Potassium (K) deficiency in wine grapes results in reduced vine growth, premature leaf drop, and yield and color loss. K can be fixed in the interlayer of clay minerals in a process called K fixation, which leads to high spatial variability in soil K. In the Lodi American Viticulture Area (AVA) management of winegrapes is complicated by a mix of K fixing soils and non-K fixing soils. Here, we leverage a digital soil mapping (DSM) framework to identify the spatial distribution of K fixation and availability, to disentangle the complexity of K management in the region. Soil samples (n = 107) were collected, analyzed for the K fixation index, K availability and cation exchange capacity (CEC), and aggregated into two depths (0–30 cm and 30–100 cm). Soil samples were intersected with remotely sensed proxies for the soil forming factors and existing soil survey data and used to train a “super learner” ensemble or combination of base models, including random forest (RF), extreme gradient boosting (XGB) and cubist. Base models were combined via model averaging (each model weighted by its R) or model stacking (linear combination of base models via OLS regression), and model performance was compared. We generated mapped uncertainties from a super learning framework by utilizing bootstrapped realizations of each base model and weighting each bootstrapped base model map via the β-coefficients generated in the ensemble fitting step. Bootstrapped maps of the super learner were utilized to generate upper and lower 90% prediction limits.

中文翻译：

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超级学习者团队绘制加州葡萄园土壤钾固定图

酿酒葡萄缺钾 (K) 会导致葡萄树生长减缓、落叶过早以及产量和颜色损失。钾可以通过称为钾固定的过程固定在粘土矿物的夹层中，这导致土壤钾的空间变异性较高。在洛迪美国葡萄栽培区 (AVA)，由于固定钾土壤和非固定钾土壤的混合，酿酒葡萄的管理变得复杂。 -K固定土壤。在这里，我们利用数字土壤测绘（DSM）框架来确定钾固定和可用性的空间分布，以理清该地区钾管理的复杂性。收集土壤样本 (n = 107)，分析钾固定指数、钾有效性和阳离子交换容量 (CEC)，并聚集到两个深度（0-30 厘米和 30-100 厘米）。土壤样本与土壤形成因子和现有土壤调查数据的遥感代理相交，并用于训练“超级学习器”集成或基础模型组合，包括随机森林（RF）、极端梯度增强（XGB）和立体派。通过模型平均（每个模型按其 R 加权）或模型叠加（通过 OLS 回归对基本模型进行线性组合）来组合基本模型，并比较模型性能。我们利用每个基本模型的自举实现，并通过集成拟合步骤中生成的 β 系数对每个自举基本模型图进行加权，从超级学习框架生成映射的不确定性。利用超级学习器的引导图来生成 90% 的预测上限和下限。