European Journal of Agronomy最新文献_第9页

Identifying optimum sowing dates for rainfed soybeans in Brazil’s new agricultural frontier 确定巴西新农业前沿旱作大豆的最佳播种日期

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-11-21 DOI: 10.1016/j.eja.2025.127927

José Wanderson Silva dos Santos , George do Nascimento Araújo Junior , Francisco Edson Paulo Ferreira , Iêdo Peroba de Oliveira Teodoro , Elvis Felipe Elli , Wemerson Saulo da Silva Barbosa , Ricardo Araújo Ferreira Junior , Guilherme Bastos Lyra , Iedo Teodoro , Ivomberg Dourados Magalhães , Adolpho Emanuel Quintela Rocha , Alexsandro Claudio dos Santos Almeida

Expanding soybean production into new, challenging environments will require advancing knowledge of agriculture management technologies to close yield gaps and enhance system’s sustainability. Crop modeling can be a powerful tool to accurately simulate crop growth and productivity under various cultivation conditions. In this study, the FAO AquaCrop model was calibrated and validated to simulate canopy cover (CC), biomass (B), grain yield (Y), soil water content (SWC), and crop evapotranspiration (ETc) for five soybean cultivars with maturity groups ranging from 6 to 9 under rainfed conditions in the Coastal Tablelands region of Alagoas, Brazil. Model performance was evaluated by modeling efficiency (EF), mean error (E), root mean square error (RMSE), normalized root mean square error (NRMSE), Willmott's index (d), and Pearson’s correlation coefficient (r). The model’s performance for CC ranged from acceptable to good, with the best results for cultivars BRS9383 and M6410. Cultivars AS3730, BMX-POTÊNCIA, and BRS9383 showed the best fit for B (20 % > NRMSE < 30 %; RMSE < 1.30 tons ha⁻¹; EF above 0.8). The model overestimated SWC (E = 16.6 %) but demonstrated low error in predicting ETc for the cultivars (-18–19 mm). After calibration, the model was applied in long-term simulations to evaluate the impacts of alternative sowing dates on crop productivity. For this purpose, a 49-year series of meteorological data was used. The period from the second half of April to the second half of June is recommended for sowing soybean cultivars in the region. During this period, the average soybean productivity can reach 2.72 (±0.38) tons ha⁻¹. Cultivars M6410 (maturity group 6.4) and BMX-POTÊNCIA (maturity group 6.7) were the most suitable for cultivation in the region. AquaCrop model is powerful to optimize rainfed soybean management for cultivation in Brazil’s new agricultural frontier.

在新的、具有挑战性的环境中扩大大豆生产将需要提高农业管理技术知识，以缩小产量差距并增强系统的可持续性。作物建模是准确模拟不同栽培条件下作物生长和产量的有力工具。在本研究中，对FAO AquaCrop模型进行了校准和验证，以模拟巴西阿拉agoas沿海高原地区5个成熟等级为6 ~ 9的大豆品种在雨养条件下的冠层盖度（CC）、生物量(B)、粮食产量(Y)、土壤含水量（SWC）和作物蒸散量（ETc）。通过建模效率（EF）、平均误差(E)、均方根误差（RMSE）、归一化均方根误差（NRMSE）、Willmott指数(d)和Pearson相关系数(r)来评价模型的性能。该模型的CC性能从一般到良好，以BRS9383和M6410为最佳。品种AS3730, BMX-POTENCIA BRS9383显示最适合B(20 %祝辞NRMSE & lt; 30 %;RMSE & lt; 1.30吨ha⁻1;EF 0.8以上)。该模型高估了SWC (E = 16.6 %)，但预测ETc的误差较低（-18 ~ 19 mm）。校正后的模型应用于长期模拟，以评估不同播期对作物生产力的影响。为此，使用了49年的一系列气象数据。建议在4月下半月至6月下半月播种大豆品种。在此期间，大豆平均产量可达2.72（±0.38）吨/公顷。品种M6410（成熟度组6.4）和BMX-POTÊNCIA（成熟度组6.7）最适合栽培。AquaCrop模型在优化巴西新农业前沿旱作大豆种植管理方面具有强大的功能。

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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 : 2026-03-01 Epub 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。

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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 : 2026-03-01 Epub 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

Dynamic parameter calibration based deep network for paddy yield prediction 基于动态参数标定的深度网络水稻产量预测

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-11-26 DOI: 10.1016/j.eja.2025.127929

S. ABARNA , D. KESAVARAJA

Paddy yield prediction plays a crucial role in agriculture, enabling farmers to make informed decisions. This work proposes an innovative approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for accurate paddy yield forecasting. The hybrid model harnesses the spatial understanding capabilities of CNNs and the sequential learning ability of RNNs to capture both local and temporal dependencies in agricultural data. A key enhancement introduced in this method is the incorporation of a dynamic parameter calibration technique. Traditional regularization methods often rely on static values, which may not adapt effectively to varying complexities in the dataset. The proposed approach dynamically adjusts the regularization strength parameters during training, allowing the model to better converge to different patterns and fluctuations in paddy growth parameters. The dataset utilized for training and evaluation comprises comprehensive agricultural variables, including soil composition, climate conditions, and historical yield data. Experiments demonstrate the effectiveness of the hybrid CNN-RNN architecture with dynamic parameter calibration which improves prediction accuracy over conventional models. This research contributes to the advancement of precision agriculture by providing a more robust and adaptable framework for paddy yield prediction. The integration of spatial and temporal features, along with dynamic parameter calibration, showcases the potential for optimizing agricultural decision-making processes and mitigating the impact of unpredictable factors on paddy production.

水稻产量预测在农业中起着至关重要的作用，使农民能够做出明智的决定。本文提出了一种结合卷积神经网络（CNN）和递归神经网络（RNN）的水稻产量预测方法。该混合模型利用cnn的空间理解能力和rnn的顺序学习能力来捕获农业数据中的局部和时间依赖关系。该方法的一个关键改进是引入了动态参数校准技术。传统的正则化方法通常依赖于静态值，这可能不能有效地适应数据集中不断变化的复杂性。该方法在训练过程中动态调整正则化强度参数，使模型能够更好地收敛于水稻生长参数的不同模式和波动。用于培训和评估的数据集包括综合农业变量，包括土壤成分、气候条件和历史产量数据。实验证明了采用动态参数校正的CNN-RNN混合结构的有效性，与传统模型相比，预测精度得到了提高。该研究为水稻产量预测提供了一个更稳健、适应性更强的框架，有助于推进精准农业的发展。时空特征的整合，以及动态参数校准，展示了优化农业决策过程和减轻不可预测因素对水稻生产影响的潜力。

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引用次数: 0

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 : 2026-03-01 Epub 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

Evaluating peat pasture performance under elevated groundwater conditions 地下水升高条件下泥炭牧场性能评价

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.eja.2025.127961

Hilde M. van Dijk , Nyncke J. Hoekstra , Raimon Ripoll-Bosch , Idse Hoving , Jeroen Pijlman , Nick van Eekeren

Historically, Dutch peatlands have been drained for agriculture, particularly as pastures for dairy production. While drainage increases productivity, it degrades the peat layer, leading to high greenhouse gas emissions and soil subsidence. Raising the depth of the groundwater table (WT) to 20 cm below field level, contrasted to commonly drained peat at approximately 50 cm below field level, is considered an effective solution to limit peat degradation. However, it is unclear how raising the WT with an active water infiltration system (AWIS) would affect grass yield and quality, and whether dairy farming can be maintained. We studied the effect of raising WT on grass production, quality, and nitrogen utilization, using experimental plots with different WT and fertilization levels in 2020–2024. Raising WT from 50 to 20 cm below field level resulted in an average decrease of 9 % in herbage dry matter yield (DMY), with significant variation between years due to varying weather conditions. The reduction in DMY could partly be explained by the observed decrease in soil N supply in wetter conditions, due to a lower mineralization rate. Increased N fertilization could mediate the lower DMY but increases nutrient losses. Herbage crude protein concentration was moderately affected by WT, and more strongly by yearly variation. Although results are on plot level, which excludes losses originating from trafficking by machinery and animals, they indicate that raising WT is compatible with relatively high grass production and similar grass quality. Consequences on farm level need to be further explored.

从历史上看，荷兰的泥炭地一直被排干用于农业，特别是作为乳制品生产的牧场。虽然排水提高了生产力，但它使泥炭层退化，导致温室气体排放高和土壤沉降。将地下水位（WT）提高到地下水位20 cm以下，而不是通常排干的泥炭地下水位50 cm以下，被认为是限制泥炭退化的有效解决方案。然而，目前尚不清楚用主动渗水系统（AWIS）饲养WT会如何影响草的产量和质量，以及奶牛养殖是否可以维持。在2020-2024年，采用不同WT和施肥水平的试验田，研究了提高WT对草地产量、品质和氮素利用的影响。将WT从低于田间水平50 ~ 20 cm提高，牧草干物质产量（DMY）平均下降9. %，且由于天气条件的不同，各年间差异显著。在较湿润的条件下，由于矿化率较低，土壤氮供应减少，可以部分解释DMY的减少。增加施氮量可以调节DMY的降低，但增加了养分损失。牧草粗蛋白质浓度受WT的影响较小，受年际变化的影响较大。虽然结果是在地块水平上，不包括机械和动物贩运造成的损失，但它们表明，饲养小毛草与相对较高的草产量和相似的草质量是相容的。对农场层面的影响需要进一步探讨。

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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 : 2026-03-01 Epub 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 : 2026-03-01 Epub 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 : 2026-03-01 Epub 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

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 : 2026-03-01 Epub 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