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Abstract Irrigated agriculture in snow-dependent regions contributes significantly to global food production. This study quantifies the impacts of climate change on irrigated agriculture in the snow-dependent Yakima River Basin (YRB) in the Pacific Northwest United States. Here we show that increasingly severe droughts and temperature driven reductions in growing season significantly reduces expected annual agricultural productivity. The overall reduction in mean annual productivity also dampens interannual yield variability, limiting yield-driven revenue fluctuations. Our findings show that farmers who adapt to climate change by planting improved crop varieties may potentially increase their expected mean annaul productivity in an altered climate, but remain strongly vulnerable to irrigation water shortages that substantially increase interannual yield variability (i.e., increasing revenue volatility). Our results underscore the importance for crop adaptation strategies to simultaneously capture the biophysical effects of warming as well as the institutional controls on water availability.
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Scaling climate change to human behavior predicting good and bad years for Maya farmers
Abstract ObjectivesHuman responses to climate variation have a rich anthropological history. However, much less is known about how people living in small‐scale societies perceive climate change, and what climate data are useful in predicting food production at a scale that affects daily lives. MethodsWe use longitudinal ethnographic interviews and economic data to first ask what aspects of climate variation affect the agricultural cycle and food production for Yucatec Maya farmers. Sixty years of high‐resolution meteorological data and harvest assessments are then used to detect the scale at which climate data predict good and bad crop yields, and to analyze long‐term changes in climate variables critical to food production. ResultsWe find that (a) only local, daily precipitation closely fits the climate pattern described by farmers. Other temporal (annual and monthly) scales miss key information about what farmers find important to successful harvests; (b) at both community‐ and municipal‐levels, heavy late‐season rains associated with tropical storms have the greatest negative impact on crop yields; and (c) in contrast to long‐term patterns from regional and state data, local measures show an increase in rainfall during the late growing season, indicating that fine‐grained data are needed to make accurate inferences about climate trends. ConclusionOur findings highlight the importance to define climate variables at scales appropriate to human behavior. Course‐grained annual, monthly, national, and state‐level data tell us little about climate attributes pertinent to farmers and food production. However, high‐resolution daily, local precipitation data do capture how climate variation shapes food production.
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- Award ID(s):
- 1632338
- PAR ID:
- 10381679
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- American Journal of Human Biology
- Volume:
- 33
- Issue:
- 4
- ISSN:
- 1042-0533
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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