The single nucleotide polymorphism (SNPs) related to resistance to Succinate dehydrogenase inhibitors (SDHIs) in <i>Botrytis cinerea</i> populations are considered spatially aggregated. A new strategy of sampling is proposed to predict mutant’s distribution which could provide a more efficient sampling in comparison with random sampling. Adaptive Cluster Sampling (ACS) was evaluated with a set of 400 <i>B. cinerea samples tested for the presence of SNPs. The sdh gene was amplified, the nucleotide sequences were determined by the dideoxynucleotide chain-termination method and the presence of SNPs (P225F, N230I and H272L/R/Y) was determined. These data were used to stochastically generate data sets with different incidences (5 to 60%) and different level of aggregation (θ of 0.1, 0.15, and 0.20). Best sampling procedures when SNP incidence was below 30% was the ACS. Considering that the presence of SNP can only be determined after a laboratory procedure, the only practical application of ACS is for selecting the samples that will be processed. In that case, a large number of geo-referenced samples can be collected and an initial sample randomly selected and tested for resistance. If the sample is positive, other samples collected in the vicinity of this initial sample will be tested; if the sample is negative, another sample will be selected at random. This sampling approach can be used to detect and monitor low incidence and emerging mutations.</i>
