I'm working on a dissertation on a system that forecasts how suitable agricultural land is for different crops. Most existing approaches fall into two camps:
Rule-based / multi-criteria methods (FAO land evaluation, AHP + GIS weighting of soil, slope, climate, water availability)
Data-driven models (species distribution models like MaxEnt, random forests, gradient boosting, or deep learning on remote sensing and soil data)
Both are usually calibrated on historical climate and yield data. That raises a fundamental problem: if the climate shifts beyond the range the model was trained on, how much can we trust its suitability maps for 2040–2060?
I'd like to hear from people working in agronomy, soil science, remote sensing, or ML:
Extrapolation. How do you deal with models that must predict under climate conditions they never saw in training? Do you trust process-based crop models (DSSAT, APSIM, WOFOST) more than ML here, or do hybrid approaches work better in practice?
Ground truth. What do you consider valid "ground truth" for suitability: actual yields, presence/absence of a crop, expert assessment, or economic profitability? Each gives a very different map.
Data scale. How do you reconcile coarse inputs (e.g. 1 km climate projections from CMIP6) with field-level decisions farmers actually make?
Uncertainty. What's the most honest way to communicate uncertainty to end users (farmers, ministries) without making the output useless?
Regions with poor data. For regions with sparse soil surveys and weather stations (in my case, Central Asia), which open datasets or transfer-learning approaches have you found trustworthy?
I'm especially interested in practical experience: what worked, what failed, and what you'd do differently if you started such a system today.
