Water scarcity is the primary constraint on crop productivity in arid and semiarid tropical areas suffering from climate alterations; in accordance, agricultural systems have to be optimized. Several concepts and strategies should be considered to improve crop yield and quality, particularly in vulnerable regions where such environmental changes cause a risk of food insecurity. In this work, we review two strategies aiming to increase drought stress tolerance: (i) the use of natural genes that have evolved over time and are preserved in crop wild relatives and landraces for drought tolerance breeding using conventional and molecular methods and (ii) exploiting the reservoir of neglected and underutilized species to identify those that are known to be more drought-tolerant than conventional staple crops while possessing other desired agronomic and nutritive characteristics, as well as introducing them into existing cropping systems to make them more resilient to water deficiency conditions. In the past, the existence of drought tolerance genes in crop wild relatives and landraces was either unknown or difficult to exploit using traditional breeding techniques to secure potential long-term solutions. Today, with the advances in genomics and phenomics, there are a number of new tools available that facilitate the discovery of drought resistance genes in crop wild relatives and landraces and their relatively easy transfer into advanced breeding lines, thus accelerating breeding progress and creating resilient varieties that can withstand prolonged drought periods. Among those tools are marker-assisted selection (MAS), genomic selection (GS), and targeted gene editing (clustered regularly interspaced short palindromic repeat (CRISPR) technology). The integration of these two major strategies, the advances in conventional and molecular breeding for the drought tolerance of conventional staple crops, and the introduction of drought-tolerant neglected and underutilized species into existing production systems has the potential to enhance the resilience of agricultural production under conditions of water scarcity.

In Colombia, the highest cassava production comes from the semi-arid region of the Atlantic Coast with relatively low yield for fresh consumption (≤11 t/ha). Development of improved varieties is based on a plant ideotype which integrates a group of desirable traits independently measured in the field. However, selecting high performance genotypes for several traits simultaneously is a complex process. Sixteen genotypes were evaluated under four environmental conditions (localities) of the Colombian Caribbean region (Cereté, Carmen de Bolivar, Agustín Codazzi, and Sevilla), and two production cycles (2016/2017–2017/2018) in order to assess phenotypic expression of selected traits, their stability, and utility in genotype selection. Selection of promising genotypes should consider both their superiority and stability. Genotypes SM3106-14, GM1692-56, CM9456-12, and GM214-62 were selected based on their agronomic performance. In addition, frequency analysis of sensorial data showed that genotypes CM9456-12, SM1127-8, SM3553-27, and SM3562-32 were preferred by panelists who assessed, color, flavor, texture, and root shape. Determination of superiority through across-environments, multi-trait selection index allows identifying genotypes with superior performance. However, selection was improved when local multi-trait selection indices were included—phenotypic stability determination (through Lin and Binns index and AMMI model) supported an adequate selection of superior and stable cassava genotypes. The inclusion of palatability response and quality features determination in cassava genotypes can be recommended to identify genotypes with higher adoption rates by farmers and consumers.

The genetic diversity of sweet potato [Ipomoea batatas (L.) Lam.] and its wild relatives has been collected and conserved in germplasm collections worldwide and explored employing several tools. The characterization of crops diversity through morphological tools produce useful information. However, the use of conventional morphological descriptions exhibits limitations due to the use of subjective and categorical parameters that affect phenotypic description and diversity estimation. In order to increase the efficiency to discriminate different phenotypes not detected by conventional morphological descriptors, new phenomic approaches were used. Seventy sweet potato accessions collected in the northern coast of Colombia were characterized by forty-nine parameters from conventional sweet potato descriptors and data obtained by RGB imaging and colourimetry. Field descriptions, RGB imaging-colourimetry and both databases integrated were analysed using Gower’s general similarity coefficient for clustering. Estimation of genotype similarity was significantly improved when quantitative data obtained by RGB imaging and colourimetry analysis were included. Variations in traits such as flesh and periderm colour in roots, leaves, vein colour and leaf shape that were not detected by field descriptors, were efficiently discriminated by measuring pixel values from images, estimation of shape descriptors (circularity, solidity, area) and colourimetry data. Expected high correlations were found for field parameters (number of lobes, lobe type, and central lobe shape) and image data (circularity, roundness and solidity). The combination of RGB imaging and colourimetry benefits the quality of morphological characterizations, resulting in a cost-effective process that is able to identify polymorphisms and target traits for diversity estimation and breeding.

Background: Sweetpotato is an important staple food crop worldwide. The genotype mainly influences the nutritional quality of its storage roots, but environmental conditions could produce significant variations in chemical composition and quality. The aim of this study was to characterize sweetpotato diversity of 20 selected genotypes and identify harvest time (90, 120, and 150 days after planting-DAP) and environmental effects on quality attributes estimated by proximate analysis (dry matter, ash, crude fiber, total protein, and total soluble solids). Red (R), green (G), and blue (B) (RGB) analysis was used to characterize the genotypes phenotypically. Results: The results of the current study revealed that flesh color was associated with proximate composition. RGB analysis showed that low B pixel values were present in yellow–orange- and purple-fleshed genotypes, which simultaneously exhibited high total protein content (TPC), ash content (AC), and crude fiber (CF), while cream- and white-fleshed genotypes showed high B pixel values and were related to high dry matter content (DMC). In these genotypes, the high DMC was maintained or increased through harvest time, however, a reduction in the proportion of accumulated AC and TPC was observed. On the other hand, in the pigmented genotypes, DMC increased up to 120 DAP, showing major stability in terms of AC and TPC. Regardless of harvest time, more intense rain events 30 days before harvest, affected the storage of DMC in roots negatively, while AC, TPC, and CF were accumulated more efficiently, or their proportion increased. High temperatures increased total soluble solids (TSS) accumulation and reduced AC, TPC, and CF accumulation. The combined analysis, according to the additive main effects and multiplicative interaction (AMMI) model, confirmed these results. Conclusions: These findings indicated that white-fleshed genotypes contain high DMC, although, with lower TPC, AC, and CF contents compared to yellow–orange- and purple-fleshed genotypes. Although there is an increase in DMC in extended harvests (in orange-fleshed genotypes up to 120 DAP), the presence of pre-harvest rain promotes its translocation and loss. On the contrary, TPC, AC, and CF can be kept stable or even increase, except in environments with high temperatures that induce low accumulation. The current study provides a better understanding of the nutritional response of sweetpotato diversity under several growing conditions, which can be recognized and used integrally to improve food quality.

Adaptation to several environmental conditions is a challenge for breeders for producing new varieties. Breeders select genotypes which show higher performance according to desired traits compared to the average of a large segregant population. In several crops, the selection index is mainly based on traits such as yield, quality, adequate plant architecture, etc. Therefore, multi-trait selection allows for the identification of genotypes that integrally exhibit a better profile and stability, in addition to the dissection of promising varieties based on their superiority in an evaluated population. In this paper, a multi-trait index included in an R tool named CropInd was used to estimate the agronomic performance of 19 sweet potato genotypes in multi-environmental evaluations (three cycles and eight locations). Here, the multi-trait index incorporated variables such as total and commercial fresh root yield, along with survival percentage, which were used in this study. Simultaneously, stability and genetic gain analysis were included to select superior sweet potato genotypes. Results showed that the CropInd script is a suitable and convenient tool for genotype selection based on multi-trait and multi-environmental data. Indexes for specific environment and general behavior (combining multi-environments) were the main output used for genotype selection. Multi-trait selection index, stability, and genetic gain analysis assisted the phenotypic selection performed by breeders. This study resulted in the selection of 0113-672COR as new variety for the Colombian Caribbean region due to its multi-trait performance and stability.