基于蒙特卡洛的海南AI驱动智慧农业平台绿色效益事前评估
Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan
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中文总结 AI 辅助
本研究构建碳核算模型并利用蒙特卡洛模拟评估海南AI智慧农业平台绿色效益，量化了农药化肥减量、节水及碳减排潜力，为事前评估提供可复现方法论。
中文摘要 AI 辅助
智慧农业平台被广泛视为实施中国农药化肥减量、节水及碳减排议程的关键载体，然而目前仍缺乏统一的量化框架来评估其绿色价值。本研究以热带农业AI驱动决策平台为对象（集成大语言模型问答、多模态病虫害诊断、物联网传感、卫星遥感及闭环田间记录系统），构建了涵盖农药和化肥生产、田间N2O、灌溉用电及水稻CH4的从摇篮到农场大门的农业碳核算模型，将平台干预转化为可量化的传递参数，并通过蒙特卡洛模拟在海南三个情景（芒果、冬季蔬菜、水稻/南繁，面积加权40%:30%:30%）中传播参数不确定性。在全面采用情况下，农药使用量中位数减少23.5%（90%区间15.0%-33.2%），化肥减少21.0%（13.8%-28.9%），灌溉用水减少16.5%（10.9%-23.5%），碳强度降低21.5%（16.1%-27.2%）。化肥减量≥15%的实现概率较高（90.6%），碳明确下降概率为98.1%，但总节水量≥20%的概率仅约20%，因此倾向于情景特定陈述。Sobol一阶指数显示，测土推荐和有机替代共同解释了总碳强度降低方差的约83%。收敛性测试表明10,000次迭代使所有统计量稳定；并报告了保守/基线/乐观情景边界。该框架为事前绿色价值评估和试点观测设计提供了可复现、可校准的方法论。
英文摘要
Smart agriculture platforms are widely regarded as key carriers for implementing China's pesticide and fertilizer reduction, water-saving and carbon-reduction agendas, yet a unified quantitative framework for assessing their green value is still lacking. Taking an AI-driven decision platform for tropical agriculture as the object (integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system), this study builds a cradle-to-farm-gate agricultural carbon accounting model covering pesticide and fertilizer production, field N2O, irrigation electricity and paddy CH4, translates platform interventions into quantifiable transmission parameters, and propagates parameter uncertainty by Monte Carlo simulation over three Hainan scenarios (mango, winter vegetable, rice/nanfan, area-weighted 40%:30%:30%). Under full adoption, median reductions are 23.5% (90% interval 15.0%-33.2%) for pesticide use, 21.0% (13.8%-28.9%) for fertilizer, 16.5% (10.9%-23.5%) for irrigation water, and 21.5% (16.1%-27.2%) for carbon intensity. Attainment probabilities are high for fertilizer reduction >=15% (90.6%) and clear carbon decline (98.1%), but only about 20% for aggregate water saving >=20%, favoring scenario-specific statements. Sobol first-order indices show soil-test recommendation and organic substitution jointly explain about 83% of the variance of aggregate carbon-intensity reduction. Convergence tests show 10,000 iterations stabilize all statistics; conservative/baseline/optimistic scenario bounds are reported. The framework offers a reproducible, calibration-ready methodology for ex-ante green-value assessment and pilot observation design.
发表机构
- Sanya University（三亚学院）
- Handan Weizhi Jingjie AI Basic Software Co., Ltd.（邯郸未至之境人工智能基础软件有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
