Regional Mapping of Perennial Weeds in Cotton with the Use of Geostatistics

Abstract

Perennial weeds constitute a serious problem in Greek cotton-growing areas, as they strongly competing against the crop and downgrade the final product. Monitoring weeds at a regional scale and relating their occurrence with abiotic factors will assist in the control of these species. Purple nutsedge, field bindweed, bermudagrass, and johnsongrass were studied in cotton crops for three consecutive growing seasons (2007 through 2009) in a large area of central Greece. Weed densities and uniformities per sampling site were assessed in relation to soil and climatic data. Abundance index (AI), which is highly dependent on abiotic factors, was also estimated, and revealed purple nutsedge to the most persistent and damaging species among the recorded weeds. Field bindweed showed the highest correlation with soil properties and especially with clay content. Furthermore, correlation analysis was used over the sampling years in order to assess the stability of weed occurrence in the sampling sites. Purple nutsedge, field bindweed, and bermudagrass proved to be stable in location and intensity. The weed density spatial distribution was evaluated by using local indicators of spatial autocorrelation (LISA) statistics, and was mapped by ordinary kriging and co-kriging interpolation methods. Only 1 to 3 spatial outliers were identified in each 1 of the 3 yr. Between the two interpolation methods co-kriging delivered better results for field bindweed and purple nutsedge, indicating that soil data could improve the estimation of weed occurrence. These co-kriging interpolated weed maps would be a very useful tool for decision makers in taking appropriate weed control measures.

Impact of High Density Planting and Weed Management Practices on Yield Attributes, Yield and Quality Characters of Bt Cotton

“…can be quoted as the most important examples (Kalivas et al, 2012;Dogan et al, 2014). Four plants of E. indica in one meter row of cotton crop were found to decrease number of bolls per plant by 25% and the cotton yield by >20% (Xiao-Yan et al, 2015).…”

Papaver rhoeas L. mapping with cokriging using UAV imagery

“…Although kriging has been widely used as an alternative to predict the spatial distributions of weed species (e.g. Donald et al 1994;Cardina et al 1997;Colbach et al 2000;Dille et al 2003;Jurado-Expósito et al 2003;Barroso et al 2005, Blanco-Moreno et al 2006Izquierdo et al 2009 andKalivas et al 2012, among many others), researchers often have few sampled sites in relation to the area to be mapped and have reported that simple interpolation procedures sometimes perform poorly for weed mapping when the number of samples that are available is restricted, e.g., by economic constraints.…”

“…Among these procedures, cokriging (COK) is commonly used when the sampling for a variable of interest (primary variable) is poor, and other variables (secondary variables) are more densely sampled because they are easier or cheaper to measure, reducing the sampling costs and increasing the prediction accuracy of the sparsely sampled target variable (Isaaks and Srivastava 1989;Webster and Oliver 2007;Chilès and Delfiner 2012;Emery 2012). COK has been successfully used to improve weed mapping by using auxiliary variables correlated with weed density, such as soil properties (Heisel et al 1999;Walter et al 2002;Kalivas et al 2012).…”

Monitoring the Spatial Variability of Knapweed (Centaurea diluta Aiton) in Wheat Crops Using Geostatistics and UAV Imagery: Probability Maps for Risk Assessment in Site-Specific Control

“…Maps based on observations at coarse resolutions will be poor in detail. Although kriging has been widely used to interpolate data and to create weed and treatment maps, e.g., [3,4,10,22,23], often because of the costs of sampling, there exist few sample sites to describe the variation in sufficient detail, which can lead to considerable uncertainty in the kriging estimations. Thus, researchers have suggested that this might not be the most suitable method for estimating weed densities [24,25].…”