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Description
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In late 2018, the Cornell Soil Health lab determined that AWC, a valuable, but time-intensive measurement, could be accurately predicted. A CASH database containing 7,232 soil samples was used to develop a Random Forest model to predict Field Capacity, Permanent Wilting Point, and AWC from a suite of measured parameters, including % sand, % silt, % clay, Organic Matter, Active Carbon also known as Permanganate Oxidizable Carbon (POxC), Respiration, Wet Aggregate Stability, Potassium, Magnesium, Iron, and Manganese. The Random Forest model was able to explain more variation in AWC than alternative multiple linear regression models. In Spring 2024, the peer reviewed manuscript, "Pedotransfer functions for field capacity, permanent wilting point, and available water capacity based on random forest models for routine soil health analysis" was published. All random forest models and the training dataset are downloadable here. (2020-07-01)
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Notes
| Dataset was compiled from 7,232 samples run through the Cornell Soil Health Laboratory between 2015-2019. Dataset contains texture data (sand, silt, and clay), wet aggregate stability (WAS), soil organic matter (SOM), 4-day soil respiration (Resp), active carbon (AC; this is also referred to as permanganate oxidizable carbon-POxC within the scientific literature), and modified morgan extractable K, Mg, Fe, and Mn in ppm. The dataset also includes field capacity, permanent wilting point, and available water capacity (AWC), which was measured on disturbed soil samples (< 2 mm) that were equilibrated after initial saturation to pressures of -10 kPa and -1500 kPa on porous ceramic pressure plates in pressure chambers (Soil Moisture Equipment Corp., Goleta, CA). Columns include: RowNumber, sand, silt, clay, WAS, SOM, Resp, AC, K, Mg, Fe, Mn, and AWC. |
