Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan

Zhaoyang Li; Ruijie Zhang; Zhaoji Sun; Lu Zhang

cs.AISep 6, 2026

Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan

Authors: Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang

Organizations: Sanya University, Sanya, Hainan 572022, China · Handan Weizhi Jingjie AI Basic Software Co., Ltd.

Abstract

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.

Figures & tables

Scenario	Unit	Platform intervention	Conventional management
Mango orchard	mu-year	diagnosis, records, drip & remote-sensing advice	calendar spraying, flood irrigation
Winter vegetable	mu-crop	diagnosis, records, interval reminders	calendar spraying, experience-based fert.
Rice/nanfan	mu-season	smart irrigation, water-layer decisions	continuous flooding, experience-based fert.

Table 1: Pilot scenario definitions and platform interventions vs. conventional management

Parameter	Distribution	Mode	Range	Grade
Blind-spray share $\varphi_{a}$	triangular	0.30	0.15–0.50	C
Soil-test reduction $\varphi_{s}$	triangular	0.15	0.05–0.30	B
Organic substitution $\varphi_{o}$	triangular	0.30	0.10–0.55	A/B
Technology adoption $\alpha$	triangular	0.55	0.30–0.80	C
Mango drip saving $\eta_{d,m}$	triangular	0.35	0.20–0.55	A
Vegetable drip saving $\eta_{d,v}$	triangular	0.30	0.15–0.50	A

Table 2: Model input parameters, probability distributions and sources

Indicator	Scenario	Mean	SD	P5	Median	P95
Pesticide reduction	all scenarios	0.238	0.055	0.150	0.235	0.332
Fertilizer reduction	all scenarios	0.211	0.046	0.138	0.210	0.289
Water saving	mango	0.202	0.055	0.120	0.197	0.300
	winter vegetable	0.174	0.051	0.098	0.169	0.267
	rice/nanfan	0.115	0.038	0.060	0.112	0.184
	aggregate	0.168	0.038	0.109	0.165	0.235

Table 3: Monte Carlo results (N=10 4 , fixed seed 20260906)

Figure 1: Distributions of the four green-value indicators and target-attainment probabilities. Dashed lines are target reference values (pesticide 20%, fertilizer 15%, water 20%, carbon intensity 15%)

Figure 2: Scenario boxplots of carbon-intensity reduction and irrigation water saving (10,000 MC samples)

Figure 3: Sobol first-order indices for aggregate carbon-intensity reduction (bracketed values are relative contribution shares)

Figure 4: Monte Carlo convergence: aggregate carbon-intensity reduction statistics vs. sample size

Scenario	Pesticide	Fertilizer	Agg. water	Carbon
	reduction	reduction	saving	intensity red.
Conservative (lower bound)	0.113	0.065	0.045	0.069
Baseline (mode)	0.225	0.195	0.160	0.200
Optimistic (upper bound)	0.375	0.375	0.380	0.388

Table 4: Three-scenario analysis (parameter endpoints)

Indicator	This study median	Literature range	Reference/basis
Pesticide reduction	0.235	0.15–0.30	IPM literature
Fertilizer reduction (N)	0.210	0.10–0.25	soil-test evaluation
Irrigation water saving	0.197 (mango)	0.20–0.50	water-fertilizer guidance moa2013
Paddy CH 4 reduction	0.300	0.20–0.50	IPCC 2019 water mgmt. IPCC2019
Carbon-intensity reduction	0.215	0.15–0.35	precision-ag. reviews

Table 5: Comparison of simulated medians with literature/official values

Precision Agriculture

Precision agriculture uses spatial and temporal data, sensors, AI, and robotics to tailor crop monitoring and farm inputs to local conditions.

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Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments

Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang

Sanya University, Sanya, Hainan 572022, China · Handan Weizhi Jingjie AI Basic Software Co., Ltd.

Precision Agriculture

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Smart agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push into a single package, so the green benefit attributable to each component remains unclear and resource-allocation decisions lack quantitative evidence. Building on a previous platform-level Monte Carlo assessment, this paper makes the components explicit and runs two controlled simulation experiments. Experiment 1 follows the chain from AI capability to farmer behavior to agrochemical input reduction, modeling pesticide/fertilizer reduction as avoidable blind-application share times prescription effectiveness times decision-touch coverage times adoption rate, and compares an experienced-extension mode with the AI mode: the probability of reaching 20% pesticide reduction is essentially zero in the extension mode but 20.7% at baseline, up to 49% with diagnosis accuracy 0.95 and adoption 0.85 under AI; the probability of 15% fertilizer reduction rises from near zero to 52.0%. Experiment 2 compares current practice (P0), IoT engineering retrofit (P1), and P1 plus AI irrigation scheduling (P2): median aggregate water saving rises from 7.8% (P0) to 11.0% (P1) and 16.0% (P2), with AI adding 5.0 percentage points beyond engineering; paddy CH4 reduction reaches 30.5% under AI scheduling versus 19.8% under manual operation, and the rice irrigation-methane subsystem carbon intensity declines 27.9%. Sensitivity analyses of both experiments consistently indicate that the primary bottleneck for meeting green targets is farmer adoption rather than algorithm accuracy, and that AI data fusion is robust to soil-moisture sensing errors. This work provides a reproducible simulation framework for component-level green-value evaluation and promotion-strategy optimization of smart agriculture platforms.

Abstract

Precision Agriculture

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