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Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture

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  • Brown, Jesslyn F.
  • Pervez, Md Shahriar

Abstract

Over 22 million hectares (ha) of U.S. croplands are irrigated. Irrigation is an intensified agricultural land use that increases crop yields and the practice affects water and energy cycles at, above, and below the land surface. Until recently, there has been a scarcity of geospatially detailed information about irrigation that is comprehensive, consistent, and timely to support studies tying agricultural land use change to aquifer water use and other factors. This study shows evidence for a recent overall net expansion of 522 thousand ha across the U.S. (2.33%) and 519 thousand ha (8.7%) in irrigated cropped area across the High Plains Aquifer (HPA) from 2002 to 2007. In fact, over 97% of the net national expansion in irrigated agriculture overlays the HPA. We employed a modeling approach implemented at two time intervals (2002 and 2007) for mapping irrigated agriculture across the conterminous U.S. (CONUS). We utilized U.S. Department of Agriculture (USDA) county statistics, satellite imagery, and a national land cover map in the model. The model output, called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the U.S. (MIrAD-US), was then used to reveal relatively detailed spatial patterns of irrigation change across the nation and the HPA. Causes for the irrigation increase in the HPA are complex, but factors include crop commodity price increases, the corn ethanol industry, and government policies related to water use. Impacts of more irrigation may include shifts in local and regional climate, further groundwater depletion, and increasing crop yields and farm income.

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  • Brown, Jesslyn F. & Pervez, Md Shahriar, 2014. "Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture," Agricultural Systems, Elsevier, vol. 127(C), pages 28-40.
    • Handle: RePEc:eee:agisys:v:127:y:2014:i:c:p:28-40
    DOI: 10.1016/j.agsy.2014.01.004
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    1. Jacinto F. Fabiosa & John C. Beghin & Fengxia Dong & JAmani Elobeid & Simla Tokgoz & Tun-Hsiang Yu, 2010. "Land Allocation Effects of the Global Ethanol Surge: Predictions from the International FAPRI Model," Land Economics, University of Wisconsin Press, vol. 86(4), pages 687-706.
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    Cited by:

    1. Zhang, Chen & Di, Liping & Lin, Li & Li, Hui & Guo, Liying & Yang, Zhengwei & Yu, Eugene G. & Di, Yahui & Yang, Anna, 2022. "Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data," Agricultural Systems, Elsevier, vol. 201(C).
    2. Minghao Bai & Shenbei Zhou & Ting Tang, 2022. "A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets," Land, MDPI, vol. 11(10), pages 1-27, September.
    3. Roger F. Auch & Danika F. Wellington & Janis L. Taylor & Stephen V. Stehman & Heather J. Tollerud & Jesslyn F. Brown & Thomas R. Loveland & Bruce W. Pengra & Josephine A. Horton & Zhe Zhu & Alemayehu , 2022. "Conterminous United States Land-Cover Change (1985–2016): New Insights from Annual Time Series," Land, MDPI, vol. 11(2), pages 1-20, February.
    4. Hsing-Hsiang Huang & Michael R. Moore, 2018. "Farming under Weather Risk: Adaptation, Moral Hazard, and Selection on Moral Hazard," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 77-124, National Bureau of Economic Research, Inc.
    5. Su, Shiliang & Hu, Yi’na & Luo, Fanghan & Mai, Gengchen & Wang, Yaping, 2014. "Farmland fragmentation due to anthropogenic activity in rapidly developing region," Agricultural Systems, Elsevier, vol. 131(C), pages 87-93.
    6. Kliment, T. & Bordogna, G. & Frigerio, L. & Stroppiana, D. & Crema, A. & Boschetti, M. & Sterlacchini, S. & Brivio, P. A., 2014. "Supporting a Regional Agricultural Sector with Geo & Mainstream ICT – the Case Study of Space4Agri Project," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(4), pages 1-12, December.
    7. Hussain, Mir Zaman & Hamilton, Stephen K. & Bhardwaj, Ajay K. & Basso, Bruno & Thelen, Kurt D. & Robertson, G.P., 2019. "Evapotranspiration and water use efficiency of continuous maize and maize and soybean in rotation in the upper Midwest U.S," Agricultural Water Management, Elsevier, vol. 221(C), pages 92-98.
    8. Dinesh Shrestha & Jesslyn F. Brown & Trenton D. Benedict & Daniel M. Howard, 2021. "Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017," Land, MDPI, vol. 10(4), pages 1-16, April.
    9. Haqiqi, Iman & Bowling, Laura C. & Jame, Sadia & Hertel, Thomas W. & Baldos, Uris Lantz C. & Liu, Jing, 2019. "Global Drivers of Land and Water Sustainability Stresses at Mid-Century," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291101, Agricultural and Applied Economics Association.
    10. Kayla A. Cotterman & Anthony D. Kendall & Bruno Basso & David W. Hyndman, 2018. "Groundwater depletion and climate change: future prospects of crop production in the Central High Plains Aquifer," Climatic Change, Springer, vol. 146(1), pages 187-200, January.
    11. Zhang, Chao & Dong, Jinwei & Zuo, Lijun & Ge, Quansheng, 2022. "Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets," Agricultural Water Management, Elsevier, vol. 263(C).
    12. Nicholas J. Pates & Nathan P. Hendricks, 2021. "Fields from Afar: Evidence of Heterogeneity in United States Corn Rotational Response from Remote Sensing Data," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1759-1782, October.
    13. Lee, Juhee & Hendricks, Nathan P., 2022. "Crop Choice Decisions in Response to Soil Salinization on Irrigated Land in California," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322602, Agricultural and Applied Economics Association.
    14. Hamze, Mohamad & Cheviron, Bruno & Baghdadi, Nicolas & Lo, Madiop & Courault, Dominique & Zribi, Mehrez, 2023. "Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model," Agricultural Water Management, Elsevier, vol. 283(C).

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