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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #320539

Title: Scheduling field operations as a function of temperature, soil moisture, and available resources

Author
item Baffaut, Claire
item STRAUCH, MICHAEL - Helmholtz Centre For Environmental Research

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/31/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Scheduling field operations in SWAT can be done by specifying fixed dates or by using the heat unit index, which considers temperature constraints. However, soil moisture and labor requirements can also limit the ability of farm operators to perform field operations at the optimal time. The SWAT2012 version 635 code was modified to introduce these constraints. Soil moisture was constrained by a user-defined maximum fraction of field capacity. Labor constraints were represented by limiting field work in the watershed to 1/14th of the watershed worked area, i.e., agricultural land, pasture, and managed forests. This means that in the absence of other field work and assuming that moisture or rain would not introduce any delay, a field operation can be completed throughout the watershed within two weeks. This algorithm was tested in the Goodwater Creek Experimental Watershed, in Northeast Missouri by comparing simulated planting dates of corn and soybeans to 15 years of planting progress records (1992-2006). On average, the model predicted planted amounts of soybeans and corn equal to those recorded within 1 day of the recorded dates for soybeans and 7 days for corn. Differences were larger at the beginning and end of the planting periods. Results indicated the need for a cutoff date beyond which these crops are abandoned and the land is left fallow. Scheduling field operations as a function of temperature, moisture, and available resources will be useful when operation timing is not well known, e.g. large river basins, scenarios analyses, or climate change studies.