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United States Department of Agriculture

Agricultural Research Service

Research Project: MECHANISTIC PROCESS-LEVEL CROP SIMULATION MODELS FOR ASSESSMENT OF AGRICULTURAL SYSTEMS

Location: Crop Systems & Global Change

Title: Evaluating county-level potential production capacity of potatoes for Maine using the crop model SPUDSIM

Authors
item Resop, Jonathan
item Fleisher, David
item Wang, Qingguo -
item Timlin, Dennis
item Reddy, Vangimalla

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: August 1, 2012
Publication Date: N/A

Technical Abstract: The United States Eastern Seaboard Region, consisting of the states from Maine to Virginia, depends on centralized and distantly produced food to supply its urban population. Non-local food sources may be vulnerable to uncertainties such as increasing fuel costs, population growth, and climate change. County-level estimates of crop productivity, as well as the associated resource requirements, are necessary to aid farmers and policy planners in making management decisions and identifying potential areas for local food production. The potato growth model SPUDSIM, part of the crop modeling package GUICS developed by the USDA-ARS, was used to simulate potato production for each county in Maine. Daily climate data generated from the model CLIGEN and NOAA weather stations was used along with SSURGO soil data and various management scenarios to quantify potential production capacity and resource requirements (fertilizer and irrigation). The variability of each of the model input parameters (climate, soil, and management) within each county was used to estimate the uncertainty associated with the output variables (potato yield, nutrient demand and water demand). The simulated crop productivity was validated using observations such as agricultural census data from NASS. The results from this research will provide valuable information for local agencies as well as provide a baseline for future research regarding predicting potential production capacity under climate change scenarios.

Last Modified: 7/30/2014