Irrigation is the primary consumptive user of water globally. Most of the land across the High Plains region in the United States is used for farming. Although irrigation is vital to prospering agricultural production, many states in the region only collect information about where, when, and how irrigation is implemented on coarse spatial levels through self-reported surveys. Recently, efforts have been made to further quantify irrigation through the classification of satellite imagery. Here, we address the differences and similarities between three most used reports of irrigation nationwide and one high resolution remotely sensed irrigation dataset (Deines et al., 2019) by applying fundamental statistical analyses to assess irrigation trends through time and to understand how they are impacted by outside drivers. We then address the unknown question of how farms are irrigated by creating a farm-level dataset of efficient sprinkler irrigation adoption across the High Plains Aquifer region from 1990 to 2012. We apply a change point detection method across the region followed by a significance filter to identify characteristic changes in irrigation patterns that are likely associated with the adoption of efficient sprinkler irrigation systems. A validation of adoption decisions showed 87% accuracy on the farm-level in Kansas, which is the only state where validation data is readily available at the scale of individual points of diversion. Irrigation adoption trends are sufficiently identified through this method. Although limitations exist, these joint methods have excellent potential to further improve our knowledge of irrigation practices on a regional level to better inform decision-making and move towards sustainable farming practices.
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