[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2537":3},{"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":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":56},2537,"A Counterfactual-Enabled Agricultural Decision Support Framework for Sustainability-Aware Groundnut Yield Prediction Using Bayesian-Optimized XGBoost","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fai7090364","Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively validated, data-driven Digital Twin prototype integrating spatiotemporal data reconstruction, sustainability-state representation, hierarchical yield forecasting, counterfactual analysis, and scenario simulation. The framework operates on historical district-level APY observations and therefore represents a retrospective approximation of Digital Twin operation rather than a continuously synchronized cyber-physical agricultural Digital Twin. The Extended Regenerative Agriculture Index (eRAI) combines crop diversity, productivity–stability, land-use efficiency, and yield-trend information to characterize district-level sustainability states. A Bayesian-optimized XGBoost model is employed for one-step-ahead yield forecasting under temporal validation, while fixed-effects and synthetic-control analyses provide complementary associational and intervention-associated evidence. Evaluation using district-level groundnut data from India during 1997–2023 demonstrates that the proposed predictor achieves an RMSE of 0.171 t\u002Fha and R2=0.92, outperforming the evaluated baselines with statistically significant differences (p\u003C0.05). The fully adjusted fixed-effects model identifies a positive association between higher sustainability states and yield, while retrospective Digital Twin replay demonstrates close temporal agreement between predicted and observed outcomes. Model-based scenario simulations indicate predicted yield increases of up to 12.4% under the evaluated sustainability-state perturbations; these estimates represent counterfactual sensitivity rather than guaranteed causal effects. TSAFI-DT provides a reproducible framework for sustainability-aware agricultural forecasting, comparative scenario exploration, and data-driven decision support.","可持续农业规划需要能够捕捉时空变异性、可持续性动态以及替代管理情景潜在结果的预测框架。本研究提出TSAFI-DT，一个经回溯验证的数据驱动数字孪生（Digital Twin）原型，集成了时空数据重建、可持续性状态表征、分层产量预测、反事实分析和情景模拟。该框架基于历史地区级APY观测数据运行，因此代表的是数字孪生运行的回溯近似，而非持续同步的网络-物理农业数字孪生。扩展再生农业指数（Extended Regenerative Agriculture Index, eRAI）综合了作物多样性、生产力-稳定性、土地利用效率和产量趋势信息，以刻画地区级可持续性状态。采用贝叶斯优化的XGBoost模型在时间验证下进行一步超前产量预测，同时固定效应和合成控制分析提供互补的关联性证据和干预关联性证据。利用印度1997—2023年地区级花生数据进行评估，结果表明所提出的预测器实现了0.171 t\u002Fha的RMSE和R²=0.92，优于所评估的基线模型且差异具有统计学显著性（p\u003C0.05）。完全调整的固定效应模型识别出较高可持续性状态与产量之间的正相关关系，而回溯性数字孪生重放表明预测结果与观测结果在时间上高度一致。基于模型的情景模拟显示，在所评估的可持续性状态扰动下，预测产量增幅最高可达12.4%；这些估计代表的是反事实敏感性而非保证的因果效应。TSAFI-DT为可持续性感知的农业预测、比较情景探索和数据驱动决策支持提供了一个可复现的框架。",null,"AI","2026-09-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,12,8,1,"提出融合数字孪生、反事实分析与贝叶斯优化XGBoost的花生产量预测框架，方法新颖、数据跨度长且验证充分，对可持续农业决策支持有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","产量预测","数字孪生","花生种植",0,"10.3390\u002Fai7090364",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":48,"direction":54,"ingested_from":55},"W7212812029",[37,40,43,45],{"name":38,"orcid":39},"Rekha R Nair","https:\u002F\u002Forcid.org\u002F0000-0002-7207-2877",{"name":41,"orcid":42},"Tina Babu","https:\u002F\u002Forcid.org\u002F0000-0001-7846-3679",{"name":44,"orcid":9},"Sumendra Yogarayan",{"name":46,"orcid":47},"Abdul Razak","https:\u002F\u002Forcid.org\u002F0000-0002-6108-3183",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"提出TSAFI-DT数字孪生框架，用贝叶斯优化XGBoost预测印度花生产量并做反事实情景分析。","基于1997-2023年印度县级花生产量数据，构建eRAI可持续性指数，采用贝叶","模型RMSE为0.171 t\u002Fha、R²=0.92，高可持续状态与产量正相关，情景模拟产量最高提升1","农业人工智能与决策模型","可延伸至实时物联网数据驱动的数字孪生，并验证反事实情景的因果效应与跨作物泛化能力。","数字乡村与农业信息化","openalex","2026-09-15T23:30:26.594281Z"]