The Crop Diversity Dashboard is an estimate of the relative diversity of crops grown on tribal lands. We use the Simpson’s Index of Diversity to estimate crop diversity from both the variety of crops grown and their respective acres within each reservation, with 0 meaning no crop diversity and 1 meaning infinite diversity. The data is available for five distinct years: 2012, 2017, 2018, 2019, and 2022.
This dashboard can be used to assess the diversity of crops grown on particular reservations to inform land-planning decisions. This is particularly important for climate-smart agriculture planning, as crop diversity is representative of a well-balanced food system and increased biodiversity. Typically, an area with a lot of intensive monocropping will show a very low score. To increase the usefulness of this dashboard, on top of our usual filter for each tribal geography represented in the data, we added a filter for NCAI regions, which enables comparing diversity within a given NCAI tribal region, which can be used for intertribal collaboration.
This dashboard applies the Simpson’s Index of Diversity to the Cropland Data Layer to estimate crop diversity from both the variety of crops grown and their respective acres within each reservation, with 0 meaning no crop diversity and 1 meaning infinite diversity. The data is available for five distinct years: 2012, 2017, 2018, 2019, and 2022.
About the Cropland Data Layer: According to the USDA, the Cropland Data Layer is a “raster, geo-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 geo-referenced output products.” The CDL data has been collected since 1997 making it an excellent tool for analyzing long-term land cover trends. The data includes 130 categories ranging from specific types of crops, pasturelands, developed lands, wetlands, etc. According to Lark et al. (2017) “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.
Species biodiversity, which is understood as “all species of plants, animals and micro-organisms existing and interacting within an ecosystem (Vandermeer and Perfecto, 1999), is essential to the healthy functioning of earth’s ecosystems. Biodiversity is associated with many ecological services, such as prevention of soil erosion, regulation of hydrological processes, recycling of nutrients, minimization of undesired or invasive organisms, detoxification, and much more (Altieri, 1999). Research on biodiversity has tended to focus on natural areas. However, in the contiguous United States, croplands comprise 22% of our land base and therefore have a major impact on our lands and ecosystems (Aguilar et al., 2015). A growing body of research seeks to explore the impact of these croplands on biodiversity.
While the importance of biodiversity in crop production is demonstrated, no data currently provides a nationwide picture of the situation for US Native lands. This dashboard aims at remedying this inequality in data access, along with providing tribes and land stewards useful information to inform sovereign and resilient crop planning. This dashboard will give you a baseline of the crop diversity of your agricultural systems, at the individual tribe, region or nationwide level. It aims at providing a well-rounded set of cutting-edge tools to support tribal land planning. Download the raw data to conduct further statistical analyses if need be. Also, check our complimentary dashboards: for instance, the Cropland Data Layer to study the crop species grown on your land, or the Soil Organic Carbon Dashboard, which looks at the increase in SOC that could be gained from agroecological production methods, which necessarily leads to more crop diversity.
Some interesting findings from our dashboard call for further analysis. The Aguilar et al. (2015) study showed a clear dropping trend in crop diversity from 1978 to 2012, with some counties forming clusters of low or high diversity over the years, creating a visible discrepancy in the resilience of local food systems in the US. However, trends in Indian Country seem to show a different narrative. From 2012 to 2022, there is a slight increase in crop diversity on US Native Lands, with the Simpson’s Index of Diversity raised from 0.46 to 0.59 overall. Regional patterns also show greater diversity in the Great Plains Region and lower diversity in the Western Region. These findings raise questions about the evolution of Native agriculture on tribal lands versus the rest of the US. The data seems to show that contrary to national trends, agriculture on Native lands is increasingly diverse. Is this another argument for the leading role that Native agriculture can play in helping the country meet the ecological transition? This narrative corroborates other findings conducted by NLAP, such as the fact that reservations have more gender-inclusive, less invasive, and more ecologically sound practices enforced by Native farmers and ranchers than their US counterparts.
The dataset does not show any particular species but provides a baseline indicator of cropland diversity from a calculation based off the Cropland Data Layer for the coterminous US. It is sourced from a publicly-available dataset to be of service to sovereign tribal land planning. We can provide details about the outlying species used in the calculations for each tribe upon request at info@nativeland.info. You can also download this enriched dataset below.
We have updated our terminology from “raw data” to “source data” to better reflect the contextual nature and origins of the information we provide. This change acknowledges that all data is influenced by the context in which it is collected, and aims to promote a more inclusive and accurate understanding of the information presented.