European Journal of Agronomy最新文献

{"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}

{"title":"A Bayesian framework for crop model calibration: A case study in the US Corn Belt","authors":"Matteo G. Ziliani ,&nbsp;Muhammad U. Altaf ,&nbsp;Trenton E. Franz ,&nbsp;Bangyou Zheng ,&nbsp;Scott Chapman ,&nbsp;Justin Sheffield ,&nbsp;Ibrahim Hoteit ,&nbsp;Matthew F. McCabe","doi":"10.1016/j.eja.2025.127650","DOIUrl":"10.1016/j.eja.2025.127650","url":null,"abstract":"<div><div>Crop models play a key role in simulating crop growth, predicting yield, and assessing interventions for improving production. Nevertheless, their reliability is often hindered by uncertainties in parameterization, soil properties, management practices, and meteorological inputs. These uncertainties can significantly affect model accuracy, especially when models are applied to different crops, cultivars, or fields. This study explores these concepts using the APSIM crop model under varying weather conditions, soil types, and management practices across multiple production years, but with a focus on a single location. Our analysis focuses on three research fields near Lincoln, Nebraska, growing different maize cultivars in either mono-cropping or rotational-crop configurations, and under both rain-fed and irrigated regimes. Initially, we perform a global sensitivity analysis to assess how variations in cultivar parameters affect key model outputs: leaf area index, biomass, and yield. We advance the analysis by conducting an intra-season sensitivity analysis to track the temporal impact of parameters over the growing cycle. Using an MCMC-based Bayesian inference approach, we estimate the most influential parameters. Results indicate that, for this specific location and agronomy, over 50 % (7 out of 13) of cultivar parameters have the greatest impact on model outputs, with the most sensitive parameters varying depending on the model output under investigation. Notably, parameters involved in the early capture of radiation were the most influential across all fields and outputs. The intra-season sensitivity analysis reveals that parameter sensitivity varies across different crop phenological stages, suggesting the potential for a targeted parameter calibration within specific windows of the season. The calibrated model using MCMC in a real-world case scenario delivers a strong agreement between predicted and observed outputs, with R² values ranging from 0.84 to 0.98, and relative RMSE between 10 % and 34 %. Compared to its uncalibrated counterpart, the calibrated model exhibits improved performance, with at least a 30 % reduction in RMSE values and enhanced correlation with in situ measurements. These findings confirm the robustness of the Bayesian calibration approach and its ability to accurately predict crop development across multiple seasons and maize cultivars. As such, this approach provides a valuable tool for calibrating crop models while simultaneously quantifying the uncertainty associated with input parameters. Extension of this analysis and model to larger regional areas would test its suitability for more generalized application of models at scale.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127650"},"PeriodicalIF":4.5,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869727","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 review of deep learning applications in weed detection: UAV and robotic approaches for precision agriculture","authors":"Puneet Saini ,&nbsp;D.S. Nagesh","doi":"10.1016/j.eja.2025.127652","DOIUrl":"10.1016/j.eja.2025.127652","url":null,"abstract":"<div><div>Deep Learning (DL) has changed the face of weed detection and has greatly improved Site-Specific Weed Management (SSWM). A comprehensive review of DL-based weed detection approaches with Unmanned Aerial Vehicles (UAVs), autonomous robots, and high-resolution orthomosaic imagery is presented in this paper. Different DL models have been used in improving the accuracy of weed detection and classification in agricultural fields such as Convolutional Neural Networks (CNNs), Transfer Learning architectures, and self-supervised models. In addition, this review addresses the interoperability of DL models in automated weeding robots, real-time edge computing systems and UAV-based precision agriculture solutions, providing an integrated view of precision weed control. The review study recognizes the recent trends in detection approaches including lightweight DL networks, multimodal data fusion and UAV related developments through a systematic analysis of 90 research papers. However, the generalizability of DL models under variable environmental settings, lack of labeled datasets and limited scalability of DL techniques for large-scale agricultural purpose, still remain an issue in the field. This paper attempts to address this by critically reviewing recent advances, highlighting knowledge gaps, and suggesting future research directions to foster integration of DL in precision agriculture and efficient weed management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127652"},"PeriodicalIF":4.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870469","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":"Estimation of the vertical attenuation coefficient of nitrogen in cotton canopy using polarized multiple-angle vegetation index","authors":"Jingang Wang ,&nbsp;Haijiang Wang ,&nbsp;Xin Lv ,&nbsp;Jing Cui ,&nbsp;Xiaoyan Shi ,&nbsp;Jianghui Song ,&nbsp;Weidi Li ,&nbsp;Wenxu Zhang","doi":"10.1016/j.eja.2025.127653","DOIUrl":"10.1016/j.eja.2025.127653","url":null,"abstract":"<div><div>The remote sensing-based estimation of the vertical attenuation coefficient <em>K</em> is of great significance to increase the accuracy of the estimation of crop canopy nitrogen vertical distribution by remote sensing technology. However, the multiple-angle information is susceptible to interference from specular reflection, which greatly limits the accuracy and stability of <em>K</em> estimation. In this research, the cotton canopy multiple-angle spectrum and polarization were acquired. Then, the spectral reflectance in the red- and blue-edge regions were combined to construct multiple-angle vegetation indices (MAVIs) using diffuse reflection component and total reflectance separately, and the MAVIs were used to estimate <em>K.</em> The estimated <em>K</em> was used to invert the nitrogen content of different vertical layers (upper, middle, and lower layers) of cotton canopy. Finally, the inversion results were compared with the inverted nitrogen content by the constructed multi-angle vegetative indices. The results showed that removing the specular reflection component from the total reflectance significantly increased the <em>K</em> estimation accuracy. The <em>K</em> estimation accuracy of MAVIs was higher than that of single-angle vegetation indices. Among the MAVIs, MNDVI_R-B (-30,45,45,45,0) had the highest <em>K</em> estimation accuracy, and the R² for the different growth season was in the range of 0.816–0.871. The estimated <em>K</em> by the MNDVI_R-B (-30,45,45,45,0) accurately inverted the nitrogen content of different vertical layers of cotton canopy, which was significantly higher than the R² of the estimation of different-layer nitrogen directly using the MAVIs. This study will provide a new method for accurately monitoring the vertical nitrogen status of crop canopy.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127653"},"PeriodicalIF":4.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863854","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":"Revealing effective strategies for cadmium reduction in rice: A meta-analysis of modified biochar applications","authors":"Hui He,&nbsp;Zhiqiang Fu","doi":"10.1016/j.eja.2025.127657","DOIUrl":"10.1016/j.eja.2025.127657","url":null,"abstract":"<div><div>Cadmium (Cd) contamination in paddy soils poses substantial environmental and health risks, particularly in regions where rice is a dietary staple. Modified biochar (MBC) has been identified as a promising approach for mitigating Cd accumulation in rice; however, the comparative effectiveness of different modifications relative to unmodified biochar (UMBC) remains insufficiently quantified. This meta-analysis integrates data from 1164 paired observations across 51 studies to assess the influence of MBC on Cd accumulation in brown rice. The findings indicate that MBC reduces Cd accumulation by 27.6 %, primarily by limiting root uptake and restricting translocation to grains. Notably, biochar modified with calcium, iron-manganese, iron-calcium, bacterial inoculants, and minerals exhibited significant Cd reduction, whereas silicon and alkaline modifications had minimal effects. The most effective Cd reduction was observed with 1–3 % biochar application at pH 7–9 under flooded conditions, with the medium-season rice crop showing the most pronounced response. These findings provide critical insights into the selection of effective biochar modifications and the development of practical, cost-efficient remediation strategies for Cd-contaminated rice paddies.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127657"},"PeriodicalIF":4.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869726","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":"Deciphering genotype × environment interaction for grain yield in durum wheat: an integration of analytical and empirical approaches for increased yield stability and adaptability","authors":"Reza Mohammadi ,&nbsp;Mozaffar Roostaei ,&nbsp;Mohammad Armion ,&nbsp;Moslem Abdipour ,&nbsp;Mahnaz Rahmati ,&nbsp;Kamal Shahbazi","doi":"10.1016/j.eja.2025.127656","DOIUrl":"10.1016/j.eja.2025.127656","url":null,"abstract":"<div><div>The development of stable and high-yielding wheat cultivars offers a sustainable solution to the challenge of food security and self-sufficiency in developing countries. The main goals of this study were to evaluate the effects of genotype, environment and genotype by environment (G×E) interaction on grain yield in durum wheat genotypes, to identify high-yielding and stable genotypes, and to identify climatic variables that significantly affect the G×E interaction. Twenty-one durum wheat breeding lines originating from ICARDA and CIMMYT, along with four national durum wheat cultivars, were evaluated using a randomized complete block design with three replications across seven locations (differing in winter temperature and rainfall) and three cropping seasons (2020–23). Four statistical models, including (i) additive main effects and multiplicative interaction (AMMI) (ii) genotype plus G×E (GGE) biplots, (iii) factorial regression (FR) and (iv) partial least squares (PLS) regression for investigating the G×E interaction for grain yield and identifying the climatic variables that significantly affect the G×E interaction, were applied. The combined analysis of variance indicated that the effects due to genotype, environment and the G×E interaction were highly significant (<em>P</em> &lt; 0.01). The environment was the main source of variation and accounted for 94.2 % of the total grain yield variation, while the G×E interaction contributed 4.7 %, and the genotype contributed 0.5 %. The combined and yearly data analysis by the “which-won-where” pattern of the GGE biplot showed consistent results across years for environmental grouping, resulting in four mega-environments in durum wheat yield trials. These results suggested that the use of these genotypes could be recommended for deployment in their respective mega-environments. Both AMMI and GGE biplots approved selecting breeding lines G19, G12, G22 and G23 as high-yield and stable genotypes across diverse environments for further breeding programs and genotype recommendation. Based on the FR model, climatic variables related to monthly rainfall and temperature explained 69.5 % of the G×E interaction variation. Using the PLS biplot, the environments were separated based on temperature and rainfall, and the genotypes with the most sensitivity (i.e., G4, G9, G24, G25) or insensitivity (i.e., G23, G21 and G14) to climatic variables were identified. These findings provide relevant information for future durum wheat breeding programs that consider improved productivity and yield stability in durum wheat under climate change conditions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127656"},"PeriodicalIF":4.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863322","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":"Maize biomass estimation by integrating spectral, structural, and textural features from unmanned aerial vehicle data","authors":"Lin Meng ,&nbsp;Bo Ming ,&nbsp;Yuan Liu ,&nbsp;Chenwei Nie ,&nbsp;Liang Fang ,&nbsp;Lili Zhou ,&nbsp;Jiangfeng Xin ,&nbsp;Beibei Xue ,&nbsp;Zhongyu Liang ,&nbsp;Huirong Guo ,&nbsp;Dameng Yin ,&nbsp;Xiuliang Jin","doi":"10.1016/j.eja.2025.127647","DOIUrl":"10.1016/j.eja.2025.127647","url":null,"abstract":"<div><div>The rapid and accurate estimation of maize aboveground biomass (AGB) and organ biomass at the field scale is crucial for monitoring crop growth and predicting yield. However, there is limited research on estimating crop organ biomass from unmanned aerial vehicle (UAV) remote sensing. This study used a multispectral (MS) camera and LiDAR sensor to acquire data at various maize growth stages across two experimental regions. The variations in maize organ biomass throughout the growing season were analyzed. Vegetation indices (VIs), canopy structure features (SFs), and texture features (TFs) were combined to create five different datasets and fed into two ensemble learning methods, i.e., Random Forest Regression (RFR) and XGBoost Regression (XGBR), to estimate maize AGB and organ biomass. The results indicated that: (i) Leaf and stalk biomass almost ceased to change after the tasseling stage. Stalk and ear biomass, compared to leaf biomass, are more strongly correlated with AGB. (ii) AGB estimation was improved by incorporating more indicators into the ensemble learning model, with the RFR model with all indicators achieving the best estimation accuracy (R² = 0.917, RMSE = 189.664 g/m², rRMSE = 21.2 %, MAE = 124.617 g/m²). (iii) Leaf and ear biomass estimation was comparable using models inputting all indicators or inputting VIs+TFs, suggesting that MS data were significant for leaf and ear biomass estimation, while SFs played an important role in stalk biomass estimation. This study accurately estimated organ-level maize biomass and AGB by combining different types of UAV remote sensing indicators and machine learning, which provides a valuable reference for organ biomass estimation of other crop types and related precision agriculture studies.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127647"},"PeriodicalIF":4.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863321","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-year assessment of seed shedding for economically important grass weed species in Italy and the UK","authors":"Donato Loddo ,&nbsp;Richard Hull ,&nbsp;Maurizio Sattin ,&nbsp;David Comont","doi":"10.1016/j.eja.2025.127648","DOIUrl":"10.1016/j.eja.2025.127648","url":null,"abstract":"<div><div>Harvest Weed Seed Control (HWSC) tactics aim to reduce weed dissemination and are considered promising approaches for future Integrated Weed Management (IWM) strategies. To be effective however, HWSC requires that target species have high seed retention at crop harvest. Here, a multi-year assessment of seed shedding was conducted across large geographical areas in the UK and Italy, for pernicious grass weed species that infest winter wheat and soybean crops. In the UK, an eight year assessment of <em>Alopecurus myosuroides</em> seed shedding was carried out in winter wheat crops. In Italy, seed shedding studies were conducted for three years, assessing <em>A. myosuroides, Avena</em> spp. and <em>Lolium perenne</em> ssp<em>. multiflorum</em> in winter wheat, and <em>Sorghum halepense</em> and <em>Echinochloa crus-galli</em> in soybean crops. Our results demonstrate low levels of seed retention (approximately 20 %) for <em>A. myosuroides</em> and <em>Avena</em> spp. at harvest, while higher mean seed retention (49 %) was found for <em>L. perenne</em> ssp<em>. multiflorum.</em> As such, <em>Avena</em> spp. and <em>A. myosuroides</em> are not good targets for HWSC across the studied locations, while HWSC could significantly contribute to <em>L. perenne</em> ssp<em>. multiflorum</em> management if combined with further control tactics. Seed retention at soybean harvest was on average 50 % for <em>E. crus-galli,</em> but higher at approximately 75 % for <em>S. halepense</em>. HWSC could therefore have a considerable impact on <em>S. halepense</em> populations in Italian soybean fields, but only an intermediate-low impact on <em>E. crus-galli</em> populations. Importantly however, we also find evidence for significant spatial and temporal variability in the extent of seed retention for all species. This study demonstrates that the potential for HWSC varies considerably between target weed species and highlights the importance of inter-annual variation in determining its expected performance.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127648"},"PeriodicalIF":4.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859394","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":"Optimizing pistachio yield and efficiency: Evaluating artificial mulch and shade nets for enhanced drought and salinity resilience","authors":"Mohammad Saeed Tadayon ,&nbsp;Seyed Majid Mousavi ,&nbsp;Seyed Mashaallah Hosseini ,&nbsp;Sohrab Sadeghi","doi":"10.1016/j.eja.2025.127655","DOIUrl":"10.1016/j.eja.2025.127655","url":null,"abstract":"<div><div>This study investigates the impact of synthetic ground cover (mulch) and shade netting on pistachio trees (<em>Pistacia vera</em> L. cv. ‘Ahmad-Aghai’) under drought and salinity stress during critical reproductive stages. Conducted over four years (2020–2023) in a semi-arid region, the experiment evaluated treatments on nutrient status, physiological traits, growth, thermal regulation, water use efficiency, and yield. The treatments included synthetic mulch, shade netting, their combination, and a control. Results showed that the combined treatment of mulch and shade netting significantly improved leaf nutrient concentrations (e.g., nitrogen 113.4 %, phosphorus 57.1 %, potassium 111.7 %) compared to the control. It also enhanced leaf area, relative water content, and photosynthetic water use efficiency by 17.1 %, 28.3 %, and 91.7 %, respectively, while reducing sodium (Na) concentrations and improving K/Na and Ca/Na ratios. This treatment increased nut yield (101.9 %) and reduced fruit abscission (37.6 %) and blank nuts (58.7 %). Canopy and soil temperatures decreased by up to 21.4 % and 30.9 %, respectively. Additionally, it reduced alternate bearing intensity (27.6 %) and other fruit complications, including endocarp lesions (53.4 %) and deformed nuts (69.1 %). Water application decreased by 39.4 %. Principal Component Analysis (PCA) indicated that the combination of mulch and shade netting improved water use efficiency and tree health. Economic analysis confirmed the cost-effectiveness of the combined treatment, yielding significant returns on investment. This study recommends adopting synthetic mulch and shade nets as sustainable practices for enhancing resilience and productivity in pistachio orchards, particularly in saline, hot, and water-stressed regions.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"168 ","pages":"Article 127655"},"PeriodicalIF":4.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855517","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}