Author
MOBERLY, JOSEPH - Kansas State University | |
AIKEN, ROBERT - Kansas State University | |
XIAOMAO, LIN - Kansas State University | |
STONE, LOYD - Kansas State University | |
SCHLEGEL, ALAN - Kansas State University | |
Baumhardt, Roland | |
Schwartz, Robert | |
O'BRIEN, DAY - Kansas State University |
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only Publication Acceptance Date: 10/7/2015 Publication Date: 11/15/2016 Citation: Moberly, J., Aiken, R.M., Xiaomao, L., Stone, L.R., Schlegel, A.J., Baumhardt, R.L., Schwartz, R.C., O'Brien, D. 2016. Crop water production functions for grain sorghum and winter wheat. ASA-CSSA-SSSA Annual Meeting Abstracts, Minneapolis, MN, November 16-18, 2015. Interpretive Summary: Technical Abstract: Productivity of water-limited cropping systems can be reduced by untimely distribution of water as well as cold and heat stress. The objective was to develop relationships among weather parameters, water use, and grain productivity to produce functions forecasting grain yields of grain sorghum and winter wheat in water-limited cropping systems. Algorithms, defined by the Kansas Water Budget (KSWB) model, solve the soil water budget with a daily time step and were implemented in Matlab. Grain yield and water use data reported in several crop sequence studies conducted in Bushland, TX, Colby, KS and Tribune, KS were compared against KSWB model results using contemporary weather data. Predictive accuracy of the KSWB model and the crop production functions was evaluated in relation to experimental results. The relative utility of yield forecasts based on the KSWB model and production functions was assessed with regard to economic risk. Field studies showed that winter wheat had stable grain yields over a wide range of water use, while sorghum had a wider range of yields over a smaller distribution of water use. Predicted water use and yield values were in general agreement with observed values with some systematic bias. These apparent biases were analyzed to identify probable sources of predictive error. |