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ARS Home » Southeast Area » Tifton, Georgia » Southeast Watershed Research » Research » Research Project #440843

Research Project: Quantifying Water Quantity and Quality at the Cook Agronomy Farm Long Term Agroecosystem Research Location

Location: Southeast Watershed Research

Project Number: 6048-13000-027-070-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Aug 1, 2021
End Date: Jul 31, 2025

Objective:
1. Evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) models on simulation of water quantity and quality for rainfed wheat production in the Palouse region of Washington. 2. Examine the potential impacts of climate change on water budgets, crop yield, nitrogen losses, green-house gas emissions, and carbon sequestration for rainfed wheat production in the Palouse region of Washington. 3. Utilize the Cropping Systems Model (CSM) of the DSSAT software to explore issues affecting wheat productivity in relation to soil depth constraints and impacts on soil carbon over time.

Approach:
Under the guidance of University of Florida (UF) personnel will work with the CAF LTAR location to facilitate testing of the DSSAT crop models. The UF personnel will work with the Washington based LTAR location to assemble data, test DSSAT, and report results. The modeling will focus upon select crop rotations studied at the CAF LTAR site where dryland wheat is produced. The model will be used to examine current (Business as Usual) as well as aspirational systems targeted at building more sustainable production rotations. Data from the CAF site will be used to evaluate the accuracy of the DSSAT models. The models will be used to quantify water and agrichemical transport for these production systems. Specifically, the model will quantify water budgets, crop yield, nitrogen losses, green-house gas emissions, and carbon sequestration. A long-term database consisting of collected data and model simulations will be produced and documented. A procedure and protocol for improved data collection and for model application will be developed and shared with LTAR scientists. Any data shortcomings will be outlined.