Multi-Factor Diagnostic and Recommendation System for Boron in Neutral and Acidic Soils

Despite its inconveniences, the most recognized method to extract boron from soils is that of hot water extraction (B_HW), which is used for diagnostics and recommendations. However, the Mehlich-3 (M3) method is widely used to extract and diagnose several elements at once (P, K, Ca, Mg, Al, B, Cu, Zn, Fe, and Mn) and is well adapted to routine analyses. The objective of our study was to develop a soil diagnostic and recommendation system for boron as a function of measured B_M3 (and other interacting elements), crop type, and spreading methods. This system is based on three databases from either the international literature or the chemical characterization of acidic-to-neutral soils typical from Qu&#233;bec (Canada). The first database came from the characterization of 365 samples typical of Qu&#233;bec soils; it has been used to predict, by the AutoML (Automatic Machine Learnig) supervised learning algorithm, B_M3 as a function of a set of parameters from the following: B_HW, pH_W, organic carbon (OC), Ca_M3, K_M3, and Mg_M3. Depending on the parameters used, the <i>R</i>² between the measured and observed B_M3 varied from 0.36 to 0.99. This database allowed us to define two classifications for soil boron diagnostics and fertility evaluation. The Cate&#8722;Nelson analysis for these two models allowed us to define three boron fertility classes: Low, medium and high; that is 0.00&#8722;0.23, 0.23&#8722;0.58, and 0.58&#8722;3.70 mg B kg^&#8722;1, respectively, for B_HW, and 0.00&#8722;0.65, 0.65&#8722;1.03, and 1.03&#8722;12.70 mg B kg^&#8722;1, respectively, for B_M3. The third database was extracted from 130 yield responses to increasing levels of boron; it was used to define a recommendation model for boron, based on AutoML, as a function of B_M3, pH_W, the crop boron requirement (medium, high), and the type of spreading (broadcast, sidedress, foliar spraying). This model resulted in an <i>R</i>² of 0.63.

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