Benchmarking Evaluation of Score-Based Diffusion Imputation for Computer Agriculture Yield Estimation Under Missing Smallholder Survey Records: An Artificial Intelligence Imputation-Error Bound for Food-Security Reporting

Smallholder yield surveys in sub-Saharan Africa routinely lose a large share of their observations to item non-response, and the resulting imputation error propagates directly into the food-security statistics that national agencies report. The argument is methodological and theoretical rather than empirical: no new survey was fielded, and the empirical anchor is the DHS Program indicator 'Women's occupation: Agriculture' (id EM_OCCP_W_AGR), which was retrieved for 35 African countries over 1994 to 2024, giving 140 country-survey observations with a mean of 40.584 and a standard deviation of 20.59. The paper formalises missingness as a conditional score-matching problem, states the assumptions under which the imputation error is controlled, and proves a bound in which the downstream yield-estimation error is bounded by the score-matching excess risk plus a term that depends on the missingness mechanism and the conditional variance of yield. The bound is then used to benchmark score-based diffusion imputation against conditional-mean and multiple-imputation baselines on the DHS indicator, and to specify the conditions under which diffusion imputation is preferable for food-security reporting. The contribution is a benchmark protocol and an error bound that make imputation quality auditable before yield statistics enter policy reporting.