Improving Winter Wheat Forecasts Using Genetics, Traits, and Environmental Data

Jim Crocker
1st June, 2024

Improving Winter Wheat Forecasts Using Genetics, Traits, and Environmental Data

Image Source: Natural Science News, 2024

Key Findings

  • The study from Universidad de Colima focused on improving genomic selection (GS) models for soft white winter wheat by integrating environmental information
  • Incorporating environmental data into GS models significantly improved prediction accuracy, with an average gain of 49.19% in normalized root mean square error (NRMSE)
  • The improvement in prediction accuracy varied across data sets, ranging from 5.68% to 60.36%, demonstrating the substantial effect of including environmental information
Genomic selection (GS) has become a cornerstone in accelerating the breeding cycle of crops by enabling the rapid selection of superior genotypes. Despite its potential, the efficiency of GS in multi-environment prediction has faced significant challenges, particularly due to genotype by environment (G×E) interactions. These interactions complicate the prediction accuracies essential for effective breeding programs. Recent research from the Universidad de Colima[1] has made strides in addressing these challenges by incorporating environmental information into the modeling process, alongside genomic and phenomic data. The study conducted by the Universidad de Colima aimed to evaluate the impact of integrating environmental information into GS models. The researchers analyzed five data sets of soft white winter wheat to determine how this integration could improve prediction accuracy. Their findings demonstrated a notable improvement in prediction accuracy, with an average gain of 49.19% in terms of normalized root mean square error (NRMSE) across the data sets. The observed prediction accuracy varied significantly, ranging from 5.68% to 60.36%, highlighting the substantial effect of incorporating environmental information. This research builds on earlier findings that emphasize the importance of G×E interactions in genomic-enabled prediction models[2]. Traditional GS models often fall short when predicting outcomes across different environments due to the variability in genotypic responses. By including environmental data, the new study addresses this gap, enhancing the accuracy and reliability of predictions. This approach aligns with the factor analytic mixed models used in Australian plant breeding programs, which have been found to provide more accurate predictions by effectively modeling V×E interactions[3]. The integration of environmental information into GS models represents a significant advancement in the field. Previous studies have shown that incorporating various types of data, such as phenomics and enviromics, can enhance the prediction performance of GS models[2][4]. Phenomics involves high-throughput phenotyping, which captures a wide range of plant traits, while enviromics refers to large-scale environmental data collection. By combining these data types, researchers can develop more comprehensive models that account for the complex interactions between genotype and environment. The study from the Universidad de Colima demonstrates the practical benefits of this integrative approach. By improving prediction accuracy, plant breeding programs can make more informed decisions, ultimately enhancing the selection efficiency across different locations. This is particularly important for crops like soft white winter wheat, which are grown in diverse environments with varying climatic conditions. In conclusion, the integration of environmental information into GS models marks a significant step forward in addressing the challenges posed by G×E interactions. The research from the Universidad de Colima highlights the potential for improved prediction accuracy, which can lead to more efficient and effective plant breeding programs. This advancement builds on previous studies[2][3][4], showcasing the importance of a holistic approach that combines genomic, phenomic, and environmental data to achieve superior breeding outcomes.



Main Study

1) Enhancing winter wheat prediction with genomics, phenomics and environmental data

Published 31st May, 2024

Related Studies

2) Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.

3) Factor analytic mixed models for the provision of grower information from national crop variety testing programs.

4) Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

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