A major concern in landscape management and precision agriculture is the variable-rate application of herbicides in order to reduce herbicide treatment load. These applications require a correct assessment and knowledge of the density and potential spatial variability of weed species within fields. This article addresses the issue of incorporating a digital elevation model as secondary spatial information into the mapping of main weed species present in two sunflower crops in Andalusia, Spain. Two prediction methods were used and compared for mapping weed density for precision agriculture. The primary information was obtained from an intensive grid weed density sampling and the secondary spatial information, e.g., elevation from a digital elevation model. The prediction methods were two geostatistical algorithms: ordinary kriging and kriging with an external drift, which takes into account the influence of landscape. Mean squared error was used to evaluate the performance of the map prediction quality. The best prediction method for mapping most of the weed species was kriging with an external drift, with the smallest mean squared error, indicating the highest accuracy. The results showed that kriging with an external drift with elevation reduced the prediction variance compared with ordinary kriging. Maps obtained from these kriged estimates showed that the incorporation of a digital elevation model as secondary exhaustive information can improve the accuracy of predicted weed densities within fields. These results suggest that kriging with an external drift of weed density data with elevation as a secondary exhaustive variable could be used in such situations, and in this way, the accuracy of maps for precision agriculture, which is the preliminary step in a precision agricultural management program, could be improved with little or no additional cost, since a digital elevation model could be obtained as part of other analyses.

中文翻译：

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数字高程模型可帮助向日葵作物中的杂草进行地统计学

景观管理和精准农业中的主要关注点是可变剂量施用除草剂，以减少除草剂的处理量。这些应用需要对田间杂草物种的密度和潜在的空间变异性进行正确的评估和了解。本文解决了将数字高程模型作为次要空间信息纳入西班牙安达卢西亚的两种向日葵作物中存在的主要杂草物种的制图问题。使用两种预测方法并比较了精确农业的杂草密度图。主要信息来自密集的网格杂草密度采样和次要空间信息，例如，来自数字高程模型的高程。预测方法是两种地统计算法：普通克里金法和带有外部漂移的克里金法，其中考虑了景观的影响。均方误差用于评估地图预测质量的性能。绘制大多数杂草种类的最佳预测方法是采用外部漂移的克里金法，其均方误差最小，表明准确性最高。结果表明，与普通克里金法相比，外部漂移随海拔升高的克里金法降低了预测方差。从这些kriged估计值获得的地图显示，将数字高程模型作为辅助性详尽信息并入可以提高田间预测的杂草密度的准确性。