For modeling social-ecological dynamics affecting maize health and production, you'll need integrated datasets spanning multiple domains. Agricultural datasets should include crop yield records, pest and disease incidence data, soil quality measurements (pH, nutrients, organic matter), weather patterns (temperature, rainfall, humidity), and farming practice surveys covering fertilizer use, irrigation methods, and crop rotation schedules. Social datasets are equally critical, encompassing farmer demographics, education levels, income data, land tenure arrangements, access to credit and markets, adoption rates of agricultural technologies, and social network structures within farming communities.

Ecological datasets should capture biodiversity indices, pollinator populations, natural pest predator abundance, land use change patterns, and ecosystem service measurements like water retention and carbon sequestration. Economic data including input costs, market prices, transportation infrastructure, and value chain analysis will help model the economic drivers of farming decisions. Institutional datasets covering agricultural extension services, policy interventions, subsidy programs, and cooperative membership can reveal how governance structures influence outcomes.

Remote sensing data from satellites can provide large-scale information on vegetation health, land cover changes, and climate variables, while temporal datasets spanning multiple growing seasons will capture long-term trends and cyclical patterns. The key is ensuring these datasets are spatially and temporally aligned, allowing you to model feedback loops between social factors (like technology adoption influenced by neighbor networks), ecological factors (like soil health affecting pest pressure), and their combined effects on maize productivity and health outcomes.

Sakshi Balyan Thank you for the response to my question,

Your response has really help me out in approaching my project.

Hello Tom Alando

For your project on modeling how social-ecological factors affect maize health and production using XGBoost, Random Forest, and Earth observation, you’ll want to use:

• Satellite data like Sentinel-2 or MODIS for vegetation health (NDVI), soil moisture, and climate info (rainfall, temperature).
• Maize yield and crop health data from local agricultural surveys or government sources.
• Social data like farm practices (planting, irrigation), farmer demographics, and access to resources (markets, extension services).
• Spatial data such as land use maps and administrative boundaries.

Combining these will help your models capture both environmental and human influences on maize production.