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Abstract Groundwater extraction in the United States (US) is unsustainable, making it essential to understand the impacts of limited water use on irrigated agriculture. To improve this understanding, we integrated a gridded crop model with satellite observations, recharge estimates, and water survey data to assess the effects of sustainable groundwater withdrawals on US irrigated agricultural production. The gridded crop model agrees with satellite‐based estimates of evapotranspiration (R2 = 0.68), as well as survey data from the United States Department of Agriculture (R2 = 0.82–0.94 for county‐level production and 0.37–0.54 for county‐level yield). Using the optimistic assumption that groundwater extraction equals effective aquifer recharge rate, we find that sustainable groundwater use decreases US irrigated production of maize, soybean, and winter wheat by 20%, 6%, and 25%, respectively. Using a more conservative assumption of groundwater availability, US irrigated production of maize, soybean, and winter wheat decreases by 45%, 37%, and 36%, respectively. The wide range of simulated losses is driven by considerable uncertainty in surface water and groundwater interactions, as well as accounting for the many aspects of sustainability. Our results demonstrate the vulnerability of US irrigated agriculture to unsustainable groundwater pumping, highlighting the difficulty of expanding or even maintaining irrigated food production in the face of climate change, population growth, and shifting dietary demands. These findings are based on reducing pumping by fallowing irrigated farmland; however, alternate pumping reduction strategies or technological advances in crop genetics and irrigation could produce different results.
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Biogeoscience opportunities to address agricultural supply chain risk: observations, methods, and applications
Food and agriculture is the largest global industry, at $7.8Tn annual value, and is also the least digitized industry. As a consequence, the inefficiencies in this industry are staggering: yield gaps below potential are 20-70% worldwide, and of the crops that are produced, 20-50% are lost from the time of harvest up to consumption. Where some frame the challenges in agriculture as “grow more with less,” a more useful analysis is around risk and uncertainty. In emerging markets, lack of geospatial data makes it difficult to recommend improved seeds or fertilizers for particular locales, therefore risky to make operating loans, impossible to accurately price crop insurance, and ultimately poses challenges in making contracts for delivery to processors that bring ag products into the food system. In developed markets, the ever increasing demands around immediacy, transparency, quality, crop novelty and food safety are straining the capacity of growers and processors to keep up. We have come to see this as a challenge in developing predictions joining both buyers and sellers around a shared set of facts on harvest timing, total yield, and post harvest quality. While these challenges have been met historically from government agencies and marketing boards reporting seasonal and regional forecasts, in many instances these are insufficient for making critical operational decisions on short timescales. In this talk, we will present a new set of measurements and analytical tools that enable unprecedented granularity in predictions to reduce risk and uncertainty in the food and ag supply chain, with special attention to applications that have potential to be economically self-sustaining.
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- Award ID(s):
- 1660146
- PAR ID:
- 10063202
- Date Published:
- Journal Name:
- AGU Fall Meeting
- Page Range / eLocation ID:
- GC31G-01
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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