European Journal of Agronomy最新文献_第8页

Fertilizer recommendations for maize production in Ghana: Comparison of machine learning, semi-mechanistic and conventional approaches 加纳玉米生产的肥料建议：机器学习、半机械和传统方法的比较

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-11-27 DOI: 10.1016/j.eja.2025.127925

Eric Asamoah , Gerard B.M. Heuvelink , Vincent Logah , Johan G.B. Leenaars , Prem S. Bindraban

Efficient fertilizer application is vital for enhancing maize production and profitability in Sub-Saharan Africa, where soil fertility varies widely across regions. This study aimed to develop a machine learning approach for generating site-specific fertilizer recommendations for maize production in Ghana and to evaluate its performance against conventional and semi-mechanistic approaches. A random forest machine learning model was trained on 482 maize yield experiments, consisting of 3136 yield observations collected from 1991 to 2020, to predict maize yield response to different fertilizer rates. The model incorporated multiple explanatory variables, including soil properties, climate conditions, and management practices, to generate fertilizer response curves from which fertilizer recommendations were derived for 14 sites across three agro-ecological zones in Ghana where field validation experiments were conducted. On these sites, the recommendations were compared with recommendations derived from the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), Conventional Fertilizer Dose Response (CFDR), and Updated Conventional Fertilizer Dose Response (UCFDR) approaches and validated through field experiments. The machine learning approach generally recommended lower rates of phosphorus and potassium than the other approaches, while nitrogen recommendations were comparable. In the Guinea Savanna zone, the recommendations from the machine learning approach outperformed those from the other approaches, producing higher mean yields for three out of the four sites in the zone. In the Forest-Savanna Transition (FST) zone, the machine learning model recommendations led to higher mean yields at four sites, while the approaches based on QUEFTS and UCFDR performed best at two other sites. In the Semi-deciduous Forest zone, the recommendations of the QUEFTS approach resulted in the highest mean yields at three sites, and CFDR at one site. Despite high input prices during the period of experimentation, the machine learning approach-based recommendations demonstrated higher net profit margins in the FST zone, suggesting cost-effectiveness in this zone. These findings indicate that site-specific fertilizer recommendations are more efficient than blanket recommendations and that machine learning approaches offer a promising and innovative approach for generating cost-effective, site-specific fertilizer recommendations in tropical climates.

在撒哈拉以南非洲，高效施肥对于提高玉米产量和盈利能力至关重要，该地区土壤肥力差异很大。本研究旨在开发一种机器学习方法，为加纳的玉米生产提供特定地点的肥料建议，并评估其与传统和半机械方法相比的性能。利用1991 - 2020年收集的482个玉米产量试验数据（包括3136个产量观测数据），对随机森林机器学习模型进行了训练，以预测不同肥料用量对玉米产量的响应。该模型结合了多个解释变量，包括土壤性质、气候条件和管理实践，生成了肥料响应曲线，根据该曲线，在加纳三个农业生态区的14个地点得出了肥料建议，并进行了实地验证试验。在这些站点上，将这些建议与热带土壤肥力定量评价（QUEFTS）、常规肥料剂量反应（CFDR）和更新常规肥料剂量反应（UCFDR）方法得出的建议进行了比较，并通过田间试验进行了验证。与其他方法相比，机器学习方法通常建议的磷和钾的用量较低，而氮的用量是相当的。在几内亚热带草原地区，机器学习方法的建议优于其他方法，在该地区的四个站点中，有三个站点的平均产量更高。在森林-热带草原过渡（FST）区，机器学习模型建议在四个地点导致更高的平均产量，而基于QUEFTS和UCFDR的方法在另外两个地点表现最好。在半落叶林带，QUEFTS方法的建议在3个站点的平均产量最高，在1个站点的CFDR最高。尽管在实验期间投入价格很高，但基于机器学习方法的建议在FST区域显示出更高的净利润率，表明该区域具有成本效益。这些发现表明，特定地点的肥料建议比一揽子建议更有效，机器学习方法为在热带气候下产生具有成本效益的特定地点肥料建议提供了一种有前途的创新方法。

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引用次数: 0

A robust estimation method for canopy chlorophyll content based on FOD and hierarchical weighting combination models 基于FOD和分层加权组合模型的冠层叶绿素含量稳健估计方法

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-17 DOI: 10.1016/j.eja.2025.127959

Jie Zhang , Xiaoyu Song , Yuanyuan Ma , Laigang Wang , Liang Sun , Li Li , Heguang Sun , Chunkai Zheng , Pingping Li , Xia Jing , Guijun Yang , Chunjiang Zhao

Assessing canopy chlorophyll content (CCC) is crucial for evaluating light capture and photosynthetic capacity, as well as for diagnosing and managing maize health. This study aims to develop a robust CCC estimation model using in situ canopy spectral data collected from two regions over a three-year period. The model employs fractional order differential (FOD) and partial least squares regression (PLSR) at multiple spectral resolutions (1 nm, 5 nm, 10 nm, 20 nm, and Sentinel-2 broadband). To mitigate the uncertainties associated with single models and enhance estimation accuracy, a hierarchical weighted combination model integrating k-means clustering and genetic algorithm (GA) is proposed. The results indicate that the CCC estimation models constructed from differential spectra generally outperform those based on original spectra across most orders. The optimal estimation orders are typically within the range of 1.2–1.6 (step: 0.2). For each resolution, the root mean square error (RMSE) of the optimal order in the test set is reduced by 1.65–25.04 % compared to the original spectra. After constructing the hierarchical weighted combination prediction model, the R² and RMSE of the combined model for each resolution are superior to those of the single models. Specifically, the RMSE of the test set is further reduced by 0.38–9.87 % compared to the optimal FOD order. Moreover, we achieved better monitoring results when we migrated the method to remotely sensed images. These findings suggest that the hierarchical weighted combination prediction model driven by fractional order differential spectra can achieve more accurate CCC estimation in maize. This method provides a basis for applying FOD to multi-resolution sensors, and this achievement contributes to the precise regulation of fertilizer and water during maize growth, offering a new technical approach for improving crop yield and resource use efficiency.

评估冠层叶绿素含量（CCC）对于评估玉米的光捕获和光合能力，以及诊断和管理玉米健康至关重要。本研究的目的是建立一个稳健的CCC估算模型，利用从两个地区收集的三年时间的原位冠层光谱数据。该模型在多个光谱分辨率（1 nm、5 nm、10 nm、20 nm和Sentinel-2宽带）下采用分数阶微分（FOD）和偏最小二乘回归（PLSR）。为了减轻单个模型的不确定性，提高估计精度，提出了一种k均值聚类与遗传算法相结合的分层加权组合模型。结果表明，在大多数阶数上，由差分谱构建的CCC估计模型总体上优于基于原始谱的CCC估计模型。最优估计阶数通常在1.2-1.6（步长：0.2）的范围内。对于每个分辨率，测试集中最优阶的均方根误差（RMSE）比原始光谱降低了1.65-25.04 %。构建层次加权组合预测模型后，各分辨率下组合模型的R²和RMSE均优于单一模型。具体而言，与最优FOD顺序相比，测试集的RMSE进一步降低了0.38-9.87 %。此外，将该方法迁移到遥感图像时，我们取得了更好的监测效果。这些结果表明，分数阶微分光谱驱动的层次加权组合预测模型可以获得更准确的玉米CCC估计。该方法为FOD在多分辨率传感器上的应用提供了基础，有助于玉米生长过程中肥水的精确调控，为提高作物产量和资源利用效率提供了新的技术途径。

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引用次数: 0

Pre-rainfall vapor pressure deficit stress and sunshine reduction govern sub-seasonal rainfall effects on China’s rice yield 雨前水汽压亏缺胁迫和日照减少控制着亚季节降雨对中国水稻产量的影响

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1016/j.eja.2025.127954

Feng Zhou , Guanghan Tang , Chengjie Wang , Yue Qin , Bo Fu , Jin Fu

The impact of rainfall on crop yields is highly heterogeneous, depending on its timing, intensity, and duration. However, existing studies have primarily focused on seasonal total rainfall, providing limited evidences on sub-seasonal (event-based) rainfall impacts on crop yields. This study combines nationwide site-level datasets (1999–2012) to assess rice yields response to sub-seasonal rainfall across China. The results reveal that the regional variation in rice yield sensitivity to rainfall (43 %) is primarily driven by the changes in the number of effective panicles per plant (PN, 28 %) and the total number of grains (TG, 12 %). These regional differences are linked to sub-seasonal rainfall impacts: reduced sunshine duration during the vegetative period affects PN, and pre-rainfall vapor pressure deficit during the reproductive period affects TG. Under a medium-range emission scenario (SSP2–4.5), projected rainfall changes are likely to cause an additional 7.3 ± 0.4 % decrease in national rice yields by the end of the century (2086–2100), compared to the historical baseline (1999–2012). This impact is particularly pronounced in southern China, where single and late rice yields are expected to decline by 9.3 % and 14.6 %, respectively. This contrasts with the previous projections (+1.3 ± 0.1 %) involving rainfall changes as the seasonal total. These findings underscore the necessity of incorporating sub-seasonal rainfall changes into site-specific rice production adaptation strategies.

降雨对作物产量的影响是高度不均匀的，取决于降雨的时间、强度和持续时间。然而，现有的研究主要集中在季节性总降雨量上，对亚季节（基于事件）降雨对作物产量的影响提供的证据有限。本研究结合1999-2012年的全国站点数据集，评估了中国各地水稻产量对次季节性降雨的响应。结果表明，水稻产量对降雨敏感性的区域差异（43 %）主要由单株有效穗数（PN, 28 %）和总粒数（TG, 12 %）的变化驱动。这些区域差异与亚季节降雨影响有关：营养期日照时数减少影响PN，生殖期雨前水汽压亏缺影响TG。在中等排放情景（SSP2-4.5）下，到本世纪末（2086-2100年），与历史基线（1999-2012年）相比，预估的降雨量变化可能导致全国水稻产量再减少7.3 ± 0.4 %。这种影响在中国南方尤为明显，预计单稻和晚稻产量将分别下降9.3% %和14.6% %。这与以前的预测结果（+1.3 ± 0.1 %）形成对比，前者将降雨变化作为季节总量。这些发现强调了将亚季节性降雨变化纳入特定地点水稻生产适应策略的必要性。

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引用次数: 0

Tobacco–rapeseed system with moderate mineral fertiliser promotes yield by synergistically improving bacterial network complexity and soil fertility 适量矿肥配烟油菜系统通过协同提高细菌网络复杂性和土壤肥力来提高产量

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.2025.127955

Qi Miao , Lu Liu , Chen Wang , Hao Ying , Yingxin Guo , Dexun Wang , Yanxia Hu , Zhenling Cui , Junwei Sun , Junying Li , Jing Tian

Optimisation of cropping and fertiliser management is essential to overcome the constraints of continuous cropping and to enhance crop yields. However, the combined effects of these practices on yield, through the regulation of soil fertility and microbial communities, remain unclear. To address this, a 13-year field experiment was conducted to assess the impacts of integrated management practices, including cropping systems (tobacco−fallow system, TFS; and tobacco–rapeseed system, TRS) and fertilisation treatments (no fertiliser, CK; moderate mineral fertiliser, NPK; and high mineral fertiliser, HNPK), on tobacco yield, soil fertility, and microbial communities. The results demonstrated that, across all fertilisation treatments, TRS increased the average yield by 46.1 % and soil fertility by 6.1 % compared to TFS. The cropping system was the primary factor shaping the overall microbial community structure (β-diversity), whereas fertilisation significantly influenced the relative abundance of specific microbial taxa (e.g., copiotrophs) within each system. TRS promoted fungal α-diversity and bacterial network complexity, whilst concomitantly suppressing pathogenic fungi. The mechanism underlying yield response to fertilisation varied with the cropping system. Under TFS, HNPK increased yield via a 'high-fertiliser nutrient compensation mechanism' by enhancing soil fertility. Specifically, HNPK increased soil fertility by 6.4 % and crop yield by 62.4 %, but it significantly reduced soil pH. In contrast, under TRS, NPK increased yield through 'soil–microbial synergy', which involved the simultaneous optimisation of soil fertility and enhancement of bacterial network complexity. Furthermore, bacterial network complexity was identified as the strongest predictor of yield. The TRS-NPK treatment outperformed TFS-HNPK in yield by 32.5 %, whilst avoiding soil acidification. This study underscores the critical role of soil bacterial network complexity in agricultural systems and demonstrates that combining crop diversification with moderate nutrient supply improves soil fertility, mitigates acidification, and enhances the sustainability of agricultural production.

优化种植和肥料管理对于克服连作的限制和提高作物产量至关重要。然而，这些做法通过调节土壤肥力和微生物群落对产量的综合影响尚不清楚。为了解决这一问题，进行了一项为期13年的田间试验，以评估综合管理措施对烟草产量、土壤肥力和微生物群落的影响，包括种植制度（烟草-休耕制度，TFS；烟草-油菜籽制度，TRS）和施肥处理（无化肥，CK；中等矿肥，NPK；高矿肥，HNPK）。结果表明，在所有施肥处理中，与TFS相比，TRS的平均产量提高46.1% %，土壤肥力提高6.1% %。种植制度是形成整体微生物群落结构（β-多样性）的主要因素，而施肥显著影响每个系统内特定微生物类群（如共养菌）的相对丰度。TRS促进真菌α-多样性和细菌网络复杂性，同时抑制致病真菌。产量对施肥的响应机制因种植制度而异。在TFS下，HNPK通过“高肥力补偿机制”提高了土壤肥力，从而提高了产量。具体而言，HNPK使土壤肥力提高了6.4 %，作物产量提高了62.4 %，但显著降低了土壤ph。相反，在TRS下，NPK通过“土壤-微生物协同作用”提高产量，这涉及土壤肥力的同时优化和细菌网络复杂性的提高。此外，细菌网络复杂性被确定为产量的最强预测因子。TRS-NPK处理的产量比TFS-HNPK处理高出32.5 %，同时避免了土壤酸化。该研究强调了土壤细菌网络复杂性在农业系统中的关键作用，并证明了作物多样化与适度养分供应相结合可以提高土壤肥力，减轻酸化，提高农业生产的可持续性。

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引用次数: 0

Boosting crop resilience to waterlogging through hormone-regulated root traits 通过激素调节根系性状提高作物抗涝能力

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.eja.2025.127948

Onusha Sharmita , Abu Bakar Siddique , Ke Liu , Sergey Shabala , Matthew Tom Harrison , Meixue Zhou , Chenchen Zhao

Soil waterlogging can result from excessive rainfall, poor soil drainage, high groundwater tables and inadequate drainage, leading to hypoxic or anoxic environments in crop rhizophores. Oxygen-deprived conditions negatively impact root growth and, in severe cases, lead to root senescence due to detrimental soil physiochemical properties, such as lack of nutrient availability and accumulation of toxic elements (elemental toxicity). As the primary plant tissue affected by waterlogging, plant roots have evolved several adaptive strategies to restore oxygen supply, optimize nutrient acquisition, and mitigate elemental toxicity. Previous research has been primarily focussed on ‘oxygen-to-roots’ traits, such as development of adventitious and lateral roots, formation of aerenchyma (air-filled cavities) and the radial oxygen loss barriers. However, underlying phytohormones regulatory networks in response to waterlogging have often been overlooked. This review synthesizes contemporary research on hormonal regulation of root adaptations under waterlogging, aiming to fill key knowledge gaps by linking hormone signalling to root-based waterlogging and soil toxicity tolerance. We highlight roles of ethylene, ABA, and auxin in regulating aerenchyma formation and development of barrier to reduce radial oxygen loss and show that auxin and cytokinin are vital for lateral and adventitious root development and regulating cellular anatomical adaptations. We propose that ethylene, gibberellins (GAs), and brassinosteroids (BRs) are all crucial hormones that play roles in nodule development for nitrogen supply under waterlogging. Importantly, for the first time, we reviewed crucial roles of phytohormones on regulating elemental toxicity underlying waterlogging tolerance. This review also highlights the mitigating roles of emerging hormones, such as melatonin and strigolactones, in enhancing root-associated adaptation. Our review comprehensively elucidates phytohormones derived waterlogging tolerance mechanisms including linking phytohormones signalling to root-associated traits and provides valuable insights for oriented breeding strategies, aiming to improve crop resilience and ensure sustainable crop production.

土壤内涝可由降雨过多、土壤排水不畅、地下水位高和排水不足造成，从而导致作物根孔缺氧或缺氧环境。缺氧条件对根系生长产生负面影响，在严重的情况下，由于有害的土壤理化性质，如缺乏养分可用性和有毒元素的积累（元素毒性），导致根系衰老。作为受内涝影响的主要植物组织，植物根系进化出多种适应策略来恢复氧气供应、优化养分获取和减轻元素毒性。以前的研究主要集中在“向根输送氧气”的特征上，如不定根和侧根的发育、通气组织（充满空气的腔）的形成和径向氧损失屏障。然而，潜在的植物激素调控网络对内涝的响应往往被忽视。本文综述了涝渍条件下根系适应激素调控的最新研究，旨在通过将激素信号与根系涝渍和土壤毒性耐受联系起来，填补关键知识空白。我们强调了乙烯、ABA和生长素在调节通气组织形成和屏障发育以减少径向氧损失中的作用，并表明生长素和细胞分裂素对侧根和不定根的发育和调节细胞解剖适应性至关重要。我们认为，乙烯、赤霉素和油菜素内酯都是涝渍条件下影响氮素供应的重要激素。重要的是，我们首次回顾了植物激素在调节内涝耐受性基础上元素毒性的重要作用。这篇综述还强调了新出现的激素，如褪黑激素和独角甾内酯，在增强根相关适应方面的缓解作用。我们的综述全面阐明了植物激素衍生的耐涝机制，包括将植物激素信号与根系相关性状联系起来，并为定向育种策略提供了有价值的见解，旨在提高作物的抗逆性，确保作物的可持续生产。

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引用次数: 0

Trait-based weed control decisions compared to economic thresholds for site-specific weed management 基于性状的杂草控制决策与特定地点杂草管理的经济阈值的比较

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.eja.2025.127946

Mona Schatke , Johanna Bensch , Lena Ulber , Bärbel Gerowitt , Christoph von Redwitz

Four decision concepts were tested for ex-ante chemical weed control decisions to be integrated into site-specific weed management (SSWM). Two concepts assess the economic profitability of a weed control treatment: one considers the abundance of weed taxonomic groups, and the other takes into account individual weed species. The third and fourth concepts utilize weed functional traits to quantify a species’ ability to provide ecosystem services (service potential) and its competitive ability (disservice potential), thereby informing management decisions. Based on grid-based manual weed assessments in five winter cereal fields in Germany, species-specific weed distribution maps were created using an interpolation approach. For each square meter of the fields, a weed control recommendation was generated using each of the four decision concepts, followed by the creation of weed control maps. Control recommendations by the two economic decision concepts showed the highest similarity across all fields, recommending weed control for 23 %–100 % (weed groups) and 6 %–100 % (species-specific) of the total field area. The concepts based on functional weed traits recommended weed control for 2 %–50 % of the area. Both economic decision concepts recommended a weed control treatment in areas recommended to be left untreated when functional traits are considered. The analysis revealed strengths and weaknesses in all concepts and recommends combining functional weed traits and economic profitability.

对四种决策概念进行了测试，以将事前化学杂草控制决策整合到特定地点杂草管理（SSWM）中。两个概念评估杂草控制处理的经济效益：一个考虑杂草分类群的丰度，另一个考虑单个杂草物种。第三和第四个概念利用杂草的功能特征来量化一个物种提供生态系统服务的能力（服务潜力）和它的竞争能力（危害潜力），从而为管理决策提供信息。基于网格人工杂草评估，采用插值方法绘制了德国5个冬季谷物田的杂草分布图。对于每一平方米的田地，使用这四个决策概念中的每一个生成一个杂草控制建议，然后创建杂草控制地图。两种经济决策概念提出的控制建议在所有领域的相似性最高，建议的杂草控制面积为总面积的23% % -100 %（杂草类群）和6 % -100 %（种特异性）。基于杂草功能性状的概念，建议对2 % ~ 50 %的面积进行杂草防治。当考虑到功能性状时，两种经济决策概念都建议在建议不处理的区域进行杂草控制处理。分析揭示了所有概念的优缺点，并建议将功能杂草性状与经济效益相结合。

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引用次数: 0

Integrating canopy structure into remote sensing inversion strategies: Optimizing plant functional trait retrieval 将冠层结构融入遥感反演策略：优化植物功能性状检索

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.eja.2025.127931

Zixi Shi , Shuo Shi , Jia Sun , Wei Gong , Lu Xu , Binhui Wang , Chenxi Liu

Accurate retrieval of plant functional traits is critical for monitoring crop growth and improving agronomic management. Canopy structural parameters, such as leaf area index (LAI) and leaf inclination distribution function (LIDFa), strongly influence inversion accuracy. Quantifying canopy structural uncertainties and developing strategies to improve retrieval accuracy are crucial. In this study, we developed an inversion framework based on the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, integrating reflectance and solar-induced fluorescence (SIF) data. Using both simulation modelling and field measurements in NEON STER crop field, we introduced multi-level prior noise and evaluated how uncertainties in LAI and LIDFa propagate into the retrieval of chlorophyll content (Cab), maximum carboxylation rate (Vcmax), and fluorescence quantum efficiency (fqe). To assess the influence of canopy structure and improve retrieval accuracy, three inversion strategies—Prior-Matched (PM), Regularized (RI), and No-Prior (NP)—were designed and tested for their accuracy and robustness. The results showed that second-order Sobol’ indices (S2) captured interactions among canopy structural parameters and functional traits, particularly between Cab-LAI, Cab-LIDFa and fqe-LAI, with sensitive spectral ranges at 680–740 nm (fluorescence) and 600–720 nm (reflectance). Error amplification analysis under six noise levels showed that structural uncertainties significant amplified reflectance and fluorescence variations, with red-edge shifts (

Δ RE

) and 740 nm fluorescence changes (

Δ F_{740}

) being most sensitive. Incorporating prior canopy structure information improved inversion accuracy by up to 7.93 % in R² and reduced RMSE by 21.25 %, although this advantage diminished under high noise levels. LAI uncertainty had a greater impact than LIDFa, and additive noise introduced more uncertainty than multiplicative noise. Comparison of the inversion strategies revealed that the RI strategy achieved higher accuracy (simulated data: R²=0.954; measured data: R²=0.799) and greater robustness to noise than the PM strategy. These findings demonstrate the value of integrating canopy structure into computational inversion models to enhance the reliability of remote sensing trait retrieval, supporting precision agriculture and sustainable crop production.

准确检索植物功能性状对监测作物生长和改进农艺管理具有重要意义。冠层结构参数如叶面积指数（LAI）和叶倾角分布函数（LIDFa）对反演精度影响较大。量化冠层结构的不确定性和制定策略是提高检索精度的关键。在这项研究中，我们开发了一个基于土壤冠层光合作用和能量通量观测（SCOPE）模型，整合反射率和太阳诱导荧光（SIF）数据的反演框架。通过模拟模型和NEON STER作物田间测量，我们引入了多层次先验噪声，并评估了LAI和LIDFa的不确定性如何传播到叶绿素含量（Cab）、最大羧基化速率（Vcmax）和荧光量子效率（fqe）的反演中。为了评估冠层结构对反演的影响，提高反演精度，设计了先验匹配（PM）、正则化（RI）和无先验（NP）三种反演策略，并对其精度和鲁棒性进行了测试。结果表明，二级Sobol指数（S2）捕获了冠层结构参数与功能性状之间的相互作用，特别是Cab-LAI、Cab-LIDFa和fqe-LAI之间的相互作用，其敏感光谱范围为680 ~ 740 nm（荧光）和600 ~ 720 nm（反射率）。6种噪声水平下的误差放大分析表明，结构不确定性显著放大了反射率和荧光变化，其中红边位移（ΔRE）和740 nm荧光变化（ΔF740）最为敏感。结合先前的冠层结构信息，在R²中反演精度提高了7.93%，RMSE降低了21.25%，尽管这种优势在高噪声水平下会减弱。LAI的不确定性比LIDFa的影响更大，加性噪声比乘性噪声引入的不确定性更大。对比反演策略，RI策略比PM策略具有更高的精度（模拟数据：R2=0.954；实测数据：R2=0.799）和更强的抗噪声鲁棒性。这些研究结果表明，将冠层结构整合到计算反演模型中，可以提高遥感性状检索的可靠性，为精准农业和作物可持续生产提供支持。

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引用次数: 0

Determination of nitrogen dilution curves and nitrogen diagnosis for oilseed flax under different nitrogen and plant density 不同氮素和密度下油籽亚麻氮素稀释曲线测定及氮素诊断

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1016/j.eja.2025.127949

Bin Yan , Zhengjun Cui , Ming Wen , Bing Wu , Haidi Wang , Yifan Wang , Yuhong Gao

<div><div>Accurate assessment of nitrogen status in oilseed flax is crucial for optimizing nitrogen use efficiency (NUE) and promoting sustainable agricultural practices. The nitrogen nutrition index (NNI) serves as a key diagnostic tool; however, its response to the interaction between nitrogen fertilization and planting density remains poorly understood. To address this knowledge gap, a field experiment was conducted using three nitrogen application rates (0 (N<sub>0</sub>), 75 (N<sub>75</sub>), and 150 (N<sub>1050</sub>) kg ha⁻¹) and three planting densities (4.5 (N<sub>450</sub>), 7.5 (N<sub>750</sub>), and 10.5 (N<sub>1050</sub>) × 10⁶ plants ha⁻¹) to reveal the effects of nitrogen and planting density on dry matter and nitrogen accumulation, leaf area index (LAI), critical nitrogen dilution curve parameters, and ecaluating subsequent impacts on nitrogen nutition status and yield. Results showed that the high-density treatment (D<sub>1050</sub>) and high-nitrogen treatment (N<sub>150</sub>) significantly enhanced dry matter (DM) accumulation during the later growth stages of flax. The effect of high nitrogen on DM shifted from inhibitory during vegetative growth to promotive during reproductive growth. Planting density had a significant positive effect on DM of oilseed flax. The combination of high nitrogen and high density (N<sub>150</sub>D<sub>1050</sub>) substantially promoted DM accumulation at Kernel and Maturity stages. Both nitrogen application and planting density exerted significant effects on leaf area index (LAI), with the highest LAI values consistently observed under the combined high nitrogen and high density treatment. High nitrogen application suppressed nitrogen accumulation under low planting density but significantly enhanced it under high density. A significant nitrogen × density interaction was evident for the critical nitrogen dilution curve parameter A1 and A2. A1 was estimated with high precision and exhibited a nonlinear response to nitrogen, peaking at moderate application rates (N<sub>75</sub>) under medium planting density (D<sub>750</sub>). In contrast, parameter A2 showed greater estimation uncertainty but pronounced sensitivity to treatments, with its value sharply declining under high nitrogen, particularly at medium density (N<sub>150</sub>D<sub>750</sub>). Higher planting densities generally reduced A2 across all nitrogen levels. A major contribution of vegetative-stage DM and LAI to parameter A1; in contrast, parameter A2 was overwhelmingly contributed by seedling DM. All nitrogen treatments resulted in nitrogen deficiency (NNI < 1) at maturity. The effectiveness of nitrogen application on NNI was highly dependent on planting density. Under low density, high nitrogen maintained superior nitrogen nutrition status through maturity. At medium density, only moderate nitrogen ensured adequate nitrogen supply at the grain-filling stage, while high nitrogen led to late-stage deficiency. Under high density, high

准确评估油籽亚麻氮素状况对优化氮素利用效率和促进可持续农业实践具有重要意义。氮营养指数（NNI）是关键的诊断工具；然而，其对氮肥和种植密度相互作用的响应尚不清楚。为了解决这一知识空白，我们进行了3种施氮量（0 （N0）、75 （N75）和150 (N1050) kg ha⁻¹）和3种种植密度（4.5 （N450）、7.5 （N750）和10.5 （N1050） × 10⁶株ha⁻¹）的田间试验，揭示了氮和种植密度对干物质和氮积累、叶面积指数（LAI）、氮关键浓度曲线参数的影响，并评估了后续对氮营养状况和产量的影响。结果表明，高密度处理（D1050）和高氮处理（N150）显著促进了亚麻生长后期干物质（DM）积累。高氮处理对DM的影响由营养生长阶段的抑制向生殖生长阶段的促进转变。种植密度对油籽亚麻的DM有显著的正影响。高氮高密度处理（N150D1050）显著促进了籽粒期和成熟期DM积累。施氮量和种植密度对叶面积指数（LAI）均有显著影响，且高氮高密度组合处理的叶面积指数一致最高。高施氮量在低密度条件下抑制氮素积累，在高密度条件下显著提高氮素积累。氮稀释曲线参数A1和A2存在显著的氮与 密度交互作用。A1的估算精度较高，对氮素具有非线性响应，在中等种植密度（D750）和中等施氮量（N75）下达到峰值。相比之下，A2参数的估计不确定性较大，但对处理的敏感性明显，在高氮条件下，特别是在中密度（N150D750）下，其值急剧下降。较高的种植密度通常会降低所有氮水平的A2。植被期DM和LAI对参数A1的主要贡献；相反，A2参数绝大部分是由幼苗DM贡献的。所有氮肥处理均导致成熟期氮素缺乏（NNI < 1）。氮肥在NNI上的施用效果高度依赖于种植密度。在低密度条件下，高氮在成熟期保持优越的氮营养状态。在中密度条件下，灌浆期只有适量施氮才能保证充足的氮素供应，而高施氮则导致后期缺氮。在高密度条件下，高施氮改善了早期营养态氮状态，高密度本身也有助于维持生殖生长过程中的氮浓度。综上所述，氮素和种植密度通过对干物质积累、LAI发育和氮动态的协同作用，交互调节油籽亚麻的生长，从而改变了关键的氮稀释曲线参数（A1和A2），塑造了整个生长周期氮营养状态（NNI）的时间格局。N75D750处理使油籽亚麻产量提高了4.1 % ~ 16.2 %，有效地平衡了产量提高和资源利用效率。

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引用次数: 0

The rhizosphere is not always the expected hotspot for nitrous oxide emissions in tea-planted soils 根际并不总是茶叶种植土壤中氧化亚氮排放的预期热点

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.eja.2025.127962

Miaomiao Cao , Yuxuan Zhang , Debang Yu , Yong Li , Nyumah Fallah , Yves Uwiragiye , Jing Wang , Yinfei Qian , Yuanyuan Huang , Zucong Cai , Minggang Xu , Scott X. Chang , Christoph Müller , Yi Cheng

Tea-planted soils are an important source of agricultural nitrous oxide (N₂O) emissions. The rhizosphere is a hotspot for N₂O emissions due to high microbial activity. This has been challenged by the fact that the lower pH and higher carbon (C) availability in the rhizosphere could reduce N₂O emissions by inhibiting nitrification and promoting the reduction of N₂O during denitrification, respectively. Resolving this contradiction is crucial to accurately estimating N₂O emissions and developing targeted N₂O mitigation practices in tea plantations. Here, using a ¹⁵N tracing technique on tea-planted soils with differing pH gradients, we found that rhizospheric N₂O emissions significantly decreased by 23.1 %-54.4 % compared with those in bulk soil. This was mostly due to lower denitrification-, autotrophic nitrification-, and heterotrophic nitrification-derived N₂O emissions in the rhizosphere. We further revealed that soil pH, gross autotrophic nitrification rate, and organic C were the most important factors controlling denitrification-, autotrophic nitrification-, and heterotrophic nitrification-derived N₂O emissions, respectively. Ammonia-oxidizing bacteria and fungi played vital roles in controlling N₂O emissions via autotrophic nitrification and denitrification, respectively. Overall, the rhizosphere is not always a hotspot for N₂O emissions in tea-planted soils, suggesting a possible overestimation of importance of the rhizosphere to terrestrial N₂O emissions. This highlights that revisiting spatial distribution of soil N₂O emissions is vital for developing appropriate N₂O mitigation strategies.

种植茶叶的土壤是农业氧化亚氮（N2O）排放的重要来源。根际微生物活跃，是N2O排放的热点。这一观点受到了挑战，因为较低的pH值和较高的根际碳(C)有效性可以通过抑制硝化作用和促进反硝化过程中N2O的减少来减少N2O的排放。解决这一矛盾对于准确估计茶园N2O排放量和制定有针对性的N2O减排措施至关重要。利用15N示踪技术对不同pH梯度的茶树土壤进行研究发现，与普通土壤相比，根际N2O排放量显著降低了23.1% % ~ 54.4% %。这主要是由于根际反硝化、自养硝化和异养硝化产生的N2O排放量较低。结果表明，土壤pH、总自养硝化速率和有机碳分别是控制反硝化、自养硝化和异养硝化N2O排放的最重要因素。氨氧化细菌和真菌分别通过自养硝化和反硝化作用控制N2O排放。总体而言，根际并不总是茶树土壤N2O排放的热点，这表明可能高估了根际对陆地N2O排放的重要性。这突出表明，重新审视土壤N2O排放的空间分布对于制定适当的N2O减缓战略至关重要。

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引用次数: 0

Traits underpinning productivity and persistence of alfalfa in rainfed mediterranean environments 地中海雨养环境下紫花苜蓿生产力和持久性的基础性状

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.eja.2025.127964

Hamza Armghan Noushahi, Alejandro del Pozo

Alfalfa (Medicago sativa L. Leguminosae) is the most important and widely grown forage legume in the world, with nearly 32 million hectares distributed across more than 80 countries. It is a perennial autotetraploid (2 n = 4x = 32) species, with a deep root system, great biological nitrogen (N₂) fixation capacity, and high forage quality and biomass production. Alfalfa, also commonly known as lucerne, is cultivated globally across diverse climates, with varying precipitation, soil fertility, and under rainfed and irrigated conditions. Under the current climate change scenario, especially in drought-prone Mediterranean regions, the increasing interannual variability of winter rainfall and the intensification of prolonged dry summers are posing significant challenges to the adaptability of alfalfa. A deeper understanding of the morpho-physiological traits associated with drought tolerance and persistence is important for developing alfalfa varieties with improved genetics and enhanced physiological performance, ultimately increasing forage yield and biomass production. In this review we examine (1) the impacts of climate change and environmental constraints in Mediterranean climate regions; (2) traits related to persistence and productivity of alfalfa in rainfed conditions, particularly in Mediterranean environments; (3) the influence of genotype x environment interactions on alfalfa trait expression; and (4) recent advances in plant high throughput phenomics and genomics aimed at identifying better adapted genotypes. This comprehensive overview provides a foundation for selecting superior alfalfa genotypes with desirable traits to improve breeding outcomes.

紫花苜蓿（Medicago sativa L. Leguminosae）是世界上最重要和最广泛种植的饲用豆科植物，在80多个国家分布着近3200万公顷。它是多年生同源四倍体（2 n = 4x = 32）种，根系深，生物固氮能力强，饲料品质和生物量高。紫花苜蓿，也被称为苜蓿，在全球不同的气候条件下种植，降雨量、土壤肥力和雨养和灌溉条件各不相同。在当前气候变化情景下，特别是在干旱易发的地中海地区，冬季降水年际变率的增加和夏季长时间干旱的加剧对苜蓿的适应性提出了重大挑战。深入了解与耐旱性和持久性相关的形态生理性状对于培育具有改良遗传和增强生理性能的苜蓿品种，最终提高饲料产量和生物量具有重要意义。在本文中，我们研究了(1)气候变化和环境约束对地中海气候区的影响；(2)在旱作条件下，特别是在地中海环境下，苜蓿的持久性和生产力相关性状；(3)基因型x环境互作对苜蓿性状表达的影响；(4)植物高通量表型组学和基因组学的最新进展，旨在确定更好的适应基因型。这为选择具有理想性状的优良苜蓿基因型以提高育种效果提供了基础。

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引用次数: 0