Estimating the total number of active wheat harvesters using big data of GNSS trajectories in China

China plants approximately 20.3 million hm² of winter wheat annually. During the recent one-month harvesting period, hundreds of thousands of combine harvesters participated in wheat harvesting from south to north. However, the total number of active harvesters remains a challenge, restricting government policy-making and industry analysis. This study proposed a nonparametric bootstrap estimation model based on big data to dynamically infer the total number of active agricultural machines by analyzing the spatio-temporal trajectories of harvesters. Through Monte Carlo simulation experiments, the performance of four nonparametric bootstrap methods was systematically evaluated from dimensions such as bias, mean squared error, and coverage probability. The results show that the bias-corrected and accelerated bootstrap method (BCa) performs best and was selected as the 95% confidence interval estimation method. The 95% confidence intervals for the total number of active harvesters in 2021, 2022, and 2023 are [447 223, 456 387], [441 708, 447 625], and [436 873, 440 608], respectively, providing a quantitative basis for regulatory supervision and capacity planning in the agricultural machinery industry.