This Is AuburnElectronic Theses and Dissertations

Evaluation of Deficit Irrigation Strategies and Management Zones Delineation for Corn Production in Alabama




Morata, Guilherme

Type of Degree

Master's Thesis


Crop Soils and Environmental Sciences


The adoption of center pivot irrigation in Alabama has increased in recent years. The reason is that farmers are achieving high crop yield by avoiding crop water stress. Although, irrigation adoption has increased, Alabama farmers still require more training to increase knowledge on irrigation timing and rate in order to increase irrigation water use efficiency. Technologies such as variable rate irrigation (VRI) and soil sensors to monitor soil water status are available to help farmer to increase their irrigation water use efficiency. Variable Rate Irrigation allows farmers to change irrigation rate as the pivot traverse the field, therefore, meeting crop water needs in relation to soil conditions of field terrain changes. Irrigation management zones (MZ) are required to create an irrigation prescription map that allows the VRI system to modify that irrigation rates according to the field and crop variability. Soil properties and topography are the most common data analyzed to delineate irrigation MZ because these properties affect soil water content on crop fields. The objectives of this study were to: (i) identify which terrain attribute, or combination of terrain attributes and soil properties better explain the variability in soil water content in a crop field, and then, can be used for irrigation management zone delineation; (ii) use a crop growth simulation modeling software CERES-Maize model (DSSAT v.4.7.5) to identify deficit irrigation strategies to initiate irrigation; and (iii) to identify irrigation amount that maximize net returns and irrigation efficiency. For the first study of this thesis (Chapter II), the experiment was conducted in Tanner and Town Creek, Alabama in 2018 and 2019. Both fields were irrigated by a center pivot irrigation system that covered 24 and 120 hectares in Tanner and Town Creek, respectively. Tanner field was planted with corn in 2018 and cotton in 2019, and Town Creek was planted with corn on both years. Spatial and temporal changes in soil water content were assessed by soil water tension sensors installed at 15, 30 and 60 cm soil depth. The daily average soil tension values were converted in volumetric water content using soil water retention curves generated through these studies. Topographic wetness index (TWI), topographic position index (TPI), elevation, and slope were considered as terrain attributes for this analysis. Apparent soil electrical conductivity (Soil ECa) collected at Tanner field and clay, silt and sand content from the Town Creek field were included in the analyses as part of the characterization of soil properties. Principal component analysis (PCA) was used to reduce the dimensionality of the volumetric water content data set and to test the hypothesis of spatial and temporal differences in soil water content. A second step involved a Spearman correlation analysis between the scores of the principal components retained in the PCA and terrain attributes and soil properties to identify parameter with significant correlation with changes in soil water content. The results of PCA indicated that three principal components were sufficient to retain more than 95% of the variance of the entire volumetric water content data set. The principal component one (PC1) was found related to soil water content spatial variability and PC2 and PC3 with temporal variability on both fields and years. At Tanner field, the PC1 explained the most of soil water content variance for the three soil layers analyzed and was high correlated only with TPI for both years. In 2018, PC2 was correlated with elevation, slope and TWI. In 2019, PC3 showed only correlation with slope and soil ECa . At the Town Creek field, PC1 was significantly correlated with slope, TWI, and sand content in 2018 and TPI in 2019. PC2 was significantly correlated with TWI, clay, and silt content in 2018 and elevation in 2019. PC3 only showed significant collection with clay and silty content in 2019. For both fields, both topographic indices were significant explaining the variability in soil water content and therefore could be considered for irrigation management zones delineation. The crop growth simulation modeling study, chapter III on this thesis, was conducted in Decatur silty clay loam soil type at Town Creek, Alabama on an irrigated corn field. The CERES-Maize model, part of the Decision Support System for Agrotechnology Transfer (DSSAT) software application program, was calibrated and validated using data collected in 2019 and 2018, respectively. Plant biomass, leaf area index, volumetric water content, phenology dates, crop yield, and yield components were used to calibrate the model. Deficit irrigation scenarios assumed for this study were considered to trigger irrigation at 20, 30, 40, 50, 60, 70, 80, and 90% of soil water depletion. This analysis was conducted using the seasonal analysis tool in DSSAT. Daily weather data used in the seasonal analyses corresponded to the period 1984 to 2019. Three different fixed irrigation rates of 12.7, 19, 25.4 mm and full rate were evaluated to refill the soil back to field capacity and the criteria for selecting the most efficient treatment was based on maximization of the net returns. The results of the calibrated model showed prediction of crop yield with RMSE = 69 kg ha-1 (0.5%) and RMSE = 450 kg ha-1 (3.5%) for the validation data set. The deficit irrigation strategy that maximized net returns resulted on triggering irrigation at 70% of soil water depletion with an average crop yield of 11,175.5 kg ha-1. Yield started to decrease when irrigation was triggered at 80% and 90% of soil water depletion. The irrigation strategies that maximized net results considered applying irrigation at the rate of 25.4 mm every time the soil reached 70% of soil water depletion. These results will require field testing to prove the results from the crop growth modeling and the results will guide Alabama farmers to manage better irrigation decisions related to irrigation timing and rate to help them increase water use efficiency, achieve higher yield and improve the profitability.