Today, the US Department of Interior’s Bureau of Indian Affairs (BIA) holds 56.2 million acres of lands in trust for various Indian tribes and individuals. Approximately 46 million acres (81 %) of this land is used for farming and grazing by livestock and game animals, yet despite these vast resources, for at least the last 100 years, Native Americans have not been the primary beneficiaries of agriculture on their lands. According to the 2012 USDA Census of Agriculture Native Americans farmers and ranchers only captured 10% of the Agriculture revenue generated on their lands. While Native Americans struggle with disproportionate levels of poverty, food scarcity, and food-related illnesses the majority of their lands are being leased by the Federal Government to non-Natives. In response, many Native communities have launched various initiatives to increase their utilization and control over their agricultural resources. However, a fundamental obstacle for these initiatives has been the lack of agriculture and land use information for their lands. In fact, the Bureau of Indian Affairs does not make available to the public even basic statistics about its agricultural leasing programs or land ownership data on Indian lands. Furthermore, while the Federal government produces a great deal of agriculture information at the county and state levels they often neglect to collect and/or format this data for American Indian lands, which can and often does overlap state and county boundaries. For example, the federal government has been collecting Agriculture Census data for every county in the United States since 1840 but it was only in 2012 when they started to collect data in earnest for a select number of American Indian Reservations. While this demonstrates progress, there are still numerous datasets that need to be formatted to calculate data for American Indian Lands.
The CDL dashboard allows you to filter by specific reservation and provides the available crop covers as a pie chart.
View the total acreage of specific crop covers over time as a line chart or table.
One of the largest and most useful of these datasets is the USDA’s Cropland Data Layer (CDL). According to the the USDA9 the Cropland Data Layer is a “raster, gee-referenced, crop-specific land cover data layer created annually for the continental United States. The CDL is created using moderate resolution satellite imagery and extensive agricultural ground truthing. The purpose of the Cropland Data Layer program is to use satellite imagery to provide acreage estimates to the Agricultural Statistics Board for major commodities and to produce digital, crop-specific, categorized georeferenced output products.” The CDL data has been collected since 1997 making it an excellent tool for analyzing long-term land cover trends. The 2019 data includes 130 categories ranging from specific types of crops, pasturelands, developed lands, wetlands, etc. Lark et al. (2017) Citing NRCS (2016) metadata, states “the CDL covers the conterminous 48 states with field-level resolution and crop classification accuracies typically upwards of 90% for major commodities like corn, cotton, rice, soybeans, and wheat.” The Cropland Data Layer has been used in hundreds of studies on a range of topics from agriculture productivity, crop variability, impacts of climate change, climate resiliency studies, estimates of carrying capacity, etc.
The CDL is important because it provides a comparative database of agriculture across the coterminous United States and across a rather long-period of time. This is different than the census of agriculture which depends on self-reported numbers and only includes a small fraction of Native American lands.
While the CDL provides a seamless coast to coast accounting of what’s growing it’s important to consider that the resolution of each pixel (the basic unit of analysis for the CDL) is the equivalent of 30m x 30m on the ground. What that means is that crops or covers that are smaller than that area will not be accurately classified. That means that that the CDL is great for measuring large-scale mono-cropped agriculture and not so great at measuring small-scale sustainable agriculture where it’s common to have mixture of crops growing in a small area.