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Title: Modeling Crop Responses to Initial Soil Water in the High Plains

Author
item SASEENDRAN, S - COLORADO STATE UNIVERSITY
item Nielsen, David
item LYON, DREW - UNIVERSITY OF NEBRASKA
item Ma, Liwang
item Ahuja, Lajpat

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2008
Publication Date: 9/15/2008
Citation: Saseendran, S.A., Nielsen, D.C., Lyon, D.J., Ma, L., Ahuja, L.R. 2008. Modeling Crop Responses to Initial Soil Water in the High Plains. Agronomy Abstract. Presented at the International American Society of Agronomy meetings, Crop Science Society of America and Soil Science Society of America (ASA/CSSA/SSSA) annual meetings. oct. 5-9, 2008. Houston, TX.

Interpretive Summary:

Technical Abstract: Dryland farming strategies in the High Plains must make efficient use of limited and variable precipitation and stored water in the soil profile for stable and sustainable farm productivity. Current research efforts focus on replacing summer fallow in the region with more profitable and environmentally friendly spring and summer crops. In the absence of advance reliable precipitation forecasts for the crop season, farmers rely mainly upon knowledge of plant available water (PAW) in the soil profile at planting for making planting decisions. To develop PAW-at-planting based decision support for crop selection, experiments were conducted involving spring triticale (X Titicosecale Wittmack), proso millet (Panicum miliaceum L.) and corn (Zea mays L.) under artificially controlled low, medium, and high PAW levels during 2004 and 2005 at Akron, Colorado, and Sidney, Nebraska. Our objectives were to model the above crops, and develop decision support for selection of a particular spring or summer crop to replace summer fallow under a given PAW scenario (50, 75 or 100% PAW) by assessing simulated productivity using long-term observed climate at the two locations {94 years (1912-2005) at Akron, and 60 years (1948-2007) at Sidney}. The RZWQM2 with DSSAT (v4.0) crop growth modules was used. Specifically, the CERES-wheat and CERES-sorghum (v4.0) modules were adapted and used for simulating triticale and proso millet, respectively, and CERES-maize (v4.0) module was used for corn simulations. The results will be presented and discussed at the meeting.