The evaluation of wild boar density in a hunting district can be performed by accurate drive counts of boars within the drive areas assigned to each hunting team. Because a complete driving of all the areas is prohibitive, only a subset is driven in a hunting occasion. Results are highly dependent on the subjective choice of these areas. In this study, an objective design-based approach is considered in which areas to be driven are randomly selected one per team in accordance with the one-per-stratum sampling scheme. Because the areas assigned to hunting teams are likely to be close to each other, the one-per-stratum sampling is likely to achieve samples of evenly spread areas. Then, the subsequent step is to choose the selection criterion for the areas and the estimation criterion for exploiting or not the information provided by area sizes. To this purpose, three sampling strategies are considered, together with methods to estimate their precision. These strategies are checked and compared by means of a simulation study performed on artificial populations constructed from the list of drive areas settled in the Province of Massa–Carrara (Italy) and partitioned among 39 hunting teams. Results from artificial populations give clear insights about the most suitable strategy to be used. Drive counts performed in this province in two hunting occasions during 2019 within 39 areas selected by one-per-stratum sampling are adopted as case studies.

狩猎区野猪密度的评估可以通过分配给每个狩猎队的驱动区域内的公猪的精确驱动计数来执行。因为完全驱动所有区域是禁止的，所以在狩猎场合只驱动一个子集。结果高度依赖于这些领域的主观选择。在这项研究中，考虑了一种基于客观设计的方法，其中根据每层一个抽样方案，每个团队随机选择一个要驱动的区域。由于分配给狩猎队的区域可能彼此靠近，因此每层一次采样很可能实现均匀分布区域的样本。然后，下一步是选择区域的选择标准和估计标准，以利用或不利用由区域大小提供的信息。为此，考虑了三种采样策略，以及估计其精度的方法。这些策略通过对人工种群进行的模拟研究进行检查和比较，该人工种群由定居在马萨-卡拉拉省（意大利）的驱动区域列表构成，并在 39 个狩猎队中进行划分。人工种群的结果对最适合使用的策略提供了清晰的见解。采用逐层抽样选取的39个区域内2019年在该省两次狩猎活动中进行的驱动计数作为案例研究。这些策略通过对人工种群进行的模拟研究进行检查和比较，该人工种群由定居在马萨-卡拉拉省（意大利）的驱动区域列表构成，并在 39 个狩猎队中进行划分。人工种群的结果对最适合使用的策略提供了清晰的见解。采用逐层抽样选取的39个区域内2019年在该省两次狩猎活动中进行的驱动计数作为案例研究。这些策略通过对人工种群进行的模拟研究进行检查和比较，该人工种群由定居在马萨-卡拉拉省（意大利）的驱动区域列表构成，并在 39 个狩猎队中进行划分。人工种群的结果对最适合使用的策略提供了清晰的见解。采用逐层抽样选取的39个区域内2019年在该省两次狩猎活动中进行的驱动计数作为案例研究。