Keywords
EQIP, BMPs, agriculture, SWAT, conservation
Start Date
16-9-2020 1:00 PM
End Date
16-9-2020 1:20 PM
Abstract
Water quality degradation is one of the world’s most pressing environmental concerns. As regulations tighten on most point source pollutants, many are now looking to nonpoint source improvements for water quality. Of particular interest is the impact of agricultural best management practices (BMPs) on water quality through incentive and voluntary programs. The Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA) has administered the Environmental Quality Incentives Program (EQIP) under the Farm Bill to assist producers with applying sustainable on-farm conservation practices. However, there has been few if any studies quantifying the benefits to water quality from the application of the EQIP-funded BMPs. The Soil and Water Assessment Tool (SWAT) model, developed by NRCS, is the predominant water quality model for many studies involving agricultural lands both in the U.S. and globally. As a result a modelling effort was undertaken in Colorado to quantify the effects of specific EQIP-funded BMPs in the Republican, South Platte, Arkansas, and Rio Grande watersheds. Edge-of-field nutrient (total nitrogen, TN and total phosphorus, TP) loadings were quantified using the SWAT model for each irrigated agricultural field in the study region. Modelling efforts included EQIP conservation practices applied between 2008 and 2018 including different types of tillage, irrigation, field borders and filter strips. Some 45,000 fields were modelled individually for 32 modelling scenarios combining the independent adoption of various BMPs (4 tillage types, 2 irrigation methods, 2 nutrient application methods, with and without field border) Results showed the highest reduction of TN under improved tillage practices; while the most significant reductions in TP were from irrigation efficiency improvements (sprinkler or drip irrigation over flood-furrow irrigation).
Demonstration of Agricultural Progress on Water Quality: Modelling the Effectiveness of the USDA-NRCS EQIP Conservation Practices
Water quality degradation is one of the world’s most pressing environmental concerns. As regulations tighten on most point source pollutants, many are now looking to nonpoint source improvements for water quality. Of particular interest is the impact of agricultural best management practices (BMPs) on water quality through incentive and voluntary programs. The Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA) has administered the Environmental Quality Incentives Program (EQIP) under the Farm Bill to assist producers with applying sustainable on-farm conservation practices. However, there has been few if any studies quantifying the benefits to water quality from the application of the EQIP-funded BMPs. The Soil and Water Assessment Tool (SWAT) model, developed by NRCS, is the predominant water quality model for many studies involving agricultural lands both in the U.S. and globally. As a result a modelling effort was undertaken in Colorado to quantify the effects of specific EQIP-funded BMPs in the Republican, South Platte, Arkansas, and Rio Grande watersheds. Edge-of-field nutrient (total nitrogen, TN and total phosphorus, TP) loadings were quantified using the SWAT model for each irrigated agricultural field in the study region. Modelling efforts included EQIP conservation practices applied between 2008 and 2018 including different types of tillage, irrigation, field borders and filter strips. Some 45,000 fields were modelled individually for 32 modelling scenarios combining the independent adoption of various BMPs (4 tillage types, 2 irrigation methods, 2 nutrient application methods, with and without field border) Results showed the highest reduction of TN under improved tillage practices; while the most significant reductions in TP were from irrigation efficiency improvements (sprinkler or drip irrigation over flood-furrow irrigation).
Stream and Session
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