Hi Clinton, Thanks for the question and apologies for the delay. The reson for this is that this irrigation data is a full dataset rather than an individual file.

If you are familiar with the AWS CLI, you can list all of these files with this command: `aws s3 ls s3://digital-atlas/domain=solution/type=suitability/source=ifpri/region=africa/processing=raw/` or you can explore them in Python using boto3 or s3fs. Unfortunately this data is all at raster level and has not been aggregated to individual regions. This includes data for 5 GCMs + 2 scenarios for each, along with the historical data for multiple different crops. For your use, I have listed out the full raster paths for the historical data below + the README to give you some context of the data.

s3://digital-atlas/domain=solution/type=suitability/source=ifpri/region=africa/processing=raw/model=historical/scenario=USD2.0/timeframe=per/variable=solar-irr_suitability/crop=chickpea.tif

You will notice this path starts with s3://. This type of s3 uri can be handled by rasterio, R Terra, etc. but if you'd prefer to download the data from the browser, you can use the http url by replacing the s3://digital-atlas with https://digital-atlas.s3.us-east-1.amazonaws.com

So the above example would become: https://digital-atlas.s3.us-east-1.amazonaws.com/domain=solution/type=suitability/source=ifpri/region=africa/processing=raw/model=historical/scenario=USD2.0/timeframe=per/variable=solar-irr_suitability/crop=chickpea.tif

And the exact same path (changing just the crop=XXX.tif) for these other crops (no capitals):

• maize
• onion
• rice
• tomato
• wheat

And for the README:

s3://digital-atlas/domain=solution/type=suitability/source=ifpri/region=africa/processing=raw/README.txt

OR

https://digital-atlas.s3.us-east-1.amazonaws.com/domain=solution/type=suitability/source=ifpri/region=africa/processing=raw/README.txt