Finding Phenotypic Biomarkers for Drought Tolerance in <i>Solanum tuberosum</i>

Abstract

Climate change models predict increased drought frequencies. Maintaining yield stability necessitates drought-tolerant crops. However, their breeding is challenging; drought tolerance is a multigene trait with significant environment interaction. Thus, the training of genomic selection models requires phenotyping a large genotype population under arid conditions. We aimed to identify phenotypic tolerance traits that facilitate the screening of large populations in the field. We performed three trials on 20 tetraploid Solanum tuberosum ssp. tuberosum genotypes with significant drought tolerance variation. Plants were subjected to early, late and long-term drought under variable climate conditions. For each stress scenario, the drought tolerance index DRYMp was calculated from the relative tuber starch yield. A laser scanner system measured canopy development continuously over the crop’s lifecycle and provided estimates of leaf movement and canopy growth features. Growth curves were evaluated by logistic regression. Different multiple regression approaches were compared for their ability to predict tolerance from phenotype data of optimally watered or stressed plants. We established that early short-term stress can be used as a proxy for long-term stress in the absence of genetic variation for drought stress recovery or memory. The gen-otypes varied significantly in most canopy features. Leaf-area-based features combined significant genotype effects with environmental stability. Multiple regression models based on single-day data outperformed those based on the regression curve parameter. The models included leaf area and leaf position parameters and partially reproduced prior findings on siblings in a genetically more diverse population.

Keywords

• abiotic stress
• water stress
• phenotyping
• LIDAR
• phenotypic markers
• multiple regression models