About this Research Topic
To overcome demand for food and other biomaterials, new technological analysis systems focused on plant breeding have emerged. Among them, genomics and phenomics consolidated their usefulness. Enviromics is more than a new layer of data, actually wherein the site is 'envirotyped'. It aims to dissect, understand, and predict environmental components for the whole Target Population of Environments (TPE) buffer, based on the similarity that exists among sites, and thus be able to interpolate inferences for a range of environments (real or virtual). Through its integration with Geographic Information Systems (GIS), we can infer cultivar recommendation, mega-environments stratification, a better understanding of G×E interaction, and optimize the local choice of breeding trial implantation. Therefore, this new tool will bring more accuracy to support breeding program decisions and increase the response to selection in current and future climate scenarios.
Many ways to collect and analyze environmental data have been used. Furthermore, several methods of using it in breeding programs and dissect complex traits have been proposed. Therefore, we would like to address the following questions in this article collection:
i. How may enviromics change breeding schemes of different crops?
ii. What is its impact on response to selection to different environments?
iii. What would be the best structure of a crop-specific breeding scheme to exploit the full potential of enviromics?
iv. How could the models be improved to bring more realism to predict future and non-observed scenarios?
v. Whence may enviromics improve the understanding of the genetic control and G×E in quantitative traits?
We wish to attract submissions around the following themes:
● Earth-based methods, targeting GIS and remote-sensing researchers:
i. Development of high-throughput Enviromics data to support a range of plant breeding programs;
ii. GIS-based modelling approaches to understand the environmental similarity among sites.
● Genetics-based methods, targeting plant breeding, eco-physiology and quantitative genetics researchers:
iii. Crop-growth modelling and eco-physiological aspects in enviromics;
iv. Quantitative genetics and genomics aspects in enviromics;
v. Coupling enviromics approaches with genomic selection and association studies;
vi. Forecasting new breeding scenarios, dealing with climatic changes challenges, via virtual environments;
vii. Envirotyping in different plant scales, at geographic and/or green-house levels.
Many ways to collect and analyze environmental data have been used. Furthermore, several methods of using it in breeding programs and dissect complex traits have been proposed. Therefore, we would like to address the following questions in this article collection:
i. How may enviromics change breeding schemes of different crops?
ii. What is its impact on response to selection to different environments?
iii. What would be the best structure of a crop-specific breeding scheme to exploit the full potential of enviromics?
iv. How could the models be improved to bring more realism to predict future and non-observed scenarios?
v. Whence may enviromics improve the understanding of the genetic control and G×E in quantitative traits?
We wish to attract submissions around the following themes:
● Earth-based methods, targeting GIS and remote-sensing researchers:
i. Development of high-throughput Enviromics data to support a range of plant breeding programs;
ii. GIS-based modelling approaches to understand the environmental similarity among sites.
● Genetics-based methods, targeting plant breeding, eco-physiology and quantitative genetics researchers:
iii. Crop-growth modelling and eco-physiological aspects in enviromics;
iv. Quantitative genetics and genomics aspects in enviromics;
v. Coupling enviromics approaches with genomic selection and association studies;
vi. Forecasting new breeding scenarios, dealing with climatic changes challenges, via virtual environments;
vii. Envirotyping in different plant scales, at geographic and/or green-house levels.
Keywords: Envirotyping, Quantitative genetics, Ecophysiology, Precision agriculture, Geoprocessing, Prediction
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