Related Concept Videos

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.

18.4K

01:25

Multiple Regression

2.9K

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

2.9K

01:57

Adaptations that Reduce Water Loss

25.1K

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

25.1K

Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and

Melina Prado¹, Adam Famoso², Kurt Guidry²

¹Department of Genetics, "Luiz de Queiroz" College of Agriculture/University of São Paulo, Piracicaba, Brazil.

Frontiers in Plant Science

|November 7, 2024

View abstract on PubMed

Summary

Optimizing multi-environmental trials (MET) for rice breeding can reduce locations by 28% without losing accuracy. This improves breeding efficiency by using environmental covariates and clustering trial sites effectively.

Keywords:

envirotyping genotype x environment market segments supervised learning target population of environments

More Related Videos

09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K

11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K

Area of Science:

Agricultural Science
Plant Breeding
Genetics

Background:

Global rice breeding aims for productive, climate-smart cultivars, but genetic gains are hindered by phenotyping costs and trial allocation challenges.
Optimizing the number and placement of multi-environmental trials (MET) is crucial for efficient rice breeding programs.

Purpose of the Study:

To develop a cost-effective and accurate method for optimizing MET design in rice breeding.
To identify key environmental covariates (ECs) influencing rice grain yield and genotype-by-environment interactions.

Main Methods:

Utilized historical weather and soil data from the USA rice belt, translating it into rice response metrics.
Applied feature selection algorithms to identify significant ECs and clustered trial locations based on environmental similarity.

Conducted joint analysis using prediction-based models under various trial scenarios to assess optimization strategies.

Main Results:

Eight ECs explained 58% of grain yield variation and 53% of genotype-by-environment interaction.
A 28% reduction in trial locations is feasible without significant loss of accuracy.
The US Rice belt was segmented into four distinct environmental clusters with varying economic importance.

Conclusions:

The developed approach optimizes MET allocation, reducing costs and improving efficiency in rice breeding.
Environmental covariates and spatial clustering are effective tools for designing more informative and cost-efficient trials.
This strategy aids in better advance trial allocation, enhancing the accuracy of breeding programs.