Quantifying Within-field Variability in Soil Moisture and Nutrients and Scheduling Site-Specific Irrigation Using Numerical Modeling
Type of DegreePhD Dissertation
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The increasing global population is expected to increase pressure on water and food demands, which means intensification and improvements in irrigated agriculture are required to satisfy future needs. More than 70% of freshwater is destined for irrigation water withdrawal from streams, lakes, and groundwater. Managing irrigation based on crop demand using soil moisture variability can help improve agricultural water management. The distribution of precipitation over the growing season plays a very important role in scheduling irrigation. To improve agricultural systems, site-specific management of irrigation and nutrients are critical components of precision agriculture. To better understand irrigation and nutrient management in cropland fields, this research was conducted in the Tennessee Valley Region of Alabama. Understanding the spatial and temporal variability in soil moisture (or soil water [L3L-3]) (Dissertation Objective 1) in different croplands can help improve the need for site-specific irrigation. We will be using soil moisture, soil moisture content, soil water, or soil water content synonymously, and all these terminologies represent volumetric soil water or moisture content in vol/vol. Estimating the mean soil water status within a field is crucial for effective irrigation water management. Various problems may arise with determining field mean soil moisture. These problems include soil physical properties, field attributes, and meteorological factors. Spatial and temporal variability in soil moisture showed maximum variability in deep and topsoil layers of the corn and cotton fields, respectively. Average soil moisture content had different patterns of variability depending on the soil depth in both corn and cotton fields. The topography of the cornfield and soil properties of the cotton field were found to be important in finding a representative location for average soil moisture content. We determined that to capture maximum spatial and temporal variability within a field, the number of soil moisture sensors to be installed can be reduced relative to the sensors installed at the beginning of crop growing season and a temporally stable location can be identified to represent average soil moisture content within a field to schedule uniform irrigation. Within-field variability in soil nutrients (phosphorus-P and nitrogen-N) has an effect on nutrient uptake by plants (Dissertation Objective 2). A cornfield divided into three irrigation management zones showed that uniform application of nutrients across the field resulted in inadequate nutrients in one zone and adequate nutrients in others, which had an impact on plant growth. The high P and N concentration zone had higher plant growth and higher corn grain yield during the 2019 growing season. However, lower P and N zone had reduced plant growth and lower corn grain yield. Plants in the high yield zone had greater nutrient uptake than plants in the low yield zone. Incorporating nutrient variability for site-specific management in management zone delineation can improve nutrient use efficiency and reduce nutrient loss during the growing season. The field hydrologic properties are important factors to be considered for irrigation and nutrient management in a field. The site-specific irrigation and nutrient management are highly dependent on site-specific properties. Soil water dynamics are controlled by soil hydraulic properties (SHPs), which can vary greatly from one zone to another. Nutrient movement within the soil profile also depends on water movement within the soil profile. For better irrigation management, knowledge about SHPs is the most critical information in determining irrigation amount (Dissertation Objective 3). Optimizing SHPs using the HYDRUS modeling of zone-specific soil matric potentials (h) can determine accurate irrigation thresholds. The SHPs had differences between the irrigation management zones (zone 1 and zone 2). Determined irrigation thresholds and amounts using optimized SHPs were reduced as compared to laboratory-drawn (raw or observed) SHPs. Also, optimized irrigation thresholds were not uniform between two zones of a cornfield. The optimizing SHPs address the discrepancies associated with laboratory and field observations since they account for measurements recorded for soil matric potential or soil water pressure heads to optimize the SHPs. However, raw SHPs cannot consider field measurements in determining accurate irrigation thresholds. Using the HYDRUS-1D model for irrigation scheduling can increase the actual root water uptake to meet the potential water demands for a crop during the growing season. Different irrigation thresholds and amounts were used to trigger the irrigation within the zones. Accurate and efficient irrigation adoption due to precipitation distribution over the growing season is important to meet crop water demands on time. Results show that farmers may delay irrigation for a few days after reaching at the irrigation threshold of h, and irrigation can be scheduled at different times in different zones. Installing soil moisture sensors at 15 cm and 30 cm depths can improve the actual to potential root water uptake ratios, which defines the water stress in the crop. The accuracy in irrigation thresholds and depths is highly dependent on accurate field capacity (FC). The determination of FC, irrigation thresholds, and irrigation depths is characterized using site-specific SHPs (Dissertation Objective 4). Determination of FC within the field conditions is onerous and time-consuming. Therefore, FC was optimized using a negligible drainage-flux criterion at 0.01 cm/day for each zone within the field. Optimized FC was different from the FC obtained from raw SHPs and benchmark FC (33 kPa) for fine-textured soils. The differences in the determination of FC and irrigation thresholds using optimized SHPs considered soil water dynamics discrepancies associated with h measurements within the zones. The relationship between accumulated crop evapotranspiration and required irrigation amount showed a strong correlation for optimized SHPs and FC. The FC based on raw SHPs and benchmarks can be a flawed irrigation strategy to determine irrigation threshold and depths during the growing season. Therefore, optimizing FC and the irrigation thresholds can improve irrigation water management. Overall, this dissertation helps improve precision management of irrigation and nutrients during the growing seasons. This study can help provide a better understanding of the site-specific problems and can provide solutions for farmers to plan future growing seasons.