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

ABSTRACT 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.

[1]  Peter M. Atkinson,et al.  Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series , 2018 .

[2]  C. Tucker,et al.  Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999 , 2001, International journal of biometeorology.

[3]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[4]  Martha C. Anderson,et al.  Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery , 2017 .

[5]  J. Greene,et al.  Within-Field Spatial Distribution of Megacopta cribraria (Hemiptera: Plataspidae) in Soybean (Fabales: Fabaceae) , 2013, Environmental entomology.

[6]  G. Hartman,et al.  Occurrence of Sclerotinia sclerotiorum in Soybean Fields in East-Central Illinois and Enumeration of Inocula in Soybean Seed Lots. , 1998, Plant disease.

[7]  Elizabeth Pattey,et al.  Narrowband vegetation indexes and detection of disease damage in soybeans , 2004, IEEE Geoscience and Remote Sensing Letters.

[8]  K. Nelson,et al.  Cultivar and Herbicide Selection Affects Soybean Development and the Incidence of Sclerotinia Stem Rot , 2002 .

[9]  A. Tenuta,et al.  Effect of Diseases on Soybean Yield in the Top Eight Producing Countries in 2006 , 2010 .

[10]  F. Gao,et al.  Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data , 2014 .

[11]  Sreekala G. Bajwa,et al.  Soybean Disease Monitoring with Leaf Reflectance , 2017, Remote. Sens..

[12]  E. Piper,et al.  Temperature and cultivar effects on soybean seed oil and protein concentrations , 1999 .

[13]  Thomas S. Colvin,et al.  Spatiotemporal variability of corn and soybean yield , 1997 .

[14]  P. Switzer,et al.  The Spatial Distribution of the Japanese Beetle, Popillia japonica, in Soybean Fields , 2013, Journal of insect science.

[15]  J. Dardanelli,et al.  Soybean Maturity Groups, Environments, and Their Interaction Define Mega-environments for Seed Composition in Argentina , 2006 .

[16]  Tim R. McVicar,et al.  Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection , 2013 .

[17]  X. Yang,et al.  Ranges and diversity of soybean fungal diseases in north america. , 2001, Phytopathology.

[18]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[19]  Mingquan Wu,et al.  An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery , 2016, Inf. Fusion.

[20]  G. Rousseau,et al.  Multivariate effects of plant canopy, soil physico-chemistry and microbiology on Sclerotinia stem rot of soybean in relation to crop rotation and urban compost amendment , 2006 .

[21]  Xiaoyang Zhang,et al.  Mapping and Quantifying White Mold in Soybean across South Dakota Using Landsat Images , 2019, Journal of Geographic Information System.

[22]  Philippe Ciais,et al.  Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades , 2007 .

[23]  Ali Soltani,et al.  Exploring spatial autocorrelation of traffic crashes based on severity. , 2017, Injury.

[24]  Le Yu,et al.  High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data , 2017, Remote. Sens..

[25]  C. D. Nickell,et al.  Yield and Seed Quality of Soybean Cultivars Infected with Sclerotinia sclerotiorum. , 1998, Plant disease.

[26]  C. Blazquez,et al.  Spatial autocorrelation analysis of cargo trucks on highway crashes in Chile. , 2018, Accident; analysis and prevention.

[27]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[28]  F. J. Moral García,et al.  Analysis of the spatio-temporal distribution of Helicoverpa armigera Hb. in a tomato field using a stochastic approach , 2006 .

[29]  S. Myint,et al.  Characterizing changes in cropping patterns using sequential Landsat imagery: an adaptive threshold approach and application to Phoenix, Arizona , 2014 .

[30]  M. Kroulík,et al.  The impact of topography on soil properties and yield and the effects of weather conditions , 2011, Precision Agriculture.

[31]  T. S. Colvin,et al.  Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field , 2003 .

[32]  T. George,et al.  Yield, Soil Nitrogen Uptake, and Nitrogen Fixation by Soybean from Four Maturity Groups Grown at Three Elevations , 1988 .

[33]  P. Moran.  Notes on continuous stochastic phenomena. , 1950, Biometrika.

[34]  M. Kozlov,et al.  Decline in Length of the Summer Season on the Kola Peninsula, Russia , 2002 .

[35]  Hans W. Linderholm,et al.  Growing season changes in the last century , 2006 .

[36]  F. J. Pierce,et al.  Geostatistical analysis of field spatial distribution patterns of soybean cyst nematode , 2003 .

[37]  T. S. Colvin,et al.  Identifying potential soybean management zones from multi-year yield data , 2005 .

[38]  Richard E. Plant,et al.  Factors underlying yield variability in two California rice fields , 2004 .

[39]  Heba Elbasiouny,et al.  Spatial variation of soil carbon and nitrogen pools by using ordinary Kriging method in an area of north Nile Delta, Egypt , 2014 .

[40]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[41]  E. S. Oplinger,et al.  Tillage, Crop Sequence, and Cultivar Effects on Sclerotinia Stem Rot Incidence and Yield in Soybean , 2001 .

[42]  D. Thompson,et al.  Using Landsat digital data to detect moisture stress , 1979 .

[43]  L. Plümer,et al.  Detection of early plant stress responses in hyperspectral images , 2014 .

[44]  G. Boland,et al.  Epidemiology of Sclerotinia stem rot of soybean in Ontario , 1988 .

[45]  A. Lahuerta-Marin,et al.  Spatiotemporal analysis of prolonged and recurrent bovine tuberculosis breakdowns in Northern Irish cattle herds reveals a new infection hotspot. , 2019, Spatial and spatio-temporal epidemiology.

[46]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[47]  Michael E. Schaepman,et al.  Unmixing-Based Landsat TM and MERIS FR Data Fusion , 2008, IEEE Geoscience and Remote Sensing Letters.

[48]  Antonio Paz González,et al.  Analysis of the spatial variability of crop yield and soil properties in small agricultural plots , 2003 .

[49]  B. Nelson,et al.  Effect of Sclerotinia Stem Rot on Yield of Soybean Inoculated at Different Growth Stages. , 2004, Plant disease.

[50]  Hankui K. Zhang,et al.  Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes , 2014 .

[51]  P. Shit,et al.  Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC) , 2016 .