The increasing threat of global climate change can impose adverse impacts on our environments and also jeopardize crop production, if the cultivars are non-resilient to unpredictable climatic events. The phenotype being a result of genotype and its expression in any given environment, it is equally important to ‘type’ the most complex ambivalent factor, the environment.

Envirotyping is the characterization of the growing environment for crops, including variables such as soil type and weather which are defined by explicit ranges of climatic factors. Envirotyping gathers the steps of collecting, processing and associating environmental data with phenotypic data to understand the various types of environments for multi-environment trials in breeding program. Thus, the envirotyping process can be continuously used to update the gene-environment relations (Costa-Neto and Fritsche-Neto, 2021).

The concept of “envirotyping” as suggested, as a third “typing” technology, complementing with genotyping and phenotyping, which dissects complex environmental interactions to pinpoint the impact of climate change on crops. Environmental factors can be collected through multiple environmental trials, geographic and soil information systems, measurement of soil and canopy properties and evaluation of companion organisms. Envirotyping entails to genotype-by-environment interaction (GEI), genes responsive to environmental signals, biotic and abiotic stresses.

The major applications of envirotypic information include characterization of environments, development of near-iso-environments, control of experimental errors, selection of experimental sites, agronomic genomics, prediction of disease epidemics and precision crop production. In plant breeding, potential trial sites can be accurately evaluated and thus the best trial sites can be selected. This strategy has helped in screening the genotypes for drought tolerance in maize (Banziger et al., 2006).

Phenotype prediction can be simplified with consideration of environmental co-variances and conditional variances. Near-iso-environments can be used to facilitate identifying genes with environment-specific effects. In rice, days-to-flowering and photo-thermo-sensitivity were studied under conditions of greenhouse for identifying different sets of quantitative trait loci. In wheat, a conceptual model for yield and heat-adaptive traits has been developed to enhance breeding efficiency (Xu, 2016).

Crossa et al. (2021) gave an overview of the interconnections between these techniques and envisaged to implement a more interconnected breeding approach that takes advantage of the modern plant breeding triangle: integrating genomics, phenomics and enviroomics. Together, they offer a pathway for conventional phenotypic breeding to envision a diverse set of opportunities to accelerate genetic gains. Connections between each of these elements can be beneficial in conducting breeding programs.

Costa-Neto et al. (2021) introduced the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data into genomics. They have highlighted literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviroomic data for a diverse set of genomic-based studies.

Envirotyping guided crop science will help us to face all the challenges through genetic studies, genotype reconstruction, variety development, abiotic/biotic stress prediction, precision agriculture and breeding. Envirotyping, as a third “typing” technology for crop science, should be used to decipher all environmental impacts on growth, reproduction and survival of crop plants. With large-scale envirotyping, in combination with known genotypic information, environments for crops can be optimized and phenotypic performance under specific environments can be largely predicted, thus enhance cost–benefit efficiency for precision breeding and crop production.

References:

Banziger, M., Setimela, P. S., Hodson, D. and Vivek, B., 2006, Breeding for improved abiotic stress tolerance in maize adapted to southern Africa. Agric. Water Manag., 80: 212-224.

Costa-Neto, G. and Fritsche-Neto, R., 2021, Enviroomics: bridging different sources of data, building one framework. Crop Breed. and Appl. Biotechnol., 21: 1-14.

Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J. and Fritsche-Neto, R., 2021, EnvRtype: a software to interplay enviroomics and quantitative genomics in agriculture. G3, 11(4): 1-21.

Crossa, J., Fritsche-Neto, R., Montesinos-Lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-Lopez, A. and Bentley, A. R., 2021, The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviroomics data. Front. Plant Sci., 12: 1-6.

Xu, Y., 2016, Envirotyping for deciphering environmental impacts on crop plants. Theor. Appl. Gen., 129: 653-673.