About this Dataset

Data From: Random Forest Regression To Predict Farinograph Traits From GlutoPeak Output In Wheat Wild Relative Backcross Lines

Flour quality is a key breeding target in hard winter wheat cultivar development. The Farinograph is perhaps the most important device for assessing quality prior to cultivar release in the United States, but large sample size requirements and long test times make in impracticable for early-stage selection. We used random forest regression to predict key Farinograph parameters from novel features we calculated from the raw data output of the GlutoPeak, which requires less time and less sample, in a winter wheat population containing wild relative introgressions. Here, we present the raw GlutoPeak data and Farinograph data used in model development.

GlutoPeak output for 68 wheat samples, contained in folder "GP_upload". Some lines including wild relative introgressions. Files with the same number prior to the underscore represent multiple replications of the same sample - one file was randomly selected for model construction.

FarinoGraph output for 68 wheat samples, some lines including wild relative introgressions.
Organization: Department of Agriculture
Last updated: 2025-01-01T22:02:57.815527
Tags: glutopeak, wheat-flour-quality

Tables

Farinograph Output

@usgov.usda_gov_data_from_random_forest_regression_to_predict_e121b746.farinograph_output

5.82 kB
67 rows
5 columns

CREATE TABLE farinograph_output (
  "sample" VARCHAR,
  "absorption" DOUBLE,
  "bake_absorption" DOUBLE,
  "mti_bu" BIGINT,
  "tolerance_stability" DOUBLE
);