chill, and Growing Degree Days combined model to project the timing of phenological phases based on an ensemble of high-resolution climate projections at 0.11° from EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) for historical (1976–2005) and future (2036–2065) periods under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results showed that more than 75 % of the study area would experience significant earlier phenological development for olive by 2050, with 5–10 days earlier relative advancement for RCP8.5 compared to other RCPs. We projected greater olive phenological advances (i.e.,&gt;20 days) within colder areas due to persistent chilling and increasing heating units following future warming condition, indicating climate suitability for olive growth, while the southern Mediterranean is still facing high potential phenological disturbance induced by advances of 10–25 days. Future differences in phenological earliness between the cultivars (5–15 days) demonstrate the vulnerability of the early cultivar in the Mediterranean despite consistent thermal suitability for the mid-late cultivar in northern Europe and colder zones. Our investigation highlights regional phenological modeling processes relevant to guide strategic management of olive cultivation under future changing climate.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127658"},"PeriodicalIF":4.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874463","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":"Better inversion of rice nitrogen nutrition index at early panicle initiation stage using spectral features, texture features, and wavelet features based on UAV multispectral imagery","authors":"Ziwei Li ,&nbsp;Yuliang Zhang ,&nbsp;Jiaming Lu ,&nbsp;Yuan Wang ,&nbsp;Can Zhao ,&nbsp;Weiling Wang ,&nbsp;Jianjun Wang ,&nbsp;Hongcheng Zhang ,&nbsp;Zhongyang Huo","doi":"10.1016/j.eja.2025.127654","DOIUrl":"10.1016/j.eja.2025.127654","url":null,"abstract":"<div><div>The early panicle initiation stage plays a pivotal role in rice yield formation and nitrogen use efficiency. Rapid and accurate estimation of the Nitrogen Nutrition Index (NNI) during this stage is essential for guiding precise fertilization in high-yield rice cultivation. Although discrete wavelet transform (DWT) serves as an effective feature extraction tool, its application to crop NNI estimation remains unexplored. In this study, three-year field experiments involving ten rice varieties and five nitrogen application levels were conducted in Jiangsu Province, China. NNI data at the early panicle initiation stage and multispectral Unmanned Aerial Vehicle (UAV) imagery were collected. The sets of vegetation indices (VIs), texture indices (TIs), and DWT feature variables were extracted and fused from the imagery. Three feature selection methods were each combined with four machine learning algorithms to build distinct NNI estimation models, followed by an assessment of model accuracy. The results indicated that the overall estimation accuracy of models developed from different feature sets followed this order: VIs+TIs+DWT &gt; VIs+DWT &gt; VIs+TIs &gt; VIs &gt; TIs+DWT. RFECV-RF models constructed with the VIs+TIs+DWT and VIs+DWT feature sets both exhibited significantly higher estimation accuracy than the two existing methods using VIs and VIs+TIs, reaching a level suitable for precise quantitative analysis. The ratio of performance to deviation (RPD) of the model built with the VIs+TIs+DWT feature set was significantly higher than that of the model using the VIs+DWT feature set. Integrating DWT with VIs and TIs has significantly enhanced the accuracy of remotely sensed NNI estimation during the early panicle initiation stage, providing a method for precise nitrogen status diagnosis in rice at this critical growth phase.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127654"},"PeriodicalIF":4.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877331","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}