European Journal of Agronomy最新文献_第3页

An agent-based model to simulate field-specific nitrogen fertilizer applications in grasslands

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-13 DOI: 10.1016/j.eja.2025.127539

A. Kaim , T.M. Schmitt , S.H. Annuth , M. Haensel , T. Koellner

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.

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引用次数: 0

Enhanced coordination of photosynthetic functions among cotton boll–leaf systems to maintain boll weight under high-density planting

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-12 DOI: 10.1016/j.eja.2025.127540

Minzhi Chen , Yinhua Yan , Fubin Liang , Jinyu An , Yuxuan Wang , Jingshan Tian , Yali Zhang , Chuangdao Jiang , Wangfeng Zhang

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 (Gossypium hirsutum 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 (>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 (<25 plants m⁻²). Under high densities (>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.

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引用次数: 0

Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-12 DOI: 10.1016/j.eja.2025.127533

Laura Delhez , Benjamin Dumont , Bernard Longdoz

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.

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引用次数: 0

Synergy between aerated drip and biodegradable film enhances sustainable maize production in arid oasis

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-11 DOI: 10.1016/j.eja.2025.127535

Yonghui Liang , Mei Wu , Jinzhu Zhang , Zhanli Ma , Yue Han , Yue Wen , Rui Chen , Jian Liu , Haiqiang Li , Zhenhua Wang

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 (P > 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.

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引用次数: 0

UAV-based rice aboveground biomass estimation using a random forest model with multi-organ feature selection

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-10 DOI: 10.1016/j.eja.2025.127529

Jing Shi , Kaili Yang , Ningge Yuan , Yuanjin Li , Longfei Ma , Yadong Liu , Shenghui Fang , Yi Peng , Renshan Zhu , Xianting Wu , Yan Gong

Background

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.

Methods

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.

Results

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).

Conclusions

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.

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引用次数: 0

Machine learning-driven analysis of greenhouse gas emissions from rice production in major Chinese provinces: Identifying key factors and developing reduction strategies

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-06 DOI: 10.1016/j.eja.2025.127536

Songhua Huan , Xiuli Liu

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.

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引用次数: 0

Assessing climate change impacts and adaptation strategies for key crops in the Republic of Moldova using the AquaCrop model

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-06 DOI: 10.1016/j.eja.2025.127530

Jorge Alvar-Beltrán , Andrea Setti , Jane Mugo , Nicolae Bucor , Gherman Bejenaru , Arianna Gialletti , Ala Druta

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 (p < 0.05) 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.

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引用次数: 0

Refining the soil and water component to improve the MoSt grass growth model

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-06 DOI: 10.1016/j.eja.2025.127520

L. Bonnard , L. Delaby , M. O’Donovan , M. Murphy , E. Ruelle

Knowledge of previous and future grass growth is an important factor for grassland management decision making. It allows the farmer to predict the availability of grass for the herd on a short-term basis and adapt grassland management practise accordingly. The Moorepark St Gilles Grass Growth Model (MoSt GG) is used to predict grass growth weekly on 84 grassland farms across Ireland. The repeated use of the model on these farms has identified areas for improvement that have been addressed in this paper. Among these improvements, the soil sub-model component has been further developed to better represent different soil types and to account for different soil depths, improving the simulations of water and soil nitrogen fluxes (V2_V1_+soil). A soil sub-layer of 10 cm was added to better simulate growth recovery after a drought period (V3_V2+water). The radiation component was improved by including the day length in the grass growth estimation (V4_V3+rad) instead of only accounting for daily cumulative solar radiation. These improvements were evaluated against several experiments conducted in Ireland and France. The developments improved model accuracy for every experiment evaluated. The RMSE in the original version of the model ranged from 322 to 1011 kg of DM/ha, whereas in the latest version of the MoSt GG model (V4_V3+rad), the RMSE ranged from 312 to 671 kg of DM/ha. The further consideration of soil characteristics resulted in a higher variability in grass production and N leaching depending on soil type and weather conditions, leading to improved growth trend representation. The addition of the soil sub-layer (V3_V2+water) improved the accuracy in drier years (French experiment) due to the more realistic grass growth recovery after a drought. The latest version of the model (V4_V3+rad) simulates grass production more accurately than the previous versions and increases the reliability of grass growth prediction.

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引用次数: 0

I-DRo: A new indicator to assess spatiotemporal diversity and ecosystem services of crop rotations

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-05 DOI: 10.1016/j.eja.2025.127531

Olivier Keichinger , Loïc Viguier , Guénaëlle Corre-Hellou , Antoine Messéan , Frédérique Angevin , Christian Bockstaller

Agroecological farming systems depend on ecosystem services (ES) to replace external anthropogenic inputs. Crop diversification is increasingly being put forward as a way to support ES service provision. Actors involved in promoting, designing and implementing diversified cropping systems presumably need to assess the degree of diversity and performance of diversified cropping systems at one stage or another. This article focuses on crop rotation diversity since it is a core component of annual cropping systems. In this study, we designed a new global indicator (I-DRo) based on a set of other indicators used to assess the temporal diversity of crop rotation (which includes functional diversity through ES provision,) as well as taxonomic diversity and spatial diversity. I-DRo covers various crop diversification strategies in the rotation: introduction of new crops or cover crops, multiple cropping, intercropping, relay cropping and strip cropping. The proposed indicators may be used separately or aggregated in a hierarchical way according to a fuzzy decision tree. Using I-DRo requires only data on field width and the main crop and cover crop species. Initial tests showed the indicator could potentially be used to support various actors in their decision-making, although results on the predictive quality were mixed given the degree of simplification. Contextualizing the calculation method for ES assessment would be one avenue of investigation. Lastly, use at EU level could support the implementation of new cross-compliance measures on crop diversification, but would require efforts to harmonize data on main crops and cover crops.

{"title":"I-DRo: A new indicator to assess spatiotemporal diversity and ecosystem services of crop rotations","authors":"Olivier Keichinger , Loïc Viguier , Guénaëlle Corre-Hellou , Antoine Messéan , Frédérique Angevin , Christian Bockstaller","doi":"10.1016/j.eja.2025.127531","DOIUrl":"10.1016/j.eja.2025.127531","url":null,"abstract":"<div><div>Agroecological farming systems depend on ecosystem services (ES) to replace external anthropogenic inputs. Crop diversification is increasingly being put forward as a way to support ES service provision. Actors involved in promoting, designing and implementing diversified cropping systems presumably need to assess the degree of diversity and performance of diversified cropping systems at one stage or another. This article focuses on crop rotation diversity since it is a core component of annual cropping systems. In this study, we designed a new global indicator (I-DRo) based on a set of other indicators used to assess the temporal diversity of crop rotation (which includes functional diversity through ES provision,) as well as taxonomic diversity and spatial diversity. I-DRo covers various crop diversification strategies in the rotation: introduction of new crops or cover crops, multiple cropping, intercropping, relay cropping and strip cropping. The proposed indicators may be used separately or aggregated in a hierarchical way according to a fuzzy decision tree. Using I-DRo requires only data on field width and the main crop and cover crop species. Initial tests showed the indicator could potentially be used to support various actors in their decision-making, although results on the predictive quality were mixed given the degree of simplification. Contextualizing the calculation method for ES assessment would be one avenue of investigation. Lastly, use at EU level could support the implementation of new cross-compliance measures on crop diversification, but would require efforts to harmonize data on main crops and cover crops.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127531"},"PeriodicalIF":4.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307193","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}

引用次数: 0

Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-02-04 DOI: 10.1016/j.eja.2025.127534

Takashi S.T. Tanaka, René Gislum

The critical nitrogen dilution curve (CNDC) and associated nitrogen nutrition index (NNI) are known to provide valuable information indicating whether the crops are experiencing luxury nitrogen (N) uptake—where they absorb more N than needed for optimal growth— or suffering from N insufficiency, where they fail to meet their optimal growth requirements. The aim of this study was to explore the potential of using UAV-based remote sensing and weather data to quantify NNI in a winter wheat crop. For that purpose, field trials with different N application strategies were conducted over three cropping seasons. The calibrated CNDC used in this study showed a better performance in detecting yield reduction caused by the N insufficiency compared to using a CNDC developed in a previous study (default CNDC). Machine learning models (i.e., random forest and partial least squares regression) were used to predict shoot biomass, N concentration, and NNI. The results showed that machine learning models could predict crop N status at medium or high accuracies (R²: 0.59–0.95). However, the default NNI predictions based on UAV data consistently indicated N insufficiency even when the crop was not suffering from N insufficiency. Whereas the calibrated NNI predictions occasionally could detect a reduction in yield caused by N deficiency. Robustness and scalability of the CNDC have rarely been discussed but based on our findings we suggest testing whether the preferred CNDC should be calibrated for a specific cultivar or region is particularly important when using remote sensing technologies for nondestructive N status measurements.

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引用次数: 0