Mumbwa Agricultural Data
Description
Mumbwa Agricultural Dataset: Field Descriptions and Research Overview Field Descriptions year: Calendar year of data collection (Integer) month: Month of data collection (Integer, 1-12) NIR: Near-infrared Spectroscopy reflectance (Percentage, %) Measures crop canopy reflectance in the near-infrared spectrum Higher values typically indicate healthier, denser vegetation GPR: Ground Penetrating Radar measurements (Unitless) Provides subsurface soil information including potential root zone characteristics Related to soil structure and moisture distribution pH: Soil acidity/alkalinity (pH scale, 0-14) Indicates soil chemical properties affecting nutrient availability Optimal range for maize is typically 5.5-7.0 nutrients: Composite soil nutrient index (Unitless, 0-100) Aggregate measure of N-P-K (Nitrogen, Phosphorus, Potassium) availability Higher values indicate better soil fertility tmin: Minimum daily temperature (Degrees Celsius, °C) Monthly average of daily minimum temperatures tmax: Maximum daily temperature (Degrees Celsius, °C) Monthly average of daily maximum temperatures humidity: Relative humidity (Percentage, %) Monthly average of atmospheric moisture content pressure: Atmospheric pressure (Hectopascals, hPa) Monthly average of barometric pressure readings rain: Monthly rainfall (Millimeters, mm) Total precipitation recorded per month cumulative_rain: Cumulative rainfall for growing season (Millimeters, mm) Running total of rainfall from season start to current month faw_infestation: Fall Armyworm infestation level (Count per plant or % infestation) Quantifies severity of FAW presence in fields Based on trap counts and field scouting observations yield_potential: Theoretical maximum yield under ideal conditions (Tons per hectare, t/ha) Estimated from soil, climate, and cultivar characteristics Represents yield ceiling without stress factors yield_avg: Actual measured yield (Tons per hectare, t/ha) Average harvested grain yield for the area Ground truth measurement for model evaluation Research Hypothesis Our research tests whether environmental, soil, and pest pressure variables can effectively predict maize yield outcomes in Mumbwa, Zambia through an integrated modeling approach. Specifically, we hypothesize that incorporating Fall Armyworm (FAW) infestation data alongside traditional agronomic parameters can significantly improve yield forecasting accuracy compared to conventional models that rely solely on weather and soil data.