A Leave-One-Out Approach to Evaluating Producers' Commodity Dependent Impact Scores on a Non-Calibrated Survey

This paper presents a novel application of the leave-one-out (LOO) methodology to evaluate the impact of individual records on national agricultural estimates produced based on non-calibrated surveys. Using the July 2020 Cattle Survey as a case study in our analysis, it is demonstrated how impact scores generated through the LOO approach can be employed in conjunction with response propensity scores to reduce sample size without significantly affecting the accuracy of estimates. Our findings indicate that strategic sample reductions can enhance resource efficiency in large-scale agricultural surveys and mitigate bias introduced by non-response through targeted imputation. This study contributes to the broader discussion on improving the cost-effectiveness of agricultural data collection while preserving statistical rigor.