Common bean (Phaseolus vulgaris L.) is an important legume for human consumption and has an important role in cropping systems as a rotational crop. Improving the sustainability in agriculture is necessary for meeting the food demands of a growing global population while lessening the environmental impact of cropping systems. Developing efficient methods of improving host-plant resistance to dry bean anthracnose (Colletotrichum lindemuthianum) and the symbiotic nitrogen fixation ability (SNF) can enhance the sustainability of common bean as a food crop. A QTL study with the black bean cultivar ‘TU’, known to possess the C. lindemuthianum race 109 resistance gene Co-5, was conducted to develop molecular markers to deploy in the MSU Dry Bean Breeding Program. Resistance to anthracnose was investigated in an F2 population developed from a cross between ‘B19504’ (a susceptible breeding line) and TU. 25 SNPs were identified between 6.84 and 24.62 Mb on linkage group 07. Improving SNF in common bean requires a method of efficiently evaluating breeding lines for the trait. Predictive models were developed from remote sensing-derived vegetation indices and machine learning algorithms to assess their ability to accurately and reliably estimate percent nitrogen derived from the atmosphere. A Random Forest model developed to predict nitrogen derived from the atmosphere (Ndfa) using yield and remote sensing (RS) data resulted in an average accuracy of r = 0.54. This model is promising in low nitrogen trials as an early selection tool to identify lines with higher SNF ability. Two prediction models for yield as an indirect indicator of SNF were developed using stepwise general linear modeling (StepwiseGLM) and Bayesian regularized artificial neural network (BRNeural Network) were determined to be accurate and reliable (StepwiseGLM r = 0.64; BRNeural Network r = 0.65). These models are promising in low nitrogen trials as an early selection tool to identify lines with higher SNF ability.
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