Applying Spatial Statistical Analysis to Ordinal Data for Soybean Iron Deficiency Chlorosis

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 field variation. Spatial analyses show that spatial models can successfully increase the quality of phenotype measurements and subsequent selection accuracy for continuous data types such as grain yield and plant height. The phenotypic data for stress traits are usually recorded in ordinal data scores but are traditionally treated as numerical values with normal distribution, such as iron deficiency chlorosis (IDC). The effectiveness of spatial adjustment for ordinal data has not been systematically compared. The research objective 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 eight different models. The models were 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 those planted in low-IDC-pressure regions. The relative efficiency of the mixed SAR model is 175%, relative to the baseline ordinary least squares model. Even though the geospatial SAR model is the best among all the compared models, the efficiency is not as good for ordinal data types as for numeric data.

Predictions and Estimations in Agricultural Production under a Changing Climate

“…In the seventh article, the authors dealt with the evaluation of methods for the spatial correction of ordinal data using the example of chlorosis symptoms resulting from iron deficiency in soybean crops [13]. Corrections for autocorrelation were carried out with the involvement of eight different models.…”

Time Series from Sentinel-2 for Organic Durum Wheat Yield Prediction Using Functional Data Analysis and Deep Learning

“…In a rapidly changing world, integrated platforms based on satellite technology, sensors, digital mapping, ecological modeling, and connectivity need to be incorporated with agronomic science to monitor, assess, and manage cropland areas and to improve yield prediction [6,10,12,13,21,[24][25][26][27][28][29][30][31][32][33]. Remotely sensed data allow monitoring with a proper spatial-, spectral-, and temporal-resolution crop over a season [11,33].…”