Cornell Soil Health Program Machine Learning Models

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

1 to 3 of 3 Results

Random Forest Models from "Cross-correlating soil aggregate stability methods to facilitate universal interpretation Feb 10, 2025 - Harvard Dataverse Amsili, Joseph; Aller, Deborah; van Es, Harold, 2024, "Random Forest Models from "Cross-correlating soil aggregate stability methods to facilitate universal interpretation", https://doi.org/10.7910/DVN/LEBT1V, Harvard Dataverse, V2, UNF:6:apWoze8lmL6L4S7uChySOw== [fileUNF] Random Forest Model Files and tutorial from manuscript: "Cross-correlating soil aggregate stability methods to facilitate universal interpretation".
Autoclaved-citrate extractable soil protein pedotransfer functions based on random forest Feb 10, 2025 Amsili, Joseph; van Es, Harold; Schindelbeck, Robert, 2024, "Autoclaved-citrate extractable soil protein pedotransfer functions based on random forest", https://doi.org/10.7910/DVN/HGBPCW, Harvard Dataverse, V2, UNF:6:9/deQyujE87Gak1OLd9RKw== [fileUNF] Autoclaved-citrate extractable soil protein (ACE protein, but hereafter referred as “soil protein”) is a novel biological soil health indicator that can indirectly capture a soil’s capacity to supply N. A dataset of 4,171 soil samples with texture, total carbon (C) and nitrogen (N), carbon-to-nitrogen ratio (C/N), soil protein, permanganate-oxidiza...
An Available Water Capacity Pedotransfer Function using Random Forest - 2020 Cornell Soil Health Model Jun 18, 2024 Amsili, Joseph; Harold van Es; Robert Schindelbeck, 2022, "An Available Water Capacity Pedotransfer Function using Random Forest - 2020 Cornell Soil Health Model", https://doi.org/10.7910/DVN/U5DAEP, Harvard Dataverse, V2, UNF:6:165Gfin1rPR9XGn56TIr9A== [fileUNF] 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, %...

Random Forest Models from "Cross-correlating soil aggregate stability methods to facilitate universal interpretation

Feb 10, 2025 - Harvard Dataverse

Amsili, Joseph; Aller, Deborah; van Es, Harold, 2024, "Random Forest Models from "Cross-correlating soil aggregate stability methods to facilitate universal interpretation", https://doi.org/10.7910/DVN/LEBT1V, Harvard Dataverse, V2, UNF:6:apWoze8lmL6L4S7uChySOw== [fileUNF]

Random Forest Model Files and tutorial from manuscript: "Cross-correlating soil aggregate stability methods to facilitate universal interpretation".

Autoclaved-citrate extractable soil protein pedotransfer functions based on random forest

Feb 10, 2025

Amsili, Joseph; van Es, Harold; Schindelbeck, Robert, 2024, "Autoclaved-citrate extractable soil protein pedotransfer functions based on random forest", https://doi.org/10.7910/DVN/HGBPCW, Harvard Dataverse, V2, UNF:6:9/deQyujE87Gak1OLd9RKw== [fileUNF]

Autoclaved-citrate extractable soil protein (ACE protein, but hereafter referred as “soil protein”) is a novel biological soil health indicator that can indirectly capture a soil’s capacity to supply N. A dataset of 4,171 soil samples with texture, total carbon (C) and nitrogen (N), carbon-to-nitrogen ratio (C/N), soil protein, permanganate-oxidiza...

An Available Water Capacity Pedotransfer Function using Random Forest - 2020 Cornell Soil Health Model

Jun 18, 2024

Amsili, Joseph; Harold van Es; Robert Schindelbeck, 2022, "An Available Water Capacity Pedotransfer Function using Random Forest - 2020 Cornell Soil Health Model", https://doi.org/10.7910/DVN/U5DAEP, Harvard Dataverse, V2, UNF:6:165Gfin1rPR9XGn56TIr9A== [fileUNF]

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, %...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications