Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

International audienceGenomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas ( Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought—two major threats to banana production—used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome

Effect of Planting Density and K2O:N Ratio on the Yield, External Quality, and Traders' Perceived Shelf Life of Pineapple Fruits in Benin

Quality, shelf life, and yield of a pineapple fruit are the important attributes for the producers and customers in the pineapple value chain of Benin, whereas poor quality, short shelf life, and low yield are the main constraints. We quantified the effects of planting density and K2O:N fertilizer ratio on the pineapple yield, external quality, and perceived shelf life in four on-farm experiments with cv. Sugarloaf in Benin; two experiments were installed in the long rainy season and two in the short rainy season. A split-plot design was used with the planting density as the main factor at three levels: 54,000, 66,600, and 74,000 plants.ha−1. The K2O:N ratio was a subfactor with three levels: K2O:N = 0.35 (farmers' practice), K2O:N = 1, and K2O:N = 2. The results showed that both factors had no effect on the crop development variables (such as the number of functional leaves and D-leaf length) at the moment of flowering induction. The planting density had no effect on the total weight per fruit, infructescence weight, total fruit length, infructescence length, crown length, or the fruit shelf life as perceived by traders. The yield increased from 54.9–69.1 up to 90.1 t.ha−1 with an increase in the planting density. The yield increase was not at the expense of the fruit weight. Increased K2O:N ratio led to a higher fruit weight whereas the fruit length was not affected. The shelf life of fruits produced at a K2O:N ratio of 1 and as perceived by traders was 6 days longer than that of fruits produced at a ratio of 0.35 (farmers' practice). Based on these results, we suggest the fresh pineapple farmers in Benin to use a combination of 66,600 plants.ha−1 with a K-fertilization scheme based on a K2O:N ratio of 1 to meet the expectation of both producers and customers in terms of fruit yield and fruit quality