Assessing spatial and temporal variability in cotton yield, soil properties, and profitability for precision agriculture in the southern High Plains

Understanding the spatio-temporal variability of soil properties, topography, crop growth, vegetative indices, and yield are the key prerequisites in developing strategies for precise management of agriculture inputs and production practices. However, very limited studies have conducted an integrated study including different aspect of soil and crops in site-specific management. Hence, the objectives of this study were to: 1) characterize the spatial variability patterns of soil microbial communities at the field scale; 2) assess the effects of soil physicochemical properties and topography on soil microbial spatial variability; 3) evaluate the pattern of spatial and temporal variation of vegetation indices at the within-field scale; 4) determine the influence of soil properties and topography on the variability of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI); 5) assess the application of NDVI and EVI in predicting cotton yield at the commercial field scale in the Southern High Plains; and 6) understand the factors affecting the spatial and temporal variability of profit and utilizing this variability in precise management of agricultural inputs. This study was conducted in ten fields located in the Southern High Plains (SHP) in the years 2000, 2001, 2002, 2003, and 2017. Soil physical, chemical, and microbial properties for a 194-ha field were derived using 212 soil samples collected from the field. Soil apparent electrical conductivity (ECa) data were obtained using an EC mapping system. Soil type information was derived for each field from the USGS soil survey database. Yield data were collected using harvesters equipped with yield monitors and global positioning systems (GPS). Digital elevation data were collected using a real-time kinematic GPS system. Elevation, slope, and curvature were derived from the digital elevation data. NDVI and EVI were derived from multiple in-season Landsat remote sensing images. The information for the cost of input and output for eight out of ten fields was collected from the farmer's crop reports. The relationship between yield, soil properties, apparent electrical conductivity, topography, and remote sensing images, and profit were evaluated. Several statistical and geostatistical methods were used to determine the spatial and temporal variation of soil properties, topography, vegetation indices, yield, and profit across the fields under study. In fields with high variability in soil physicochemical properties and topography, soil organic carbon and soil water content were the main factors influencing the spatial variability of total soil microbial biomass. The spatio-temporal patterns of vegetation indices, such as NDVI and EVI were also found to be significantly influenced by the spatial distribution of soil and topographic properties, specifically, elevation and apparent soil electrical conductivity. NDVI and EVI predicted cotton yield in all the years under study with good accuracy, but EVI showed better prediction performance compared to NDVI due to its higher sensitivity to vegetation. Moreover, average yield and profit also showed significant spatial and temporal variability in the fields under study. For the fields with lower variability in soil and topographic properties, profit was significantly different in different clusters. In fields with higher variability in soil and topographic properties, profit was inconsistent in the clusters across different growing seasons. The information on soil microbial variability could be used to develop strategies for site-specific management to enhance soil health, especially in semi-arid crop production systems. Understanding the influence of soil and topographic properties on the spatio-temporal distribution of vegetation indices and yield prediction performance can help select the proper vegetation indices for yield prediction and devising strategies for precise input management. Moreover, the knowledge on variability of profit at spatial and temporal scales provides the potential for site-specific management of agricultural inputs such as irrigation in these clusters or management zones based on the variability of the field. Overall, this study suggested that variability in soil and topography can influence crop growth, yield, and profit in the field. And this information can be utilized to create site-specific management strategies that can have real-world practical application in the sustainability of agriculture and conservation of the environment.