Policy, Technology, and Management Options for Water Conservation in the Ogallala Aquifer in Kansas, USA

The Ogallala Aquifer underlies 45 million ha, providing water for approximately 1.9 million people and supporting the robust agriculture economy of the US Great Plains region. The Ogallala Aquifer has experienced severe depletion, particularly in the Southern Plains states. This paper presents policy innovations that promote adoption of irrigation technology, and management innovations. Innovation in Kansas water policy has had the dual effects of increasing the authority of the state to regulate water while also providing more flexibility and increasing local input to water management and regulation. Technology innovations have focused on improved timing and placement of water. Management innovations include soil water monitoring, irrigation scheduling, soil health management and drought-tolerant varieties, crops, and cropping systems. The most noted success has been in the collective action which implemented a Local Enhanced Management Area (LEMA), which demonstrated that reduced water pumping resulted in low to no groundwater depletion while maintaining net income. Even more encouraging is the fact that irrigators who have participated in the LEMA or other conservation programs have conserved even more water than their goals. Innovative policy along with creative local–state–federal and private–public partnerships are advancing irrigation technology and management. Flexibility through multi-year allocations, banking of water not used in a given year, and shifting water across multiple water rights or uses on a farm are promising avenues to engage irrigators toward more sustainable irrigation in the Ogallala region.

Inspecting the Food–Water Nexus in the Ogallala Aquifer Region Using Satellite Remote Sensing Time Series

Agricultural production in the Great Plains provides a significant amount of food for the United States while contributing greatly to farm income in the region. However, recurrent droughts and expansion of crop production are increasing irrigation demand, leading to extensive pumping and attendant depletion of the Ogallala aquifer. In order to optimize water use, increase the sustainability of agricultural production, and identify best management practices, identification of food–water conflict hotspots in the Ogallala Aquifer Region (OAR) is necessary. We used satellite remote sensing time series of agricultural production (net primary production, NPP) and total water storage (TWS) to identify hotspots of food–water conflicts within the OAR and possible reasons behind these conflicts. Mean annual NPP (2001–2018) maps clearly showed intrusion of high NPP, aided by irrigation, into regions of historically low NPP (due to precipitation and temperature). Intrusion is particularly acute in the northern portion of OAR, where mean annual TWS (2002–2020) is high. The Oklahoma panhandle and Texas showed large decreasing TWS trends, which indicate the negative effects of current water demand for crop production on TWS. Nebraska demonstrated an increasing TWS trend even with a significant increase of NPP. A regional analysis of NPP and TWS can convey important information on current and potential conflicts in the food–water nexus and facilitate sustainable solutions. Methods developed in this study are relevant to other water-constrained agricultural production regions.

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths—an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.