Agricultural landscape genomics to increase crop resilience

Abstract

Populations are continually adapting to their environment. Knowledge of which populations and individuals harbor unique and agriculturally useful variations has the potential to accelerate crop adaptation to the increasingly challenging environments predicted for the coming century. Landscape genomics, which identifies associations between environmental and genomic variation, provides a means for obtaining this knowledge. However, despite extensive efforts to assemble and characterize ex situ collections of crops and their wild relatives, gaps remain in the genomic and environmental datasets needed to robustly implement this approach. This article outlines the history of landscape genomics, which, to date, has mainly been used in conservation and evolutionary studies, provides an overview of crops and wild relative collections that have the necessary data for implementation and identifies areas where new data generation is needed. We find that 60% of the crops covered by the International Treaty on Plant Genetic Resources for Food and Agriculture lack the data necessary to conduct this kind of analysis, necessitating identification of crops in need of more collections, sequencing, or phenotyping. By highlighting these aspects, we aim to help develop agricultural landscape genomics as a sub-discipline that brings together evolutionary genetics, landscape ecology, and plant breeding, ultimately enhancing the development of resilient and adaptable crops for future environmental challenges.

Keywords: Crop wild relatives; genome–environment association; local adaptation; plant breeding.

Figure 1

Comparison of GEA-enabled breeding to a general traditional breeding scheme. Breeding timeline, population size, and relative cost of activities are considered here. Basic breeding timelines are generally formulated in a species-specific way to account for calendar time; they can also be conceptualized as the number of generations. With the ease and reduced costs of high-throughput sequencing and genotyping platforms, phenotyping is generally considered the primary bottleneck that limits the identification and validation of lines, traits, and QTL due to the high costs (Bazakos et al., 2017; Langstroff et al., 2022). Whether an individual breeding program can use GEA-enabled breeding depends on many factors, including generation time, ploidy, ability to use tissue culture methods, transformation potential, relationship of experimental lines to elite material, and trait genetic architecture. However, cultivar turnover is often slow, particularly when moving material between geographies (Lucier, 1991; Singh et al., 2020). Implications of yield protection from climate resilience breeding imply that there is a need to make decisions now to ensure that cultivars will thrive under the projected climate regimens of the 21st century.

Figure 2

GEA analyses in sunflower. (A and B) Here, we see the expected output of a GEA study, a Manhattan plot showing associations between SNPs and (A) degree days below 18°C (DD < 18) for H. annuus, with HaHIT1, a sunflower ortholog of HEAT-INTOLERANT 1 (HIT1), as one of the top associations, and (B) cation exchange capacity, a measure of soil fertility, in H. petiolaris ssp. fallax. The purple line represents a Bayes factor (BFis) of 20 deciban (dB). Modified with permission from Todesco et al. (2020).

Figure 3

Exploration of the workflow for the agricultural landscape genomics process. Different parts of the analysis are shown in different colors.