Lifecycle assessment systems and methods for determining emissions from animal production

Abstract

Approaches provide for machine learning or training algorithms that apply modifications to models based on a type of data obtained, including, for example, producer-specific management practice data, genetic data, among other such data. The animal-centric models can be configured to, for example, quantify gas emissions (e.g., greenhouse gas emissions) that an animal may be expected to emit over a period of time, including, for example, over the animal's lifetime. The emissions in certain embodiments can further enable the certification of emissions for individual animals.

What is claimed is: 
     
         1 . A computing system for generating emissions models, the computing system comprising:
 a computing device processor; and   a memory device including instructions that, when executed by the computing device processor, enables the computing system to:
 obtain, by the computing device processor of the computing system, historic animal data from a plurality of different disaggregated sources, 
 identify, by the computing device processor of the computing system, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions, 
 generate, by the computing device processor of the computing system, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways, 
 receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal, 
 obtain in real-time from a database, by the computing device processor of the computing system, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal, 
 identify, by the computing device processor of the computing system, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and 
 apply, by the computing device processor of the computing system, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal. 
   
     
     
         2 . The computing system of  claim 1 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 receive a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date,   wherein the animal-centric emissions model is based on the pathway.   
     
     
         3 . The computing system of  claim 2 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 identify, by the computing device processor of the computing system, equation components associated with the pathway,   wherein the animal-centric emissions model is based on the equation components.   
     
     
         4 . The computing system of  claim 1 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 determine, by the computing device processor of the computing system, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data.   
     
     
         5 . The computing system of  claim 1 , wherein the amount of emissions by the selected animal is for a particular lifecycle emissions pathway. 
     
     
         6 . The computing system of  claim 1 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 display, for the selected animal associated with the unique identifier, in a graphical user interface one or more views of the amount of emissions during the emissions lifecycle.   
     
     
         7 . The computing system of  claim 1 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 associate at least one certification with the selected animal based on the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.   
     
     
         8 . The computing system of  claim 7 , wherein the at least one certification indicates the amount of emissions that the animal has emitted or is expected to emit. 
     
     
         9 . The computing system of  claim 1 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 iteratively update the animal-centric emissions model based on additional data from the plurality of sensors.   
     
     
         10 . The computing system of  claim 9 , wherein a machine learning technique is utilized to iteratively update the animal-centric emissions model. 
     
     
         11 . The computing system of  claim 1 , wherein a machine learning technique is utilized to generate the emissions model. 
     
     
         12 . The computing system of  claim 1 , wherein the plurality of sensors includes at least one of a camera, a scale, a ruler, a timer, a feeder, a temperature sensor, a pressure sensor, a flow meter, an electrical sensor, a radiation sensor, a gas sensor, a liquid sensor, a humidity sensor, a movement sensor, a global positioning sensor (GPS), a soil composition sensor, a pH sensor, a body composition sensor, a health sensor, animal identification sensor, facial identification sensor, biomedical sensor, an x-ray sensor, nuclear magnetic resonance sensor, or an ultrasound sensor, and wherein the performance data includes expected progeny performance data, expected progeny differences data, genotypic data, phenotypic data, and farm practices management data associated with the selected animal. 
     
     
         13 . The computing system of  claim 1 , wherein the instructions, when executed by the computing device processor, further enables the computing system to:
 generate control instructions to control an appliance to alter at least one task affecting the amount of emissions by the selected animal during the emissions lifecycle of the selected animal.   
     
     
         14 . A computer-implemented method for generating animal-centric emissions models, comprising:
 obtaining, by a computing device processor, historic animal data from a plurality of different disaggregated sources,   identifying, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions,   generating, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways,   receiving a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal,   obtaining in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal,   identifying, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and   applying, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.   
     
     
         15 . The computer-implemented method of  claim 14 , further comprising:
 receiving a selection of a pathway from the plurality of potential lifecycle emissions pathways for the selected animal, the pathway comprising an entry point corresponding to a start date and an exit point corresponding to an end date,   wherein the animal-centric emissions model is based on the pathway.   
     
     
         16 . The computer-implemented method of  claim 15 , further comprising:
 identifying, by the computing device processor, equation components associated with the pathway,   wherein the animal-centric emissions model is based on the equation components.   
     
     
         17 . The computer-implemented method of  claim 14 , further comprising:
 determining, by the computing device processor, the amount of emissions by the selected animal during the emissions lifecycle of the selected animal by evaluating the animal-centric emissions model on the historic animal data and the performance data.   
     
     
         18 . The computer-implemented method of  claim 17 , further comprising:
 determining, by the computing device processor, an emissions offset based on the total amount of emissions by the group of animals and the amount of emissions by the selected animal.   
     
     
         19 . The computer-implemented method of  claim 14 , wherein the database comprises a blockchain database. 
     
     
         20 . A non-transitory computer readable storage medium storing instructions that, when executed by a computing device processor of a computing system, causes the computing system to:
 obtain, by the computing device processor, historic animal data from a plurality of different disaggregated sources,   identify, by the computing device processor, a plurality of equation components based on the historic animal data, individual equation components configured to quantify an amount of emissions,   generate, by the computing device processor, an emissions model comprising the plurality of equation components, the emissions model quantifying a total amount of emissions by a group of animals for an emissions lifecycle of the group of animals, wherein the emissions lifecycle includes a plurality of potential lifecycle emissions pathways,   receive a selection of an animal associated with the group of animals to identify a selected animal, the animal associated with a unique identifier identifying the selected animal,   obtain in real-time from a database, by the computing device processor, wherein the database is comprised of information obtained by at least one sensor of a plurality of sensors monitoring the selected animal, performance data associated with the unique identifier of the selected animal,   identify, by the computing device processor, one or more data variables associated with at least one equation component of the plurality of equation components of the emissions lifecycle based on the performance data, and   apply, by the computing device processor, at least one adjustment to the at least one equation component to generate an animal-centric emissions model, the animal-centric emissions model quantifying an amount of emissions by the selected animal during the emissions lifecycle of the selected animal.