Amir Haghverdi

Perspectives on delineating management zones for variable rate irrigation

Up to 40% of soil available water content variance was explained by pie shape zoning.Dynamic zoning strategy may be needed if soil spatial arrangement varies by depth.Soil ECa and satellite images were useful attributes for irrigation zone delineation. This study aimed at investigating the performance of multiple irrigation zoning scenarios on a 73ha irrigated field located in west Tennessee along the Mississippi river. Different clustering methods, including k-means, ISODATA and Gaussian Mixture, were selected. In addition, a new zoning method, based on integer linear programming, was designed and evaluated for center pivot irrigation systems with limited speed control capability. The soil available water content was used as the main attribute for zoning while soil apparent electrical conductivity (ECa), space-borne satellite images and yield data were required as ancillary data. A good agreement was observed among delineated zones by different clustering methods. The new zoning method explained up to 40% of available water content variance underneath center pivot irrigation systems. The ECa achieved the highest Kappa coefficient (=0.79) among ancillary attributes, hence exhibited a considerable potential for irrigation zoning.

Studying uniform and variable rate center pivot irrigation strategies with the aid of site-specific water production functions

Novel site-specific water production functions (WPFs) were developed and tested.New zoning procedures for variable rate irrigation were established.The k-NN WPF accurately predicted cotton yield under supplemental irrigation.Sector zoning was predicted to enhance cotton yield under supplemental irrigation. Irrigation management has evolved into a top priority issue since available fresh water resources are limited. Water production functions (WPFs), mathematical relationships between applied water and crop yield, are useful tools for irrigation management and economic analysis of yield reduction due to deficit irrigation. This study aimed at (i) designing and evaluating site-specific WPFs (using k nearest neighbors (k-NN), multiple linear regression, and neural networks), (ii) simulating yield maps for uniform, sector control VRI, and zone control VRI center pivot systems using the site-specific WPFs, (iii) using the best WPF to investigate different cotton irrigation and zoning strategies using integer linear programming, and (iv) comparing soil-based and WPF-based zones for sector control VRI systems. A two-year cotton irrigation experiment (2013-2014) was implemented to study irrigation-cotton lint yield relationship across different soil types. The site-specific k-NN WPFs showed the highest performance with root mean square error equal to 0.131Mgha-1 and 0.194Mgha-1 in 2013 and 2014, respectively. The result indicated that variable rate irrigation with limited sector control capability could enhance cotton lint yield under supplemental irrigation when field-level spatial soil heterogeneity is significant. The temporal changes in climate and rainfall patterns, however, had a great impact on cotton response to irrigation in west Tennessee, a moderately humid region with short season environment. We believe site-specific WPFs are useful empirical tools for on-farm irrigation research.

Prediction of cotton lint yield from phenology of crop indices using artificial neural networks

Abstract A primary utility of satellite remote sensing technology is monitoring and assessment of agricultural lands for determining the area, amount, type, and quality of crop production. Since the mid-1970s agricultural scientists have sought to advance this utility through development of precision agriculture (PA) methods and technologies. Consequently, PA has taken advantage of freely available medium-spatial resolution remote sensing technology and instrumented fields to monitor crop biomass, phenology, and yield of crops at the sub-field to larger scales. The main goal of this study was to determine cotton lint yield in a 73-ha irrigated field in western Tennessee using remote sensing technology. We used two growing seasons (2013 and 2014) of Landsat 8 transformed to 8 input predictors including Red, near infra-red (NIR), the simple ratio (SR), normalized difference vegetation index (NDVI), green NDVI (GNDVI), and the tasselled cap transformation’s greenness, wetness, and soil brightness indices: GI, WI, and SBI, respectively, as proxies for cotton lint yield and crop phenology (in this study all input predictors are being referred to as crop indices, CIs). We used artificial neural network (ANN) approach to generate 61,200 models relating individual CIs and CI phenology to field estimates of lint yield to predict and map the field’s cotton lint yield in two cropping seasons. The correlation between cotton lint yield and CIs ranged from -0.20 to 0.60 in 2013 and from −0.79 to 0.84 in 2014. The best ANN models were in 2013 (r = 0.68 and the normalized MAE = 11%) and 2014 (r = 0.86 and the normalized MAE = 8%) growing seasons. The WI and GI were the best CI predictors of cotton lint yield, and overall for the early to mid-season prediction, CI phenologies had better performance than single date CI models. Consequently, we recommend the use of Landsat 8 derived WI or GI phenology to predict crop yields.