As climate changes, maintenance of yield stability requires efficient selection for drought tolerance. Drought-tolerant cultivars have been successfully but slowly bred by yield-based selection in arid environments. Marker-assisted selection accelerates breeding but is less effective for polygenic traits. Therefore, we investigated a selection based on phenotypic markers derived from automatic phenotyping systems. Our trial comprised 64 potato genotypes previously characterised for drought tolerance in ten trials representing Central European drought stress scenarios. In two trials, an automobile LIDAR system continuously monitored shoot development under optimal (C) and reduced (S) water supply. Six 3D images per day provided time courses of plant height (PH), leaf area (A3D), projected leaf area (A2D) and leaf angle (LA). The evaluation workflow employed logistic regression to estimate initial slope (k), inflection point (Tm) and maximum (Mx) for the growth curves of PH and A2D. Genotype × environment interaction affected all parameters significantly. Tm(A2D)s and Mx(A2D)s correlated significantly positive with drought tolerance, and Mx(PH)s correlated negatively. Drought tolerance was not associated with LAc, but correlated significantly with the LAs during late night and at dawn. Drought-tolerant genotypes had a lower LAs than drought-sensitive genotypes, thus resembling unstressed plants. The decision tree model selected Tm(A2D)s and Mx(PH)c as the most important parameters for tolerance class prediction. The model predicted sensitive genotypes more reliably than tolerant genotype and may thus complement the previously published model based on leaf metabolites/transcripts.

Potato is an important food crop with high water-use-efficiency but low drought tolerance. The bottleneck in drought tolerance breeding is phenotyping in managed field environments. Fundamental research on drought tolerance is predominantly done in container-based test systems in controlled environments. However, the portability of results from these systems to performance under field conditions is debated. Thus, we analyzed the effects of climate conditions, container size, starting material, and substrate on yield and drought tolerance assessment of potato genotypes compared to field trials. A leave one out assessment indicated a minimum of three field trials for stable tolerance prediction. The tolerance ranking was highly reproducible under controlled-conditions, but weakly correlated with field performance. Changing to variable climate conditions, increasing container size, and substituting cuttings by seed tubers did not improve the correlation. Substituting horticultural substrate by sandy soil resulted in yield and tuber size distributions similar to those under field conditions. However, as the effect of the treatment × genotype × substrate interaction on yield was low, drought tolerance indices that depend on relative yields can be assessed on horticultural substrate also. Realistic estimates of tuber yield and tuber size distribution, however, require the use of soil-based substrates.

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.

No abstract