Knowledge on gene action and trait expression are important for effective breeding. The objective of this study was to determine the general combining ability (GCA), specific combining ability (SCA), maternal effects and heritability of drought tolerance, yield and yield components of candidate sweetpotato clones. Twelve genotypes selected for their high yield, dry matter content or drought tolerance were crossed using a full diallel mating design. Families were field evaluated at Masoro, Karama, and Rubona Research Stations of Rwanda Agriculture Board. Success rate of crosses varied from 1.8 to 62.5% with a mean of 18.8%. Family by site interaction had significant effect (P < 0.01) on storage root and vine yields, total biomass and dry matter content of storage roots. The family effects were significant (P < 0.01) for all parameters measured. Broad sense heritability estimates were 0.95, 0.84, 0.68, 0.47, 0.74, 0.75, 0.50, and 0.58 for canopy temperature (CT), canopy wilting (CW), root yield, skin color, flesh color, dry matter content, vine yield and total biomass, respectively. The GCA effects of parents and SCA effects of crosses were significant (P < 0.01) for CT, CW, storage root, vine and biomass yields, and dry matter content of storage root. The ratio of GCA/SCA effects for CT, CW, yield of storage roots and dry matter content of storage roots were higher than 50%, suggesting the preponderance of additive over non-additive gene action in the expression of these traits. Maternal effects were significant (P < 0.05) among families for CT, CW, flesh color and dry matter content, vine yield and total biomass. Across sites, the best five selected families with significant SCA effects for storage root yield were, Nsasagatebo × Otada 24, Otada 24 × Ukerewe, 4-160 × Nsasagatebo, K513261 × 2005-034 and Ukerewe × K513261 with 11.0, 9.7, 9.3, 9.2, 8.6 t/ha, respectively. The selected families are valuable genetic resources for sweetpotato breeding for drought tolerance, yield and yield components in Rwanda or similar environments.

Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.

The identification of drought tolerant banana varieties under natural environment is complicated by difficulties in field management, variation in phenotype and unexpected rainfall events. A study to develop an alternative and rapid technique to screen for drought tolerant banana varieties by an in vitrotechnique was carried out. Effects of 0.09 M sucrose, 0.09 M sorbitol and 0.0 M sugar on growth of banana plantlets were compared under in vitro conditions. Results from this experiment proved that sorbitol is not a source of energy for in vitro banana plantlets and it could be used as a neutral osmotic inducer. Exploration of different levels of osmotic stress induced by 0.1 to 0.5 M sorbitol in the media and their effects on the growth of banana plantlets proved that the concentration of 0.2 M sorbitol is the highest concentration to reveal different growth parameters. The application of this concentration on banana varieties of Williams, Popoulou, Obino l'Ewai, Lep Chang Kut, Mbwazirume (negative control: drought sensitive), and Cachaco (positive control: drought tolerant) showed that all varieties were affected by sorbitol osmotic stress but the degree of sensitivity is different. Significant differences in reduction of gain of fresh and dry weight, new roots and leaves, and leaf area were observed between Cachaco and Mbwazirume. For most growth parameters, Cachaco showed the lowest reduction and Mbwazirume presented the highest reduction due to osmotic stress. The varieties Williams and Lep Chang Kut showed a level of drought tolerance after Cachaco. Lep Chang Kut had the lowest reduction of gain of new root and fresh weight, and water content; whereas, Williams occupied the second position of low reduction of gain of leaf area, number of leaves, and the third position in low reduction of gain of new root and dry weight. After, Obino l'Ewai occupied the fourth position and Popoulou came as the fifth position. Mbwazirume was the last in the tolerance of sorbitol induced osmotic stress with high reduction in many growth parameters evaluated. From this study, an in vitro technique to screen drought tolerant banana varieties was developed, and the drought tolerance of Cachaco and Lep Chang Kut and drought sensitivity of Mbwazirume were proved. The total gain of fresh and dry weight, number of new leaves and leaf area were identified to be appropriate growth parameters for identifying drought tolerant banana varieties under in vitro condition.

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