Monsanto Technology LLC

Zi Wang

Sambarta Dasgupta

Srinivas Phani Kumar Chavali

Anthony Paul Kovacs

Jorge Moran

Nalini Polavarapu

Qianni Dong

Silvano Assanga Ocheya

Yiduo Zhan

Aviral Shukla

Mahdi Jadaliha

Xiaobo Zhou

Hui Zhang

Anand Pramod Deshmukh

Jesse B Grote

Chong Yu

Benjamin Bruce Stewart-Brown

Humberto Ignacio Gutierrez Gaitan

Hongwei Luo

Jorge Luis Moran

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.

Methods and systems for use in implementing resources in plant breeding

A routing processor implements a multi-stage prescriptive routing model engine based on harvest input data relating to the harvesting of crops at a plurality of locations by a plurality of combines and based on a harvest characteristic representing an attribute of the crops to be harvested by the combines. The multi-stage prescriptive routing model engine generates a combine routing program prescribing the movement of each combine between the locations and includes a demand stage configured to identify combine harvesting demand as a function of the harvest input data and the harvest characteristic and a scheduling stage configured to generate the combine routing program as a function of the harvesting demand.

Systems and methods for combine routing

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.

Methods and systems for identifying progenies for use in plant breeding

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.

Methods and systems for identifying hybrids for use in plant breeding

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San Mateo, CA, United States of America

Zi Wang