[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1414":3,"related-1414":116},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":82,"slug":85,"view_count":21,"doi":86,"paper":87,"created_at":115},1414,"Beyond Nitrogen Fixation: Multifunctional Roles of Common Bean in Organic Agroecosystems and Ecosystem-Oriented Breeding Strategies","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.0047.v1","Common bean (Phaseolus vulgaris L.) is the world’s most widely consumed grain legume and a foundational species for organic and low-input agriculture, yet its agroecological value extends far beyond nitrogen fixation and nutritional provisioning. This review reframes P. vulgaris as a breeding target defined by ecosystem function rather than by yield alone, synthesizing evidence on functional biology, biological nitrogen fixation, rhizosphere microbiome assembly and soil ecosystem engineering to identify the trait complexes that ecosystem-oriented breeding programs must select for. We examine the constraints limiting expression of these traits under organic management, including weed competition, biotic and abiotic stress, nutrient limitation and the scarcity of cultivars and certified seed adapted to low-input environments, and evaluate the agroecological management practices, resource-use efficiency metrics and emerging technologies, spanning precision agriculture, phenomics, molecular breeding, genome editing and microbiome engineering, that can accelerate their genetic improvement. Central to this synthesis is a paradigm shift in breeding objectives: common bean is conceptualized as a holobiont whose performance emerges from coordinated plant-microbiome interaction, requiring breeding programs to select explicitly for nitrogen-fixation efficiency, root system architecture, rhizosphere recruitment capacity, nutrient-use efficiency and multi-stress resilience as heritable, ecosystem-oriented traits. We conclude by outlining priority breeding research directions integrating genomics, phenomics, envirotyping and rhizosphere ecology, positioning common bean breeding as a strategic lever for the transition toward regenerative, climate-smart organic agriculture.","菜豆（*Phaseolus vulgaris* L.）是全球消费最广泛的食用豆类作物，也是有机农业和低投入农业的基础性物种，然而其农业生态价值远不止于生物固氮和营养供给。本综述将菜豆重新定位为以生态系统功能而非单纯产量为目标的育种对象，综合功能生物学、生物固氮、根际微生物组组装及土壤生态系统工程等方面的证据，识别以生态系统为导向的育种计划必须选择的目标性状复合体。我们考察了有机管理条件下限制这些性状表达的制约因素，包括杂草竞争、生物与非生物胁迫、养分限制以及适应低投入环境的品种和认证种子稀缺性，并评估了可加速其遗传改良的农业生态管理实践、资源利用效率指标及新兴技术，涵盖精准农业、表型组学、分子育种、基因组编辑和微生物组工程。本综述的核心在于育种目标的范式转变：菜豆被概念化为一个全生物体（holobiont），其表现源于植物-微生物组的协同互作，这就要求育种计划将固氮效率、根系构型、根际募集能力、养分利用效率及多重胁迫韧性作为可遗传的、以生态系统为导向的性状进行明确选择。最后，我们概述了整合基因组学、表型组学、环境型鉴定（envirotyping）和根际生态学的优先育种研究方向，将菜豆育种定位为向再生型、气候智能型有机农业转型的战略杠杆。",null,"Preprints.org","2026-09-01T00:00:00Z","论文",25,false,65,{"impact":17,"substance":18,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,10,7,1,"综述提出菜豆作为生态系统功能育种目标，涵盖多学科交叉，对有机农业和育种研究有参考价值。",[24,25],{"name":10,"url":6},{"name":26,"url":27},"Plants","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15182884",[29,30,31,32,33],"微生物组","智慧育种","农业生态","有机农业","菜豆育种",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,56,63,71,77],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":55},9,true,[],[52,53,36,54],"土壤","根际","rhizosphere",[],{"code":57,"score":20,"supported":49,"title":58,"abstract":59,"tags":62},"F2",[],[60,61],"分子育种","molecular breeding",[30],{"code":64,"score":65,"supported":49,"title":66,"abstract":67,"tags":70},"I1",6,[],[68,69],"精准农业","precision agriculture",[],{"code":72,"score":73,"supported":49,"title":74,"abstract":75,"tags":76},"H6",4,[],[31],[31],{"code":78,"score":21,"supported":14,"title":79,"abstract":80,"tags":81},"H8",[],[],[32],[83,84],"农业生态 微生物组 智慧育种 有机农业","农业生态 微生物组","农业生态微生物组智慧育种有机农业-1414","10.20944\u002Fpreprints202609.0047.v1",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":113,"ingested_from":114},"W7205017877",[90,93,95,97,100,103],{"name":91,"orcid":92},"D.I. Avasiloaiei","https:\u002F\u002Forcid.org\u002F0000-0003-3316-168X",{"name":94,"orcid":9},"Mariana Calara",{"name":96,"orcid":9},"Petre Marian Brezeanu",{"name":98,"orcid":99},"Barbara Pipan","https:\u002F\u002Forcid.org\u002F0000-0002-3860-119X",{"name":101,"orcid":102},"Lovro Sinkovič","https:\u002F\u002Forcid.org\u002F0000-0002-6123-8085",{"name":104,"orcid":105},"C. Brezeanu","https:\u002F\u002Forcid.org\u002F0000-0002-3352-3081",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"综述菜豆在有机农业中的多功能生态作用，提出以生态系统为导向的育种策略。","综述功能生物学、生物固氮、根际微生物组、土壤生态工程及精准农业、表型组学、分子育","菜豆应作为全生物体育种，选择固氮效率、根系构型、根际招募等生态系统导向性状。","农业绿色发展与碳","可研究菜豆-微生物互作性状的遗传基础，结合表型组与环境型，开发低投入适应性品种。","智慧农业 \u002F 农业物联网","openalex","2026-09-02T23:30:13.807287Z",{"total":65,"page":21,"page_size":65,"items":117},[118,213,272,362,424,502],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":126,"score_detail":127,"sources":132,"tags":134,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":184,"slug":187,"view_count":106,"doi":188,"paper":189,"created_at":212},5466,"Microbial and enzymatic response of soil to organic and natural management practices in chickpea-based cropping systems at the Satpura foothills","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1814274","Inroduction Natural and organic farming systems are increasingly recognized as sustainable agroecological approaches that promote regenerative practices, ecological balance, and reduced reliance on external inputs. This study aimed to assess the effects of different nutrient management strategies on the soil enzymatic and biological properties of chickpea-based cropping systems. Methods A field experiment was conducted during the rabi seasons of 2022–23 and 2023–24 at the Centre for Organic Agriculture Research and Training, Department of Agronomy, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra, India. The experiment followed a strip-plot design with four replications. Four cropping systems were evaluated in a 2:2 row proportion: sole chickpea, chickpea intercropped with coriander, chickpea intercropped with linseed, and chickpea intercropped with sorghum. Three nutrient management strategies were assessed: organic farming, natural farming, and an integrated approach combining organic and natural farming inputs. Results Across both years, sole chickpea consistently supported greater soil biological activity than the intercropping systems. Compared with chickpea–sorghum intercropping under natural farming, sole chickpea managed using integrated organic and natural farming practices significantly increased soil nitrogen, phosphorus, and potassium by 60%, 100%, and 192%, respectively. Fungal, bacterial, and actinomycete populations increased by 78.0%, 85.4%, and 109.8%, respectively. Soil microbial biomass carbon and nitrogen increased by 42.4% and 64.6%, respectively. Urease, dehydrogenase, acid phosphatase, and alkaline phosphatase activities increased by 75.6%, 100%, 116.8%, and 42.4%, respectively. Among the nutrient management strategies, the integrated application of organic and natural farming inputs showed superior performance in improving soil biological properties compared with either approach alone. Discussion The findings indicate that sole chickpea combined with integrated organic and natural nutrient management can improve soil microbial populations, enzymatic activities, nutrient availability, and overall soil biological health. This management strategy may contribute to long-term soil fertility and sustainable agricultural production in Inceptisols of the Satpura foothill region of India under semi-arid conditions.","引言 自然农业与有机农业体系日益被视为可持续的农业生态方法，能够促进再生性实践、生态平衡并减少对外部投入的依赖。本研究旨在评估不同养分管理策略对鹰嘴豆（chickpea）种植体系土壤酶学及生物学特性的影响。方法 于2022—23年度和2023—24年度rabi季在印度马哈拉施特拉邦阿科拉市Dr. Panjabrao Deshmukh Krishi Vidyapeeth农学系有机农业研究与培训中心开展田间试验。试验采用条区设计（strip-plot design），设4次重复。以2：2行比评估4种种植体系：鹰嘴豆单作、鹰嘴豆与芫荽间作、鹰嘴豆与亚麻间作、鹰嘴豆与高粱间作。评估3种养分管理策略：有机农业、自然农业以及有机与自然农业投入相结合的集成方式。结果 两年间，鹰嘴豆单作始终较间作体系支持更高的土壤生物学活性。与自然农业下的鹰嘴豆—高粱间作相比，采用有机与自然农业集成实践的鹰嘴豆单作使土壤氮、磷、钾分别显著提高60%、100%和192%；真菌、细菌和放线菌数量分别增加78.0%、85.4%和109.8%；土壤微生物生物量碳和氮分别增加42.4%和64.6%；脲酶、脱氢酶、酸性磷酸酶和碱性磷酸酶活性分别提高75.6%、100%、116.8%和42.4%。在养分管理策略中，有机与自然农业投入的集成施用较任一单一方式在改善土壤生物学特性方面表现更优。讨论 研究结果表明，鹰嘴豆单作结合有机与自然养分集成管理可改善土壤微生物种群、酶活性、养分有效性及整体土壤生物学健康。该管理策略可能有助于印度萨特普拉山麓地区半干旱条件下始成土（Inceptisols）的长期土壤肥力和可持续农业生产。","Frontiers in Sustainable Food Systems","2026-10-20T00:00:00Z",69,{"impact":19,"substance":128,"depth":129,"authority":130,"freshness":48,"relevant":21,"comment":131},20,17,13,"田间试验数据扎实、结论明确，但属印度区域尺度的农学论文，对国内智慧农业主题页聚合价值有限，可作国际有机农业参考。",[133],{"name":124,"url":121},[135,32,136,137,138],"间作系统","养分管理","土壤微生物","鹰嘴豆",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":142},{"candidates":143,"agricultural_evidence":49},[144,149,156,163,168,173,178],{"code":35,"score":145,"supported":49,"title":146,"abstract":147,"tags":148},15,[36],[52,36],[137],{"code":150,"score":65,"supported":49,"title":151,"abstract":152,"tags":155},"A5",[],[153,154],"高粱","sorghum",[],{"code":157,"score":65,"supported":49,"title":158,"abstract":159,"tags":162},"A7",[],[160,161],"间作","intercropping",[],{"code":164,"score":73,"supported":49,"title":165,"abstract":166,"tags":167},"F3",[],[136],[136],{"code":78,"score":73,"supported":49,"title":169,"abstract":170,"tags":172},[],[171],"可持续农业",[32],{"code":72,"score":174,"supported":49,"title":175,"abstract":176,"tags":177},3,[],[31],[],{"code":179,"score":174,"supported":49,"title":180,"abstract":181,"tags":183},"H7",[],[182],"干旱",[],[185,186],"鹰嘴豆 有机农业 土壤微生物","Satpura 鹰嘴豆 间作","鹰嘴豆有机农业土壤微生物-5466","10.3389\u002Ffsufs.2026.1814274",{"doi":188,"openalex_id":190,"authors":191,"venue":124,"cited_by_count":106,"oa_url":206,"card":207,"direction":111,"ingested_from":114},"W7223012759",[192,194,196,198,200,202,204],{"name":193,"orcid":9},"Nalla Charitha",{"name":195,"orcid":9},"Adinath N. Paslawar",{"name":197,"orcid":9},"Vilas Kashinath Kharche",{"name":199,"orcid":9},"Madastu Saikrishna",{"name":201,"orcid":9},"Yogesh V. Ingle",{"name":203,"orcid":9},"Akanksha Singh",{"name":205,"orcid":9},"Anita B. Chorey","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1814274\u002Fpdf",{"tldr":208,"method":209,"finding":210,"direction":111,"opportunity":211},"比较有机与自然农法下鹰嘴豆种植体系对土壤微生物和酶活性的影响。","两年条区试验，4种种植体系×3种养分管理，测土壤微生物数量、生物量碳氮及酶活性。","单作鹰嘴豆配合有机+自然综合管理显著提升土壤养分、微生物数量与酶活性。","可探究综合有机-自然管理下微生物群落结构与碳固持的长期耦合机制。","2026-10-21T23:30:06.876518Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":220,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":221,"score_detail":222,"sources":227,"tags":229,"primary_topic":233,"secondary_topics":234,"classification":235,"search_phrases":253,"slug":256,"view_count":21,"doi":257,"paper":258,"created_at":271},4696,"Knowledge and Practices in Organic Agriculture Production among Grade 12 Students of Gamu Agri-Fishery School, Isabela","https:\u002F\u002Fdoi.org\u002F10.65339\u002Fijsis.v1.i3.157","This study assessed the knowledge and practices of Grade 12 Organic Agriculture students of Gamu Agri-Fishery School in Linglingay, Gamu, Isabela, and examined the relationship between these two variables. Anchored on a Knowledge–Practice Framework informed by experiential and competency-based learning, the study employed a quantitative, cross-sectional descriptive-correlational design. Total enumeration was used involving all 35 Grade 12 Organic Agriculture students. Data were gathered through a researcher-developed structured questionnaire consisting of an objective knowledge assessment and a frequency-based practice scale. Descriptive statistics and Spearman’s rank-order correlation were employed in analyzing the data. Results showed a high level of knowledge, with an overall performance of 85.43% and 88.57% of the respondents classified as having high or very high knowledge. Organic agriculture practices were also consistently demonstrated, with an overall mean of 3.57 and 94.29% of respondents classified under the “Always” category. Students demonstrated their strongest competencies in composting, organic-input utilization, soil fertility, and farm management, while pest and disease management obtained comparatively lower results. A moderately strong positive and statistically significant relationship was found between knowledge and practices (ρ = .641, p \u003C .001), indicating that greater knowledge tended to correspond with more frequent application of organic agriculture practices. The study concludes that the students possess substantial theoretical understanding and practical competencies in organic agriculture production. Continued integration of classroom instruction with hands-on agricultural activities is recommended, with greater emphasis on organic pest and disease management. The study supports SDG 2 on sustainable food production, SDG 4 on quality education, and related goals concerning responsible production, decent work, climate action, and environmental protection. Its sustainability impact lies in strengthening agricultural education, environmental responsibility, youth agricultural competency, and preparation for sustainable livelihoods and food-production systems.","本研究评估了位于伊莎贝拉省加穆市林林盖的加穆农业-渔业学校12年级有机农业学生的知识与实践，并考察了这两个变量之间的关系。本研究以基于体验式学习和能力本位学习的知识-实践框架为理论基础，采用定量、横断面描述性-相关性设计。研究采用全体枚举法，涵盖全部35名12年级有机农业学生。数据通过研究者自行开发的结构化问卷收集，该问卷由客观知识评估和基于频率的实践量表组成。数据分析采用描述性统计和斯皮尔曼等级相关。结果显示，学生的知识水平较高，总体表现为85.43%，88.57%的受访者被归类为具有高或极高知识水平。有机农业实践也得到了持续体现，总体均值为3.57，94.29%的受访者被归类为“总是”类别。学生在堆肥、有机投入品利用、土壤肥力和农场管理方面表现出最强的能力，而病虫害管理的结果相对较低。知识与实践之间存在中等强度的正相关且具有统计学显著性（ρ = .641，p \u003C .001），表明知识水平越高，有机农业实践的应用频率往往越高。研究得出结论，学生具备扎实的有机农业生产理论理解和实践能力。建议继续将课堂教学与动手农业活动相结合，并更加重视有机病虫害管理。本研究支持可持续发展目标2（可持续粮食生产）、可持续发展目标4（优质教育），以及有关负责任生产、体面工作、气候行动和环境保护的相关目标。其可持续性影响在于加强农业教育、环境责任、青年农业能力，以及为可持续生计和粮食生产体系做好准备。","International Journal of Sustainable and Integrated Studies","2026-10-09T00:00:00Z",45,{"impact":223,"substance":224,"depth":17,"authority":65,"freshness":225,"relevant":21,"comment":226},5,14,8,"菲律宾一校35名学生的有机农业知识—实践相关性研究，样本小、地域性强，对国内三农信息化参考价值有限，不宜进入每日精选。",[228],{"name":219,"url":216},[171,230,231,32,232],"农业教育","病虫害防治","青年农技",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":236},{"candidates":237,"agricultural_evidence":49},[238,242,248],{"code":35,"score":65,"supported":49,"title":239,"abstract":240,"tags":241},[],[52,36],[],{"code":243,"score":174,"supported":49,"title":244,"abstract":245,"tags":247},"D",[],[246],"渔业",[],{"code":78,"score":249,"supported":14,"title":250,"abstract":251,"tags":252},2,[],[],[171,32],[254,255],"Gamu Agri-Fishery School 有机农业","Isabela 有机农业 学生","GamuAgri-FisherySchool有机农业-4696","10.65339\u002Fijsis.v1.i3.157",{"doi":257,"openalex_id":259,"authors":260,"venue":219,"cited_by_count":106,"oa_url":263,"card":264,"direction":270,"ingested_from":114},"W7221078739",[261],{"name":262,"orcid":9},"Ricardo B. Vigan","https:\u002F\u002Fgraspepress.com\u002Fijsis\u002Farticle\u002Fdownload\u002F179\u002F170",{"tldr":265,"method":266,"finding":267,"direction":268,"opportunity":269},"评估12年级有机农业学生的知识与实践水平及其相关性。","对35名学生进行问卷调查，用描述统计和Spearman相关分析。","知识与实践呈中度正相关（ρ=.641），病虫害管理相对薄弱。","数字乡村与农业信息化","可开发数字化学习工具强化有机病虫害管理，并追踪知识向实践转化的长期效果。","农业人工智能与决策模型","2026-10-11T23:30:58.324864Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":291,"secondary_topics":292,"classification":299,"search_phrases":321,"slug":324,"view_count":106,"doi":325,"paper":326,"created_at":361},4366,"Article topic: comprehensive assessment of soil and flora contamination by heavy metals in the zone of influence of the Nikolaevskaya enrichment plant","https:\u002F\u002Fdoi.org\u002F10.51886\u002F1999-740x_2026_3_46","Agricultural lands in East Kazakhstan region cover a significant area, with large portions of pasture and cropland situated in close proximity to operating industrial facilities. Despite this, no comprehensive integrated assessment combining remote sensing, ground-based soil monitoring, and flora survey had previously been conducted for the zone of influence of the Nikolaevskaya enrichment plant. This study aimed to fill that gap by a 40-year land cover analysis (1985-2024) using publicly available satellite data processed in Google Earth Engine with soil sampling and vegetation surveys across 17 sites. Soil analyses revealed not only elevated heavy metal concentrations but also a sharp pH decrease, conditions under which heavy metals become significantly more mobile and available for plant uptake. Technosol formation was observed at multiple sampling points, indicating a permanent transformation of the natural soil profile. The flora survey recorded 72 species from 62 genera, with ruderal and synanthropic species domina-ting disturbed habitats and native species persisting only at control points more distant from the facility. Taken together, these findings point to ongoing ecosystem degradation and raise concrete concerns for the regional food chain given the pastureland and cropland adjacent to the plant.","东哈萨克斯坦地区的农业用地面积广阔，其中大片牧场和农田与正在运营的工业设施相距不远。尽管如此，此前尚未针对尼古拉耶夫斯克选矿厂影响区域开展过将遥感、地面土壤监测与植物区系调查相结合的综合评估。本研究旨在填补这一空白，通过为期40年的土地覆盖分析（1985—2024年），利用Google Earth Engine处理的公开卫星数据，并结合17个样点的土壤采样与植被调查。土壤分析不仅揭示了重金属浓度升高，还发现pH值急剧下降，在这种条件下，重金属的迁移性和植物可利用性显著增强。在多个采样点观察到技术土（Technosol）的形成，表明自然土壤剖面发生了永久性改变。植物区系调查共记录到62属72种植物，其中扰动生境以杂草类和伴人植物物种为主，原生物种仅存于距设施较远的对照点。综合来看，这些发现表明生态系统正在持续退化，并且鉴于该厂邻近牧场和农田，对区域食物链构成了切实的担忧。","Pochvovedenie i agrokhimiya","2026-10-05T00:00:00Z",73,{"impact":17,"substance":282,"depth":18,"authority":130,"freshness":225,"relevant":21,"comment":283},22,"结合40年遥感与地面采样的重金属污染综合评估，方法扎实、结论具体，但属区域案例研究，公共影响有限。",[285],{"name":278,"url":275},[287,31,288,289,290],"耕地保护","遥感监测","Google Earth Engine","土壤重金属",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[293],{"code":294,"slug":295,"name":296,"path":297,"parent_code":298},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":42,"locked":14,"version":43,"method":44,"evidence":300},{"candidates":301,"agricultural_evidence":49},[302,306,312,317],{"code":35,"score":145,"supported":49,"title":303,"abstract":304,"tags":305},[36],[52,36],[290],{"code":294,"score":225,"supported":49,"title":307,"abstract":308,"tags":311},[],[309,310],"遥感","remote sensing",[289,288],{"code":313,"score":21,"supported":14,"title":314,"abstract":315,"tags":316},"H1",[],[],[287],{"code":72,"score":21,"supported":14,"title":318,"abstract":319,"tags":320},[],[],[31],[322,323],"Nikolaevskaya 选矿厂 土壤污染","东哈萨克斯坦 农田 重金属","Nikolaevskaya选矿厂土壤污染-4366","10.51886\u002F1999-740x_2026_3_46",{"doi":325,"openalex_id":327,"authors":328,"venue":278,"cited_by_count":106,"oa_url":354,"card":355,"direction":360,"ingested_from":114},"W7220713350",[329,332,334,337,340,343,346,349,352],{"name":330,"orcid":331},"Yuliya Tugunova","https:\u002F\u002Forcid.org\u002F0009-0006-1037-1480",{"name":333,"orcid":9},"M. Altynbayeva",{"name":335,"orcid":336},"Жанар Абилда","https:\u002F\u002Forcid.org\u002F0000-0001-8505-5243",{"name":338,"orcid":339},"Рахим Канат","https:\u002F\u002Forcid.org\u002F0009-0009-2418-5920",{"name":341,"orcid":342},"Abylay Begaly","https:\u002F\u002Forcid.org\u002F0009-0009-2848-336X",{"name":344,"orcid":345},"D. Kairkhanov","https:\u002F\u002Forcid.org\u002F0009-0006-1017-1980",{"name":347,"orcid":348},"Iskander Isgandarov","https:\u002F\u002Forcid.org\u002F0009-0001-4386-3303",{"name":350,"orcid":351},"ELENA A. ISAKOVA","https:\u002F\u002Forcid.org\u002F0000-0002-9056-6504",{"name":353,"orcid":9},"Kh. Choi","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1135\u002F802",{"tldr":356,"method":357,"finding":358,"direction":111,"opportunity":359},"综合遥感、土壤采样与植被调查，评估哈萨克斯坦一选矿厂影响区40年重金属污染与生态退化。","Google Earth Engine处理1985-2024卫星数据，结合17个","土壤重金属升高且pH骤降，重金属迁移性增强，形成技术土，植被以杂草为主，威胁食物链。","可延伸研究重金属活化与pH耦合机制，并构建污染区农田食物链风险预警模型。","农业遥感与作物表型","2026-10-07T23:30:29.146414Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":367,"content":9,"source_name":368,"source_url":365,"published_at":369,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":370,"score_detail":371,"sources":373,"tags":375,"primary_topic":381,"secondary_topics":382,"classification":388,"search_phrases":404,"slug":407,"view_count":106,"doi":408,"paper":409,"created_at":423},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":225,"substance":128,"depth":129,"authority":48,"freshness":19,"relevant":21,"comment":372},"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。",[374],{"name":368,"url":365},[376,377,378,379,380],"智慧农业","农业人工智能","主动学习","线虫检测","土壤采样",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[383],{"code":384,"slug":385,"name":386,"path":387,"parent_code":298},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":42,"locked":14,"version":43,"method":44,"evidence":389},{"candidates":390,"agricultural_evidence":49},[391,395,400],{"code":35,"score":224,"supported":49,"title":392,"abstract":393,"tags":394},[36],[52,36],[],{"code":384,"score":48,"supported":49,"title":396,"abstract":398,"tags":399},[397],"artificial intelligence",[],[377],{"code":64,"score":20,"supported":49,"title":401,"abstract":402,"tags":403},[],[68,69],[376],[405,406],"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","10.5281\u002Fzenodo.23544145",{"doi":408,"openalex_id":410,"authors":411,"venue":368,"cited_by_count":106,"oa_url":365,"card":418,"direction":270,"ingested_from":114},"W7223082195",[412,414,416],{"name":413,"orcid":9},"Amine Ben Salah",{"name":415,"orcid":9},"Ines Trabelsi",{"name":417,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":419,"method":420,"finding":421,"direction":270,"opportunity":422},"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。","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。","2026-10-22T11:20:58.901747Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":125,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":442,"secondary_topics":443,"classification":444,"search_phrases":464,"slug":467,"view_count":106,"doi":468,"paper":469,"created_at":501},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",79,{"impact":433,"substance":282,"depth":18,"authority":224,"freshness":48,"relevant":21,"comment":434},16,"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。",[436],{"name":430,"url":427},[288,438,439,440,441],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":445},{"candidates":446,"agricultural_evidence":49},[447,451,455,460],{"code":35,"score":224,"supported":49,"title":448,"abstract":449,"tags":450},[36],[52,36],[],{"code":294,"score":20,"supported":49,"title":452,"abstract":453,"tags":454},[],[309,310],[288],{"code":179,"score":65,"supported":49,"title":456,"abstract":457,"tags":459},[],[458,182],"气候适应",[],{"code":72,"score":21,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[440],[465,466],"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":468,"openalex_id":470,"authors":471,"venue":430,"cited_by_count":106,"oa_url":427,"card":496,"direction":360,"ingested_from":114},"W7223069171",[472,474,476,478,481,483,486,489,491,493],{"name":473,"orcid":9},"Yue Li",{"name":475,"orcid":9},"Mengfei Shi",{"name":477,"orcid":9},"Fei Zhang",{"name":479,"orcid":480},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":482,"orcid":9},"Gowhar Meraj",{"name":484,"orcid":485},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":487,"orcid":488},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":490,"orcid":9},"Yuanfang Chai",{"name":492,"orcid":9},"Hongtao Xu",{"name":494,"orcid":495},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":497,"method":498,"finding":499,"direction":360,"opportunity":500},"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。","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":503,"title":504,"url":505,"summary":506,"summary_zh":507,"content":9,"source_name":508,"source_url":505,"published_at":369,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":509,"score_detail":510,"sources":513,"tags":515,"primary_topic":519,"secondary_topics":525,"classification":528,"search_phrases":562,"slug":565,"view_count":106,"doi":566,"paper":567,"created_at":584},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。","Agronomy",77,{"impact":433,"substance":511,"depth":129,"authority":130,"freshness":19,"relevant":21,"comment":512},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。",[514],{"name":508,"url":505},[376,516,288,517,518],"机器学习","土壤墒情","冬小麦",{"code":520,"slug":521,"name":522,"path":523,"parent_code":524},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[526,527],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":384,"slug":385,"name":386,"path":387,"parent_code":298},{"status":42,"locked":14,"version":43,"method":44,"evidence":529},{"candidates":530,"agricultural_evidence":49},[531,537,541,546,551,558],{"code":520,"score":532,"supported":49,"title":533,"abstract":535,"tags":536},26,[534,521],"winter wheat",[522,534,521],[518],{"code":35,"score":145,"supported":49,"title":538,"abstract":539,"tags":540},[36],[52,36],[517],{"code":384,"score":145,"supported":49,"title":542,"abstract":544,"tags":545},[543],"machine learning",[516,543],[516],{"code":294,"score":19,"supported":49,"title":547,"abstract":548,"tags":550},[],[309,310,549],"multispectral",[288],{"code":552,"score":65,"supported":49,"title":553,"abstract":554,"tags":557},"H3",[],[555,556],"灌溉","irrigation",[],{"code":64,"score":73,"supported":49,"title":559,"abstract":560,"tags":561},[],[376],[376],[563,564],"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":566,"openalex_id":568,"authors":569,"venue":508,"cited_by_count":106,"oa_url":505,"card":579,"direction":113,"ingested_from":114},"W7223156204",[570,573,575,577],{"name":571,"orcid":572},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":574,"orcid":9},"Yi Zhang",{"name":576,"orcid":9},"WEI Lei",{"name":578,"orcid":9},"Chengcai Zhang",{"tldr":580,"method":581,"finding":582,"direction":360,"opportunity":583},"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。","2026-10-22T11:20:18.887301Z"]