Guatemala and Haiti are two of the most food insecure nations in the Western Hemisphere. Measurements of food availability and access are instrumental in developing targeted hunger reduction strategies yet no estimates of cropped area (a critical input in the calculation of food production) at either a national or sub-national-level exist. The purpose of this research is to produce estimates of cropped area for Guatemala and Haiti using an area frame sampling approach and very high resolution (∼1. m) satellite imagery. Related research has combined livelihood data with topographic information to construct cropped area estimates in other settings using generalized additive models. We expand this approach with the inclusion of specific population variables in place of the livelihood data. We produce estimates of cropped area for the two countries and sub-national units and our results highlight the significance and complexity of incorporating explicit population characteristics into models of cropped area.
Bibliographical noteFunding Information:
The authors wish to thank the editor of Applied Geography, Dr. Jay Gatrell, for his assistance and guidance in the preparation of this manuscript for publication. Thanks also to Lindsey Everett and Trevor Cunningham for cartographic support and data assistance. Thanks to Joaquin Polania for his assistance and support. The authors also wish to thank Dr. Jim Verdin, Jim Rowland, Gary Eilerts and Dr. Jude Mikal for their comments on earlier versions of this paper. Finally, the authors appreciate the very helpful and insightful comments, questions and suggestions of two anonymous reviewers. This work was supported by the US Agency for International Development through a US Geological Survey cooperative agreement (04HQAG0001) and a US Department of Agriculture foreign agriculture grant (58-3148-6-137). The very high resolution imagery is licensed under NextView (c) 2010 DigitalGlobe.
Copyright 2011 Elsevier B.V., All rights reserved.
- Cropped area
- Food availability
- Remote sensing