Traditional bean (Phaseolus vulgaris L.) and maize (Zeas mays L.) cropping systems provide multiple ecosystem services to the smallholder farmers that grow them worldwide, yet plant genotypes are rarely developed for this type of cropping environment due to the complexity of the system. Farmers have been growing these systems for generations and may have additional insight into when and how to select cultivars for intercropping systems. The objectives of this study were to investigate with farmers, climbing bean genotype by cropping system interactions (G x CS) in bean-maize intercrops and to use farmer criteria in the evaluation of the provisional services provided by three cropping systems. This research was carried out in Northern Province, Rwanda on two research stations and 7 farmers' fields. Four cropping systems were planted in a randomized complete block design for two seasons. The cropping systems were a maize sole crop (MO), a bean sole crop (BO), a bean-maize intercrop in rows (IC), and a traditional bean-maize intercrop (TC). There were six bean genotypes and one maize genotype. Yield and morphological traits were collected and analyzed. Averaged across season and location, on-station bean yields were 3.4 mt/ha in the BO, 1.5 mt/ha in the IC, and 1.9 mt/ha in the TC. Averaged across season and location, on-station maize yields were 5.1 mt/ha in the MO, 4.8 mt/ha in the IC, and 3.1 mt/ha in the TC. There were no differences in bean yield between the genotypes in the BO, but one genotype, RWV 2070, yielded significantly higher (>0.0001) than the other genotypes in the IC. Pods/plant, the major component of yield, had a significant G x CS interaction and was the only plant trait in the IC that was different between cultivars. On-station results indicate there are genotypes that have greater competitive ability than others in the IC, but aren't identifiable in the BO. Participatory variety selection (PVS), group discussions and interviews were used to determine farmer genotype preferences and intercrop evaluation methods. Farmers evaluated on-farm trials in the same four cropping systems. Farmers' preferred the same genotypes for both cropping systems but they discussed different traits depending on the system. When selecting a genotype for an intercrop environment, farmers consider plant traits, adaptation, trait-based competitive ability, an intrinsic competitive ability, and various management strategies. Bean farmers in Rwanda use complex methods for identifying genotypes adapted to field conditions and different cropping systems, and add new insight into selection for bean genotypes in low-input environments. Current agricultural policy in Rwanda encourages farmers to shift from diverse intercrop systems to sole crop systems but this may impact the types of services farmers gain from the cropping system. To identify trade-offs in cropping systems, on-station data was analyzed in terms of grain yield, protein content, caloric value, and economic returns including market value and land-use efficiencies. The IC intercrop system, planted in rows, provided more services than any other system and could be a viable alternative to the sole crop systems recommended by the government. Combined, these studies underscore the importance of intercrop systems and show that integrating knowledge systems improves our understanding of genotype by cropping system interactions.
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