Apply spatial statistics analysis to ordinal data for soybean iron deficiency chlorosis

Authors: Xu, Zhanyou; Cannon, Steven; BEAVIS, WILLIAMS - Iowa State University

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/26/2022
Publication Date: 9/1/2022
Citation: Xu, Z., Cannon, S.B., Beavis, W.D. 2022. Apply spatial statistics analysis to ordinal data for soybean iron deficiency chlorosis. Agronomy. 12(9). Article 2095. https://doi.org/10.3390/agronomy12092095.
DOI: https://doi.org/10.3390/agronomy12092095

Interpretive Summary: Plant breeding is generally time-consuming, often requiring ten years and many evaluations (trials) to develop a new variety. Further, performance trials are often difficult to interpret due to varying conditions within or across test sites. A field may have patches of richer or poorer soil, or differences in soil moisture or compaction, or patchy disease pressure. The present work describes statistical methods to help correct for these kinds of local differences. Using iron deficiency chlorosis in soybean (which causes leaf-yellowing and low seed yield), this research tests three types of statistical approaches, and concludes that one of these methods generally did the best job of predicting and correcting variation due to localized soil conditions. These results will help breeders to make more accurate selections during performance trials, thereby speeding up the breeding process and more efficiently producing new, improved varieties for farmers and consumers.

Technical Abstract: Accounting for field variation patterns plays a crucial role in interpreting phenotype data and, thus, in plant breeding. Several spatial models have been developed to account for spatial variation, showing that spatial models can successfully increase the quality of phenotype measurement and subsequent selection accuracy for continuous data types such as grain yield and plant height. For stress traits in which the phenotypic data is recorded in ordinal data scores, such as in iron deficiency chlorosis (IDC), the spatial adjustment has not been well studied. The objective of the research described here is to evaluate methods for spatial adjustment of ordinal data, using soybean IDC as an example. Comparisons of adjustment effectiveness for spatial autocorrelation were conducted among three different groups of models. The models can be divided into three groups: group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and group III, tensor product penalized P-splines. Results from the model comparison show that the effectiveness of the models depends on the severity of field variation, the irregularity of the variation pattern, and the model used. The geospatial SAR models outperform the other models for ordinal IDC data. Prediction accuracy for the lines planted in the IDC high-pressure area is 11.9% higher than that of the lines planted in low IDC pressure regions. The relative efficiency of the mixed SAR model is 175%, relative to the baseline ordinary least square model.