Ortiz B V, Perry C, Goovaerts P, Vellidis G, Sullivan D
Auburn University. 204 Extension Hall, Auburn University, Auburn, AL, 36849, USA.
Geoderma. 2010 May;156(3-4):243-252. doi: 10.1016/j.geoderma.2010.02.024.
Identifying the spatial variability and risk areas for southern root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) is key for site-specific management (SSM) of cotton (Gossypium hirsutum L.) fields. The objectives of this study were to: (i) determine the soil properties that influence RKN occurrence at different scales; and (ii) delineate risk areas of RKN by indicator kriging. The study site was a cotton field located in the southeastern coastal plain region of the USA. Nested semivariograms indicated that RKN samples, collected from a 50×50 m grid, exhibited a local and regional scale of variation describing small and large clusters of RKN population density. Factorial kriging decomposed RKN and soil properties variability into different spatial components. Scale dependent correlations between RKN data showed that the areas with high RKN population remained stable though the growing season. RKN data were strongly correlated with slope (SL) at local scale and with apparent soil electrical conductivity deep (EC(a-d)) at both local and regional scales, which illustrate the potential of these soil physical properties as surrogate data for RKN population. The correlation between RKN data and soil chemical properties was soil texture mediated. Indicator kriging (IK) maps developed using either RKN, the relation between RKN and soil electrical conductivity or a combination of both, depicted the probability for RKN population to exceed the threshold of 100 second stage juveniles/100 cm(3) of soil. Incorporating EC(a-d) as soft data improved predictions favoring the reduction of the number of RKN observations required to map areas at risk for high RKN population.
识别南方根结线虫[南方根结线虫（Kofoid & White）Chitwood]（RKN）的空间变异性和风险区域是棉花（陆地棉）田块精准管理（SSM）的关键。本研究的目的是：（i）确定在不同尺度上影响RKN发生的土壤性质；（ii）通过指示克里格法划定RKN的风险区域。研究地点是位于美国东南沿海平原地区的一块棉田。嵌套半方差图表明，从50×50米网格采集的RKN样本呈现出局部和区域尺度的变异，描述了RKN种群密度的小集群和大集群。因子克里格法将RKN和土壤性质的变异性分解为不同的空间成分。RKN数据的尺度依赖性相关性表明，RKN种群数量高的区域在整个生长季节保持稳定。RKN数据在局部尺度上与坡度（SL）以及在局部和区域尺度上与深层表观土壤电导率（EC（a-d））密切相关，这说明了这些土壤物理性质作为RKN种群替代数据的潜力。RKN数据与土壤化学性质之间的相关性受土壤质地介导。使用RKN、RKN与土壤电导率之间的关系或两者结合开发的指示克里格（IK）图描绘了RKN种群超过100条二龄幼虫/100立方厘米土壤阈值的概率。将EC（a-d）作为软数据纳入，改善了预测效果，有利于减少绘制高RKN种群风险区域所需的RKN观测数量。
