Using spatial crop modeling to improve the regional agricultural water planning.

Climate change is increasingly affecting agriculture water resources. This adverse situation can be addressed by developing approaches focusing on optimization for agricultural water management.  Groundwater depletion is a serious issue for the sustainability of irrigated agriculture in the southern and central parts of the High Plains Aquifer (HPA), USA. Crops that require more water to grow (e.g., maize) may not receive sufficient irrigation due to decline in pumping capacities, and growers can experience yield loss, jeopardizing the farm profits. Geospatial crop modeling can be seen as a tool to simulate different scenarios of water availability for crops in regions like Texas and Oklahoma Panhandle. Open-source version of AquaCrop (AC-OSPy) was run under a gridded environment on the maize pixels of crop frequency layer developed by National Agricultural Statistics Service. Long-term simulations for past 30-year period (1991-2020) were run using the historical weather data for multiple irrigation application rates. Also, deficit irrigation was tested to assess the impact of skipping irrigation in different crop stages. The simulations were able to capture the variation of weather and soil patterns in the region. Mean irrigation requirement ranged between 78 mm and 314 mm under 50% available water capacity irrigation threshold, and mean yield varied from 8.8 to 14.3 Mg-ha^-1. Deficit irrigation showed a potential of water saving during initial and vegetative stages (up to 113 mm), whereas a significant decline in yields was noted for skipping irrigation during flowering. Overall, the results of the study showed great potential of using geospatial crop modeling approach for regional agricultural water planning and drought mitigation efforts.