Soil sampling strategy optimization to support predictive soil mapping

政策制定者、规划者和生产者已经认识到，高质量的土壤信息对于有效的决策至关重要，但他们往往面临着缺乏足够高分辨率的信息。预测性土壤制图越来越多地被用来解决这一短缺问题，利用基于机器学习的方法的力量来解决全国受过实地培训的土壤专家短缺的问题。然而，预测性测绘仍然需要现场样本收集。就生产者而言，样本采集通常由他们的农学团队进行，这些数据通过设备安装的传感器进行补充，但有效预测土壤制图的具体采样需求不同于传统的土壤测试或产量监测，以提供营养建议。 目前，关键的方法空白之一是确定支持预测性土壤制图和评估所需的样本点的数量。虽然已经对这一主题进行了研究，但大多数工作都集中在抽样策略上，以完全表征景观中的统计变异性。关于多少抽样在经济上是最佳的和实用的，特别是在加拿大草原的背景下，文献中仍然存在空白。 该项目的目标将是制定一种抽样战略，其基础是一种结合了经济因素的改进的拉丁超立方体设计。这项工作将与我们的研究合作伙伴CropPro Consulting一起进行。CropPro对这个项目很有帮助，因为他们将为研究地点提供必要的数据以及宝贵的农学专业知识。 文献中的这一缺失部分是阻碍预测土壤制图广泛应用的关键障碍之一，因为预测土壤制图产生的空间信息的准确性和实用性直接受到为用作预测模型的训练数据而收集的土壤数据的影响。

Policy makers, planners and producers have recognized that high-quality soil information is essential for effective decision making, but they are often faced with a shortage of sufficiently high-resolution information. Predictive soil mapping is increasingly used to address this shortage, leveraging the power of machine-learning based approaches to overcome a national shortage of field-trained soils specialists. However, predictive mapping still requires in-field sample collection. In the case of producers, sample collection is commonly carried out by their agronomy team and these data are supplemented via equipment-mounted sensors, but the specific sampling needs for effective predictive soil mapping are not the same as for traditional soil testing or yield monitoring to inform nutrient recommendations. Currently, one of the key methodology gaps is determining the amount of sample points required to support predictive soil mapping and assessments. While there has been research conducted on this subject, most of this work has focused on sampling strategies to completely characterize the statistical variability in the landscape. There is still a gap in the literature regarding how much sampling is economically optimal and practical, specifically in the context of the Canadian Prairies. The goal of this project will be to develop a sampling strategy based on a modified conditioned Latin hypercube design that incorporates economic factors. This work will be undertaken with our research partner, CropPro Consulting. CropPro is instrumental for this project as they will be providing both essential data for the research sites along with valuable agronomic expertise. This missing piece in the literature is one of the key obstacles preventing widespread application of predictive soil mapping, as the accuracy and utility of spatial information generated from predictive soil mapping is directly influenced by the soil data collected for use as training data for predictive models.