Abstract
Methodology for precision agriculture is, perhaps, too focused on methods that allow for spatial correlation in the ANOVA error term. While sound inference about differences between local yields can be computed, no understanding of what is driving these differences is achieved. A completely general form for a spatial model can include suitable covariates. Most research in precision agriculture includes gathering a variety of site-specific information. Through the presentation of the analysis of data from a published soybean [Glycine max (L.) Merr.] study, one specific type of covariate is developed - a duration index for soybean canopy light interception over the growing season. The relationship of the index to grain yield is reasonably well determined (R² = 0.82). We, therefore, suggest that the quest for modeling an appropriate covariate or covariates is primary. Treating spatial variation by other methods should only be used when the quest has failed.
Keywords
mixed-model, nonlinear regression, repeated measures, segmented regression, weighted least-squares
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Meek, D. W. and Singer, J. W.
(2004).
"AN EXAMPLE OF DEVELOPING COVARIATES FOR PROBLEMS IN PRECISION AGRICULTURE,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1165
AN EXAMPLE OF DEVELOPING COVARIATES FOR PROBLEMS IN PRECISION AGRICULTURE
Methodology for precision agriculture is, perhaps, too focused on methods that allow for spatial correlation in the ANOVA error term. While sound inference about differences between local yields can be computed, no understanding of what is driving these differences is achieved. A completely general form for a spatial model can include suitable covariates. Most research in precision agriculture includes gathering a variety of site-specific information. Through the presentation of the analysis of data from a published soybean [Glycine max (L.) Merr.] study, one specific type of covariate is developed - a duration index for soybean canopy light interception over the growing season. The relationship of the index to grain yield is reasonably well determined (R² = 0.82). We, therefore, suggest that the quest for modeling an appropriate covariate or covariates is primary. Treating spatial variation by other methods should only be used when the quest has failed.
