diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index af33ecfce..dd4ab42fc 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,7 +1,22 @@ - +## PR Checklists +(Select the checklist that applies to your PR) -## Why are you creating this Pull Request? +### Adding a Dataset Overview: +- [ ] Dataset is available in the production VEDA STAC +- [ ] Production STAC url is referenced in the dataset overview +- [ ] All metadata, including those for the E&A page, is completed in the mdx file +- [ ] Any images are compressed, ideally <500 KB +- [ ] At least one code owner has reviewed the PR -- [Adding Datasets or Stories](?title=Content%3A%20%3Cname%3E&expand=1&template=content.md) -- [Version Release](?title=Deploy%20vX.X.X&expand=1&template=version_release.md) -- [Other](?expand=1&template=default.md) \ No newline at end of file +### Adding a Data Story: +- [ ] Any data referenced in the story is already published to VEDA STAC and referenced in a separate dataset overview page (prior to merging or as part of the same PR) +- [ ] All metadata is completed in the mdx file +- [ ] Any images are compressed, ideally <500 KB +- [ ] Published Date is as close to the actual release date as possible (work with veda-ui team to determine potential release date when story is close to ready) +- [ ] At least one code owner has reviewed the PR + +### Updating VEDA-UI +- [ ] Confirmed that [updating the `veda-ui` submodule](https://github.com/NASA-IMPACT/veda-config/blob/main/docs/DEVELOPMENT.md#development) is needed. +- [ ] Ensured that the correct version of veda-ui is being used +- [ ] Reviewed the veda-ui changelogs for relevant updates +- [ ] Tested that the changes work as expected with the current instance configuration \ No newline at end of file diff --git a/.veda/ui b/.veda/ui index 179976ec2..6e929e8a9 160000 --- a/.veda/ui +++ b/.veda/ui @@ -1 +1 @@ -Subproject commit 179976ec2da0fff8c9ba3e46a59a69ac5c2aa568 +Subproject commit 6e929e8a9d075776a206bfd020c58107e71435ea diff --git a/datasets/soil-texture.data.mdx b/datasets/soil-texture.data.mdx new file mode 100644 index 000000000..aebd943e2 --- /dev/null +++ b/datasets/soil-texture.data.mdx @@ -0,0 +1,469 @@ +--- +id: soil-texture +name: 'ISRIC World Soil Texture Classifications' +description: "250 meter resolution global soil texture dataset from ISRIC, produced in 2017. Available at seven soil layer depths." +media: + src: ::file ./soil-texture-background.jpeg + alt: Examples of two different soil types + author: + name: Soil Sensor + url: https://soilsensor.com/articles/soil-textures/ +taxonomy: + - name: Topics + values: + - Agriculture + - Land Cover + - name: Source + values: + - ISRIC +layers: + - id: soil-texture-0cm + stacCol: soil-texture + name: Soil Texture at the Surface (0 cm Depth) + type: raster + description: 'ISRIC Soil Texture Classification at 0 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_0cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + + - id: soil-texture-5cm + stacCol: soil-texture + name: Soil Texture at 5 cm Depth + type: raster + description: 'ISRIC Soil Texture Classification at 5 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_5cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + + - id: soil-texture-15cm + stacCol: soil-texture + name: Soil Texture at 15 cm Depth + type: raster + description: 'ISRIC Soil Texture Classification at 15 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_15cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + + - id: soil-texture-30cm + stacCol: soil-texture + name: Soil Texture at 30 cm Depth + type: raster + description: 'ISRIC Soil Texture Classification at 30 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_30cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + + - id: soil-texture-60cm + stacCol: soil-texture + name: Soil Texture at 60 cm Depth + type: raster + description: 'ISRIC Soil Texture Classification at 60 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_60cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + + - id: soil-texture-100cm + stacCol: soil-texture + name: Soil Texture at 100 cm Depth + type: raster + description: 'ISRIC Soil Texture Classification at 100 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_100cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + + - id: soil-texture-200cm + stacCol: soil-texture + name: Soil Texture at 200 cm Depth + type: raster + description: 'ISRIC Soil Texture Classification at 200 cm' + zoomExtent: + - 0 + - 20 + sourceParams: + assets: soil_texture_200cm_250m + colormap_name: soil_texture + nodata: 255 + + legend: + type: categorical + stops: + - color: "#F89E61" + label: "Clay" + - color: "#BA8560" + label: "Silty Clay" + - color: "#D8D2B4" + label: "Sandy Clay" + - color: "#AE734C" + label: "Clay Loam" + - color: "#9E8478" + label: "Silty Clay Loam" + - color: "#C6A365" + label: "Sandy Clay Loam" + - color: "#B4A67D" + label: "Loam" + - color: "#E1D4C4" + label: "Silty Loam" + - color: "#BEB56D" + label: "Sandy Loam" + - color: "#777C7A" + label: "Silt" + - color: "#A89B6F" + label: "Loamy Sand" + - color: "#E9E2AF" + label: "Sand" + info: + source: ISRIC + spatialExtent: Global + temporalResolution: Annual + unit: N/A + +--- + + + ## Dataset Details + - **Temporal Extent:** 2017 + - **Temporal Resolution:** N/A + - **Spatial Extent:** Global + - **Spatial Resolution:** 250 m + - **Data Units:** N/A + - **Data Type:** Research + - **Data Latency:** N/A + +
+ + + ISRIC surface soil texture classifications (0 cm depth) in the Ohio River Valley. + +
+
+ + +
+ Soil Texture Classification Triangle. + + ISRIC Soil Texture Classification Triangle showing the percentage of clay, silt, and sand in each type. + +
+ + + ### About + The ISRIC Soil Texture dataset (SoilGrids 250) provides detailed soil texture classifications, which are critical for understanding soil properties, water retention, and agricultural potential. Maintained by the International Soil Reference and Information Centre (ISRIC), this dataset has been instrumental in various environmental and agricultural studies across the globe. This classification system breaks soils down into different categories based on their percentage of sand, silt, and clay, providing an essential resource for researchers, land managers, and farmers. + +
+ + + + ### What the ISRIC Soil Texture Dataset Offers + - Soil Texture Classes: This dataset classifies soils into twelve texture categories, ranging from sand, silt, and clay, to more complex combinations such as loam, clay loam, and silty clay. Each category offers a distinct combination of sand, silt, and clay content, helping users determine soil characteristics. + + - Soil Texture Triangle: A visualization tool used to depict the relationships between the percentage of sand, silt, and clay in soil. It allows users to easily locate and interpret soil types based on their texture composition. + + - Applications in Agriculture and Environmental Studies: The soil texture classification is pivotal in determining soil fertility, drainage capabilities, and erosion potential, making it crucial for land use planning, crop management, and climate studies. + + + + + + + + ### Access the Data + + Visit the [SoilGrids](https://www.soilgrids.org) webpage to explore all of the data options that SoilGrids 250 offers. + + + + + + + + ### Citing this Dataset + + Hengl, T., de Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748 + + + + + + + + ## Disclaimer + + All data provided in VEDA has been transformed from the original format (TIFF) into Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org)). Careful quality checks are used to ensure data transformation has been performed correctly. + + + + + + + + ### Key Publication + + Hengl, T., de Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagotić, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748 + + + + + + + + ### Other Publications + + Rossiter, D. G. (2001). Methodology for soil resource inventories. Computers and Electronics in Agriculture, 35(2), 189–214. + + + + + + + + + ## Data Stories Using This Dataset + + **[Climate Extremes Aggrevate Social Vulnerabilities in Alabama's Black Belt](https://www.earthdata.nasa.gov/dashboard/stories/black-belt-climate)** + + + + + + + + ## License + + [Creative Commons Attribution 1.0 International](https://creativecommons.org/publicdomain/zero/1.0/legalcode) (CC BY 1.0) + + + \ No newline at end of file diff --git a/docs/DEVELOPMENT.md b/docs/DEVELOPMENT.md index 4dd7859af..bd0886c21 100644 --- a/docs/DEVELOPMENT.md +++ b/docs/DEVELOPMENT.md @@ -7,7 +7,7 @@ You can see the version running: ./.veda/veda --info ``` -The commit you may want to pin the `veda-ui` to will depend, but to get the latest changes from the `main` branch you could do: +The commit you may want to pin the `veda-ui` to will depend, but to update veda-ui you can do: ``` # Go into the submodule cd .veda/ui @@ -15,12 +15,10 @@ cd .veda/ui # Update the submodule info git fetch -# Switch to the branch you want. -# You can also choose a specific tag. -git switch main +# Switch to the release you want. +# You can see the list of releases on https://github.com/NASA-IMPACT/veda-ui/releases -# Get the latest changes -git pull +git checkout vx.x.x # Get back to the root directory cd - diff --git a/stories/BB_AgeOperators_Sumter.png b/stories/BB_AgeOperators_Sumter.png index 5cceff841..07dc6a0ce 100644 Binary files a/stories/BB_AgeOperators_Sumter.png and b/stories/BB_AgeOperators_Sumter.png differ diff --git a/stories/BB_Estab_AL.png b/stories/BB_Estab_AL.png index a9cd37f33..e85d1ef56 100644 Binary files a/stories/BB_Estab_AL.png and b/stories/BB_Estab_AL.png differ diff --git a/stories/BB_trends_Choctaw.png b/stories/BB_trends_Choctaw.png index 846435533..3e4f0a03b 100644 Binary files a/stories/BB_trends_Choctaw.png and b/stories/BB_trends_Choctaw.png differ diff --git a/stories/BB_trends_Marengo.png b/stories/BB_trends_Marengo.png index 2aeb493c8..eb6e5b5ce 100644 Binary files a/stories/BB_trends_Marengo.png and b/stories/BB_trends_Marengo.png differ diff --git a/stories/BB_trends_Pickens.png b/stories/BB_trends_Pickens.png index 3dfaf318e..31f0446c0 100644 Binary files a/stories/BB_trends_Pickens.png and b/stories/BB_trends_Pickens.png differ diff --git a/stories/black-belt-age-trends.png b/stories/black-belt-age-trends.png new file mode 100644 index 000000000..a4cc1740f Binary files /dev/null and b/stories/black-belt-age-trends.png differ diff --git a/stories/black-belt-climate-ej.stories.mdx b/stories/black-belt-climate-ej.stories.mdx new file mode 100644 index 000000000..c8cb44f09 --- /dev/null +++ b/stories/black-belt-climate-ej.stories.mdx @@ -0,0 +1,268 @@ +--- +id: black-belt-climate +name: Climate Extremes Aggravate Social Vulnerabilities in Alabama's Black Belt +description: "Rising temperatures worsen social vulnerabilities for the elderly population by increasing their heat-related health risks." +media: + src: ::file ./black_belt_cover.jpg + alt: Farmer in the fields. + author: + name: NASA + url: https://widerimage.reuters.com/photographer/lucy-nicholson.html +pubDate: 2024-11-11 +taxonomy: + - name: Topics + values: + - Environmental Justice + - Heat + - Land Use + - Natural Disasters + +--- + + + +

+ Authors: Maheshwari Neelam [1], Udaysankar Nair [2], Natalie P.Thomas [3], and Andrew Blackford [2] +

+

+ [1] USRA and National Aeronautics and Space Administration (NASA) +

+

+ [2] The University of Alabama in Huntsville (UAH) +

+

+ [3] University of Maryland and Global Modeling and Assimilation Office, NASA +

+

+ Mission: NASA Earth Action: A thriving world, driven by trusted, actionable Earth science +

+

+ This study demonstrates innovative applications of NASA and other datasets to highlight environmental inequities. Please note that these results are preliminary and have not yet undergone peer review. +

+
+
+ + + +

+ The Alabama Black Belt, a region integral to the Southern United States, derives its name from its dark, fertile soil. This rich land is ideal for agriculture, especially cotton, and owes its fertility to a unique geological history. Millions of years ago, when the Gulf of Mexico extended further inland, plankton deposits formed soft limestone called Selma Chalk. Over time, this limestone weathered, creating the calcium-rich, chalky subsoil that characterizes the Black Belt. +

+
+ +
+ + + Surface soil textures classified from the International Soil Reference and Information Centre (ISRIC) show the fertile 'black belt' region of Alabama quite well. + +
+ +
+ + +
+ + + This composite True Color satellite imagery from MODIS highlights the difference in land use - land cover in the Black Belt Prairie compared to the majority of the Southeastern United States. + +
+ +

+ The Black Belt's crescent shape is captured in a natural-color image by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. This image is a composition of segments from several images taken between 2015 and 2018, allowing for the removal of clouds and haze. +

+
+ +
+ + + +

+ The Black Belt’s rich soil played a pivotal role in shaping the region's history and demographics. This fertile land transformed the area into an agricultural powerhouse, making it a cornerstone of the cotton economy, which was heavily reliant on enslaved African American labor. After the Civil War, many former slaves remained in the area, working as sharecroppers and tenant farmers. The region became a significant cultural and political area, particularly noted for its role in the civil rights movement. Despite its agricultural legacy, the Black Belt has faced economic and social challenges, including poverty and limited access to education and healthcare, which continue to impact its predominantly Black population. +

+
+
+ +
+
+ + + + ## Socioeconomic Vulnerability + [Social Vulnerability Index (SVI)](https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html) data from the Centers for Disease Control and Prevention (CDC) between 2000 and 2022 show a concerning demographic shift: the younger generation, including those aged 17 and younger, is decreasing in the region, leaving behind an aging population, particularly those aged 65 and above. A linear trend is observed, with color coding representing the slope of this shift. This trend exacerbates the region's vulnerabilities, as the older population becomes more isolated and dependent. +
+ + + Significant Trends in Elderly and Young Populations (SVI data): Slope Analysis (p < 0.1) + +
+
+ + + ## Climate Extremes + The region is now facing a significant new challenge - climate change. MERRA-2 Climate Indices from 1980 to 2023 reveal significant shifts in temperature extremes. The region is witnessing a marked increase in warm days (TX90P, % of days), warm nights (TN90P, % of days), heatwave frequency (HWF, days) alongside a decrease in cool days (TX10P, % of days), cool nights (TN10P, % of days), 1-day precipitation amount (RX1Day, mm) and 5-day precipitation amount (RX5Day, mm). +
+ + + Significant Trends in Climate Extremes from MERRA-2 (1980-2023) in Choctaw County, AL: Normalized Slope Analysis (p < 0.1) + +
+
+ + + ## Butler County +
+ + + Significant Trends in Climate Extremes from MERRA-2 (1980-2023) in Butler County, AL: Normalized Slope Analysis (p < 0.1) + +
+
+ + + ## Pickens County +
+ + + Significant Trends in Climate Extremes from MERRA-2 (1980-2023) in Pickens County, AL: Slope Analysis (p < 0.1) + +
+
+ + + ## Marengo County +
+ + + Significant Trends in Climate Extremes from MERRA-2 (1980-2023) in Marengo County, AL: Slope Analysis (p < 0.1) + +
+
+
+ + + +

+ Agriculture remains the backbone of the Black Belt's economy, with many older adults working in the fields. This reliance on an aging workforce is concerning, particularly as the physical demands of farming intensify with rising temperatures. Analyzing USDA's [National Agricultural Statistics Service](https://www.nass.usda.gov/) data for Sumter County, the number of farm operators aged 55 and above is increasing, reflecting a broader national trend where the average age of U.S. farm producers has risen to 58.1 years by 2022. +

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+ +
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+ + + +
+ +
+ +

+ Additionally, outdoor occupations such as transportation and construction have become crucial sources of income and have shown growth over the past few years. Data from the [U.S. Census Bureau](https://www.census.gov/topics/employment/industry-occupation.html), which examines the number of establishments by employment category in counties like Choctaw and Butler, underscores the economic importance of these sectors. +

+
+ +
+ + + +

+ The combination of rising temperatures and an older workforce underscores the urgent need for adaptive strategies to protect both the health of the population and the viability of these economic sectors.Addressing these intertwined challenges requires targeted policies and investments in climate resilience measures. By enhancing education, healthcare, and job opportunities, along with adopting sustainable agricultural practices, the Black Belt can work towards a more resilient and prosperous future in the face of climate change. + ### To mitigate heat-related risks, several safeguards can be implemented: + - Improved Access to Cooling Centers: Establishing more cooling centers in rural areas can provide relief during extreme heat events + - Health Monitoring Programs: Implementing regular health check-ups for older adults working in agriculture can help prevent heat-related illnesses + - Education and Training: Providing training on heat safety and recognizing symptoms of heat stress can empower workers to take preventive actions + - Flexible Work Schedules: Adjusting work hours to cooler parts of the day can reduce exposure to extreme heat +

+
+
+ + + +

+ ### Data Access + * [NASA Description for MERRA-2 Climate Indices ](https://search.earthdata.nasa.gov/search/granules?p=C1949649168-GES_DISC&pg[0][v]=f&pg[0][gsk]=-start_date&q=MERRA-2%20extremes&tl=1724265744.288!3!!/) + * [CDC/SVI Data and Documentation Download | Place and Health](https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html) + * [United States Department of Agriculture (USDA) | National Agricultural Statistics Service (NASS)](https://www.nass.usda.gov/) + * [Census Bureau Data | Occupation](https://www.census.gov/topics/employment/industry-occupation.html) +

+

+ **Editors**: Maheshwari Neelam and Derek Koehl + + **Developers**: Maheshwari Neelam, Andrew Blackford, Brian Freitag, and Aaron Kaulfus + + **Science and Content Contributors**: Maheshwari Neelam and Udaysankar Nair + + **Questions / Feedback (email address)**: maheshwari.neelam@nasa.gov +

+

+ #### Additional Resources + * Thomas N. P., A. B. Marquardt Collow, M. G. Bosilovich, et al. 2023. Effect of Baseline Period on Quantification of Climate Extremes Over the United States. Geophysical Research Letters Volume 50, Issue 17, https://doi.org/10.1029/2023GL105204 . + * [The growing threat of heat for farmworkers](https://blogs.edf.org/growingreturns/2023/08/02/heat-threat-for-farmworkers/) + * [How Thousands of Black Farmers Were Forced Off Their Land](https://www.thenation.com/article/society/black-farmers-pigford-debt/) + * [“It Tears You Apart Mentally and Physically”: The Health Crisis Afflicting Black Farmers](https://inthesetimes.com/article/black-farmers-stress-debt-land-loss-racism-mental-health-crisis) +

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