Weather-driven synergistic water-economy-environment regulation of farmland ecosystems

Farmland ecosystems (FEs) constitute the most important source of food production, and water is one of the most im-portant factors influencing FEs. The amount of water can affect the yield and thus the economic efficiency. Water mi-gration can generate environmental effects through the migration of fertilizers. Interlinkages and constraints exist between the water, economy and environment, which require synergistic regulation. Meteorological elements influ-ence the reference crop uptake amount and thus the water cycle processes and are key drivers of regulation at the water-economy-environment nexus. However, the weather-driven, synergistic water-economy-environment regula-tion of FEs has not been sufficiently researched. As such, this paper employed a dynamic Bayesian prediction of the reference evapotranspiration (ETo) and a quantitative characterization of the total nitrogen (TN) and total phosphorus (TP) contents in agricultural crops and soils via field monitoring and indoor experimental analysis. Consequently, multiobjective optimization modeling was conducted to weigh the mutual trade-offs and constraints between water, the economy and the environment. The proposed method was verified via an example involving the modern agricul-tural high-tech demonstration park in Harbin, Heilongjiang Province, China. The results indicated that (1) the effect of meteorological factors gradually decreased over time, but the prediction results were very accurate, and the higher the delay order of the dynamic Bayesian network (DBN) was, the more accurate the predictions; (2) ETo was significantly driven by meteorological elements, and the most important meteorological factor influencing ETo throughout the year was average temperature. When the average temperature was reduced by 10.0 %, ETo was reduced by 1.4 %, the re-quired amount of irrigation water was reduced by 4.9 %, and the economic benefits of a single cube of water increased by 6.3 %; (3) resource-economy-environment multidimensional synergy enabled a 12.8 % reduction in agricultural ecosystem pollutant emissions, while the economic benefits per unit of water increased by 8.2 % and the system syn-ergy increased by 23.2 %.