[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1269":3,"related-1269":101},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":29,"secondary_topics":34,"classification":35,"search_phrases":80,"slug":83,"view_count":20,"doi":84,"paper":85,"created_at":100},1269,"Second-crop corn in the State of Paraná","https:\u002F\u002Fdoi.org\u002F10.33158\u002Fasb.r272.v12.2026","The second-crop corn has become one of the main agricultural production systems in Brazil, with the state of Paraná being one of the largest national producers of this crop. The expansion of the soybean-corn system has significantly contributed to increased grain production and the strengthening of animal protein production chains, playing a strategic role in the state and national economy. However, second-crop corn cultivation is subject to several factors that limit productivity, notably abiotic stresses such as water deficit, low temperatures, frosts, and irregular rainfall distribution, as well as biotic factors such as the corn leafhopper (Dalbulus maidis), the corn stunt complex, foliar diseases, insect pests, and weeds. These factors directly affect the physiological and biochemical processes of the plant, compromising the growth, development, and yield of cultivated hybrids. In this context, genetic improvement plays a fundamental role in the development of cultivars better adapted to the edaphoclimatic conditions of Paraná. The use of modern tools, such as marker-assisted selection, genomic selection, high-precision phenotyping, digital agriculture, remote sensing, and gene editing, has contributed to accelerating the development of more productive, resistant, and stable hybrids. Furthermore, the integration of breeding technologies, agronomic management, and climate monitoring represents a promising strategy to minimize the impacts of climate change and increase the sustainability of the production system. Therefore, this review gathers and discusses the main aspects related to the cultivation of second-crop maize in the state of Paraná, addressing the factors that influence productivity, the physiological and genetic mechanisms of resistance, the technological advances applied to genetic improvement, and the prospects for the development of more resilient and efficient production systems.",null,"Agronomy Science and Biotechnology","2026-08-29T00:00:00Z","论文",10,false,62,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},18,16,15,3,1,"综述巴西二季玉米生产制约因素与育种技术进展，对国内玉米产业有参考价值，但时效性较低。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","玉米","遗传育种","巴西",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":36,"locked":13,"version":37,"method":38,"evidence":39},"classified","2026-10-22.2","rules",{"candidates":40,"agricultural_evidence":42},[41,47,55,62,68,74],{"code":30,"score":18,"supported":42,"title":43,"abstract":45,"tags":46},true,[44],"corn",[31,44],[26],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":54},"F2",6,[],[52,53],"gene editing","genomic selection",[],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":61},"I1",4,[],[60],"digital agriculture",[25],{"code":63,"score":19,"supported":42,"title":64,"abstract":65,"tags":67},"A4",[],[66],"soybean",[],{"code":69,"score":19,"supported":42,"title":70,"abstract":71,"tags":73},"H7",[],[72],"climate change",[],{"code":75,"score":19,"supported":42,"title":76,"abstract":77,"tags":79},"I3",[],[78],"remote sensing",[],[81,82],"智慧农业 遗传育种 巴西 玉米","智慧农业 遗传育种","智慧农业遗传育种巴西玉米-1269","10.33158\u002Fasb.r272.v12.2026",{"doi":84,"openalex_id":86,"authors":87,"venue":9,"cited_by_count":91,"oa_url":92,"card":93,"direction":97,"ingested_from":99},"W7204666901",[88],{"name":89,"orcid":90},"Helio Souza","https:\u002F\u002Forcid.org\u002F0000-0001-9683-0655",0,"https:\u002F\u002Fwww.mecenaspublishing.com\u002Fjournals\u002Findex.php\u002Fasbjournal\u002Farticle\u002Fdownload\u002F272\u002F487",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"综述巴拉那州二季玉米生产，探讨限制因素、遗传改良技术及提升可持续性的策略。","综述遗传改良、标记辅助选择、基因组选择、表型分析、遥感及基因编辑等技术。","整合育种、农艺管理和气候监测可减轻气候变化影响，提升二季玉米产量和可持续性。","智慧农业 \u002F 农业物联网","可研究将遥感与气候监测数据整合到育种决策中，开发适应二季玉米特定胁迫的智能管理系统。","openalex","2026-09-01T04:03:13.010485Z",{"total":49,"page":20,"page_size":49,"items":102},[103,173,252,307,389,455],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":8,"source_name":109,"source_url":106,"published_at":110,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":124,"classification":131,"search_phrases":152,"slug":155,"view_count":91,"doi":156,"paper":157,"created_at":172},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":113,"substance":16,"depth":17,"authority":114,"freshness":115,"relevant":20,"comment":116},8,12,9,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[118],{"name":109,"url":106},[25,120,121,26,122],"农业人工智能","机器学习","耐盐育种",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},[125],{"code":126,"slug":127,"name":128,"path":129,"parent_code":130},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":36,"locked":13,"version":37,"method":38,"evidence":132},{"candidates":133,"agricultural_evidence":42},[134,141,145],{"code":126,"score":135,"supported":42,"title":136,"abstract":138,"tags":140},19,[137],"machine learning",[121,137,139],"decision support",[120,121],{"code":30,"score":18,"supported":42,"title":142,"abstract":143,"tags":144},[31],[26,31],[26],{"code":56,"score":146,"supported":42,"title":147,"abstract":148,"tags":151},7,[],[149,150],"精准农业","precision agriculture",[25],[153,154],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":156,"openalex_id":158,"authors":159,"venue":109,"cited_by_count":91,"oa_url":106,"card":166,"direction":170,"ingested_from":99},"W7221462188",[160,163],{"name":161,"orcid":162},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":164,"orcid":165},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","农业人工智能与决策模型","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":180,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":181,"score_detail":182,"sources":187,"tags":189,"primary_topic":193,"secondary_topics":194,"classification":201,"search_phrases":234,"slug":237,"view_count":20,"doi":238,"paper":239,"created_at":251},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架","Journal of Experimental Agriculture International","2026-10-10T00:00:00Z",71,{"impact":114,"substance":183,"depth":184,"authority":185,"freshness":115,"relevant":20,"comment":186},20,17,13,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[188],{"name":179,"url":176},[25,190,26,149,191,192],"变量施肥","遥感监测","养分管理",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},[195],{"code":196,"slug":197,"name":198,"path":199,"parent_code":200},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":36,"locked":13,"version":37,"method":38,"evidence":202},{"candidates":203,"agricultural_evidence":42},[204,208,212,219,226,230],{"code":56,"score":17,"supported":42,"title":205,"abstract":206,"tags":207},[150],[149,150],[25,149],{"code":30,"score":18,"supported":42,"title":209,"abstract":210,"tags":211},[31],[26,31],[26],{"code":196,"score":213,"supported":42,"title":214,"abstract":215,"tags":218},11,[],[216,217,192],"施肥","肥料",[192,190],{"code":220,"score":49,"supported":42,"title":221,"abstract":222,"tags":225},"H2",[],[223,224],"土壤","soil",[],{"code":126,"score":19,"supported":42,"title":227,"abstract":228,"tags":229},[],[121],[],{"code":75,"score":20,"supported":13,"title":231,"abstract":232,"tags":233},[],[],[191],[235,236],"玉米 精准施肥 氮肥管理","遥感 变量施肥 养分利用效率","玉米精准施肥氮肥管理-4694","10.9734\u002Fjeai\u002F2026\u002Fv48i104559",{"doi":238,"openalex_id":240,"authors":241,"venue":179,"cited_by_count":91,"oa_url":176,"card":246,"direction":170,"ingested_from":99},"W7221366291",[242,244],{"name":243,"orcid":8},"Piyali Pal",{"name":245,"orcid":8},"Disha Chattopadhyay",{"tldr":247,"method":248,"finding":249,"direction":170,"opportunity":250},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","2026-10-11T23:30:51.450082Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":8,"content":8,"source_name":257,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":259,"sources":262,"tags":264,"primary_topic":267,"secondary_topics":268,"classification":269,"search_phrases":293,"slug":296,"view_count":297,"doi":8,"paper":298,"created_at":306},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":16,"substance":260,"depth":16,"authority":185,"freshness":19,"relevant":20,"comment":261},22,"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[263],{"name":257,"url":255},[25,265,121,26,149,266],"产量预测","遥感",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":270},{"candidates":271,"agricultural_evidence":42},[272,276,280,285,289],{"code":56,"score":185,"supported":42,"title":273,"abstract":274,"tags":275},[149],[149],[25,149],{"code":30,"score":114,"supported":42,"title":277,"abstract":278,"tags":279},[26],[26],[26],{"code":75,"score":281,"supported":42,"title":282,"abstract":283,"tags":284},5,[],[266],[265,266],{"code":126,"score":57,"supported":42,"title":286,"abstract":287,"tags":288},[],[121],[121],{"code":220,"score":19,"supported":42,"title":290,"abstract":291,"tags":292},[],[223],[],[294,295],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",2,{"doi":8,"openalex_id":8,"authors":299,"venue":8,"cited_by_count":91,"oa_url":8,"card":300,"direction":97,"ingested_from":305},[],{"tldr":301,"method":302,"finding":303,"direction":97,"opportunity":304},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","agent","2026-10-09T00:05:02.451957Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":8,"source_name":313,"source_url":310,"published_at":314,"category":11,"cover_url":8,"hotness":315,"is_selected":13,"score":316,"score_detail":317,"sources":319,"tags":324,"primary_topic":328,"secondary_topics":329,"classification":336,"search_phrases":365,"slug":368,"view_count":91,"doi":369,"paper":370,"created_at":388},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。","Frontiers in Sustainable Food Systems","2026-10-07T00:00:00Z",25,80,{"impact":16,"substance":260,"depth":16,"authority":185,"freshness":115,"relevant":20,"comment":318},"基于12个玉米主产省2010—2024年面板数据构建农业区块链就绪环境指数，发现其与玉米碳排放强度负相关，方法规范、数据规模可观，但结论仅为关联性而非因果，属细分领域实证进展。",[320,321],{"name":313,"url":310},{"name":322,"url":323},"Frontiers in Sustainable Food Systems 2026 年 10 月 07 日 Volume 10 Sec. Land, Livelihoods and Food Security","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Ffull",[325,25,26,326,327],"数字乡村","农业碳排放","区块链",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},[330],{"code":331,"slug":332,"name":333,"path":334,"parent_code":335},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":36,"locked":13,"version":37,"method":38,"evidence":337},{"candidates":338,"agricultural_evidence":42},[339,343,349,354,360],{"code":30,"score":18,"supported":42,"title":340,"abstract":341,"tags":342},[31],[26,31],[26],{"code":331,"score":114,"supported":42,"title":344,"abstract":346,"tags":348},[345],"carbon emission",[347],"碳排放",[326],{"code":56,"score":146,"supported":42,"title":350,"abstract":351,"tags":353},[],[352,60],"数字农业",[25],{"code":355,"score":19,"supported":42,"title":356,"abstract":357,"tags":359},"G2",[],[358],"社会化服务",[],{"code":361,"score":20,"supported":13,"title":362,"abstract":363,"tags":364},"I6",[],[],[325],[366,367],"农业区块链 玉米 碳排放","玉米主产区 区块链 碳强度","农业区块链玉米碳排放-4416","10.3389\u002Ffsufs.2026.1942225",{"doi":369,"openalex_id":371,"authors":372,"venue":313,"cited_by_count":91,"oa_url":381,"card":382,"direction":97,"ingested_from":99},"W7220869193",[373,376,378],{"name":374,"orcid":375},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":377,"orcid":8},"Zhiyi Hu",{"name":379,"orcid":380},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Fpdf",{"tldr":383,"method":384,"finding":385,"direction":386,"opportunity":387},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","农业绿色发展与碳","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","2026-10-08T23:30:07.668939Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":8,"source_name":395,"source_url":392,"published_at":396,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":397,"score_detail":398,"sources":401,"tags":403,"primary_topic":407,"secondary_topics":408,"classification":413,"search_phrases":438,"slug":441,"view_count":20,"doi":442,"paper":443,"created_at":454},4346,"Response of OPV Corn (Zea mays) to Antitranspirants (Chitosan) Under Drought Stress","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i4-1723642","Chitosan is gaining recognition in crop science as a natural antitranspirant that improves plant water-use efficiency and enhances resilience under drought stress. This study investigated the effects of chitosan derived from golden apple snail shells on the growth, physiological response, and yield performance of glutinous open-pollinated variety (OPV) corn (Zea mays) under drought conditions. Conducted from February 23 to April 29, 2025, in Macatoc, Victoria, Oriental Mindoro, the experiment followed a 3×4 factorial design using a Randomized Complete Block Design, with Factor A being water stress intervals (regular, 15 days, and 25 days) and Factor B being chitosan levels (0, 50, 100, and 150 ml\u002FL). Results showed that the first factor (water stress interval) had a significant effect (p\u003C0.05) on key growth traits such as plant height, number of leaves, leaf area, and fresh and dry biomass, with regular watering (A1) producing superior values compared to 15-day (A2) and 25-day (A3) intervals. However, yield components like corn ear diameter, length, and weight showed comparable results between A1 and A2, suggesting moderate water stress did not drastically affect reproductive traits. The second factor (chitosan level) significantly influenced all parameters, with 100 ml\u002FL (B3) producing the best performance in plant height, leaf area, number of leaves, biomass, corn ear traits, Relative Water Content (RWC), and Excised Leaf Water Retention (ELWR). The 150 ml\u002FL treatment (B4) also improved traits but with diminishing gains or slight physiological suppression. The interaction between water stress intervals and chitosan levels was significant for plant height, number of leaves, leaf area, and RWC, especially at 6 and 8 weeks after planting. Notably, the combination of 100 ml\u002FL chitosan and 15-day water stress interval (A2B3) showed performance comparable to the regularly watered treatment, indicating chitosan’s mitigating role under moderate drought. These findings support the application of chitosan at optimal concentrations as an eco-friendly, biodegradable strategy to enhance maize performance under water-limited conditions, reinforcing its value in climate-smart agriculture.","壳聚糖作为一种天然抗蒸腾剂，能够提高植物水分利用效率并增强干旱胁迫下的恢复力，在作物科学领域日益受到关注。本研究探讨了源自金苹果螺壳的壳聚糖对干旱条件下糯性开放授粉品种（OPV）玉米（Zea mays）生长、生理响应及产量表现的影响。试验于2025年2月23日至4月29日在东民都洛省维多利亚市马卡托克进行，采用3×4析因设计，以随机完全区组设计（RCBD）为基础，因素A为水分胁迫间隔（常规浇水、15天、25天），因素B为壳聚糖浓度（0、50、100和150 ml\u002FL）。结果表明，第一因素（水分胁迫间隔）对株高、叶片数、叶面积及鲜重和干重生物量等关键生长性状具有显著影响（p\u003C0.05），常规浇水（A1）的各项指标优于15天（A2）和25天（A3）间隔处理。然而，玉米穗直径、长度和重量等产量构成因素在A1和A2之间表现相当，表明适度水分胁迫未对生殖性状产生剧烈影响。第二因素（壳聚糖浓度）对所有参数均有显著影响，其中100 ml\u002FL（B3）在株高、叶面积、叶片数、生物量、玉米穗性状、相对含水量（RWC）和离体叶片保水能力（ELWR）方面表现最佳。150 ml\u002FL处理（B4）虽也改善了各性状，但增益递减或出现轻微生理抑制。水分胁迫间隔与壳聚糖浓度之间的交互作用对株高、叶片数、叶面积和RWC具有显著影响，尤其在种植后第6周和第8周。值得注意的是，100 ml\u002FL壳聚糖与15天水分胁迫间隔的组合（A2B3）表现与常规浇水处理相当，表明壳聚糖在适度干旱条件下具有缓解作用。这些发现支持将壳聚糖以最优浓度施用作为一种生态友好、可生物降解的策略，以提升玉米在水分受限条件下的表现，进一步印证了其在气候智慧型农业中的价值。","Iconic Research and Engineering Journals","2026-10-06T00:00:00Z",54,{"impact":113,"substance":16,"depth":399,"authority":49,"freshness":113,"relevant":20,"comment":400},14,"小规模田间试验，结论有一定参考价值但影响范围有限，属细分领域研究进展。",[402],{"name":395,"url":392},[25,26,404,405,406],"抗旱栽培","壳聚糖","水分利用效率",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},[409],{"code":69,"slug":410,"name":411,"path":412,"parent_code":335},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":36,"locked":13,"version":37,"method":38,"evidence":414},{"candidates":415,"agricultural_evidence":42},[416,420,426,433],{"code":30,"score":16,"supported":42,"title":417,"abstract":418,"tags":419},[44],[26,31,44],[26],{"code":69,"score":399,"supported":42,"title":421,"abstract":423,"tags":425},[422],"drought",[424,422],"干旱",[],{"code":427,"score":49,"supported":42,"title":428,"abstract":429,"tags":432},"B2",[],[430,431],"苹果","apple",[],{"code":56,"score":57,"supported":42,"title":434,"abstract":435,"tags":437},[],[436],"smart agriculture",[25],[439,440],"壳聚糖 玉米 干旱胁迫","OPV玉米 抗旱 壳聚糖","壳聚糖玉米干旱胁迫-4346","10.64388\u002Firev10i4-1723642",{"doi":442,"openalex_id":444,"authors":445,"venue":395,"cited_by_count":91,"oa_url":448,"card":449,"direction":97,"ingested_from":99},"W7220744122",[446],{"name":447,"orcid":8},"Sharlene D. Andaya","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1723642.pdf",{"tldr":450,"method":451,"finding":452,"direction":386,"opportunity":453},"研究壳聚糖抗蒸腾剂对干旱胁迫下糯玉米生长、生理和产量的影响。","3×4析因试验，水分间隔与壳聚糖浓度，测定生长、生理及产量指标。","100 ml\u002FL壳聚糖配合15天水分间隔可媲美常规灌溉，缓解中度干旱。","可探究壳聚糖与其它生物刺激素复配及在不同作物和田间规模下的节水增产机制。","2026-10-07T23:30:10.272335Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":8,"source_name":461,"source_url":458,"published_at":462,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":463,"score_detail":464,"sources":466,"tags":468,"primary_topic":472,"secondary_topics":473,"classification":478,"search_phrases":494,"slug":497,"view_count":57,"doi":498,"paper":499,"created_at":524},4168,"High-throughput phenotyping of maize genotype using drone-based multispectral imagery for NDVI estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12892-026-00392-w","High-throughput phenotyping of maize genotype using drone-based multispectral imagery for NDVI estimation。Journal of Crop Science and Biotechnology","利用基于无人机的多光谱影像对玉米基因型进行高通量表型分析以估算NDVI。《作物科学与生物技术杂志》","Journal of Crop Science and Biotechnology","2026-10-03T00:00:00Z",67,{"impact":114,"substance":16,"depth":17,"authority":185,"freshness":113,"relevant":20,"comment":465},"核心期刊论文，方法具体但属细分技术进展，影响范围有限，适合主题聚合而非每日精选。",[467],{"name":461,"url":458},[25,26,469,470,471],"NDVI","无人机遥感","高通量表型",{"code":30,"slug":31,"name":26,"path":32,"parent_code":33},[474],{"code":75,"slug":475,"name":476,"path":477,"parent_code":130},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":36,"locked":13,"version":37,"method":38,"evidence":479},{"candidates":480,"agricultural_evidence":42},[481,485,490],{"code":30,"score":18,"supported":42,"title":482,"abstract":483,"tags":484},[31],[26,31],[26],{"code":75,"score":185,"supported":42,"title":486,"abstract":488,"tags":489},[487],"multispectral",[487],[469,470],{"code":56,"score":20,"supported":13,"title":491,"abstract":492,"tags":493},[],[],[25],[495,496],"玉米 无人机 多光谱 NDVI","高通量表型 玉米 基因型","玉米无人机多光谱NDVI-4168","10.1007\u002Fs12892-026-00392-w",{"doi":498,"openalex_id":500,"authors":501,"venue":461,"cited_by_count":91,"oa_url":8,"card":518,"direction":522,"ingested_from":99},"W7218576117",[502,505,508,511,514,516],{"name":503,"orcid":504},"Nining Nurini Andayani","https:\u002F\u002Forcid.org\u002F0000-0002-4069-1307",{"name":506,"orcid":507},"Muhammad Azrai","https:\u002F\u002Forcid.org\u002F0000-0002-5546-9485",{"name":509,"orcid":510},"Roy Efendi","https:\u002F\u002Forcid.org\u002F0000-0002-6515-1198",{"name":512,"orcid":513},"Muhammad Aqil","https:\u002F\u002Forcid.org\u002F0000-0002-1842-9230",{"name":515,"orcid":8},"Dwitari Lestary",{"name":517,"orcid":8},"Haikal Akbar",{"tldr":519,"method":520,"finding":521,"direction":522,"opportunity":523},"利用无人机多光谱影像对玉米基因型进行高通量表型分析并估算NDVI。","无人机多光谱成像与NDVI植被指数估算。","无人机多光谱影像可高效获取玉米基因型NDVI表型数据。","农业遥感与作物表型","可探索多光谱NDVI与产量基因型关联，构建高通量表型育种决策模型。","2026-10-04T23:30:18.734738Z"]