license: apache-2.0
Dataset
For the present study, we used data from the GxE competition advocated by the G2F project in 2022 (https://www.maizegxeprediction2022.org/), including genetic markers (G2F-G) for maize inbred lines, phenotypic measurements (G2F-P) collected throughout each growing season, metadata (G2F-M) for each field trial, environmental covariate (EC) data, and environmental (G2F-E) data. G2F-E data were mainly climatic and soil variables captured during crop development in each experimental trial.
In order to explore the influence of environmental factors on yield prediction results, we designed two sets of prediction scenarios: 1) yield prediction based on the whole genome, and 2) yield prediction integrating genome, weather and soil factors. Different data sets are generated for different prediction scenarios.
For a detailed description of this dataset, please refer to the methods section of the paper.
Dataset file structure directory
├─test_set
│ New_test_values.csv
│ test_G.csv
│ test_GE.csv
│
└─train_set
G.csv
GE.csv
New_Yield_values.csv
train_Yield_folds.csv
Description
train_set
- G.csv
Genome-wide principal component data used to train the G2P model. - GE.csv
The data was integrated from genome-wide principal component data, weather data and soil data. - train_Yield_folds.csv
The dataset is a ten-fold cross-validation dataset generated by the kfolds.py script for model training and testing. - New_Yield_values.csv
This dataset is assembled from the base model predictions and is primarily used to train the second layer of models in the stacking framework.
test_set
- test_G.csv
This dataset is a predicted population of target genotypes from an untested environment and is used to validate the predictive performance of the model when environmental effects are ignored.
- test_GE.csv
This dataset was integrated from genotype and environment to validate the predictive performance of the model across environments under environmental stress.
- New_test_values.csv This dataset is composed of the predicted values of the base model in the new environment and is used as a prediction set for the second layer of the model in the stacking framework.