Abstract: The present disclosure is directed to methods (and associated systems) for training a machine learning model for predictive plant breeding using phenomic selection based on diverse data streams to predict grain composition comprising: collecting, with a processor, training data, stored in a database, from the group consisting essentially of phenomic data; selecting, with the processor, a machine learning model based on the training data, the machine learning model selected from the group comprising supervised learning models, unsupervised learning models, and combinations thereof; training, with the processor, the machine learning model using the training data from the database; and inputting, via the processor, a new set of phenotypic data from a plurality of grain bearing plants into the trained machine learning model to generate a predictive breeding crosses list ranked on an aggregate probability that a progeny of the cross will exhibit one or more desired phenotypic characteristics.

Abstract: Applicants have identified that three critical phenotypic/genetic measures are highly correlated with transition period health and may be used in selection and breeding protocols and/or in combination with traditional breeding and marker assisted selection methods to improve predictability of transition period health. According to the invention genetic evaluations for mastitis, ketosis, and metritis have been found to be highly predictive of overall transition health. The genetic evaluations are produced by directly measuring thousands of clinical cases of mastitis, ketosis, and metritis in ancestors of a particular animal and using this data in selection. Applicant's selection criteria and quickly impact a breeders population by reducing transition cow disease incidence in the initial population and in progeny.

Abstract: Applicants have identified that three critical phenotypic/genetic measures are highly correlated with transition period health and may be used in selection and breeding protocols and/or in combination with traditional breeding and marker assisted selection methods to improve predictability of transition period health. According to the invention genetic evaluations for mastitis, ketosis, and metritis have been found to be highly predictive of overall transition health. The genetic evaluations are produced by directly measuring thousands of clinical cases of mastitis, ketosis, and metritis in ancestors of a particular animal and using this data in selection. Applicant's selection criteria and quickly impact a breeders population by reducing transition cow disease incidence in the initial population and in progeny.

Abstract: Applicants have identified that three critical phenotypic/genetic measures are highly correlated with transition period health and may be used in selection and breeding protocols and/or in combination with traditional breeding and marker assisted selection methods to improve predictability of transition period health. According to the invention genetic evaluations for mastitis, ketosis, and metritis have been found to be highly predictive of overall transition health. The genetic evaluations are produced by directly measuring thousands of clinical cases of mastitis, ketosis, and metritis in ancestors of a particular animal and using this data in selection. Applicant's selection criteria can quickly impact a breeders population by reducing transition cow disease incidence in the initial population and in progeny.

Abstract: Applicants have identified that three critical phenotypic/genetic measures are highly correlated with transition period health and may be used in selection and breeding protocols and/or in combination with traditional breeding and marker assisted selection methods to improve predictability of transition period health. According to the invention genetic evaluations for mastitis, ketosis, and metritis have been found to be highly predictive of overall transition health. The genetic evaluations are produced by directly measuring thousands of clinical cases of mastitis, ketosis, and metritis in ancestors of a particular animal and using this data in selection. Applicant's selection criteria and quickly impact a breeders population by reducing transition cow disease incidence in the initial population and in progeny.