It is of critical importance to understand the relationships between crop yield, soil properties and topographic characteristics for agricultural management. This study’s objective was to compare techniques to quantify the relationship between soil and topographic characteristics for predicting crop yield using high-resolution data and analytical techniques. The study was conducted on a multiple field dataset located in Southwestern Ontario, Canada, where few studies have assessed the impact of applications for precision agriculture and machine learning (ML) to the soil property-yield relationship in this region. The dataset included 145,500 observations of corn and soybean yield, topographic and soil nutrient characteristics. The attributes considered for this study included pH, soil organic matter (OM) content, cation exchange capacity (CEC), soil test phosphorus, zinc (Zn), potassium (K), elevation and topographic wetness index. Multiple linear regression (MLR), artificial neural networks, decision trees and random forests were compared to identify methods able to relate soil properties and crop yields on a subfield scale (2 m). Random forests were the most successful at predicting yield with an R² value of 0.85 for corn and 0.94 for soybeans. MLR was the least successful with an R² of 0.40 for corn and 0.45 for soybeans. Cross-validation experiments showed that random forest models in most cases could predict low- and high-yield areas from fields excluded from training datasets, but this was not possible in all cases. Techniques tested the models and identified significant soil and topographic attributes when predicting yield, though the identification was subject to some uncertainty. These results suggest that ML techniques might be used to predict high yield areas of fields without existing yield maps, if those fields have similar relationships of soil properties to yield.

了解作物产量、土壤特性和地形特征之间的关系对于农业管理至关重要。本研究的目的是比较技术以量化土壤和地形特征之间的关系，以使用高分辨率数据和分析技术预测作物产量。该研究是在位于加拿大安大略省西南部的一个多场数据集上进行的，很少有研究评估精准农业和机器学习 (ML) 应用对该地区土壤特性-产量关系的影响。该数据集包括对玉米和大豆产量、地形和土壤养分特征的 145,500 次观测。本研究考虑的属性包括 pH、土壤有机质 (OM) 含量、阳离子交换容量 (CEC)、土壤测试磷、锌（Zn）、钾（K）、海拔和地形湿度指数。对多元线性回归 (MLR)、人工神经网络、决策树和随机森林进行比较，以确定能够在子田规模 (2 m) 上关联土壤特性和作物产量的方法。随机森林在用 R 预测产量方面是最成功的²玉米的值为 0.85，大豆的值为 0.94。MLR 是最不成功的，玉米的 R ²为 0.40，大豆为 0.45。交叉验证实验表明，在大多数情况下，随机森林模型可以从训练数据集中排除的字段中预测低产和高产区域，但这并非在所有情况下都是可能的。技术测试了模型并在预测产量时确定了重要的土壤和地形属性，尽管识别存在一些不确定性。这些结果表明，如果这些田地具有相似的土壤特性与产量的关系，则可以使用 ML 技术来预测没有现有产量图的田地的高产区域。