European Journal of Agronomy最新文献

{"title":"Non-destructive and on-site estimation of grape total soluble solids by field spectroscopy and stack ensemble learning","authors":"Hongyi Lyu ,&nbsp;Miles Grafton ,&nbsp;Thiagarajah Ramilan ,&nbsp;Matthew Irwin ,&nbsp;Eduardo Sandoval","doi":"10.1016/j.eja.2025.127558","DOIUrl":"10.1016/j.eja.2025.127558","url":null,"abstract":"<div><div>Accurately estimating the total soluble solids (TSS) of berries with a non-destructive method is crucial for wine grape growers if wine quality improvements are to be made. At present, the methods employed with the best statistical results are implemented under stable lab conditions, using spectroscopic analysis in the visible-near infrared (VNIR) region. This study explores using field spectroscopy to estimate the TSS of berries directly in the vineyard. A portable visible-near infrared-shortwave infrared (VNIR-SWIR) spectroradiometer measured the reflectance data of grape berries in the 350–2500 nm spectral region. A large in-field multi-season spectral database (<em>n</em> = 1830) over two years (2023–2024) from three ‘Pinot Noir’ commercial vineyards were selected to develop spectral-region specific (VNIR, SWIR or VNIR-SWIR) machine learning models. Different machine learning modeling pipelines were built using data collected from 2023 and validated using data from 2024 to predict grape TSS based on in-field spectral databases. Subsequently, the performance of using stack ensemble learning (ES) to predict grape TSS was evaluated and compared with three commonly used methods: K-nearest neighbors (KNN), random forest regression (RFR), and support vector regression (SVR). The result on the independent test set showed that, the ES model based on MSC+SG+ 1D spectral data, in the VNIR-SWIR region provided the highest prediction accuracy for grape TSS value, with a coefficient of determinations (<em>R</em>²) of 0.815, root mean square error (RMSE) of 1.131 °Brix, and a ratio of performance to deviation (RPD) of 2.236, with a Lin’s concordance correlation coefficient (CCC) of 0.897. This study demonstrated the potential of using an ES model to assess the grape TSS rapidly and non-destructively from field spectroscopy data.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"167 ","pages":"Article 127558"},"PeriodicalIF":4.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446118","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":"Leaf phosphorus concentration as a diagnostic tool for predicting grape composition in subtropical viticulture","authors":"Lincon Stefanello ,&nbsp;Raissa Schwalbert ,&nbsp;Rai Schwalbert ,&nbsp;Filipe Nunes ,&nbsp;Luana Garlet ,&nbsp;Lucas Dotto ,&nbsp;Amanda Krug ,&nbsp;Matheus Kulmann ,&nbsp;Ignacio Ciampitti ,&nbsp;Gustavo Brunetto","doi":"10.1016/j.eja.2025.127555","DOIUrl":"10.1016/j.eja.2025.127555","url":null,"abstract":"<div><div>In subtropical viticulture, grapevines are cultivated in soils with low phosphorus (P) availability, requiring P fertilization to enhance their productivity. However, the relationship between grapevine yield and grape quality remains a matter of discussion. This study aimed to (i) investigate the effect of different rates of P application on grapevine yield and grape chemical composition, (ii) identify the trade-off between grapevine yield and grape composition, and (iii) characterize leaf P dynamics in response to P supply and use foliar P as a tool to predict grape composition. The investigation was carried out in southern Brazil, from 2016 to 2020, on 'Chardonnay' and 'Pinot Noir' (<em>Vitis vinifera</em> L.), fertilized with the following rates of (0, 10, 20, 40, 60, and 100 kg P₂O₅ ha⁻¹) applied from 2011 to 2015. Hierarchical Bayesian models were used to obtain the most probable relationship between yield and P concentration in leaves for each grapevine cultivar. 'Chardonnay' and 'Pinot Noir' achieved their highest probable yields at 40 and 60 kg P₂O₅ ha⁻¹, respectively. However, high grape yields triggered lower berry total soluble solids, total titratable acidity, pH; polyphenols, and anthocyanins. ‘Pinot Noir’ presented more stability for maintaining grape composition than ‘Chardonnay’ when compared at similar yield levels. Finally, the leaf P concentration at flowering resulted in an adequate indicator of soluble solids in berries, with a linear plateau association and a breakpoint for leaf P at 2.6 g P kg⁻¹. These outcomes will assist the winegrower by anticipating the characteristics of grape products.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"167 ","pages":"Article 127555"},"PeriodicalIF":4.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454613","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":"Best cultivar: Optimization of maturity group classification for reaching soybean yield potential","authors":"Eduardo Lago Tagliapietra ,&nbsp;José Eduardo Minussi Winck ,&nbsp;Nereu Augusto Streck ,&nbsp;Michel Rocha Da Silva ,&nbsp;Alexandre Ferigolo Alves ,&nbsp;Darlan Scapini Balest ,&nbsp;Cristian Savegnago ,&nbsp;Marcos Dalla Nora ,&nbsp;Romulo Pulcinelli Benedetti ,&nbsp;Leonardo Silva Paula ,&nbsp;Victória Brittes Inklman ,&nbsp;Alencar Junior Zanon","doi":"10.1016/j.eja.2025.127556","DOIUrl":"10.1016/j.eja.2025.127556","url":null,"abstract":"<div><h3>Context</h3><div>The expansion of the sowing season, motivated by the search for greater yield potential and higher efficiency of the production system, results that the classification system of relative maturity groups cannot satisfactorily capture the interaction between genotype and environment in a subtropical environment. Knowledge of the duration of the crop development cycle at the time of sowing, combined with the optimal agronomic cycle, makes it possible to optimize the positioning of cultivars and increase crop yield.</div></div><div><h3>Objective</h3><div>The objective of this study was twofold: (i) to propose optimization in maturity group classification using adjusted 3rd degree polynomial equations have been fundamental to understanding the developmental cycle of soybean in relation to sowing date. in the maturity group classification system taking into account the sowing date to achieve yield potential and (ii) to develop a framework for indicating soybean cultivars based on the optimal agronomic cycle for Southern Brazil.</div></div><div><h3>Methods</h3><div>A database containing 72 field experiments in eleven agricultural years (from 2010 to 2011–2022–2023), covering 44 municipalities in Southern Brazil, was used to estimate the development cycle and the optimal agronomic cycle depending on the sowing date. To better represent the development cycle of soybean as a function of sowing date, the maturity group classification system was further developed, using adapted 3rd order polynomial equations.</div></div><div><h3>Results and conclusions</h3><div>The optimal agronomic cycle ranged from 141 to 111 days. A framework to indicate soybean cultivars based on the optimal agronomic cycle resulted in the Best Cultivar software, which serves as a tool for the best positioning of cultivars at the sowing times and thus maximizes the interaction between genotype and environment.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"166 ","pages":"Article 127556"},"PeriodicalIF":4.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427721","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":"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}