European Journal of Agronomy最新文献_第6页

Monitoring crop leaf area index using improved global structure-from-motion and multi-feature data fusion on a phenotyping robot 利用改进的全局运动结构和多特征数据融合在表型机器人上监测作物叶面积指数

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-01-02 DOI: 10.1016/j.eja.2025.127974

Miao Su , Weixing Cao , Yaze Yun , Peng Xia , Yue Guo , Xiangming Zhou , Chongya Jiang , Jie Jiang , Yan Zhu , Xia Yao , Dong Zhou

Reconstruction of crop three-dimensional (3D) point clouds is essential for monitoring phenotypic parameters, like plant height and leaf area index (LAI), which is a critical phenotype predictor for smart crop breeding. The main 3D reconstruction technologies include image-based approaches, laser scanning, and depth camera methods. Among these methods, image-based structure-from-motion (SfM) is widely used due to its low cost and high accuracy. However, field crop canopy image data for high-resolution point cloud construction are often large-scale, unordered, and uncalibrated. Conventional SfM methods struggle with 3D reconstruction due to high computational costs and long processing times, delaying phenotypic analysis. To address this issue, we developed an improved global SfM algorithm, which increases the point cloud reconstruction speed by an average of 1.39 times compared to traditional incremental SfM methods and by more than 10 % on average compared to two mainstream global SfM algorithms. In addition, we integrated three types of predictors, point cloud features, color indices and texture features, through multi-feature data fusion and machine learning. A random forest algorithm for the prediction of LAI for a combined data set of four different crops, and using all three categories of predictors, achieved higher monitoring accuracy compared to using a single feature category (R²=0.78 vs R²=0.71–0.74). This new method, which includes an improved global SfM algorithm and a three-predictor fusion-based LAI monitoring approach, offers an efficient and reliable solution for precise crop phenotyping and continuous growth monitoring in complex field environments, enabling accurate assessment of crop morphology and developmental dynamics.

作物三维点云的重建是监测作物表型参数的必要条件，如株高和叶面积指数（LAI），这是智能作物育种的重要表型预测因子。主要的三维重建技术包括基于图像的方法、激光扫描和深度相机方法。其中，基于图像的运动结构法（SfM）以其成本低、精度高等优点得到了广泛的应用。然而，用于高分辨率点云构建的农田作物冠层图像数据往往是大规模的、无序的和未校准的。由于计算成本高，处理时间长，延迟了表型分析，传统的SfM方法难以进行3D重建。为了解决这个问题，我们开发了一种改进的全局SfM算法，与传统的增量SfM方法相比，该算法将点云重建速度平均提高了1.39倍，与两种主流的全局SfM算法相比，平均提高了10 %以上。此外，我们通过多特征数据融合和机器学习，集成了点云特征、颜色指数和纹理特征三种类型的预测因子。与使用单一特征类别相比，用于预测四种不同作物组合数据集的LAI的随机森林算法（R²=0.78 vs R²= 0.71-0.74）取得了更高的监测精度。该新方法包括改进的全局SfM算法和基于三预测因子融合的LAI监测方法，为复杂田间环境下的精确作物表型和连续生长监测提供了高效可靠的解决方案，能够准确评估作物形态和发育动态。

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引用次数: 0

Development and validation of a crop yield prediction framework accounting for regional heterogeneity: A case study of spring maize in Jilin Province 考虑区域异质性的作物产量预测框架的构建与验证——以吉林省春玉米为例

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-01-02 DOI: 10.1016/j.eja.2025.127977

Jun Gao , Daming Dong , Wenjiang Huang , Yingying Dong , Tongren Xu , Chongya Jiang , Kun Wang

Accurate and timely crop-yield prediction is essential for ensuring food security, managing agricultural risk, and supporting policy formulation. To address the respective limitations of traditional crop growth models and deep learning methods under complex environmental conditions, a hybrid modeling framework is proposed that integrates remote-sensing data assimilation, a process-based crop growth model, and deep learning techniques. Using spring maize in Jilin Province, China (2015–2020) as the case study, leaf area index (LAI) retrieval accuracy is first improved by coupling the PROSAIL model with machine-learning algorithms. A complete meteorological sequence for the target year is then constructed using a dynamic time warping (DTW) algorithm to overcome early-season prediction challenges caused by missing real-time weather data. Retrieved LAI is assimilated into the WOFOST crop growth model through an ensemble Kalman filter (ENKF) to calibrate state variables and enable dynamic yield prediction across growth stages. Finally, a deep learning model (Convolutional Neural Network–Attention Long Short-Term Memory with Multi-Task Learning, CNN-ALSTM-MTL) is developed to fuse assimilation outputs with multi-source heterogeneous data, leveraging multi-task learning to enhance adaptability to regional heterogeneity and improve yield prediction performance at the regional scale. Assimilation is found to substantially improve maize-yield estimation, increasing R² by 0.2 and reducing RMSE by 276 kg ha⁻¹. Compared with the assimilated crop growth model alone, the hybrid framework further increases R² by 35 % and decreases RMSE by 23 % by hierarchically capturing feature information relevant to maize-yield estimation. The best performance is achieved during the key growth stage (jointing to tasseling stage), with an R² of 0.75 and an RMSE of 592 kg ha⁻¹, enabling reliable yield prediction approximately two months before harvest. This framework demonstrates potential for cross-crop and cross-regional applications and provides robust methodological support for regional-scale yield forecasting and food-security early warning.

准确、及时的作物产量预测对于确保粮食安全、管理农业风险和支持政策制定至关重要。针对传统作物生长模型和深度学习方法在复杂环境条件下各自存在的局限性，提出了一种融合遥感数据同化、基于过程的作物生长模型和深度学习技术的混合建模框架。以中国吉林省春玉米（2015-2020）为例，首先将PROSAIL模型与机器学习算法相结合，提高了叶面积指数（LAI）的检索精度。然后使用动态时间规整（DTW）算法构建目标年的完整气象序列，以克服由于缺少实时天气数据而导致的早期季节预测挑战。通过集合卡尔曼滤波（ensemble Kalman filter， ENKF）将反演到的LAI同化到WOFOST作物生长模型中，校准状态变量，实现跨生长阶段的动态产量预测。最后，建立了卷积神经网络-注意长短期记忆与多任务学习的深度学习模型（CNN-ALSTM-MTL），将同化输出与多源异构数据融合，利用多任务学习增强对区域异质性的适应性，提高区域尺度上的产量预测性能。发现同化能显著改善玉米产量估算，使R²增加0.2，使RMSE减少276 kg ha⁻¹。与单独同化的作物生长模型相比，混合框架通过分层捕获与玉米产量估算相关的特征信息，进一步提高R²35 %，降低RMSE 23 %。在生育关键期（拔节至抽雄期）表现最佳，R²为0.75，RMSE为592 kg ha - 1，可以在收获前两个月左右进行可靠的产量预测。该框架显示了跨作物和跨区域应用的潜力，并为区域尺度的产量预测和粮食安全预警提供了强有力的方法支持。

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引用次数: 0

From soil stress to sustainable solutions: A bibliometric and systematic review of plant growth promoting rhizobacteria in climate-resilient agriculture 从土壤胁迫到可持续解决方案：气候适应型农业中促进根瘤菌植物生长的文献计量学和系统综述

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-01-02 DOI: 10.1016/j.eja.2025.127972

Deepak Kumar , Meenakshi Suhag , Deepak Malik

Due to the change of climate conditions and excessive use of xenobiotic compounds, soil fertility is compromised, which ultimately affects the plant-soil interaction, resulting in nutrient imbalances, reduced photosynthesis, decreased plant height and other metabolic disruptions. Plant Growth Promoting Rhizobacteria (PGPR) inoculants are considered one of the solutions for sustainable farming techniques that enhance crop productivity while conserving the ecosystem. This work uses bibliometric and systematic literature review methodologies to examine the recent interest in the scientific communities for mitigating environmental stresses. By evaluating 284 articles in the Web of Science (WoS) database from 2015 to 2024. Keywords co-occurrence analysis revealed that PGPRs enhance plant growth by improving nutrient uptake, increasing stress tolerance, producing phytohormones, activating key stress-tolerance genes, and facilitating phytoremediation and metal detoxification. The study analysis depicts a growing shift towards sustainable farming techniques that enhance crop productivity while conserving the ecosystem by using PGPR-based inoculants, as compared to synthetic fertilizers and pesticides. PGPR enhances plant growth through direct and indirect mechanisms, which are elaborated upon in this work. The positive role of PGPR on plant growth development and yield is also highlighted. Challenges and limitations of PGPR in agriculture are also discussed. There is a growing shift toward multi-strain consortia that offer broader and more stable benefits compared to single isolates. Advances in omics technologies are enabling a deeper understanding of PGPR plant-soil interactions and guiding the selection of highly efficient strains. Researchers are also emphasizing improved inoculant formulations, including encapsulation and biofilm/EPS-based approaches for better root colonization and field performance. Further research work in the area of PGPR-based products will be essential for maximizing their effectiveness in diverse farming environments for sustainable farming in the future.

由于气候条件的变化和外源化合物的过量使用，土壤肥力受到损害，最终影响植物与土壤的相互作用，导致养分失衡、光合作用减少、植物高度下降等代谢紊乱。促进植物生长的根瘤菌（PGPR）接种剂被认为是可持续农业技术的解决方案之一，可以提高作物生产力，同时保护生态系统。这项工作使用文献计量学和系统的文献综述方法来检查科学界最近对减轻环境压力的兴趣。通过对Web of Science （WoS）数据库2015 - 2024年的284篇论文进行评价。共现分析表明，PGPRs通过改善植物养分吸收、提高植物的抗逆性、产生植物激素、激活关键抗逆性基因、促进植物修复和金属解毒等途径促进植物生长。该研究分析表明，与合成肥料和农药相比，可持续农业技术正日益转向使用基于pgpr的接种剂来提高作物生产力，同时保护生态系统。PGPR通过直接和间接的机制促进植物生长，这些机制在本工作中得到了详细阐述。强调了PGPR对植物生长发育和产量的积极作用。讨论了PGPR在农业中的挑战和局限性。与单一菌株相比，多菌株联合体提供更广泛和更稳定的好处。组学技术的进步使人们能够更深入地了解PGPR植物与土壤的相互作用，并指导高效菌株的选择。研究人员还强调了接种剂配方的改进，包括包封和基于生物膜/ eps的方法，以获得更好的根定植和田间表现。进一步开展基于pgpr产品的研究工作对于在未来的可持续农业中最大限度地提高其在不同农业环境中的有效性至关重要。

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引用次数: 0

Modeling mustard water use and its effects on soil water content and subsequent maize performance under projected climate scenarios 模拟预测气候情景下芥菜水分利用及其对土壤水分含量和后续玉米生产性能的影响

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-01-02 DOI: 10.1016/j.eja.2025.127971

Uzair Ahmad , Xuejun Dong , Shah Jahan Leghari , Gerrit Hoogenboom

Mustard (Brassica spp.) grown as a cover crop retains root-zone soil water; however, if terminated late, it may lead to excessive water uptake. We calibrated and evaluated the decision support system for agrotechnology transfer (DSSAT) using field data from 2019 to 20 to simulate soil water content (SWC), leaf area index (LAI), aboveground biomass (AGB), and grain yield for maize (Zea mays L.) in southwest Texas. We applied DSSAT with climate projections from seven global climate models (GCMs) to assess soil water dynamics and crop responses under projected climate scenarios. The model accurately calibrated and evaluated LAI for maize (RMSE = 0.28) and mustard (RMSE = 1.42), and AGB for maize (RMSE = 1092.51 kg ha⁻¹) and mustard (RMSE = 772.54 kg ha⁻¹). Simulated SWC matched observed values in 2019 and closely followed field observations at 10 cm depth in 2020, confirming the model's sensitivity to root-zone moisture dynamics. Future climate impacts were assessed using Seasonal Analysis tool with bias-corrected projections from seven GCMs under RCP 4.5 and RCP 8.5. Results showed that maize yield is projected to peak in 2050 under RCP 4.5 and declined under RCP 8.5. Mustard cover cropping improved SWC (0.29 m³ m⁻³) and subsequently maize yield under moderate to high rainfall scenarios, but had neutral effects under drier conditions (SWC as 0.13 m³ m⁻³). It is critical to maintain SWC above 0.27 m³ m⁻³ for maize yield stability in this region. Maize irrigation demand is projected to increase 20 % by 2100. Sensitivity analysis showed that maize yield is strongly influenced by genetic parameters such as P1, P5, and G2 that regulate phenology and grain filling duration. Our study recommends targeted irrigation at flowering and physiological maturity in maize to optimize WUE, while demonstrating that projected warming and altered rainfall patterns are expected to intensify soil moisture deficits in mustard–maize rotation.

作为覆盖作物种植的芥菜（芸苔属）保持根区土壤水分；然而，如果终止较晚，则可能导致过量的水分吸收。利用2019 - 20年的田间数据，对美国德克萨斯州西南部玉米（Zea mays L.）的土壤含水量（SWC）、叶面积指数（LAI）、地上生物量（AGB）和籽粒产量进行了模拟，对农业技术转移决策支持系统（DSSAT）进行了校准和评估。我们将DSSAT与七个全球气候模式（GCMs）的气候预估相结合，评估了预估气候情景下的土壤水分动态和作物响应。该模型对玉米（RMSE = 0.28）和芥菜（RMSE = 1.42）的LAI以及玉米（RMSE = 1092.51 kg ha−1）和芥菜（RMSE = 772.54 kg ha−1）的AGB进行了精确校准和评估。模拟的SWC与2019年的观测值相匹配，并与2020年10 cm深度的野外观测值密切相关，证实了模型对根区水分动态的敏感性。利用季节分析工具对RCP 4.5和RCP 8.5下七个gcm的偏差校正预估进行了未来气候影响评估。结果表明，在RCP 4.5条件下，2050年玉米产量将达到峰值，而在RCP 8.5条件下，玉米产量将下降。在中至高降雨条件下，芥菜覆盖种植提高了SWC(0.29 m3 m−3)，随后提高了玉米产量，但在干旱条件下没有任何影响（SWC为0.13 m3 m−3）。维持该地区0.27 m3 m−3以上的SWC对玉米产量稳定至关重要。预计到2100年，玉米灌溉需求将增加20% %。敏感性分析表明，调控物候和灌浆期的遗传参数P1、P5和G2对玉米产量的影响较大。我们的研究建议在玉米开花和生理成熟时进行有针对性的灌溉，以优化水分利用效率，同时表明预计的变暖和降雨模式的改变预计会加剧芥末-玉米轮作中土壤水分不足的情况。

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引用次数: 0

Process-based assessment of wheat yield gap in Mediterranean rainfed conditions: Cultivar, nutrient, and sowing optimization 地中海雨养条件下小麦产量差距的过程评估：品种、养分和播种优化

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-30 DOI: 10.1016/j.eja.2025.127967

Lahcen Ousayd , El Houssaine Bouras , Achraf Mamassi , Abdelghani Chehbouni , Victor Ongoma

Wheat production in Morocco remains constrained by persistent yield gaps arising from climatic variability, soil fertility decline, and suboptimal farm management. This study aimed to quantify attainable and actual yields of bread wheat (Triticum aestivum L.) and to identify management pathways for reducing these gaps under rainfed conditions in Morocco. The Environmental Policy Integrated Climate (EPIC) model was calibrated and validated for five representative cultivars across major rainfed agroecological zones using multi-season field observations. Model evaluation showed high predictive accuracy for grain yield (R² = 0.87, RMSE = 0.25 t/ha, NRMSE = 12.5 %), above-ground biomass (R² = 0.93, RMSE = 1.2 t/ha, NRMSE = 8.7 %), and leaf area index (R² = 0.81, RMSE = 0.73, NRMSE = 15.2 %). Simulated potential yield (

Y_{p}

) averaged 5.4 t/ha, of which 66 % was attainable under water-limited rainfed conditions (

Y_{w}

= 3.6 t/ha) and 48 % was realized as observed farmer yield (

Y_{a}

= 2.6 t/ha), resulting in an exploitable yield gap of approximately 52 % (2.8 t/ha). Scenario analysis indicated yield gains of 25–35 % from improved nitrogen use and 30–40 % from supplemental irrigation, while integrated management closed 40–65 % of the exploitable gap and raised mean yields to 3.8–4.2 t/ha. Across management strategies, intermediate-input scenarios delivered strong economic performance, achieving marginal rate of return values of 257–433 % with modest additional costs (266–1389 MAD/ha). These findings demonstrate that targeted integration of water and nutrient management can substantially enhance wheat productivity and resilience, offering a practical framework for advancing sustainable intensification and national food self-sufficiency.

摩洛哥的小麦生产仍然受到气候变化、土壤肥力下降和不理想的农场管理造成的持续产量差距的制约。本研究旨在量化面包小麦（Triticum aestivum L.）的可获得和实际产量，并确定在摩洛哥雨养条件下减少这些差距的管理途径。通过多季野外观测，对主要雨养农业生态区5个代表性品种的环境政策综合气候（EPIC）模型进行了标定和验证。模型对粮食产量（R²= 0.87,RMSE = 0.25 t/ha, NRMSE = 12.5 %）、地上生物量（R²= 0.93,RMSE = 1.2 t/ha, NRMSE = 8.7 %）和叶面积指数（R²= 0.81,RMSE = 0.73, NRMSE = 15.2 %）具有较高的预测精度。模拟潜在产量（Yp）平均为5.4吨/公顷，其中66%是在限水雨养条件下实现的（Yw = 3.6吨/公顷），48%是根据观察到的农民产量实现的（Ya = 2.6吨/公顷），导致可利用产量差距约为52%（2.8吨/公顷）。情景分析表明，改善氮肥利用可使产量增加25 - 35%，补充灌溉可使产量增加30 - 40%，而综合管理可弥补40 - 65%的可利用缺口，将平均产量提高到3.8-4.2吨/公顷。在各种管理策略中，中等投入方案带来了强劲的经济表现，以适度的额外成本（266-1389 MAD/ha）实现了257 - 433%的边际回报率。这些发现表明，有针对性地整合水和养分管理可以大大提高小麦的生产力和抵御力，为推进可持续集约化和国家粮食自给提供了一个实用框架。

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引用次数: 0

Legume-cereal mixed culture as green manure enhanced the yield stability of baby Chinese cabbage via disease suppressing 豆粕混交绿肥通过抑制病害提高了小白菜产量的稳定性

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-29 DOI: 10.1016/j.eja.2025.127956

Zhilong Fan , Yunyou Nan , Wen Yin , Falong Hu , Cai Zhao , Hong Fan , Xiaohua Yan , Weidong Cao , Qiang Chai

Global agriculture must enhance productivity while mitigating environmental degradation, particularly in intensive vegetable systems vulnerable to soil-borne diseases and nutrient imbalances. A seven-year field study (2018–2024) in Northwest China’s arid irrigation region was conducted to investigate the efficacy of legume-cereal green manure mixed cultures in suppressing the soft rot and the tipburn in baby Chinese cabbage (Brassica rapa subsp. Pekinensis cv. ‘Wawacai’), while improving soil health and yield stability. Six green manure regimes—common vetch (Vicia sativa L.) (CV), hairy vetch (Vicia vilosa Roth.) (HV), barley (Hordeum vulgare L.) (BL), and their mixed cultures (CV×BL, CV×HV, HV×BL)—were evaluated against post-harvest fallow (CF) in a randomized block design. The CV×BL emerged as the most effective intervention, significantly reducing the incidence rate by 20.7–72.4 % for soft rot and by 27.5–80.2 % for tipburn compared to CF, outperforming monocultures and other mixed cultures. Structural equation modeling revealed that yield stability was not only due to direct growth promotion from improved soil properties, but was substantially driven by the effective suppression of soft rot and tipburn. Consequently, CV×BL significantly increased yield by 22.4 % and improved yield stability by 3.6-fold relative to CF. These findings establish legume-cereal mixtures as sustainable alternatives to chemical-intensive practices, effectively addressing soil degradation and disease pressure in arid intensive systems. The common vetch and barley mixed culture as green manure specifically offers a scalable solution for reconciling productivity and sustainability in vegetable production through its dual capacity for disease suppression and yield stabilization.

全球农业必须在提高生产力的同时减轻环境退化，特别是在易受土壤传播疾病和养分失衡影响的集约化蔬菜系统中。在西北干旱灌区进行了为期7年（2018-2024）的豆豆-谷物绿肥混合培养对小白菜软腐病和赤烧病的防治效果研究。学报的履历。‘ Wawacai ’)，同时改善土壤健康和产量稳定性。六种绿肥方案-普通豌豆（Vicia sativa L.）（CV），毛豌豆（Vicia vilosa Roth）。（HV），大麦（Hordeum vulgare L.）（BL）及其混合培养物（CV×BL, CV×HV, HV×BL）在收获后休耕（CF）中进行随机区组设计。CV×BL是最有效的干预措施，与CF相比，软腐病的发病率显著降低20.7-72.4 %，烧伤的发病率显著降低27.5-80.2 %，优于单一培养和其他混合培养。结构方程模型表明，产量稳定不仅是由于土壤性质的改善直接促进了生长，而且在很大程度上是由有效抑制软腐病和倒烧所驱动的。因此，CV×BL与CF相比，产量显著提高22.4% %，产量稳定性提高3.6倍。这些发现确立了豆类-谷物混合物作为化学密集型做法的可持续替代品，有效解决干旱集约化系统中的土壤退化和疾病压力。作为绿肥的普通豌豆和大麦混合栽培通过其抑制疾病和稳定产量的双重能力，为协调蔬菜生产的生产力和可持续性提供了一种可扩展的解决方案。

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引用次数: 0

Impacts of atmospheric CO2 enrichment on nitrous oxide emissions in wheat and rice cropping systems at global and local scales 大气CO2富集对全球和地方尺度小麦和水稻种植系统氧化亚氮排放的影响

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-27 DOI: 10.1016/j.eja.2025.127970

Shengji Yan , Guiyao Zhou , Daniel Revillini , Yunlong Liu , Haoyu Qian , Aixing Deng , Yanfeng Ding , Yu Jiang , Manuel Delgado-Baquerizo , Xin Zhang , Weijian Zhang

Human activity is boosting atmospheric carbon dioxide (CO₂), which further compounds the contributions to climate change through interaction effects with other greenhouse gases, especially nitrous oxide (N₂O). However, the global-scale role of elevated CO₂ (eCO₂) in shaping N₂O emissions across major cereal systems remains insufficiently studied. This effect can be especially important in cereal crops such as rice and wheat, which are among the most predominant crops on the planet and require different management strategies. Here, we combined meta-analysis of a global database (including rice and wheat cropping systems) with a two-year rice-wheat cropping experiment in eastern China; and found that eCO₂ consistently promotes N₂O emissions both at local and global scales in wheat cropping systems. For meta-analysis, we show that wheat cropping increased eCO₂-induced N₂O emissions by 19.6 %, whereas rice cropping showed no significant changes. Local experiments supported the global results and revealed a potential functional mechanism for the positive relationship between eCO₂ and N₂O emissions, where eCO₂ experimentally increased the ratio of microbial nitrite reductase gene abundances (nirK, nirS) to N₂O reductase gene abundance (nosZ) in soil. Taken together, our study highlights the potential positive feedback among eCO₂ and N₂O, as well as the crucial role of cereal type in governing the eCO₂ effect on N₂O emissions, which is an important consideration for management to both mitigate climate change under global change and promote agricultural sustainability.

人类活动正在增加大气中的二氧化碳（CO2），通过与其他温室气体，特别是一氧化二氮（N2O）的相互作用，进一步加剧了对气候变化的贡献。然而，在全球范围内，二氧化碳（eCO2）升高对主要谷物系统N2O排放的影响仍未得到充分研究。这种影响在水稻和小麦等谷类作物中尤为重要，它们是地球上最主要的作物之一，需要不同的管理策略。在这里，我们将全球数据库（包括水稻和小麦种植系统）的荟萃分析与中国东部为期两年的水稻-小麦种植试验相结合；并发现eCO2在小麦种植系统的地方和全球尺度上都持续促进N2O的排放。荟萃分析显示，小麦种植增加了19.6% %的co2诱导的N2O排放量，而水稻种植没有显著变化。局部实验支持全局结果，并揭示了eCO2与N2O排放正相关的潜在功能机制，其中eCO2实验增加了土壤中微生物亚硝酸盐还原酶基因丰度（nirK, nirS）与N2O还原酶基因丰度（nosZ）的比值。综上所述，我们的研究强调了eCO2和N2O之间潜在的正反馈，以及谷物类型在控制eCO2对N2O排放的影响方面的关键作用，这是在全球变化下减缓气候变化和促进农业可持续性管理的重要考虑因素。

{"title":"Impacts of atmospheric CO2 enrichment on nitrous oxide emissions in wheat and rice cropping systems at global and local scales","authors":"Shengji Yan , Guiyao Zhou , Daniel Revillini , Yunlong Liu , Haoyu Qian , Aixing Deng , Yanfeng Ding , Yu Jiang , Manuel Delgado-Baquerizo , Xin Zhang , Weijian Zhang","doi":"10.1016/j.eja.2025.127970","DOIUrl":"10.1016/j.eja.2025.127970","url":null,"abstract":"<div><div>Human activity is boosting atmospheric carbon dioxide (CO<sub>2</sub>), which further compounds the contributions to climate change through interaction effects with other greenhouse gases, especially nitrous oxide (N<sub>2</sub>O). However, the global-scale role of elevated CO<sub>2</sub> (eCO<sub>2</sub>) in shaping N<sub>2</sub>O emissions across major cereal systems remains insufficiently studied. This effect can be especially important in cereal crops such as rice and wheat, which are among the most predominant crops on the planet and require different management strategies. Here, we combined meta-analysis of a global database (including rice and wheat cropping systems) with a two-year rice-wheat cropping experiment in eastern China; and found that eCO<sub>2</sub> consistently promotes N<sub>2</sub>O emissions both at local and global scales in wheat cropping systems. For meta-analysis, we show that wheat cropping increased eCO<sub>2</sub>-induced N<sub>2</sub>O emissions by 19.6 %, whereas rice cropping showed no significant changes. Local experiments supported the global results and revealed a potential functional mechanism for the positive relationship between eCO<sub>2</sub> and N<sub>2</sub>O emissions, where eCO<sub>2</sub> experimentally increased the ratio of microbial nitrite reductase gene abundances (<em>nirK</em>, <em>nirS</em>) to N<sub>2</sub>O reductase gene abundance (<em>nosZ</em>) in soil. Taken together, our study highlights the potential positive feedback among eCO<sub>2</sub> and N<sub>2</sub>O, as well as the crucial role of cereal type in governing the eCO<sub>2</sub> effect on N<sub>2</sub>O emissions, which is an important consideration for management to both mitigate climate change under global change and promote agricultural sustainability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127970"},"PeriodicalIF":5.5,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839790","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}

引用次数: 0

Re-evaluating onion varieties in organic farming: Evidence from a decade of multi-environment trials 重新评估有机农业中的洋葱品种：来自十年多环境试验的证据

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-24 DOI: 10.1016/j.eja.2025.127969

M.L. Romo-Pérez, A. Rekowski, C. Zörb

Onion landraces and traditional open-pollinated varieties are gaining renewed interest in organic farming due to their potential for regional adaptation, on-farm seed use, and distinct flavor profiles. However, comprehensive long-term field-based evaluations of their agronomic and quality traits remain scarce. This study assessed the agronomic performance, compositional quality, and environmental responsiveness of traditional open-pollinated onion varieties grown under certified organic conditions across diverse environments in Germany. Between 2014 and 2024, 19 field trials were conducted at seven research and on-farm locations. Three open-pollinated varieties, including one landrace, were evaluated alongside commercial references. Yield, quality traits, and environmental interactions were analyzed using meta-analyses, linear mixed-effects models (including site-year as a random effect), and principal component analysis. The landrace Birnenförmige (Bif) and the traditional open-pollinated variety Stunova (Stu) generally matched or approached the performance of commercial varieties. Birnenförmige showed the most consistent and statistically robust advantages in compositional traits, combining early maturity with high dry matter, sugar, and pyruvate levels. In contrast, Stunova—tested only at research stations—showed positive but more variable responses, with high fructan concentrations and favorable yield trends in early trial years. By comparison, the traditional open-pollinated variety Rijnsburger 4 (Rij) showed increased sensitivity to humid conditions, with reduced marketable yield and quality. Relative humidity negatively affected dry matter and sugar accumulation but tended to increase pyruvate levels, particularly in Stu. These findings underscore the importance of variety selection tailored to environmental conditions and organic production goals. Traditional open-pollinated onion varieties can contribute to the diversification and resilience of organic farming systems. Their distinct compositional profiles and climatic responses offer valuable options for producers seeking alternatives to hybrids in the context of organic seed system development and climate adaptation.

洋葱地方品种和传统的开放授粉品种由于其潜在的区域适应性，农场种子使用和独特的风味特征，正在重新获得有机农业的兴趣。然而，对其农艺和品质性状的长期综合田间评价仍然很少。本研究评估了在德国不同环境下有机认证条件下种植的传统开放授粉洋葱品种的农艺性能、组成质量和环境响应性。2014年至2024年间，在7个研究和农场地点进行了19次田间试验。三个开放授粉的品种，包括一个地方品种，在商业参考资料的基础上进行了评估。利用荟萃分析、线性混合效应模型（包括地点年作为随机效应）和主成分分析分析了产量、品质性状和环境相互作用。地方品种Birnenförmige （Bif）和传统的开放授粉品种Stunova （Stu）的表现一般与商品品种相匹配或接近。Birnenförmige在组成性状上表现出最一致和统计上稳健的优势，早熟与高干物质、糖和丙酮酸水平相结合。相比之下，stunova（仅在研究站进行测试）显示出积极但更可变的反应，在试验早期具有较高的果聚糖浓度和有利的产量趋势。相比之下，传统的开放授粉品种Rijnsburger 4 （Rij）对潮湿条件的敏感性增加，市场产量和品质下降。相对湿度对干物质和糖积累有负面影响，但倾向于增加丙酮酸水平，特别是在Stu。这些发现强调了根据环境条件和有机生产目标进行品种选择的重要性。传统的开放授粉洋葱品种可以促进有机农业系统的多样化和复原力。它们独特的成分特征和气候响应为生产者在有机种子系统发展和气候适应的背景下寻找杂交品种的替代品提供了有价值的选择。

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引用次数: 0

Assessing climate-smartness of agronomic practices in oil palm production under changing climate conditions 评估气候变化条件下油棕生产中农业实践的气候适应性

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-22 DOI: 10.1016/j.eja.2025.127966

Lisma Safitri , Marcelo V. Galdos , Iput Pradiko , Alexis Comber , Andrew Challinor

Assessing the climate-smartness of oil palm (OP) agronomic practices is critical for ensuring sustainable, resilient, and low-emission production that meets growing demand and complies with international climate-friendly regulations. This study aims to assess the climate-smartness, defined as improved productivity, enhanced resilience and reduced GHG emissions, of OP agronomic practices under a changing climate. Climate-smartness of irrigation and empty fruit bunch (EFB) application with standard and reduced N fertiliser was assessed using yield change, carbon balance change and two climate-smart indices. The Agricultural Production SIMulator (APSIM) model was used to simulate yield, carbon balance components and water use over a 25-year plantation cycle. Uncertainty analysis included ten different sites, five GCMs (IPSL, GFDL, MPI, MRI, UKESM1), two emission scenarios (SSP1–2.6 and SSP5–8.5) and three periods (baseline: 1998–2022; mid-century: 2041–2065; end-century: 2071–2095). Irrigation emerges as the most climate-smart practice for OP production under climate change, showing strong synergy among mitigation, adaptation, and sustainable production. While gains in yield and soil organic carbon (SOC) are modest (median yield increase: 4.48 %; IQR: −12.10–10.79), emissions remain low, maintaining OP systems as carbon sinks (lowest carbon balance change, median: 0.21 tC_eq ha⁻¹ yr⁻¹; IQR: 0.08–0.43). Irrigation also shows highest synergy in water productivity and GHG intensity (median index score: 0.36; IQR: 0.25–0.48). All EFB application scenarios improve productivity and adaptation through higher yields and SOC, though gains are offset by higher emissions from EFB decomposition in warmer conditions. Elevated temperature, higher N fertiliser and reduced plant density lower the climate-smartness of OP productions. This study improves understanding of balanced climate-smart practices. Choosing the climate-smart practices and maintaining optimised N fertiliser and plant density enhance synergy in sustainable production, mitigation and adaptation of OP under climate change.

评估油棕（OP）农艺实践的气候智能性对于确保可持续、有弹性和低排放的生产，满足不断增长的需求并符合国际气候友好型法规至关重要。本研究旨在评估气候智能，定义为气候变化下OP农艺实践的生产力提高、复原力增强和温室气体排放减少。采用产量变化、碳平衡变化和2个气候智能指标，评价了标准施氮和减量施氮灌溉空果串（EFB）的气候智能性。采用农业生产模拟器（APSIM）模型模拟了25年人工林周期内的产量、碳平衡成分和水分利用。不确定性分析包括10个不同的站点、5个gcm （IPSL、GFDL、MPI、MRI、UKESM1）、2个排放情景（SSP1-2.6和SSP5-8.5）和3个时期（基线：1998-2022；本世纪中叶：2041-2065；世纪末：2071-2095）。在气候变化条件下，灌溉成为最具气候智能型的有机肥生产方式，在减缓、适应和可持续生产之间表现出强大的协同作用。虽然产量和土壤有机碳（SOC）的增加是适度的（产量增加中位数：4.48 %;IQR:−12.10-10.79），但排放仍然很低，维持了OP系统作为碳汇（最低碳平衡变化，中位数：0.21 tCeq ha（⁻¹yr）； IQR: 0.08-0.43）。灌溉在水生产力和温室气体强度方面也表现出最高的协同效应（指数得分中位数：0.36;IQR: 0.25-0.48）。所有的EFB应用场景都通过提高产量和有机碳来提高生产力和适应性，尽管这些收益会被更温暖条件下EFB分解产生的更高排放所抵消。温度升高、施氮量增加和植株密度降低降低了有机磷产量的气候适应性。这项研究提高了对平衡的气候智能型实践的理解。选择气候智能型做法和保持优化的氮肥和植物密度，增强了气候变化下可持续生产、减缓和适应有机磷的协同作用。

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引用次数: 0

Response of maize productivity and protein quality to straw incorporation combined with nitrogen fertilizer in Northeast China: A 10-year field experiment 10年秸秆配施氮肥对东北玉米产量和蛋白质品质的响应

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-22 DOI: 10.1016/j.eja.2025.127965

Anran Long , Xinjie Ji , Xiangyu Li , Xuelian Wang , Lv Yang , Liyun Chang , Jingwen Yang , Ying Jiang , Hua Qi , Xiangwei Gong

The combination of straw and nitrogen (N) fertilizer enhances ecological sustainability and food security; however, few studies have examined how maize (Zea mays L.) productivity, protein synthesis, and protein structure–function relationships respond to these practices. In 2015, a study used a two-factor split design with eight treatments: two straw incorporation methods, rotary (RTS) and plow tillage (PTS), and four N fertilizer levels (N0, N1, N2, N3; 0, 112, 187, and 262 kg ha⁻¹). Compared with RTS, PTS significantly increased the maize biomass accumulation by 9.7 % and grain yield by 3.5 % over three years. In contrast, higher N use efficiency, activities of N metabolism enzymes, and increased protein components during the grain-filling stage were observed under RTS conditions than under PTS conditions, thereby increasing the protein and amino acid contents of mature grains. N2 treatment resulted in a smoother protein surface and a more stable secondary and tertiary structure than the other N fertilizer treatments, which was conducive to optimizing the physicochemical properties of maize proteins in RTS combined with N2 practices. Partial least squares path modeling and random forest analyses revealed that high yield and superior protein quality could not be enhanced simultaneously under the current management practices. Overall, PTS increased maize yield, whereas RTS optimized protein quality in Northeast China in a 10-year field experiment. Our results provide important references for improving productivity and protein quality from the perspective of straw incorporation and N fertilizer management at the field scale when considering different requirements.

秸秆与氮肥配施提高了生态可持续性和粮食安全；然而，很少有研究调查玉米（Zea mays L.）的生产力、蛋白质合成和蛋白质结构-功能关系如何响应这些做法。2015年，本研究采用双因素分割设计，共8个处理：两种秸秆还田方式，旋转（RTS）和犁耕（PTS）， 4个氮肥水平（N0、N1、N2、N3； 0、112、187和262 kg ha−1）。与RTS相比，PTS在3年内显著提高了玉米生物量积累9.7% %，增产3.5 %。与PTS相比，RTS处理提高了籽粒灌浆期氮素利用效率、氮素代谢酶活性和蛋白质成分，提高了成熟籽粒蛋白质和氨基酸含量。与其他氮肥处理相比，N2处理使玉米蛋白质表面更光滑，二级和三级结构更稳定，有利于优化RTS与N2相结合条件下玉米蛋白质的理化性质。偏最小二乘路径模型和随机森林分析表明，在目前的管理方式下，高产和优质蛋白质不能同时得到提高。总体而言，在10年的田间试验中，PTS提高了东北玉米产量，而RTS优化了蛋白质品质。本研究结果为在考虑不同需求的条件下，从秸秆还田和氮肥管理的角度提高产量和蛋白质品质提供了重要参考。

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引用次数: 0