Plant Pathology & Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY 14456.
Cornell Vegetable Program, Cornell Cooperative Extension, Canandaigua, NY 14424.
Plant Dis. 2021 Sep;105(9):2453-2465. doi: 10.1094/PDIS-07-20-1619-RE. Epub 2021 Oct 19.
Sampling strategies that effectively assess disease intensity in the field are important to underpin management decisions. To develop a sequential sampling plan for the incidence of Cercospora leaf spot (CLS), caused by , 31 table beet fields were assessed in the state of New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of three to six linear transects per field. Spatial pattern analyses were performed, and results were used to develop sequential sampling estimation and classification models. CLS incidence () ranged from 0.13 to 0.92 with a median of 0.31, and beta-binomial distribution, which is reflective of aggregation, best described the spatial patterns observed. Aggregation was commonly detected (>95%) by methods using the point-process approach, runs analyses, and autocorrelation up to the fourth spatial lag. For Spatial Analysis by Distance Indices, or , 45% of the datasets were classified as a random pattern. In the sequential sampling estimation and classification models, disease units are sampled until a prespecified target is achieved. For estimation, the goal was sampling CLS incidence with a preselected coefficient of variation (). Achieving the = 0.1 was challenging with <51 sampling units, and only observed on datasets with incidence >0.3. Reducing the level of precision, i.e., increasing to 0.2, allowed the preselected to be achieved with a lower number of sampling units and with an estimated incidence ([Formula: see text]) close to the true value of . For classification, the goal was to classify the datasets above or below prespecified thresholds () used for CLS management. The average sample number, or ASN, was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to , and approximately 11 when far from . Correct decisions occurred in >76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.
在田间有效评估疾病强度的抽样策略对于支持管理决策至关重要。为了制定由引起的叶斑病（CLS）发病率的序贯抽样计划，在纽约州评估了 31 个食用甜菜田。在每个田间的三到六条线性样带中的每一条的 51 个采样位置中随机选择的 6 片叶子上进行 CLS 发病率的评估。进行了空间模式分析，并利用结果开发了序贯抽样估计和分类模型。CLS 发病率（）范围从 0.13 到 0.92，中位数为 0.31，β二项式分布反映了聚集，最能描述观察到的空间模式。聚集通常通过使用点过程方法、运行分析和自相关到第四个空间滞后的方法检测到（>95%）。对于距离指数的空间分析，或，45%的数据集被归类为随机模式。在序贯抽样估计和分类模型中，直到达到预定目标，才会对疾病单位进行抽样。对于估计，目标是用预先选择的变异系数（）对 CLS 发病率进行抽样。使用<51 个采样单元很难达到目标=0.1，仅在发病率>0.3 的数据集上观察到。降低精度水平，即增加到 0.2，允许使用较少的采样单元达到预先选择的，并且估计的发病率接近真实值。对于分类，目标是将数据集分类为高于或低于用于 CLS 管理的预定阈值（）。通过蒙特卡罗模拟确定平均样本数，或 ASN，在接近发病率接近时，ASN 在 20 到 45 之间，而在远离时，ASN 约为 11。验证数据集的>76%做出了正确的决策。结果表明，这些序贯抽样计划可用于有效评估食用甜菜田的 CLS 发病率。
