[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1148":3,"related-1148":78},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":72,"slug":75,"view_count":76,"doi":8,"paper":8,"created_at":77},1148,"人工智能在土壤表征与农业中的应用:技术、模型与应用系统综述(Agronomy 16(13) 1241)","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241","发表于《Agronomy》(Basel) 2026年第16卷第13期1241:基于PRISMA 2020框架,对Scopus数据库2018-2026年196项合格研究进行系统综述,聚焦土壤理化性质、数字土壤制图、灌溉管理和作物产量预测。研究显示机器学习与深度学习模型主导近期研究,随机森林、支持向量机、梯度提升、人工神经网络、卷积神经网络、长短期记忆架构是最常报告的技术。主要数据来源包括原位传感器、实验室测量、遥感影像和环境协变量,常通过多源数据融合框架集成。结果表明基于树的集成模型在不同土壤属性上提供稳健性能,深度学习模型对时空预测和遥感应用特别有效。挑战涉及数据质量与可用性、跨区域模型可迁移性和复杂模型有限的可解释性。",null,"Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: agronomy-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagronomy-logo.png?010a42cd82f83819)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagronomy)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Systematic Review\n\nby \nCesar Augusto Navarro Rubio\n\n[](mailto:cnavarro15@alumnos.uaq.mx)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0009-0003-7541-5841), \nHugo Martínez Ángeles\n\n *[](mailto:hmartinez15@alumnos.uaq.mx)[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0002-8552-705X), \nMario Trejo Perea\n\n *[](mailto:mtp@uaq.mx)[![Image 4: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0002-7014-3379), \nJosé Luis Reyes Araiza\n\n[](mailto:lreyes@uaq.mx)[![Image 5: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0003-2609-102X), \nGuillermo Ronquillo-Lomeli\n\n[](mailto:gronquillo@cidesi.edu.mx)[![Image 6: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0003-2459-1602), \nIvan Gonzalez-Garcia\n\n[](mailto:ivan.glez@uaq.mx)[![Image 7: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0001-6331-5761), \nEusebio Ventura Ramos\n\n[](mailto:eventura@uaq.mx)[![Image 8: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0003-4414-7875) and \nJosé Gabriel Ríos Moreno\n\n[](mailto:riosg@uaq.mx)[![Image 9: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788001719)](https:\u002F\u002Forcid.org\u002F0000-0002-8988-2729)\n\nFacultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico\n\n*\n\nAuthors to whom correspondence should be addressed.\n\nSubmission received: 22 May 2026 \u002F Revised: 19 June 2026 \u002F Accepted: 24 June 2026 \u002F Published: 26 June 2026\n\n## Abstract\n\nArtificial Intelligence (AI) has become a key enabler in soil science and agriculture, supporting advanced modeling, monitoring, and decision-making processes. This systematic review synthesizes recent developments in AI-based soil characterization and agricultural applications, with emphasis on soil physicochemical properties, digital soil mapping, irrigation management, and crop yield prediction. Following the PRISMA 2020 framework, a structured search of the Scopus database identified 196 eligible studies published between 2018 and 2026. The reviewed literature reveals a clear transition toward data-driven approaches, with machine learning and deep learning models dominating recent research. Random Forest, Support Vector Machines, gradient boosting methods, artificial neural networks, Convolutional Neural Networks, and Long Short-Term Memory architectures were the most frequently reported techniques. The primary data sources included in situ sensors, laboratory measurements, remote sensing imagery, and environmental covariates, often integrated through multi-source data fusion frameworks. The results indicate that tree-based ensemble models provide robust performance across diverse soil properties, whereas deep learning models are particularly effective for spatiotemporal prediction and remote sensing applications. AI-driven systems are increasingly used to support precision agriculture through irrigation optimization, crop yield forecasting, digital soil mapping, and soil health monitoring. However, challenges remain regarding data quality and availability, model transferability across regions, and the limited interpretability of complex models. The findings highlight current research trends, methodological challenges, and future opportunities for the development of reliable and scalable AI-driven soil and agricultural systems.\n\n## 1. Introduction\n\nSoil is a fundamental component of terrestrial ecosystems and plays a critical role in agricultural productivity, environmental sustainability, and land management. The accurate characterization of soil properties such as moisture content, texture, salinity, temperature, and nutrient availability is essential for optimizing agricultural practices and ensuring efficient resource utilization. These properties are inherently variable in both space and time, making soil systems highly complex and challenging to model using traditional analytical approaches.\n\nAmong the various soil parameters investigated in the literature, soil electrical resistivity (SER) has gained increasing attention due to its sensitivity to multiple physicochemical properties and its suitability for data-driven analysis. Electrical conduction in soils occurs primarily through the pore fluid and dissolved ions, making resistivity strongly dependent on moisture content, salinity, temperature, and soil structure. As a result, SER is widely used as an indirect proxy for assessing soil conditions in applications ranging from geophysical exploration to precision agriculture and environmental monitoring [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241)].\n\nThe relationship between soil electrical resistivity and environmental variables is highly nonlinear and influenced by complex interactions among multiple parameters. Moisture content has been consistently identified as the dominant factor controlling resistivity, exhibiting a strong inverse correlation (r ≈\n\n$- 0.88$\n), while temperature and soil suction generally show weaker positive relationships [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241)]. In addition, soil properties such as clay content, plasticity, and dry unit weight significantly affect electrical conduction mechanisms, highlighting the need for multivariable modeling approaches capable of capturing these interactions [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241)]. Furthermore, electrical conductivity, which represents the inverse of resistivity, is closely linked to soil salinity and chemical properties such as pH and nutrient concentration, enabling its use in irrigation management and soil fertility assessment [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241),[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B5-agronomy-16-01241)].\n\nTraditional methods for soil characterization and resistivity measurement, including laboratory testing and field-based techniques such as the Wenner four-electrode method, provide reliable results but are often labor-intensive, time-consuming, and limited in spatial resolution. These limitations become particularly significant in agricultural contexts, where large-scale monitoring and real-time decision-making are required. Consequently, there has been a growing interest in developing data-driven approaches capable of estimating soil properties based on easily measurable variables.\n\nIn this context, Artificial Intelligence (AI) has emerged as a powerful tool for modeling complex soil systems. Machine learning (ML) and deep learning (DL) algorithms have demonstrated strong capabilities in capturing nonlinear relationships between soil variables and predicting key properties with high accuracy. Models such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) have been widely applied for soil resistivity and property prediction. Some studies have reported coefficients of determination (R 2) values approaching 0.99 under specific experimental settings, controlled conditions, or dataset characteristics; however, model performance varies substantially depending on soil type, environmental conditions, data quality, and validation procedures [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B6-agronomy-16-01241)]. In addition, advanced deep learning architectures, including Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU), have shown strong performance in modeling temporal and spatial variability in soil systems, particularly for moisture prediction and hydraulic property estimation [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B7-agronomy-16-01241),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B8-agronomy-16-01241),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B9-agronomy-16-01241)].\n\nThe integration of AI with sensing technologies has further enhanced soil characterization capabilities. Real-time monitoring systems based on electrical resistivity measurements, such as robotic Wenner arrays, enable rapid spatial mapping of soil properties across agricultural fields [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B3-agronomy-16-01241)]. Similarly, Electrical Resistivity Tomography (ERT) combined with soil moisture measurements has been successfully applied to monitor irrigation processes and analyze wetting–drying cycles in crops such as cotton and tomatoes [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B10-agronomy-16-01241),[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B11-agronomy-16-01241)]. Moreover, multi-sensor platforms incorporating electrical, dielectric, optical (Vis–NIR), and acoustic measurements, coupled with AI models, have enabled the development of high-resolution digital representations of soil systems, often referred to as “digital soil twins” [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B12-agronomy-16-01241)].\n\nBeyond resistivity-focused applications, AI techniques have been widely applied in broader agricultural contexts, including soil fertility mapping, crop yield prediction, crop recommendation systems, and smart irrigation management. These applications leverage large-scale datasets combining soil, climate, and remote sensing information to support data-driven decision-making in precision agriculture [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B13-agronomy-16-01241),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B14-agronomy-16-01241),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B15-agronomy-16-01241),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B16-agronomy-16-01241),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B17-agronomy-16-01241)].\n\nDespite these advances, several challenges remain in the application of AI to soil characterization and agricultural systems. Soil heterogeneity, environmental variability, and the dependence of models on site-specific conditions limit the generalizability of AI approaches [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241)]. Additionally, the availability of high-quality labeled data remains a major constraint, particularly when integrating field measurements, laboratory analyses, and remote sensing data at different spatial and temporal scales [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B12-agronomy-16-01241),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B14-agronomy-16-01241)]. To address these issues, recent studies have explored synthetic data generation techniques, such as variational autoencoders, and multi-sensor data fusion strategies to improve model robustness and predictive performance [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B1-agronomy-16-01241),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B12-agronomy-16-01241),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B16-agronomy-16-01241)]. Furthermore, there is a growing emphasis on the development of hybrid models that combine physical principles with AI techniques, as well as interpretable machine learning approaches, to enhance both predictive accuracy and model transparency [[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B2-agronomy-16-01241),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B4-agronomy-16-01241),[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B5-agronomy-16-01241)].\n\nDespite the rapidly expanding body of research in this field, the existing literature remains fragmented due to variations in datasets, modeling approaches, soil types, environmental conditions, and application domains. Many studies focus on specific regions or individual soil properties, while others emphasize particular AI techniques, making it difficult to establish a unified understanding of current advancements and limitations.\n\nThis fragmentation highlights the need for a systematic and comprehensive synthesis of the existing literature on AI applications in soil characterization and agriculture. A structured review is essential to identify dominant research trends, evaluate the performance of different AI models, and determine the most relevant soil parameters for predictive modeling.\n\nAlthough several review studies have examined specific aspects of AI in agriculture, such as crop yield prediction, remote sensing applications, digital soil mapping, or precision agriculture technologies, a comprehensive synthesis integrating AI methodologies, soil properties, sensing technologies, and agricultural applications within a unified framework remains limited. Addressing these interconnected dimensions is essential to better understand how AI supports soil characterization and agricultural decision-making across multiple scales and application domains.\n\nAccordingly, this study conducts a systematic review of the applications of Artificial Intelligence in soil characterization and agricultural systems, with particular emphasis on soil electrical resistivity, multivariable modeling approaches, and precision agriculture applications.\n\nTo structure this systematic analysis, the following research questions are defined. Addressing these questions provides insights into the current state of knowledge on AI-driven soil analysis and agricultural applications, while acknowledging that the findings are conditioned by the scope and characteristics of the selected studies.\n\n*   What types of AI models are most commonly used for soil characterization and agricultural applications?\n\n*   Which soil properties are most frequently modeled using AI techniques?\n\n*   What sensing technologies and data sources are employed in AI-driven soil studies?\n\n*   What are the main agricultural applications of AI in soil analysis, including precision agriculture and irrigation management?\n\n*   What limitations, challenges, and research gaps exist in current AI-based soil modeling approaches?\n\nThis systematic review is not hypothesis-driven; rather, it is structured around predefined research questions that provide a framework for synthesizing and analyzing the existing literature. The objective is to analyze and compare AI-based methodologies, identify key soil variables influencing predictive performance, and evaluate their applicability in agricultural systems.\n\nThis paper is structured as follows. [Section 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec1-agronomy-16-01241) presents the Introduction, describing the importance of soil characterization, the role of electrical resistivity, and the emergence of AI techniques in agricultural applications. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec2-agronomy-16-01241) describes the Materials and Methods, including the systematic review protocol, search strategy, and selection criteria. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec3-agronomy-16-01241) presents the Results, summarizing the main AI techniques, soil properties, and application domains identified in the literature. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec4-agronomy-16-01241) provides the Discussion, analyzing methodological trends, limitations, and future research directions. Finally, [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#sec5-agronomy-16-01241) presents the Conclusions, highlighting the main findings and contributions of this study.\n\n## 2. Materials and Methods\n\n### 2.1. Systematic Review Protocol\n\nThis systematic review was conducted following the principles established in the PRISMA 2020 framework [[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#B18-agronomy-16-01241)], which provides a standardized methodology for identifying, screening, and selecting scientific literature. The application of this protocol ensures a transparent, structured, and reproducible workflow throughout the review process.\n\nThe methodological procedure was organized into four main phases: (i) identification of relevant studies through database searching, (ii) screening of records based on titles and abstracts, (iii) eligibility assessment through full-text evaluation, and (iv) final inclusion of studies that met the predefined criteria. This sequential approach allows for a clear traceability of the selection process while minimizing potential biases associated with subjective study selection.\n\nThe adoption of the PRISMA methodology enables a systematic synthesis of the existing literature on Artificial Intelligence applications in soil characterization and agricultural systems, with particular attention to soil electrical resistivity, multivariable soil analysis, and precision agriculture technologies. The PRISMA 2020 checklist is provided as [Supplementary Material](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F13\u002F1241#app1-agronomy-16-01241) to ensure compliance with reporting standards.\n\nTo further support transparency and reproducibility, the review protocol was registered in the Open Science Framework (OSF) platform (registration DOI: [https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F6ACSK](https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F6ACSK), accessed on 30 March 2026). The literature search, screening, and study selection processes were completed prior to the final preparation of the manuscript, and the year 2026 represents the final time boundary defined in the search strategy. The OSF access date corresponds solely to the consultation and verification of the registered review protocol.\n\n### 2.2. Search Strategy and Database Selection\n\nThe literature search was performed using the Scopus database, which was selected due to its extensive coverage of peer-reviewed journals, robust indexing system, and advanced search capabilities. Scopus offers a comprehensive representation of research across multiple disciplines, including soil science, agriculture, environmental engineering, and artificial intelligence, making it particularly suitable for the multidisciplinary scope of this study.\n\nAlthough other databases such as Web of Science and Google Scholar also provide valuable sources of scientific information, Scopus was chosen to ensure consistency in metadata, reproducibility of results, and reliable citation tracking. Nevertheless, it is acknowledged that relying on a single database may lead to the omission of some relevant studies, particularly those published in regional or non-indexed journals.\n\nTo mitigate this limitation, the search strategy was carefully designed to capture a broad range of relevant publications by combining keywords related to artificial intelligence, soil characterization, agricultural applications, and soil electrical resistivity.\n\nThe final search query used was as follows:\n\nTITLE-ABS-KEY ((“machine learning” OR “deep learning” OR “artificial intelligence”) AND (“soil characte","《Agronomy》2026, 16(13), 1241","2026-08-28T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,13,4,1,"系统综述总结AI在土壤与农业应用，方法全面，但时效性稍差，影响限于学术领域。",[25],{"name":10,"url":6},[27,28,29,30],"系统综述","人工智能","土壤表征","Agronomy",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,59,66],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},11,true,[48],"土壤",[48],[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"I2",7,[],[56,57],"深度学习","机器学习",[28],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":65},"H3",3,[],[64],"灌溉",[],{"code":67,"score":61,"supported":46,"title":68,"abstract":69,"tags":71},"I3",[],[70],"遥感",[],[73,74],"人工智能 土壤表征 系统综述 Agronomy","人工智能 土壤表征","人工智能土壤表征系统综述Agronomy-1148",2,"2026-08-31T00:08:57.336490Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,153,239,319,389,461],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":8,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":103,"secondary_topics":104,"classification":110,"search_phrases":130,"slug":133,"view_count":134,"doi":135,"paper":136,"created_at":152},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":91,"substance":18,"depth":92,"authority":93,"freshness":13,"relevant":22,"comment":94},8,17,9,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[96],{"name":87,"url":84},[98,99,100,101,102],"智慧农业","农业人工智能","主动学习","线虫检测","土壤采样",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[105],{"code":52,"slug":106,"name":107,"path":108,"parent_code":109},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":111},{"candidates":112,"agricultural_evidence":46},[113,118,123],{"code":32,"score":114,"supported":46,"title":115,"abstract":116,"tags":117},14,[33],[48,33],[],{"code":52,"score":93,"supported":46,"title":119,"abstract":121,"tags":122},[120],"artificial intelligence",[],[99],{"code":124,"score":53,"supported":46,"title":125,"abstract":126,"tags":129},"I1",[],[127,128],"精准农业","precision agriculture",[98],[131,132],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583",0,"10.5281\u002Fzenodo.23544145",{"doi":135,"openalex_id":137,"authors":138,"venue":87,"cited_by_count":134,"oa_url":84,"card":145,"direction":149,"ingested_from":151},"W7223082195",[139,141,143],{"name":140,"orcid":8},"Amine Ben Salah",{"name":142,"orcid":8},"Ines Trabelsi",{"name":144,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":146,"method":147,"finding":148,"direction":149,"opportunity":150},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","openalex","2026-10-22T11:20:58.901747Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":8,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":166,"tags":168,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":200,"slug":203,"view_count":134,"doi":204,"paper":205,"created_at":238},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":163,"substance":164,"depth":19,"authority":114,"freshness":93,"relevant":22,"comment":165},16,22,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[167],{"name":159,"url":156},[169,170,171,172,173],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":177},{"candidates":178,"agricultural_evidence":46},[179,183,188,195],{"code":32,"score":114,"supported":46,"title":180,"abstract":181,"tags":182},[33],[48,33],[],{"code":67,"score":53,"supported":46,"title":184,"abstract":185,"tags":187},[],[70,186],"remote sensing",[169],{"code":189,"score":79,"supported":46,"title":190,"abstract":191,"tags":194},"H7",[],[192,193],"气候适应","干旱",[],{"code":196,"score":22,"supported":14,"title":197,"abstract":198,"tags":199},"H6",[],[],[172],[201,202],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":204,"openalex_id":206,"authors":207,"venue":159,"cited_by_count":134,"oa_url":156,"card":232,"direction":236,"ingested_from":151},"W7223069171",[208,210,212,214,217,219,222,225,227,229],{"name":209,"orcid":8},"Yue Li",{"name":211,"orcid":8},"Mengfei Shi",{"name":213,"orcid":8},"Fei Zhang",{"name":215,"orcid":216},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":218,"orcid":8},"Gowhar Meraj",{"name":220,"orcid":221},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":223,"orcid":224},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":226,"orcid":8},"Yuanfang Chai",{"name":228,"orcid":8},"Hongtao Xu",{"name":230,"orcid":231},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":233,"method":234,"finding":235,"direction":236,"opportunity":237},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","农业遥感与作物表型","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":8,"source_name":30,"source_url":242,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":249,"tags":251,"primary_topic":254,"secondary_topics":260,"classification":263,"search_phrases":295,"slug":298,"view_count":134,"doi":299,"paper":300,"created_at":318},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。",77,{"impact":163,"substance":247,"depth":92,"authority":20,"freshness":13,"relevant":22,"comment":248},21,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[250],{"name":30,"url":242},[98,57,169,252,253],"土壤墒情","冬小麦",{"code":255,"slug":256,"name":257,"path":258,"parent_code":259},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[261,262],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":52,"slug":106,"name":107,"path":108,"parent_code":109},{"status":39,"locked":14,"version":40,"method":41,"evidence":264},{"candidates":265,"agricultural_evidence":46},[266,272,276,281,286,291],{"code":255,"score":267,"supported":46,"title":268,"abstract":270,"tags":271},26,[269,256],"winter wheat",[257,269,256],[253],{"code":32,"score":17,"supported":46,"title":273,"abstract":274,"tags":275},[33],[48,33],[252],{"code":52,"score":17,"supported":46,"title":277,"abstract":279,"tags":280},[278],"machine learning",[57,278],[57],{"code":67,"score":13,"supported":46,"title":282,"abstract":283,"tags":285},[],[70,186,284],"multispectral",[169],{"code":60,"score":79,"supported":46,"title":287,"abstract":288,"tags":290},[],[64,289],"irrigation",[],{"code":124,"score":21,"supported":46,"title":292,"abstract":293,"tags":294},[],[98],[98],[296,297],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":299,"openalex_id":301,"authors":302,"venue":30,"cited_by_count":134,"oa_url":242,"card":312,"direction":317,"ingested_from":151},"W7223156204",[303,306,308,310],{"name":304,"orcid":305},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":307,"orcid":8},"Yi Zhang",{"name":309,"orcid":8},"WEI Lei",{"name":311,"orcid":8},"Chengcai Zhang",{"tldr":313,"method":314,"finding":315,"direction":236,"opportunity":316},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":8,"source_name":325,"source_url":322,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":329,"tags":331,"primary_topic":337,"secondary_topics":338,"classification":339,"search_phrases":359,"slug":362,"view_count":134,"doi":363,"paper":364,"created_at":388},5549,"Characterising climate mitigation and adaptation performance in oil palm plantation: evidence from Indonesia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1871877","Despite growing demand for ‘climate-friendly’ palm oil, little is known about how existing soil and management conditions align with climate mitigation and adaptation goals. This study characterised the climate mitigation and adaptation performance of oil palm (OP) production across different soil and management conditions based on simulated carbon balance and productivity. The Agricultural Production Systems SIMulator (APSIM)-OilPalm model was used to simulate productivity and carbon balance in 25 industrial OP sites on mineral soils in North Sumatra, Indonesia. K-means clustering was used to group similarity patterns between simulated carbon balance and productivity with observed soil and management conditions data. The model has good skill in simulating OP productivity at sites with standard plant density, achieving coefficient of determination (R 2 ) yield up to 0.79, though performance is poor at plantations with plant density outside the standard range or very low observed yields. Simulated OP productivity exhibits moderate variability from 15.38 to 28.67 t fresh fruit bunch (FFB) ha −1 yr. −1 . Simulated carbon balance, which is comparable to measured values in the region, indicates that all observed OP sites serve as a carbon sink with low variability from −2.09 to −3.86 t C eq. ha −1 yr. −1 , driven by uniformity in climate factors and plant density. Eleven of 25 sites have good mitigation and adaptation performance, indicated by high carbon sinks, yield and soil organic carbon (SOC) increment. These sites are characterised by greater plant density and moderate levels of N fertilisation, sand content and Ganoderma boninense (G. boninense) infection, with low carbon nitrogen (CN) ratio. Over half of sites face climate adaptation challenges, indicated by either low productivity or SOC increment and characterised by low plant density and nitrogen fertiliser with high G. boninense infection and sandy soils. This study contributes information about mitigation and adaptation in determining the management of OP and supports the tracking of climate-friendly palm oil within broader supply chains.","尽管对“气候友好型”棕榈油的需求日益增长，但人们对现有土壤和管理条件如何与气候减缓及适应目标相协调知之甚少。本研究基于模拟碳平衡和生产力，表征了油棕（OP）生产在不同土壤和管理条件下的气候减缓与适应表现。研究采用农业生产系统模拟器（APSIM）-OilPalm模型，模拟了印度尼西亚北苏门答腊25个矿质土壤工业油棕样点的生产力和碳平衡。运用K均值聚类方法，将模拟碳平衡与生产力之间的相似性模式与观测到的土壤和管理条件数据进行分组。该模型在标准种植密度样点的油棕生产力模拟中具有良好能力，决定系数（R²）最高达0.79，但在种植密度超出标准范围或观测产量极低的种植园中表现较差。模拟油棕生产力呈现中等变异性，范围为15.38至28.67 t鲜果串（FFB）ha⁻¹ yr.⁻¹。模拟碳平衡与该地区实测值相当，表明所有观测油棕样点均为碳汇，变异性较低，范围为−2.09至−3.86 t C eq. ha⁻¹ yr.⁻¹，这归因于气候因子和种植密度的一致性。25个样点中有11个具有良好的减缓和适应表现，表现为高碳汇、产量和土壤有机碳（SOC）增量。这些样点的特征是种植密度较大，氮肥施用量、砂含量和灵芝（Ganoderma boninense，G. boninense）感染程度适中，碳氮（CN）比低。超过半数样点面临气候适应挑战，表现为生产力或SOC增量较低，其特征是种植密度和氮肥用量低、灵芝感染率高及砂质土壤。本研究为确定油棕管理中的减缓和适应提供了信息，并支持在更广泛供应链中追踪气候友好型棕榈油。","Frontiers in Sustainable Food Systems",80,{"impact":19,"substance":164,"depth":19,"authority":20,"freshness":93,"relevant":22,"comment":328},"基于APSIM模型对印尼25个油棕园碳平衡与生产力的模拟研究，方法清晰、数据具体，对气候友好型油棕管理与供应链追踪有参考价值，但属区域性论文，影响范围有限。",[330],{"name":325,"url":322},[332,333,334,335,336],"作物模型","气候智慧农业","油棕","碳汇","可持续种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":340},{"candidates":341,"agricultural_evidence":46},[342,346,351,355],{"code":32,"score":79,"supported":46,"title":343,"abstract":344,"tags":345},[],[48,33],[],{"code":347,"score":21,"supported":46,"title":348,"abstract":349,"tags":350},"H4",[],[335],[335],{"code":189,"score":21,"supported":46,"title":352,"abstract":353,"tags":354},[],[192],[333],{"code":52,"score":22,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[332],[360,361],"APSIM-OilPalm 油棕 碳平衡","印尼 油棕 气候适应","APSIM-OilPalm油棕碳平衡-5549","10.3389\u002Ffsufs.2026.1871877",{"doi":363,"openalex_id":365,"authors":366,"venue":325,"cited_by_count":134,"oa_url":322,"card":382,"direction":386,"ingested_from":151},"W7223112249",[367,370,373,376,379],{"name":368,"orcid":369},"Lisma Safitri","https:\u002F\u002Forcid.org\u002F0000-0002-7188-1058",{"name":371,"orcid":372},"Marcelo Valadares Galdos","https:\u002F\u002Forcid.org\u002F0000-0002-6080-0726",{"name":374,"orcid":375},"Iput Pradiko","https:\u002F\u002Forcid.org\u002F0000-0001-7877-8400",{"name":377,"orcid":378},"Alexis Comber","https:\u002F\u002Forcid.org\u002F0000-0002-3652-7846",{"name":380,"orcid":381},"Andrew Juan Challinor","https:\u002F\u002Forcid.org\u002F0000-0002-8551-6617",{"tldr":383,"method":384,"finding":385,"direction":386,"opportunity":387},"用APSIM模型模拟印尼油棕园碳平衡与产量，评估其气候减缓与适应表现。","APSIM-OilPalm模型模拟25个站点，结合K-means聚类分析土壤与管","所有站点均为碳汇，11个站点减缓与适应表现良好，过半站点面临适应挑战。","农业绿色发展与碳","可扩展至不同气候带与泥炭地油棕园，结合遥感数据构建区域尺度气候友好型油棕认证体系。","2026-10-22T11:20:15.883770Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":8,"source_name":325,"source_url":392,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":406,"secondary_topics":412,"classification":414,"search_phrases":433,"slug":436,"view_count":134,"doi":437,"paper":438,"created_at":460},5547,"Apple quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。",66,{"impact":91,"substance":18,"depth":163,"authority":20,"freshness":93,"relevant":22,"comment":397},"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[399],{"name":325,"url":392},[401,402,403,404,405],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":407,"slug":408,"name":409,"path":410,"parent_code":411},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[413],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":415},{"candidates":416,"agricultural_evidence":46},[417,424,428],{"code":407,"score":92,"supported":46,"title":418,"abstract":420,"tags":423},[419],"apple",[421,422,419],"苹果","orchard",[],{"code":32,"score":114,"supported":46,"title":425,"abstract":426,"tags":427},[33],[48,33],[],{"code":196,"score":61,"supported":46,"title":429,"abstract":430,"tags":432},[],[431],"生物多样性",[],[434,435],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547","10.3389\u002Ffsufs.2026.1943487",{"doi":437,"openalex_id":439,"authors":440,"venue":325,"cited_by_count":134,"oa_url":392,"card":455,"direction":386,"ingested_from":151},"W7223104118",[441,444,447,449,452],{"name":442,"orcid":443},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":445,"orcid":446},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":448,"orcid":8},"K. Kahu",{"name":450,"orcid":451},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":453,"orcid":454},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":456,"method":457,"finding":458,"direction":386,"opportunity":459},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","2026-10-22T11:20:15.557635Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":8,"source_name":467,"source_url":464,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":161,"score_detail":468,"sources":470,"tags":472,"primary_topic":478,"secondary_topics":481,"classification":483,"search_phrases":507,"slug":510,"view_count":134,"doi":511,"paper":512,"created_at":532},5541,"Year-round modeling of soil water-heat-salt transport and crop growth under film-mulched drip irrigation with freeze-thaw cycles","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105024","Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.","背景 季节性冻土区覆膜滴灌农田的土壤水热盐迁移与作物生长，受生育期内部分覆膜和滴灌以及休止期土壤冻融过程的影响。理解全年土壤水热盐动态及作物生长对于农业可持续用水至关重要。目的 然而，现有全年模型在生育期多采用一维（1D）方法，忽略了部分覆膜滴灌条件下的水平盐分迁移，导致对盐分淋洗效率的高估。这一局限可通过开发兼顾生育期与休止期不同动态特征的全年模型加以解决。方法 本研究将前期开发的基于HYDRUS-2D的作物与水分模型（CAWM-H2D）与考虑土壤冻融过程的SHAW模型相耦合，构建了全年耦合模型CAWM-S。CAWM-S的主要特点包括：详细刻画生育期二维土壤水热盐迁移、作物生长与产量形成以及覆膜土壤表面能量平衡，同时描述休止期冻融过程引起的土壤水盐再分布。模型采用南疆季节性冻土区覆膜滴灌棉田连续两个全年周期的监测数据，在不同灌溉水矿化度水平下进行率定与验证。该验证按处理进行，且仅限于同一站点和两年时段；仍需在其他站点、年份、作物及灌溉系统下开展独立验证。结果与结论 在两个不同矿化度水平的年份中，土壤水分、温度、盐分、作物生长指标及产量的模拟结果与实测数据总体吻合，处理平均R²为0.48~0.98，NSE为0.42~0.97。此外，分析了不同灌水量与矿化度组合对土壤脱盐率、蒸散及其组分、作物产量和水分生产率的影响，揭示了覆膜滴灌条件下最优灌水量与灌溉水矿化度之间的二次关系。验证结果表明，CAWM-S能够有效量化土壤水热盐耦合动态与作物生长，并预测","Agricultural Systems",{"impact":163,"substance":164,"depth":19,"authority":114,"freshness":93,"relevant":22,"comment":469},"构建了覆盖冻融期的全年水热盐与作物生长耦合模型，方法有新意、验证数据扎实，对盐碱地滴灌节水有实用价值，但属单点两年验证的学术成果，产业影响有限。",[471],{"name":467,"url":464},[473,474,475,476,477],"棉花","盐碱地治理","节水灌溉","覆膜滴灌","土壤水盐运移",{"code":60,"slug":289,"name":479,"path":480,"parent_code":36},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[482],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":484},{"candidates":485,"agricultural_evidence":46},[486,491,495,501],{"code":60,"score":19,"supported":46,"title":487,"abstract":488,"tags":490},[289],[64,289,489],"evapotranspiration",[475],{"code":32,"score":17,"supported":46,"title":492,"abstract":493,"tags":494},[33],[48,33],[474],{"code":496,"score":21,"supported":46,"title":497,"abstract":498,"tags":500},"A6",[],[499],"cotton",[473],{"code":502,"score":61,"supported":46,"title":503,"abstract":504,"tags":506},"H8",[],[505],"sustainable agriculture",[],[508,509],"南疆 覆膜滴灌 棉花","CAWM-S 模型 冻融","南疆覆膜滴灌棉花-5541","10.1016\u002Fj.agsy.2026.105024",{"doi":511,"openalex_id":513,"authors":514,"venue":467,"cited_by_count":134,"oa_url":464,"card":527,"direction":236,"ingested_from":151},"W7223120642",[515,518,520,523,525],{"name":516,"orcid":517},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":519,"orcid":8},"Tongkai Guo",{"name":521,"orcid":522},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":524,"orcid":8},"Wenping Bi",{"name":526,"orcid":8},"Xiaomin Mao",{"tldr":528,"method":529,"finding":530,"direction":236,"opportunity":531},"构建全年耦合模型CAWM-S，模拟覆膜滴灌棉田水热盐运移与作物生长。","将HYDRUS-2D的CAWM-H2D与冻融模型SHAW耦合，用南疆两年监测数据","模型精度较高（R²0.48-0.98），揭示最优灌溉量与灌水盐度呈二次关系。","可扩展至多站点、多作物与不同灌溉制度的独立验证，并耦合遥感数据实现区域尺度模拟。","2026-10-22T11:20:12.238655Z"]