Using household survey data to identify large-scale food security patterns across Uganda.

Area of Science:

• Agricultural Economics
• Spatial Analysis
• Development Studies

Background:

• Targeting food security interventions for smallholder households requires large-scale poverty and food security data.
• Current mapping efforts often rely on aggregated data, potentially ignoring crucial household-level dynamics and local variability.
• The contribution of household-level information to spatial prediction of welfare indicators remains unclear.

Purpose of the Study:

• To assess if and how geo-referenced household-level data can improve spatial predictions of welfare indicators.
• To quantify local and large-scale variations in food security and livelihood activities.
• To evaluate the extent to which current mapping efforts overlook local variability.

Main Methods:

• Applied a stepwise regression kriging procedure to translate point data to spatially explicit patterns.
• Utilized geo-referenced household survey data from Uganda for country-wide predictions.
• Incorporated both household and environmental explanatory variables.

Main Results:

• Spatial predictions for food availability and livelihood indicators were generally weak, indicating difficulty in capturing large-scale variability.
• Household explanatory variables added little variation beyond environmental factors.
• Predictability was strongest for indicators influenced by environmental gradients; ubiquitously present crops showed high local variation.

Conclusions:

• Local variation in household welfare is substantial and often overrides large-scale patterns.
• Understanding the diversity of livelihood activities within local areas is critical for effective poverty targeting.
• Interventions must acknowledge that intra-area livelihood diversity is more significant than inter-area variability.