非点源污染
SWAT模型
肥料
水土评价工具
分水岭
环境科学
农业
污染
农业工程
计算机科学
流域
农学
地理
生态学
工程类
考古
机器学习
生物
地图学
水流
作者
L. Arrueta,Douglas B. Jackson-Smith,Margaret Kalcic
出处
期刊:Journal of Soil and Water Conservation
[Soil and Water Conservation Society]
日期:2022-02-10
卷期号:: 00055-00055
被引量:1
标识
DOI:10.2489/jswc.2022.00055
摘要
Nonpoint source pollution is the primary cause of reduced water quality in the United States. This pollution results primarily from fertilizer and manure application in farmland. One of the hydrological models most widely used to predict the effect of nitrogen (N) and phosphorus (P) fertilizer application on nutrient loadings is the Soil and Water Assessment Tool (SWAT). While important advances have been made to improve the representation of biophysical processes within SWAT, modelers often fail to capture the complexity of human land management behaviors. Meanwhile, decades of social science research have demonstrated that farmers are not a homogeneous group, but rather exhibit complex and diverse behaviors. In this paper we present a systematic review of recently published papers that use SWAT to document how the modeling community typically represents fertilizer application behaviors. We compare these representations with findings from recent farmer surveys that captured information about fertilizer application rates. We found that most SWAT model applications assume that farmer field management behaviors are relatively homogeneous (e.g., all farmers behave in the same way), and that farmers generally apply fertilizer using rational agronomic or economic criteria. These simplifying assumptions conflict with the reality of farmers’ fertilizer behavior. Farmer surveys in Minnesota and Ohio show considerable variability in N and P fertilizer application rates on corn (Zea mays L.) fields following soybeans (Glycine max [L.] Merr.). The disconnect between SWAT modeling approaches and results of farmer surveys point to opportunities to better represent the heterogeneity of farmers’ fertilizer behavior in SWAT and other hydrologic models, which could improve our ability to link changes in land use and management to water quality and increase the effectiveness of conservation program interventions.
科研通智能强力驱动
Strongly Powered by AbleSci AI