European Journal of Agronomy最新文献

{"title":"Optimizing UAV-based herbicide applications for sustainable wheat weed management by a novel multi-parameter field evaluation under variable environmental conditions","authors":"Biljana Boskovic ,&nbsp;Dragana Bozic ,&nbsp;Milan Drazic ,&nbsp;Kosta Gligorevic ,&nbsp;Velibor Spalevic ,&nbsp;Shuraik Kader ,&nbsp;Milos Pajic","doi":"10.1016/j.eja.2025.127945","DOIUrl":"10.1016/j.eja.2025.127945","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) offer potential for precise, sustainable herbicide application in wheat, but their efficacy under variable field conditions requires robust evaluation. This study aims to rectify these research gaps and introduces a novel multi-parameter field evaluation of UAV-based herbicide applications for sustainable wheat weed management, explicitly evaluating the critical impact of real-time meteorological variability and application parameters. Field experiments over two growing seasons (2021/2022 and 2022/2023) compared UAV applications (30 L ha⁻¹, fine-to-medium droplets) at 1.5 m and 2.5 m altitudes with conventional spraying (200 L ha⁻¹, medium-coarse droplets), using constant doses of two commercial herbicide formulations (1. <em>tritosulfuron</em> + <em>florasulam</em>; 2. <em>iodosulfuron-methyl-sodium</em> + <em>amidosulfuron</em> + <em>mefenpyr-diethyl</em>). Efficacy was measured via species-specific weed density and fresh mass reduction. UAV treatments achieved significantly higher or equivalent suppression of <em>Capsella bursa-pastoris</em>, <em>Lactuca serriola</em>, <em>Sinapis arvensis</em>, and <em>Veronica persica</em> compared to conventional spraying. Despite more challenging conditions (high temperature, lower humidity, stronger wind) in the second season, overall efficacy increased, attributed to using a more effective herbicide formulation, underscoring the critical role of herbicide selection for UAV systems. <em>Lamium purpureum</em> exhibited significant weather sensitivity, with lower flight altitude enhancing fresh mass reduction. Correlation analysis suggested temperature positively (r = 0.586, p = 0.045) and wind velocity negatively (r = ̵ 0.588, p = 0.045) influenced treatment efficacy. UAV applications achieved up to 90 % efficacy in the second year by 31 DAHA (i.e., herbicide application) while using 85 % less water. This research provides the first multi-parameter field validation of UAV herbicide application under variable environmental conditions, demonstrating its viability and significant water-saving potential. The findings offer crucial, actionable inferences for optimizing UAV parameters (altitude, droplet size) with herbicide selection and real-time weather data, benefiting global precision agriculture efforts towards resource-efficient and environmentally responsible weed management.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127945"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619438","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}

{"title":"Improving winter wheat yield and water use efficiency using soil moisture sensor-driven precision furrow irrigation","authors":"Liguang Cheng ,&nbsp;Younggu Her ,&nbsp;Chul Soo Park ,&nbsp;Dong-Hyeon Kim ,&nbsp;Taeil Jang","doi":"10.1016/j.eja.2025.127958","DOIUrl":"10.1016/j.eja.2025.127958","url":null,"abstract":"<div><div>Effective water management is critical for enhancing winter wheat yield, grain quality, and resilience in rice-wheat double-cropping systems of South and East Asia. However, production in paddy soils remains constrained by spring droughts, post-anthesis waterlogging, and the absence of region-specific irrigation guidelines. This study evaluated precision irrigation strategies integrating real-time soil moisture monitoring to improve water use efficiency (WUE) and crop performance under variable climates. Field experiments conducted over three seasons (2021–2024) in South Korea compared three treatments: conventional rainfed (CRF), soil moisture-based irrigation at 55 % available soil water (SIA), and at 55 % saturation water content (SIS). SIA consistently outperformed CRF and SIS, increasing grain yield by 20–27 %, WUE by 10–22 %, and leaf area index by up to 16 %. Maintaining soil moisture within the 0–40 cm available water range between jointing and grain filling optimized growth and resource use, whereas SIS induced oversaturation and CRF suffered from moisture deficits. This study offers an integrated framework linking agronomic performance to sensor-trigger logic and on-farm constraints. By converting real-time soil-moisture readings into stage-specific irrigation rules, this work shows that the available soil water-based threshold (SIA) increases yield and WUE over rainfed and saturation-based approaches while revealing how soils and seasonal climate shape outcomes.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127958"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747146","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}

{"title":"Identification of regional agricultural drought in the North China Plain and its attribution factors","authors":"Shaofeng Huang,&nbsp;Qi Zhang,&nbsp;Siyuan Dai","doi":"10.1016/j.eja.2025.127930","DOIUrl":"10.1016/j.eja.2025.127930","url":null,"abstract":"<div><div>Regional agricultural drought (RAD) can cause great losses and is a complex phenomenon with multiple attribution factors. Previous studies have rarely examined agricultural droughts from the perspective of regional events, overlooking the temporal and spatial synchronicity in their development processes. In this study, we enhanced the conventional three-dimensional (3D, latitude × longitude × time) connectivity approach by modifying the spatial connectivity criteria and objectively establishing two critical minimum area thresholds to identify RADs. The remote sensing-based Crop Water Stress Index (CWSI) was employed to characterize agricultural drought in the North China Plain (NCP). A total of 114 RADs were detected across the NCP from 2000 to 2023, and their occurrence characteristics and attribution factors were analyzed. The results suggested that setting the minimum area threshold for spatially contiguous agricultural drought clusters at 2.0 % of the total study area yielded more stable identification outcomes. The average duration of the 114 RADs was 52.24 days, with 23.68 % of the events lasting longer than three months and 31.58 % covering more than 90 % of the study area. In the NCP, spring and autumn were periods characterized by frequent and severe agricultural droughts, with spring droughts more intense than autumn droughts. From 2000, the severity and intensity of RADs exhibited a slight decreasing trend. RADs occurred much more frequently in the northwestern region, and the southwestward-moving events were the most common. Using the Geodetector method, precipitation, relative humidity, and evaporation were detected as the top three meteorological factors attributed the spatial distribution of RADs in the NCP. Potential evaporation and precipitation were the predominant meteorological factors influencing the interannual fluctuation of RADs. The Atlantic Multidecadal Oscillation and Western Pacific Subtropical High were identified as the primary teleconnection attributors of interannual variability of RADs. These findings provide novel insight into the characteristics and drivers of RADs, and can offer valuable references for agricultural planning and management from a regional perspective.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127930"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567790","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}

{"title":"Optimization of tillage and nitrogen fertilization improves yield and resource utilization efficiency of rainfed winter wheat","authors":"Pengfei Shen ,&nbsp;Feiyang He ,&nbsp;Quan Wang ,&nbsp;Yuanbo Zhang ,&nbsp;Miao Li ,&nbsp;Fei Chen ,&nbsp;Xiaoxia Wen ,&nbsp;Weiyan Wang ,&nbsp;Yuncheng Liao","doi":"10.1016/j.eja.2025.127957","DOIUrl":"10.1016/j.eja.2025.127957","url":null,"abstract":"<div><div>How to improve tillage practices and nitrogen management for enhancing nitrogen absorption and redistribution, thereby increasing crop yields and resource utilization efficiency remains a current challenge in agriculture. However, these effects exhibit marked heterogeneity across agroecological regions and warrant further scrutiny, particularly in dryland farming systems. To address this issue, a two-factor split-plot field experiment, including two tillage practices in the main plots (i.e., rotary tillage and plow tillage, represented by RT and PT, respectively) and three nitrogen fertilizer gradients in the subplots (i.e., 180, 240, and 300 kg N ha⁻¹, represented by N1, N2, and N3, respectively) was carried out over three consecutive winter wheat seasons in the dryland wheat-maize intercropping agricultural area of Northwest China. Results demonstrated that RT effectively enhanced the sustained supply capacity of soil moisture and nutrients, increased dry matter accumulation and promoted plant photosynthetic performance of the winter wheat plants. When integrated with the N2 treatment, this practice further elevates grain yield and substantially enhances resource-use efficiency. Over the 3-year growing seasons, compared with the PT–N2, the RT–N2 treatment significantly increased winter wheat yield, net income, and water-use efficiency by 13.12–15.02 %, 45.12–58.02 %, and 10.72–19.97 %, respectively. In addition, the RT–N2 treatment optimised pre-anthesis nitrogen accumulation and post-anthesis nitrogen uptake in the aboveground tissues of winter wheat, thereby increasing nitrogen uptake efficiency, nitrogen agronomic efficiency, nitrogen recovery efficiency, and nitrogen partial factor productivity by averages of 20.53 %, 37.3 %, 59.07 %, and 11.63 %, respectively. Meanwhile, the RT-N2 also reduced nitrate nitrogen leaching within the 0–200 cm soil profile after winter wheat harvest by an average of 17.89 % compared with the PT-N2 treatment. The partial least squares path model revealed that soil moisture and nutrient availability are the primary drivers of winter wheat productivity, whereas nitrate-N leaching from the 60–140 cm soil layer emerges as the dominant limiting factor. Overall, the management strategy integrating RT with N2 delivers synergistic gains in winter wheat productivity while simultaneously advancing environmental sustainability. This finding provides a robust basis for optimising tillage and nitrogen management in wheat–maize double-cropping systems, thereby facilitating the concurrent realisation of high yields and ecological stability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127957"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731761","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}

{"title":"From climate knowledge to adaptive action: Crop water requirement and agricultural water risk in the Yellow River Basin","authors":"Lei Sun ,&nbsp;Quanzhong Huang ,&nbsp;Dongyang Ren ,&nbsp;Min Li ,&nbsp;Xu Xu ,&nbsp;Yunwu Xiong ,&nbsp;Guanhua Huang","doi":"10.1016/j.eja.2025.127950","DOIUrl":"10.1016/j.eja.2025.127950","url":null,"abstract":"<div><div>Agricultural water demand in the Yellow River Basin (YRB) is increasingly shaped by climate change and human activities, posing challenges to sustainable water management. This study analyzed the temporal evolution and driving factors of crop water requirement (CWR) for four major crops—spring wheat, winter wheat, spring maize, and summer maize—using a dynamic crop coefficient model based on MODIS NDVI (2000–2020), combined with the FAO Penman–Monteith method. Pearson correlation and random forest model were employed to identify dominant climatic and anthropogenic influences. Future CWR trends (2021–2100) were projected under nine combined scenarios, integrating three CMIP6 climate pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5) with three cropping area strategies (baseline, +15 %, –15 %). Finally, an assessment framework was established to evaluate agricultural water stress under future scenarios. Results show that the NDVI-based model effectively captures intra-seasonal crop variation and improves CWR estimation accuracy. From 2000–2020, per-unit CWR showed a significant increase for spring wheat and spring maize, whereas a decreasing trend was observed for summer maize. Human activities, especially the irrigated area, were the main driver of CWR change, surpassing climatic factors. Future projections indicate a significant upward trend in per-unit CWR (<em>p</em> &lt; 0.001), with wheat being more sensitive to emission scenarios. Spatially, high-CWR zones are expected to shift from the arid northwest to the central and lower plains. Scenario combined high emission with planting expansion exhibit an approximately 16 % increase in mean annual CWR and result in the highest projected water stress. These findings provide a scientific basis for adaptive water governance and climate-resilient agricultural planning in large river basins.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127950"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697313","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}

{"title":"A robust estimation method for canopy chlorophyll content based on FOD and hierarchical weighting combination models","authors":"Jie Zhang ,&nbsp;Xiaoyu Song ,&nbsp;Yuanyuan Ma ,&nbsp;Laigang Wang ,&nbsp;Liang Sun ,&nbsp;Li Li ,&nbsp;Heguang Sun ,&nbsp;Chunkai Zheng ,&nbsp;Pingping Li ,&nbsp;Xia Jing ,&nbsp;Guijun Yang ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.eja.2025.127959","DOIUrl":"10.1016/j.eja.2025.127959","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127959"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784869","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}

{"title":"Pre-rainfall vapor pressure deficit stress and sunshine reduction govern sub-seasonal rainfall effects on China’s rice yield","authors":"Feng Zhou ,&nbsp;Guanghan Tang ,&nbsp;Chengjie Wang ,&nbsp;Yue Qin ,&nbsp;Bo Fu ,&nbsp;Jin Fu","doi":"10.1016/j.eja.2025.127954","DOIUrl":"10.1016/j.eja.2025.127954","url":null,"abstract":"<div><div>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 (<em>PN</em>, 28 %) and the total number of grains (<em>TG</em>, 12 %). These regional differences are linked to sub-seasonal rainfall impacts: reduced sunshine duration during the vegetative period affects <em>PN</em>, and pre-rainfall vapor pressure deficit during the reproductive period affects <em>TG</em>. 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.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127954"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747147","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}

{"title":"Rotation and organic fertilization alleviate soil microbial phosphorus limitation and improve soil health and productivity in a continuous cropping pineapple orchard","authors":"Chengming Yan ,&nbsp;Dongsheng An ,&nbsp;Yanan Liu ,&nbsp;Baoshan Zhao ,&nbsp;Qiufang Zhao ,&nbsp;Zhiling Ma ,&nbsp;Haiyang Ma","doi":"10.1016/j.eja.2025.127980","DOIUrl":"10.1016/j.eja.2025.127980","url":null,"abstract":"<div><div>Long-term pineapple monoculture severely degrades soil in tropical regions, necessitating sustainable remediation strategies. This study evaluated the effectiveness of crop rotation and organic fertilization in improving soil health and productivity through a field experiment in a decade-long continuously cropped pineapple orchard, with four treatments: continuous pineapple (PP, control); PP with 30 % organic fertilizer substitution (OP); <em>Alpinia officinarum–</em>pineapple rotation (GP); banana–pineapple rotation (BP). Soil health was evaluated using three metrics: the soil quality index (SQI) derived from conventional biochemical indicators, and soil multifunctionality (SMF) based on seven enzymatic activities, and microbial community structure via phospholipid fatty acid (PLFA) profiling. Microbial metabolic limitation was determined via extracellular enzyme stoichiometry. Compared to PP, both remediation strategies significantly alleviated microbial phosphorus (P) limitation, increasing EEA_N:P by 12.4<img>16.6 % and reducing the vector angle by 20.1<img>27.7 %. Consequently, they enhanced microbial abundance (PLFA increased 30.9–84.1 %) and diversity, with increases in SQI (21.2<img>47.4 %), SMF (26.8<img>45.7 %), and pineapple yield (9.3<img>25.5 %). Crop rotation, particularly BP, consistently outperformed organic fertilization across these metrics—e.g., BP rotation increased total PLFA by 84.1 %, markedly exceeding the 30.9 % increase under OP. Structural equation modeling (SEM) analysis confirmed a critical pathway whereby P limitation alleviation enhanced microbial abundance and SMF, directly boosting yield. Thus, while both strategies were beneficial, crop rotation was superior, with BP most improving soil health and productivity. These findings inform sustainable tropical agriculture and soil health indicator selection.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127980"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883347","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}

{"title":"Fertilizer recommendations for maize production in Ghana: Comparison of machine learning, semi-mechanistic and conventional approaches","authors":"Eric Asamoah ,&nbsp;Gerard B.M. Heuvelink ,&nbsp;Vincent Logah ,&nbsp;Johan G.B. Leenaars ,&nbsp;Prem S. Bindraban","doi":"10.1016/j.eja.2025.127925","DOIUrl":"10.1016/j.eja.2025.127925","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127925"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611856","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}

{"title":"Tobacco–rapeseed system with moderate mineral fertiliser promotes yield by synergistically improving bacterial network complexity and soil fertility","authors":"Qi Miao ,&nbsp;Lu Liu ,&nbsp;Chen Wang ,&nbsp;Hao Ying ,&nbsp;Yingxin Guo ,&nbsp;Dexun Wang ,&nbsp;Yanxia Hu ,&nbsp;Zhenling Cui ,&nbsp;Junwei Sun ,&nbsp;Junying Li ,&nbsp;Jing Tian","doi":"10.1016/j.eja.2025.127955","DOIUrl":"10.1016/j.eja.2025.127955","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"174 ","pages":"Article 127955"},"PeriodicalIF":5.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731778","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}