TDNet Discover

The capstone study titled “Impact of Limited Water Supply in Agricultural Yields and Sales Forecasts in the South Texas Produce Industry” investigates the issue of irrigation water scarcity and its influence on agricultural yields and market sales predictions. The project's scope includes exploring the socio-technical dynamics in the South Texas agricultural sector to enhance market forecasting and decision-making for agribusinesses in drought conditions. By incorporating elements of agroecological resilience theory and socio-technical systems theory, the study offers a thorough analysis of the challenges and potential technological interventions in this critical agricultural zone. The study is underscored by the need for agribusinesses to adapt to the socio-technical complexities of modern agriculture and to make informed decisions amid climatic adversities that affect crop yields and market stability. The study utilises a mixed-methods approach, using geospatial, predictive, and regression analyses to explore these dynamics. The guiding research question is: “In what ways does the availability of irrigation water influence crop yield and sales forecasting in the South Texas produce sector, and how can Information Technology be leveraged to enhance forecasting accuracy and decision-making?” The study’s geographic focus is on Hidalgo, Starr, and Cameron counties, the region’s agricultural hub, which is heavily dependent on irrigation from the Rio Grande. The sample comprises yield records for regional produce types (such as citrus fruits, onions, and leafy greens), water allocation and delivery reports from local irrigation authorities, and sales data from the USDA and local produce cooperatives. Data will be collected through remote sensing technologies (NDVI, thermal imaging), historical weather databases, irrigation water data, and market price information. Data analysis will include GIS mapping of agricultural areas, time-series analysis for forecasting (ARIMA/SARIMA models), and 3 multivariate regression to assess the relationships between yield, water availability, and market trends. This approach aims to provide stakeholders with data-driven insights for more effective management and planning.