Soil sampling strategy optimization to support predictive soil mapping

土壤采样策略优化以支持预测土壤绘图

• 批准号: 556793-2020
• 负责人: BedardHaughn, Angela
• 金额: $ 1.72万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706278
• 关键词: Soil; sampling; strategy; optimization; support

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.

决策者、规划者和生产者已经认识到，高质量的土壤信息对于有效决策至关重要，但他们往往面临缺乏足够高分辨率的信息的问题。预测性土壤制图越来越多地用于解决这一短缺问题，利用基于机器学习的方法来克服全国缺乏经过实地培训的土壤专家的问题。然而，预测绘图仍然需要现场样品收集。对于生产商而言，样品收集通常由其农艺团队进行，这些数据通过安装在设备上的传感器进行补充，但有效预测土壤测绘的具体采样需求与传统土壤测试或产量监测不同，以提供营养建议。 目前，方法上的一个主要差距是确定支持预测性土壤制图和评估所需的样本点数量。虽然已经对这一主题进行了研究，但大多数工作都集中在采样策略上，以完全描述景观的统计变异性。在文献中仍然有一个差距，关于多少采样是经济上最佳和实用的，特别是在加拿大大草原的背景下。 这个项目的目标是开发一个采样策略的基础上修改条件拉丁超立方体设计，纳入经济因素。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沿着有价值的农学专业知识的基本数据。 在文献中的这一缺失部分是阻碍预测土壤制图广泛应用的主要障碍之一，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收集的土壤数据的影响，这些数据用作预测模型的训练数据。