Smallholder Farming and Global Crop Masks

Suggested by: Sophia Klaußner, Lorenz Wendt (CDL GEOHUM)

Short description: 
Studies have shown that landcover models are not sufficient in correctly identifying crop land in Sub-Saharan Africa. This has grave implications as early warning systems and the distribution of support in case of emergencies therefore gets significantly delayed.

In this study the student will investigate an area with small-holder farming to evaluate the accuracy of global models for small-holders and develop usability guidance from this. They will create a validation dataset and evaluate several global models for performance with small-scale agriculture and explore the implications related to this. Further they will create a help for deciding what land cover model to use in different contexts.

Suggested Reading:

• Dlamini, L., Crespo, O., Van Dam, J., & Kooistra, L. (2023). A global systematic review of improving crop model estimations by assimilating remote sensing data: Implications for small-scale agricultural systems. Remote Sensing, 15(16), 4066. https://doi.org/10.3390/rs15164066
• Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., & Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? Scientific Data, 11(1), 486. https://doi.org/10.1038/s41597-024-03306-z
• Ketema, H., Wei, W., Legesse, A., Wolde, Z., Temesgen, H., Yimer, F., & Mamo, A. (2020). Quantifying smallholder farmers’ managed land use/land cover dynamics and its drivers in contrasting agro-ecological zones of the East African Rift. Global Ecology and Conservation, 21, e00898. https://doi.org/10.1016/j.gecco.2019.e00898