Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: Exemplary systems and methods are disclosed for allocating resources in a breeding pipeline to multiple origins. One exemplary method includes accessing a data structure including data representative of multiple origins, in which the data includes, for each of the multiple origins, a trait performance expression or genotypic component information. The exemplary method further includes determining a resource allocation, which allocates n resources among the multiple origins based on a probability associated with the trait performance expressions and/or the genotypic components for the origins, and then allocating the n resources in the breeding pipeline for the multiple origins, based on the determined resource allocation.

Abstract: Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes generating population prediction scores for each potential cross within a set of potential crosses, where each population prediction score is associated with a prediction of commercial success for the associated potential cross within the set of potential crosses. The method also includes selecting a subgroup of potential crosses, based on thresholds associated with the population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, and directing ones of the selected target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: Exemplary systems for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary system includes a computing device configured to access phenotypic data related to a pool of hybrids from a data structure and determine a prediction score for each of the hybrids in the pool of hybrids based on the accessed phenotypic data. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The computing device is also configured to select a group of hybrids from the pool of hybrids based on the prediction score, identify a set of hybrids, from the selected group of hybrids, based on one or more factors associated with the hybrids, and then direct the set of hybrids to a validation phase of the plant breeding pipeline for planting and/or testing.

Abstract: Exemplary systems and methods are disclosed for allocating resources in a breeding pipeline to multiple origins. One exemplary method includes accessing a data structure including data representative of multiple origins, in which the data includes, for each of the multiple origins, a trait performance expression or genotypic component information. The exemplary method further includes determining a resource allocation, which allocates n resources among the multiple origins based on a probability associated with the trait performance expressions and/or the genotypic components for the origins, and then allocating the n resources in the breeding pipeline for the multiple origins, based on the determined resource allocation.

Abstract: Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes selecting a subgroup of potential crosses, based on thresholds associated with population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, filtering the target crosses based on a rule (or rules) defining a threshold (or thresholds) for at least one characteristic and/or trait, selecting ones of the filtered target crosses based on risk associated with the selected one of the filtered target crosses, and directing the selected ones of the filtered target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.

Abstract: Exemplary methods for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of hybrids and determining a prediction score for at least a portion of the hybrids included in the pool based on the data included in the data structure. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The method further includes selecting a group of hybrids from the pool based on the prediction score, identifying a set of hybrids, from the group of hybrids, based on an expected performance of the set of hybrids and/or one or more factors associated with the hybrids and/or lines making up the hybrids, and also directing the set of hybrids to a further iteration or different phase in the breeding pipeline.

Abstract: Exemplary systems and methods are disclosed for allocating resources in a breeding pipeline to multiple origins. One exemplary method includes accessing a data structure including data representative of multiple origins, in which the data includes, for each of the multiple origins, a trait performance expression or genotypic component information. The exemplary method further includes determining a resource allocation, which allocates n resources among the multiple origins based on a probability associated with the trait performance expressions and/or the genotypic components for the origins, and then allocating the n resources in the breeding pipeline for the multiple origins, based on the determined resource allocation.

Abstract: Exemplary systems and methods are disclosed for allocating resources in a breeding pipeline to multiple origins. One exemplary method includes accessing a data structure including data representative of multiple origins, in which the data includes, for each of the multiple origins, a trait performance expression or genotypic component information. The exemplary method further includes determining a resource allocation, which allocates n resources among the multiple origins based on a probability associated with the trait performance expressions and/or the genotypic components for the origins, and then allocating the n resources in the breeding pipeline for the multiple origins, based on the determined resource allocation.

Abstract: Exemplary systems and methods are disclosed for allocating resources in a breeding pipeline to multiple origins. One exemplary method includes accessing a data structure including data representative of multiple origins, in which the data includes, for each of the multiple origins, a trait performance expression or genotypic component information. The exemplary method further includes determining a resource allocation, which allocates n resources among the multiple origins based on a probability associated with the trait performance expressions and/or the genotypic components for the origins, and then allocating the n resources in the breeding pipeline for the multiple origins, based on the determined resource allocation.

Abstract: Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes selecting a subgroup of potential crosses, based on thresholds associated with population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, filtering the target crosses based on a rule (or rules) defining a threshold (or thresholds) for at least one characteristic and/or trait, selecting ones of the filtered target crosses based on risk associated with the selected one of the filtered target crosses, and directing the selected ones of the filtered target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.

Abstract: Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes selecting a subgroup of potential crosses, based on thresholds associated with population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, filtering the target crosses based on a rule (or rules) defining a threshold (or thresholds) for at least one characteristic and/or trait, selecting ones of the filtered target crosses based on risk associated with the selected one of the filtered target crosses, and directing the selected ones of the filtered target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: Exemplary methods for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of hybrids and determining a prediction score for at least a portion of the hybrids included in the pool based on the data included in the data structure. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The method further includes selecting a group of hybrids from the pool based on the prediction score, identifying a set of hybrids, from the group of hybrids, based on an expected performance of the set of hybrids and/or one or more factors associated with the hybrids and/or lines making up the hybrids, and also directing the set of hybrids to a further iteration or different phase in the breeding pipeline.

Abstract: Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes selecting a subgroup of potential crosses, based on thresholds associated with population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, filtering the target crosses based on a rule (or rules) defining a threshold (or thresholds) for at least one characteristic and/or trait, selecting ones of the filtered target crosses based on risk associated with the selected one of the filtered target crosses, and directing the selected ones of the filtered target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.