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Efficient irrigation technologies, which seem to promise reduced production costs and water consumption in heavily irrigated areas, may instead be driving increased irrigation use in areas that were not traditionally irrigated. As a result, the total dependence on supplemental irrigation for crop production and revenue is steadily increasing across the contiguous United States. Quantifying this dependence has been hampered by a lack of comprehensive irrigated and dryland yield and harvested area data outside of major irrigated regions, despite the importance and long history of irrigation applications in agriculture. This study used a linear regression model to disaggregate lumped agricultural statistics and estimate average irrigated and dryland yields at the state level for five major row crops: corn, cotton, hay, soybeans, and wheat. For 1945–2015, we quantified crop production, irrigation enhancement revenue, and irrigated and dryland areas in both intensively irrigated and marginally-dependent states, where both irrigated and dryland farming practices are implemented. In 2015, we found that irrigating just the five commodity crops enhanced revenue by ~$7 billion across all states with irrigation. In states with both irrigated and dryland practices, 23% of total produced area relied on irrigation, resulting in 7% more production than from dryland practices. There was a clear response to increasing biofuel demand, with the addition of more than 3.6 million ha of irrigated corn and soybeans in the last decade in marginally-dependent states. Since 1945, we estimate that yield enhancement due to irrigation has resulted in over $465 billion in increased revenue across the contiguous United States (CONUS). Example applications of this dataset include estimating historical water use, evaluating the effects of environmental policies, developing new resource management strategies, economic risk analyses, and developing tools for farmer decision making.
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Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data
High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas.
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- PAR ID:
- 10112655
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 11
- Issue:
- 3
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 370
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs11030370
