| Sumario: | Biotic constraints limit bean productivity across all growing regions, with most severe impact in tropical developing countries.
Evaluation for disease resistance can be influenced by environment, management, and evaluation expertise, which imposes challenges for the development of better varieties with improved resistance on-farm. To increase data reliability, CIAT developed and implemented a coordinated strategy to capture disease severity information in the Lwengo district in Uganda where bean is widely cultivated by small farmers and disease pressure is high. We merged capacity from CIAT-Palmira, CIAT-Kawanda, and NARO-NaCRRI to define the best conditions and genotypes to generate meaningful phenotypic information. A bean nursery (Meso and Andean) representing elite breeding lines, resistant parents, local varieties, some international differentials, and disease resistant and susceptible checks, was sown in two farms at Lwengo. CIAT’s evaluation 1-9 scale was used to evaluate severity for all diseases observed, considering three evaluations on leaves and one on pods. The maximum score on leaves was used to calculate cumulative probabilities considering three phenotypic categories: resistant, intermediate, and susceptible.
Results showed that Lwengo was indeed an optimal location to perform bean diseases assessment, particularly for Angular Leaf Spot (ALS), followed by Rust. At farm 1, no genotype showed resistance reaction to ALS on leaves, even the widely known source of Phg2 Mexico 54. However, intermediate response was observed in AAB08b, Amendoin, NABE 14, and G5686. Similar results were obtained at farm 2 in which no resistance response was observed in any genotype. However, Amendoin was susceptible in this site which suggests pathogen diversity between farms. Interestingly, resistance to ALS in pods was observed in both sites in several genotypes. Results for Rust were more encouraging where several genotypes showed a high level of resistance and only a few showed a susceptible reaction.
On-farm testing combining optimal environment, susceptible checks, and no pesticide use are key elements for assuring enough disease pressure and to obtain reliable data. These results support better decisions about potential sources of resistance to be included in further testing and elimination of those that are not meeting the resistant threshold score.
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