|Stone, Kenneth - Ken|
Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 1/22/2015
Publication Date: 11/10/2015
Citation: Stone, K.C., Sadler, E.J. 2015. Assessing spatial variation of corn response to irrigation using a bayesian semiparametric model. In: Proceedings of the 2015 ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation-A tribute to the Career of Terry Howell, Sr., November 10-12, 2015, Long Beach, California. p 1-23. (doi:10.13031/irrig.20152142961). Conference Proceedings Paper No. 152142961,
Technical Abstract: Spatial irrigation of agricultural crops using site-specific variable-rate irrigation (VRI) systems is beginning to have wide-spread acceptance. However, optimizing the management of these VRI systems to conserve natural resources and increase profitability requires an understanding of the spatial crop responses. In this research, we utilize a recently developed spatially explicit analysis model to analyze spatial corn yield data. The specific objectives of this research are to 1) to calculate a suite of estimates needed for the types of analyses mentioned above and to provide credible intervals (measures of uncertainty) around these estimates and 2) to examine whether the conclusions from this rigorous re-analysis are different from the prior analysis and if the results force any modifications to the conclusions obtained with the prior analyses. The spatially explicit analysis was achieved using a mixed model formulation of bivariate penalized smoothing splines and was implemented in a Bayesian framework. This model simultaneously accounted for spatial correlation as well as relationships within the treatments and has the ability to contribute information to nearby neighbors. The model-based yield estimates were in excellent agreement with the observed spatial corn yields and were able to estimate the high and low yields more accurately. Credible intervals were calculated around the estimates and the majority encompassed the observed yields. After calculating estimates of yield, we then calculated estimates of other response variables such as rainfed yield, maximum yield, and irrigation at maximum yield. These estimated response variables were then compared with previous results from a classical statistical analysis. Our conclusions supported the original analysis in identifying significant spatial differences in crop responses across and within soil map units. These spatial differences were great enough to be considered in irrigation system design and management. The major improvement in the 2014 analysis is that the model explicitly considered the spatial dependence in calculation of the estimated yields and other variables and, thus, should provide improved estimates of their impact in system design and management.