Data-Driven Multi-Objective Optimization for Balancing Agricultural Productivity and Ecological Conservation in the Peiqiao River Watershed
Year: 2026
Type: article
Abstract: The sustainable management of agricultural watersheds requires reconciling agricultural productivity with ecological conservation, particularly in data-scarce regions. This study develops a data-driven multi-objective optimization framework (MOOF) for cultivated land use in the Peiqiao River watershed, a tributary of the Yangtze River. A comprehensive dataset was compiled, including high-resolution land use, soil physicochemical properties from 270 field-sampled cores, meteorological and hydrological records (2018–2023), and agricultural cost–benefit statistics. These data drove a coupled simulation system comprising the AnnAGNPS model for sediment and nutrient loads and an Economic Benefit Evaluation Model (EBEM) for net returns under various land-use configurations. A Multi-flip Tabu Search Algorithm (MTSA) iteratively linked simulation outputs with a penalty-constrained multi-objective function. Under predefined policy constraints and expert weights, the optimization identified a relative Pareto-improving land-use configuration rather than a global optimum. Sediment, total nitrogen, and total phosphorus loads decreased by 4.06%, 1.52%, and 1.74%, respectively, while economic benefit increased by 12.08% (from 32.75 to 36.71 million yuan). However, the TN and TP reductions are smaller than the calibrated model error and should be interpreted as indicative. The framework offers a managerial synergy pathway for green growth under ecological constraints in data-scarce agricultural watersheds.
Source: Data
DOI: 10.3390/data11110287
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7224849343