European Journal of Agronomy最新文献

{"title":"Enhance the accuracy of rice yield prediction through an advanced preprocessing architecture for time series data obtained from a UAV multispectral remote sensing platform","authors":"Xiangqian Feng ,&nbsp;Ziqiu Li ,&nbsp;Peixin Yang ,&nbsp;Weiyuan Hong ,&nbsp;Aidong Wang ,&nbsp;Jinhua Qin ,&nbsp;Haowen Zhang ,&nbsp;Pavel Daryl Kem Senou ,&nbsp;Yunbo Zhang ,&nbsp;Danying Wang ,&nbsp;Song Chen","doi":"10.1016/j.eja.2025.127542","DOIUrl":"10.1016/j.eja.2025.127542","url":null,"abstract":"<div><div>High-resolution temporal spectral data captured by unmanned aerial vehicles (UAVs) have become increasingly important in predicting crop yields. Effective preprocessing of these temporal datasets is crucial for improving yield estimation accuracy and facilitating the broader application of predictive models. Despite its growing importance, a comprehensive guide detailing the preprocessing procedures for UAV temporal data is currently lacking. Consequently, this research is dedicated to constructing a robust preprocessing framework tailored to UAV time series spectral remote sensing data, with a particular emphasis on assessing its impact on the accuracy of yield predictions. We developed a multi-level threshold segmentation (MLT) method specifically for rice particle swarm optimization (ricePSO). Three field experiments were executed under diverse nutritional regimes to contrast the efficacy of yield predictions derived from UAV temporal dynamic threshold segmentation against those achieved through temporal data smoothing. Results showed that the ricePSO multi-level threshold segmentation outperformed the conventional Otsu threshold segmentation method, enhancing yield prediction accuracy by 1–11 %. Meanwhile, data smoothing effectively reduced errors in the temporal data acquisition process. Combining MLT, Gaussian smoothing, and the Bidirectional Long Short-Term Memory (Bi-LSTM) model resulted in the highest yield prediction accuracy, with an <em>R²</em> value of 87.52 %. Overall, this study achieved improvements in yield prediction accuracy through the use of multilevel dynamic threshold segmentation and data smoothing, providing new strategies for the preprocessing of temporal multispectral remote sensing data from UAV.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127542"},"PeriodicalIF":4.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419949","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":"Assessing genetics, biophysical, and management factors related to soybean seed protein variation in Brazil","authors":"María Sol Zelaya Arce ,&nbsp;Eduardo Lago Lago Tagliapietra ,&nbsp;José Eduardo Minussi Winck ,&nbsp;Alexandre Ferigolo Alves ,&nbsp;Felipe Schmidt Dalla Porta ,&nbsp;Tiago Broilo Facco ,&nbsp;Nereu Augusto Streck ,&nbsp;Mauricio Fornalski Soares ,&nbsp;Gregori Da Encarnação Ferrão ,&nbsp;Daniel Debona ,&nbsp;Claudio Hideo Martins da Costa ,&nbsp;Rodrigo Merighi Bega ,&nbsp;Elizandro Fochesatto ,&nbsp;Everton Luis Krabbe ,&nbsp;Alencar Junior Zanon","doi":"10.1016/j.eja.2025.127541","DOIUrl":"10.1016/j.eja.2025.127541","url":null,"abstract":"<div><div>The demand for high-quality soybeans is increasing. The composition of soybean grain can vary with genetics, biophysical, and management factors. In particular, studies on protein concentration are increasing worldwide. The objectives in this study were: (i) to quantify the genetic effects on seed protein concentration and (ii) to identify the biophysical and management factors affecting seed protein concentration in soybean production systems in Brazil. We collected soybean samples and crop management data through surveys in 194 soybean farms in two growing seasons (2018/2019; 2022/2023) across eleven states in Brazil. Seed protein was determined by the Kjeldahl method. Random forest regressions and comparisons between high and low protein fields to identify the main causes of variation in soybean protein concentration were used. Fields with highest protein concentration were observed in older cultivars released in (2011), at lower yields (3082 kg ha⁻¹), late sowing (DOY 313), higher temperatures (25.6 °C⁻¹) and a lower photothermal coefficient (0.79 MJ m⁻² d⁻¹ °C⁻¹). Conversely, low protein concentration was observed in fields with higher yields (4220 kg ha⁻¹), early sowing (DOY 313), lower temperatures (24.8°C⁻¹) and a higher photothermal coefficient (0.84 MJ m⁻² d⁻¹ °C⁻¹) and newer cultivars released in (2016). The regression tree and random forest explained 58 % of the protein variability, including cultivar (39 %), latitude (12 %) and sowing date (7 %). Cultivar was the most important factor affecting soybean protein concentration, followed by sowing date. The year of cultivar release, breeding company, latitude, temperature, photothermal coefficient and water supply also affected the final concentration of soybean seed protein. The results emphasize the need for breeding programs to evaluate protein concentration in new soybean varieties. Additionally, we now have clear biophysical and management indicators to help achieve higher protein concentrations in soybean crops.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127541"},"PeriodicalIF":4.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419948","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":"Projecting the impacts of climate change on soybean production and water requirements using AquaCrop model","authors":"Wilfredo Jr. Barrera ,&nbsp;Carmelo Maucieri ,&nbsp;Maurizio Borin ,&nbsp;Francesco Morbidini ,&nbsp;Tjaša Pogačar ,&nbsp;Marko Flajšman ,&nbsp;Graziano Ghinassi ,&nbsp;Leonardo Verdi ,&nbsp;Anna Dalla Marta ,&nbsp;Roberto Ferrise","doi":"10.1016/j.eja.2025.127538","DOIUrl":"10.1016/j.eja.2025.127538","url":null,"abstract":"<div><div>Soybean production under rainfed conditions is vulnerable to climate uncertainties, particularly in semi-arid and semi-humid regions. This study assessed the impacts of climate change (SSP1–2.6, SSP2–4.5, SSP5–8.5) on soybean production and water requirements in the near (2041–2060), mid (2061–2080) and far (2081–2100) future. Simulations were conducted in specific locations in Italy (Castelfranco and Cesa) and Slovenia (Ljubljana) under rainfed and irrigated conditions, considering different thresholds of readily available water (RAW) depletion (25–100 %) to start irrigation. The results showed predominantly negative impacts of climate change under rainfed conditions. Under SSP1–2.6 and SSP2–4.5, irrigation mitigated these negative effects, leading to improved soybean performance in Italy in the near and mid future. In contrast, the mitigating potential of irrigation in Ljubljana was reduced, affecting negatively the soybean performance even under irrigated conditions. Nevertheless, the yield potential of Ljubljana remains higher compared to Castelfranco and Cesa. Soybean water productivity (WP_ET) followed similar trend as yield, showing minimal change except under SSP5–8.5 in the mid and far future. Climate change reduced the soybean crop water requirement (CWR) which decreased progressively from SSP1–2.6 to SSP5–8.5 across all time periods. The net irrigation requirement (NIR) was highest under SSP5–8.5, increasing from near to far future but remained stable under SSP1–2.6 and SSP2–4.5. Increasing the RAW depletion threshold for irrigation reduced soybean NIR but significantly decreased yield. Therefore, the results suggest that irrigating soybean at 50 % RAW depletion could be a viable adaptation strategy to climate change, effectively balancing the trade-offs between NIR and yield.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127538"},"PeriodicalIF":4.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419950","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":"An agent-based model to simulate field-specific nitrogen fertilizer applications in grasslands","authors":"A. Kaim ,&nbsp;T.M. Schmitt ,&nbsp;S.H. Annuth ,&nbsp;M. Haensel ,&nbsp;T. Koellner","doi":"10.1016/j.eja.2025.127539","DOIUrl":"10.1016/j.eja.2025.127539","url":null,"abstract":"<div><div>Grasslands have a large share of the world’s land cover and their sustainable management is important for the protection and provisioning of grassland ecosystem services. The question of how to manage grassland sustainably is becoming increasingly important, especially in view of climate change, which on the one hand extends the vegetation period (and thus potentially allows use intensification) and on the other hand causes yield losses due to droughts. Fertilization plays an important role in grassland management and decisions are usually made at farm level. Data on fertilizer application rates are crucial for an accurate assessment of the effects of grassland management on ecosystem services. However, these are generally not available on farm/field scale. To close this gap, we present an agent-based model for Fertilization In Grasslands (FertIG). Based on animal, land-use, and cutting data, the model estimates grassland yields and calculates field-specific amounts of applied organic and mineral nitrogen on grassland (and partly cropland). Furthermore, the model considers different legal requirements (including fertilization ordinances) and nutrient trade among farms. FertIG was applied to a grassland-dominated region in Bavaria, Germany comparing the effects of changes in the fertilization ordinance as well as nutrient trade. The results show that the consideration of nutrient trade improves organic fertilizer distribution and leads to slightly lower N_min applications. On a regional scale, recent legal changes (fertilization ordinance) had limited impacts. Limiting the maximum applicable amount of N_org to 170 kg N/ha fertilized area instead of farm area as of 2020 hardly changed fertilizer application rates. No longer considering application losses in the calculation of fertilizer requirements had the strongest effects, leading to lower supplementary N_min applications. The model can be applied to other regions in Germany and, with respective adjustments, in Europe. Generally, it allows comparing the effects of policy changes on fertilization management at regional, farm and field scale.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127539"},"PeriodicalIF":4.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402498","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":"Enhanced coordination of photosynthetic functions among cotton boll–leaf systems to maintain boll weight under high-density planting","authors":"Minzhi Chen ,&nbsp;Yinhua Yan ,&nbsp;Fubin Liang ,&nbsp;Jinyu An ,&nbsp;Yuxuan Wang ,&nbsp;Jingshan Tian ,&nbsp;Yali Zhang ,&nbsp;Chuangdao Jiang ,&nbsp;Wangfeng Zhang","doi":"10.1016/j.eja.2025.127540","DOIUrl":"10.1016/j.eja.2025.127540","url":null,"abstract":"<div><div>High planting density curtails the boll number per plant more significantly than the single boll weight, yet it is hard to estimate the boll weight from single-leaf photosynthesis with increasing boll abscission. We speculated that high plant density may lead to coordination among photosynthetic organs to maintain boll weight. Therefore, cotton (<em>Gossypium hirsutum</em> L.) yield formation, the photosynthetic characteristics of the leaves and boll–leaf system were studied under various plant densities. The results showed that the boll number per plant or boll number per boll–leaf system decreased more greatly than the boll–leaf system number per plant with increasing plant density. Leaf area, single leaf photosynthetic rate, and CO₂ assimilation of the boll–leaf system all gradually decreased with the increase of plant density. There was a significant positive linear correlation between integrated CO₂ assimilation of the boll–leaf system and boll biomass per boll–leaf system. After girdling treatment, the boll biomass of the boll–leaf system decreased more greatly compared with non-girdling treatment with increasing plant density. Moreover, the girdling/non-girdling of boll biomass per boll–leaf system reached 0.8–1.0 at 19–25 plants m⁻². The removal of the lower-canopy bolls caused a significant increase in the boll biomass of the upper canopy, and the biomass per boll at high densities (&gt;25 plants m⁻²) increased more greatly than at low densities. Therefore, the rapid decrease in CO₂ assimilation of the boll–leaf system resulted in a decreased boll number per boll–leaf system as plant density increased (&lt;25 plants m⁻²). Under high densities (&gt;25 plants m⁻²), the boll biomass not only depends on the photosynthetic rate of the corresponding boll–leaf system, but also on the coordination of photosynthetic functions among adjacent cotton boll–leaf systems. Optimal planting density (19–25 plants m⁻²) means that the assimilate production and utilization of the boll–leaf system can be balanced. At this density, the coordination of boll number and boll weight is conducive to maximizing the yield per plant and unit ground area.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127540"},"PeriodicalIF":4.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388477","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":"Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model","authors":"Laura Delhez ,&nbsp;Benjamin Dumont ,&nbsp;Bernard Longdoz","doi":"10.1016/j.eja.2025.127533","DOIUrl":"10.1016/j.eja.2025.127533","url":null,"abstract":"<div><div>Dynamic crop models, such as the Daisy soil-plant-atmosphere model, simulate many processes and encompass a large number of parameters. Global sensitivity analysis (GSA) aims to identify the most influential parameters and understand model structure and behaviour. However, little attention has been paid to the temporal dynamics of parameter sensitivity in crop models, even though it can provide greater insight into model structure. This study performs a comprehensive GSA on the Daisy model, including the soil-vegetation-atmosphere transfer (SVAT) module, focusing on crop yield as well as CO₂, N₂O and energy fluxes. The Sobol’ method was applied to two types of outputs: (i) outputs aggregated into a scalar with an objective function (RMSE or cumulative) and (ii) vector outputs analysed at each time step. The main objectives of this paper were to compare the temporal and aggregated applications of GSA and to identify influential parameters of Daisy under different environmental conditions. Both aggregated and temporal methods identified the same main parameters. Nevertheless, temporal analysis provided deeper insight into model behaviour and calibration guidelines, revealing dynamic changes in parameter sensitivity at weekly and hourly resolutions and identifying critical periods for calibration. Aggregated analysis was less time-consuming and focused on specific aspects due to the definition of the objective function. Finally, we discussed the risks and solutions for Daisy over-parameterisation as well as methods for parameter estimation based on information provided by the GSA.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127533"},"PeriodicalIF":4.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395811","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":"Synergy between aerated drip and biodegradable film enhances sustainable maize production in arid oasis","authors":"Yonghui Liang ,&nbsp;Mei Wu ,&nbsp;Jinzhu Zhang ,&nbsp;Zhanli Ma ,&nbsp;Yue Han ,&nbsp;Yue Wen ,&nbsp;Rui Chen ,&nbsp;Jian Liu ,&nbsp;Haiqiang Li ,&nbsp;Zhenhua Wang","doi":"10.1016/j.eja.2025.127535","DOIUrl":"10.1016/j.eja.2025.127535","url":null,"abstract":"<div><div>Biodegradable film (BF) is considered a promising and environmentally friendly alternative to polyethylene film (PE). However, its benefits for soil and crop growth are generally weaker than those of PE, particularly during the later stages of crop growth. In contrast, aerated drip irrigation demonstrates significant advantages in soil environment, carbon balance, and crop yield. To evaluate the feasibility of BF mulching under aerated drip irrigation, we examined soil volumetric water content, oxygen concentration, respiration, CO₂ emissions, maize photosynthetic characteristics, harvested biomass, yield, water use efficiency, and net carbon sequestration under aerated drip irrigation, non-aerated drip irrigation, PE mulching, BF mulching with 60-day and 100-day induction periods. The field experiment was conducted in Shihezi, Xinjiang, during the growing seasons of maize in 2021 and 2022. Results indicated that both aerated drip irrigation and BF mulching reduced shallow soil volumetric water content and enhanced soil oxygen concentration. Although BF mulching resulted in declines in maize growth, carbon balance, and economic indicators, aerated drip irrigation effectively mitigated these reductions. Aerated drip irrigation improved soil conditions, enhanced root biomass, and boosted agricultural productivity. Notably, both single indicator analysis and entropy-weighted TOPSIS evaluation revealed that aerated drip irrigation combined with BF mulching, featuring a 100-day induction period, ensured economic and ecological benefits comparable to those of PE mulching (<em>P</em> &gt; 0.05). This combination sustains economic benefits, improves soil conditions, preserves field carbon balance, mitigates residual plastic pollution, and supports the sustainable production of maize.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"165 ","pages":"Article 127535"},"PeriodicalIF":4.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388476","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":"UAV-based rice aboveground biomass estimation using a random forest model with multi-organ feature selection","authors":"Jing Shi ,&nbsp;Kaili Yang ,&nbsp;Ningge Yuan ,&nbsp;Yuanjin Li ,&nbsp;Longfei Ma ,&nbsp;Yadong Liu ,&nbsp;Shenghui Fang ,&nbsp;Yi Peng ,&nbsp;Renshan Zhu ,&nbsp;Xianting Wu ,&nbsp;Yan Gong","doi":"10.1016/j.eja.2025.127529","DOIUrl":"10.1016/j.eja.2025.127529","url":null,"abstract":"<div><h3>Background</h3><div>Aboveground biomass (AGB) is important for monitoring crop growth and field management. Accurate estimation of AGB helps refine field strategies and advance precision agriculture. Remote sensing with Unmanned Aerial Vehicles (UAVs) has become an effective method for estimating key parameters of rice.</div></div><div><h3>Methods</h3><div>This study involved four experiments conducted across varied locations and timeframes to collect field sampling data and UAV imagery. Feature extraction, including Vegetation Index (VI), textures, and canopy height, was performed. Key factors influencing biomass estimation across different rice organs were analyzed. Based on these insights, a Random Forest model was developed for AGB estimation.</div></div><div><h3>Results</h3><div>The VIS-Leaf factor-Spike factor-Stem factor (VIS-L-Sp-St) model proposed in this study outperformed traditional methods. The training set achieved an R² of 0.89 with a reduced RMSE of 191.30 g/m², surpassing the traditional VIS model (R²=0.64, RMSE=363.53 g/m²). Notably, in the validation set, the VIS-L-Sp-St model showed good transferability, with an R² of 0.85 and RMSE of 196.55 g/m², outperforming MLR (R²=0.02, RMSE=5944.09 g/m²), PLSR (R²=0.18, RMSE=934.27 g/m²) methods, BP (R²=0.14, RMSE=581.61 g/m²) method and SVM method((R²=0.45, RMSE=600.91 g/m²).</div></div><div><h3>Conclusions</h3><div>Sensitivity analysis showed that different rice organs respond differently to specific features. This insight improves feature selection efficiency and enhances AGB estimation accuracy. The organ-specific AGB estimation model highlights its potential to support precision agriculture and field management, contributing to advancements in agricultural research and application.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127529"},"PeriodicalIF":4.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376892","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":"Machine learning-driven analysis of greenhouse gas emissions from rice production in major Chinese provinces: Identifying key factors and developing reduction strategies","authors":"Songhua Huan ,&nbsp;Xiuli Liu","doi":"10.1016/j.eja.2025.127536","DOIUrl":"10.1016/j.eja.2025.127536","url":null,"abstract":"<div><div>Rice cultivation is a significant contributor to global greenhouse gas (GHG) emissions. However, the complex nonlinear relationship between driving factors and GHG emission intensity (GHGI) remains poorly understood, and effective reduction strategies are still needed. This study integrates machine learning models and SHapley Additive Explanations (SHAP) to assess the nonlinear relationship and design GHGI reduction strategies based on data from 14 provinces in China from 2012 to 2022. The key findings are as follows. (1) For GHGI reduction, the optimal conditions include an annual average sunshine duration of 47–75 days, an annual average temperature of 15.3–17.9℃, annual average precipitation levels of either 1000.0–1368.4 or 1680.0–2004.7 mm, soil pH below 5.6 or above 6.5, soil total nitrogen content of 17.0–20.3 g/kg, and soil organic carbon content of 15.0–22.5 g/kg. The recommended application rates for nitrogen, phosphate, and potassium fertilizers are 160.0–311.0 kg/ha, 124.9–129.9 kg/ha and 144.0–194.3 kg/ha, respectively. Agricultural practices such as transplanting, mixed farming, tillage and mid-season drainage demonstrate higher GHGI reduction potential compared to other measures. (2) For lowest-cost GHGI reduction strategies in major provinces, Heilongjiang, Jilin, and Liaoning provinces could reduce GHGI to 0.28, 0.15, and 0.05 tCO₂e/t, respectively, by adjusting sunshine conditions. Hainan, Guangdong, Fujian, Jiangsu, Jiangxi, Zhejiang and Guangxi provinces could achieve GHGI reductions to 0.62, 0.31, 0.21, 0.47, 0.57, 0.92 and 0.28 tCO₂e/t, respectively, by optimizing nitrogen fertilizer application and labor practices. Hunan and Anhui provinces could reduce GHGI to 0.57 and 0.85 tCO₂e/t by adjusting irrigation modes. Implementing these strategies would result in an average GHGI reduction of 28.75 %, although production costs per mu for early, mid-to-late indica and japonica rice in major provinces would increase by 28.87 %, 27.95 % and 27.38 %, respectively, compared to the original production costs. These findings provide valuable insights and a scientific basis for developing GHGI reduction strategies in rice production and enhancing the sustainability of this critical agricultural sector.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127536"},"PeriodicalIF":4.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing climate change impacts and adaptation strategies for key crops in the Republic of Moldova using the AquaCrop model","authors":"Jorge Alvar-Beltrán ,&nbsp;Andrea Setti ,&nbsp;Jane Mugo ,&nbsp;Nicolae Bucor ,&nbsp;Gherman Bejenaru ,&nbsp;Arianna Gialletti ,&nbsp;Ala Druta","doi":"10.1016/j.eja.2025.127530","DOIUrl":"10.1016/j.eja.2025.127530","url":null,"abstract":"<div><div>Impact-based models are an essential tool to better understand the effects of climate change on crop production and to aid in the adaptation planning processes. However, in the Republic of Moldova (RoM), there is limited integration of crop simulation findings into adaptation policies and plans (see National Adaptation Plan (NAP) adopted in 2024). To bring novelty to this topic, the Food and Agriculture Organization (FAO), in conjunction with the State Hydrometeorological Service and the State Commission for Plant Variety Testing of the RoM, aims to assess the effect of future climate on five crops of national importance (maize, tomatoes, sunflowers, green peas, and wheat). We use state-of-the-art climate (Coordinated Regional Downscaling Experiment (CORDEX) and Coordinated Output for Regional Evaluations (CORE)) and crop models (AquaCrop) for two climate change scenarios: Representative Concentration Pathways (RCPs) 2.6 and 8.5. Adaptation solutions across the RoM are explored by advancing or delaying the sowing dates and enhancing field management decisions by improving soil fertility and reducing weed stress. Statistically significant (<em>p &lt; 0.05</em>) higher yields are simulated when advancing the sowing date of maize and when growing medium cycle varieties as opposed to short cycle. A CO₂-enriched environment (RCP 8.5) leads to statistically significantly higher yields among C3 crops (wheat and green peas) but has detrimental effects on C4 crops (maize). Limiting climatic drivers include decreasing seasonal rainfall, a higher number of dry days and heat-stress conditions during the summertime, and, conversely, fewer cold days during the wintertime necessary for wheat vernalization. As a result, this research not only provides valuable insights for stakeholders mandated to provide evidence-based adaptation, such as the National Commission on Climate Change, but also uncovers potential adaptation solutions to mitigate the adverse effects of climate change.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127530"},"PeriodicalIF":4.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307191","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}