|Paz, Joel - IOWA STATE UNIVERSITY|
|Batchelor, William - IOWA STATE UNIVERSITY|
Submitted to: American Society of Agricultural Engineers Transactions
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 20, 1998
Publication Date: N/A
Interpretive Summary: Modeling will be important in Precision Farming for crops because we will need to predict the effect of management decisions on crop growth. This project was an early attempt to use a crop growth model to predict soybean yield at over 200 locations on a 13 ha field. The model, CROPGRO-Soybean, is a process based model, which means that it simulates plant processes such as nutrient uptake, photosynthesis, and growth of roots, stems, leaves, flowers, and seeds. The idea being tested was that variability in water stress (too much or too little) in the soil was largely responsible for the soybean yield variability that has been documented on this field over many years. Soil conditions at each of the 224 locations on the field with historic yield information were identified from soil survey information and special tests run at some of the locations. These soil conditions and their interactions with weather and root growth were used to control the growth of the simulated plants at the 224 individual locations. The results were that water stress simulated by the model was able to explain 69% of the variation in yield over 3 years. The difference between simulated and actual yield was about 12% of the actual yield. This is very good for a first attempt, but more development of the model and method of using it will be needed. When fully developed, this approach should allow managers to study the use of variable application of inputs such as fertilizer and herbicides or other pesticides. The ability to reduce inputs and maintain yields or to increase selected inputs and optimize net returns will be important as we attempt to produce abundant food with limited effect on the environment.
Technical Abstract: Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability have had limited success. In this paper, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybeans, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16 ha field in Iowa using 3 years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over 3 years. The root mean square error was 286 ka ha**-1, representing approximately 12% of the 3-year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation of surface water runon into grids. Results of this research show that it is important to include measurements of soil moisture holding capacity, and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.