|Iqbal, Javed -|
|Whisler, Frank -|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 20, 2013
Publication Date: October 7, 2013
Repository URL: http://http:handle.nal.usda.gov/10113/58647
Citation: Iqbal, J., Read, J.J., Whisler, F. 2013. Using remote sensing and soil physical properties for predicting the spatial distribution of cotton lint yield. Turkish Journal of Field Crops. 18:158-165. Interpretive Summary: Variability in cotton lint yield across a field can be caused by natural spatial variability in soils, soil-plant water relations, and man-made variability from an intended or unintended management practice. This research was conducted to analyze digital remote sensing images of crop reflectance, specifically the normalized difference vegetation index (NDVI), to help predict cotton vigor and potential lint production on a site by site basis in a grower’s field. Cotton growers and consultants have interest in this technology to reduce costs by applying production inputs more efficiently and to decrease potential environmental impact from agricultural activities. Results found NDVI during the first-flowering stage, between 800 and 1500 growing degree days (GDD), is a good predictor of final lint yield and would be useful in scheduling cotton defoliation practices. Analysis of NDVI at pre-bloom stage (or first square), between 300 and 600 GDD, may be useful in deciding areas of the field to replant due to low plant density, particularly in dry conditions and droughty soil. The research presents a time line for obtaining critical multispectral image data from cotton during the growing season that may reduce costs to the grower and enhance management of crop production inputs.
Technical Abstract: Timely reflectance data from cotton (Gossypium hirsutum L.) production fields provide a useful tool for crop health assessment and site-specific crop management decisions. This field study investigated the relationships among site-specific normalized difference vegetation index (NDVI), soil physical properties, and final lint yield in order to develop a time line for obtaining critical remotely sensed data. Ten (1998) and 17 (1999) multispectral airborne images (10-nm band width) with 2-m spatial resolution were collected between April and September and analyzed at 12 sites in two North-South transects. Surface-soil textural class ranged from sandy loam to silt loam. Significant correlation (P<0.01) was found between NDVI and lint yield on 5 and 17 July, with the drainage areas having the lowest NDVI. Because NDVI during flowering stage of development, 800 to 1500 growing degree days (GDD), was a good predictor of cotton lint yield, spatial analysis of NDVI may provide critical information on when and where to use chemical defoliants in the field. Additionally, NDVI measurements at earlier stages of cotton development, 300 to 600 GDD, appear to have potential to delineate field areas with low plant density, which was associated with surface soils that were either coarse-textured and of low water-holding capacity or low in saturated hydraulic conductivity (porosity) and localized with the main water-drainage areas of the field.