European Journal of Agronomy最新文献_第7页

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。

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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

Intelligent retrieval of leaf traits using hyperspectral reflectance and deep learning 基于高光谱反射率和深度学习的叶片特征智能检索

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-18 DOI: 10.1016/j.eja.2025.127960

Wenchao Qi , Le Yu , Tao Liu , Hui Wu , Qiang Zhao , Linsheng Wu , Xiaoyan Kang , Yibo Wang , Lifu Zhang

Reliable and intelligent retrieval of leaf traits from hyperspectral reflectance is crucial for assessing ecosystem functions, yet conventional approaches struggle with spectral complexity and nonlinearities. To address these challenges, we developed the Leaf Trait Retrieval Network (LTRN), a novel deep learning framework that integrates Kolmogorov–Arnold Network (KAN), Transformer, and Temporal Convolutional Networks (TCN) for end-to-end trait estimation. Model validation was carried out using a large spectral–trait database covering hundreds of plant species and four functional traits. Experimental results demonstrated that LTRN model outperforms state-of-the-art deep learning models, achieving R² values greater than 0.78 for estimating chlorophyll content (Chl_a+b), equivalent water thickness (EWT), carotenoid content (C_car), and leaf mass per area (LMA). Further analyses indicated that the LTRN model delivers stable estimation performance across spectral resolutions of 10–25 nm. Moreover, the model demonstrates strong stability across varying proportions of training samples. These findings underscore the robustness and stability of LTRN for large-scale vegetation trait retrieval, offering a valuable framework for advancing the intelligent estimation of other ecological parameters.

从高光谱反射率中可靠和智能地检索叶片特征对于评估生态系统功能至关重要，但传统方法难以解决光谱复杂性和非线性问题。为了解决这些挑战，我们开发了叶片性状检索网络（LTRN），这是一种新的深度学习框架，集成了Kolmogorov-Arnold网络（KAN）、Transformer和Temporal Convolutional Networks (TCN)，用于端到端性状估计。利用包含数百种植物和4种功能性状的大型光谱性状数据库对模型进行验证。实验结果表明，LTRN模型优于最先进的深度学习模型，在估计叶绿素含量（Chla+b）、等效水厚度（EWT）、类胡萝卜素含量（Ccar）和每面积叶质量（LMA）方面的R2值大于0.78。进一步分析表明，LTRN模型在10-25 nm的光谱分辨率范围内具有稳定的估计性能。此外，该模型在不同比例的训练样本中表现出很强的稳定性。这些发现强调了LTRN在大尺度植被特征检索中的鲁棒性和稳定性，为推进其他生态参数的智能估计提供了有价值的框架。

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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 : 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 : 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

Impact of controlled-release fertilizer on nitrogen use efficiency, greenhouse gas emissions, and environmental sustainability in sunflower cropping systems 控释肥对向日葵种植系统氮素利用效率、温室气体排放和环境可持续性的影响

IF 5.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2025-12-17 DOI: 10.1016/j.eja.2025.127963

Wenhao Ren , Xianyue Li , Tingxi Liu , Ning Chen , Maoxin Xin , Qian Qi , Bin Liu , Hongxing Liu

As environmental concerns related to agricultural practices intensify, the excessive use of nitrogen fertilizers has become a major global challenge. Controlled-release fertilizers (CRF) offer a promising strategy to improve agricultural productivity, enhance nitrogen utilization, mitigate ecological impacts, and increase economic returns. This study examined the effectiveness of CRF in enhancing sunflower production and nitrogen use efficiency, as well as in reducing nitrogen losses (including ammonia (NH₃) and nitrous oxide (N₂O) emissions and nitrogen leaching) across multiple nitrogen treatments during a three-year field experiment. The results showed that CRF significantly increased nitrogen use efficiency by 14.33 % and reduced nitrogen volatilization by 26.56 %, particularly within the first 20 d after fertilization, during which NH₃ and N₂O emissions were markedly lower than those under traditional nitrogen fertilizer treatments. Furthermore, life cycle assessment (LCA) indicated that CRF substantially decreased environmental impacts, with reductions of 13.03 % in greenhouse gas emissions, 29.05 % in acidification potential, and 29.02 % in eutrophication potential. Under low to medium nitrogen application rates (e.g., N225), delayed nitrogen release further reduced nitrogen loss and alleviated environmental pressure. By integrating LCA with the ecological-economic benefits (BETA) model, this study quantified the ecological and economic value of CRF. The findings demonstrated that CRF delivered high economic returns and considerable environmental benefits within the optimal nitrogen application range, making it an effective approach for sustainable agricultural development. These evaluation methods offer a systematic framework for assessing the environmental and economic outcomes of CRF, providing theoretical support for its broader adoption in agricultural production.

随着与农业实践相关的环境问题加剧，氮肥的过度使用已成为一个重大的全球性挑战。控释肥料（CRF）为提高农业生产力、提高氮素利用率、减轻生态影响和增加经济效益提供了一种很有前景的策略。在为期三年的田间试验中，研究了CRF在提高向日葵产量和氮利用效率方面的有效性，以及在多个氮肥处理中减少氮损失（包括氨（NH3）和氧化亚氮（N2O）排放和氮淋失）的有效性。结果表明，CRF显著提高了氮素利用效率14.33 %，减少了氮素挥发量26.56 %，特别是在施肥后的前20 d内，NH3和N2O排放量显著低于传统氮肥处理。此外，生命周期评价（LCA）表明，CRF显著降低了环境影响，温室气体排放降低了13.03 %，酸化潜势降低了29.05 %，富营养化潜势降低了29.02 %。在低至中等施氮量（如N225）下，延迟氮素释放进一步减少了氮素损失，缓解了环境压力。通过将LCA与生态经济效益（BETA）模型相结合，量化了CRF的生态经济价值。研究结果表明，在最佳施氮范围内，CRF具有较高的经济效益和可观的环境效益，是农业可持续发展的有效途径。这些评价方法为评估CRF的环境和经济成果提供了一个系统框架，为其在农业生产中的广泛应用提供了理论支持。

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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 : 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