Spatiotemporal characteristics of white mold and impacts on yield in soybean fields in South Dakota

• Main Authors: Confiance Mfuka, Emmanuel Byamukama, Xiaoyang Zhang
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-04-01
• Series: Geo-spatial Information Science
• Subjects: soybean; white mold; landsat; modis; fusion; kriging; ndvi
• Online Access: http://dx.doi.org/10.1080/10095020.2020.1712265

White mold of soybeans is one of the most important fungal diseases that affect soybean production in South Dakota. However, there is a lack of information on the spatial characteristics of the disease and relationship with soybean yield. This relationship can be explored with the Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 and a fusion of Landsat 8 and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. This study investigated the patterns of yield in two soybean fields infected with white mold between 2016 and 2017, and estimated yield loss caused by white mold. Results show evidence of clustering in the spatial distribution of yield (Moran’s I = 0.38; p < 0.05 in 2016 and Moran’s I = 0.45; p < 0.05 in 2017) that can be explained by the spatial distribution of white mold in the observed fields. Yield loss caused by white mold was estimated at 36% in 2016 and 56% in 2017 for the worse disease pixels, with the most accurate period for estimating this loss on 21 August and 8 September for 2016 field and 2017 field, respectively. This study shows the potential of free remotely sensed satellite data in estimating yield loss caused by white mold.