2415628
Project Grant
Overview
Grant Description
Sbir Phase I: Improving Domestic Small Ruminant Reproduction Through Computer Assisted Embryo Analysis -The broader/commercial impact of this Small Business Innovation Research (or Small Business Technology Transfer) Phase I project will be to accelerate the quality and growth of U.S. sheep and goat production through improved embryo transfer rates. The United States is forced to import 1.5 billion USD yearly of small ruminant protein to fulfill national demand.
Embryo transfer use to improve domestic herds is currently limited due to low success rates and almost exclusively used in a small margin of elite herds. By improving the success of this technology and lowering the cost, it will democratize it for wider industry use. Accelerating national sheep and inventory numbers through more productive and prolific animals will significantly bolster the health, safety, and welfare of the American populace through increased access to economical, lean protein.
More animals entering the food supply means job creation and an expansion of tax revenue through increased demand for animal feedstuffs, routine animal care, veterinarian services, transportation, animal processing, and distribution of value-added products. Fulfilling U.S. consumer needs with U.S. grown sheep and goats means job creation and internal food security.
In this project, machine learning models with computer vision and multifactorial herd qualities will be studied to significantly improve sheep and goat breeding success rates. Key identified features in the embryo will be used as a baseline that will enable evaluation of a variety of intrinsic and extrinsic factors related to the ewe during the gestational period to better understand environmental factors related to pregnancy failure and success.
This analysis will produce a comprehensive embryo and animal health analysis that can be used in sheep and goat embryoology laboratories to enable veterinarians, embryologists, and producers to improve breeding success rates. The resulting user interface incorporates the data collection and processing with herd breeding management to serve as a minimum viable product for immediate wider industry adoption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.- Subawards are planned for this award.
Embryo transfer use to improve domestic herds is currently limited due to low success rates and almost exclusively used in a small margin of elite herds. By improving the success of this technology and lowering the cost, it will democratize it for wider industry use. Accelerating national sheep and inventory numbers through more productive and prolific animals will significantly bolster the health, safety, and welfare of the American populace through increased access to economical, lean protein.
More animals entering the food supply means job creation and an expansion of tax revenue through increased demand for animal feedstuffs, routine animal care, veterinarian services, transportation, animal processing, and distribution of value-added products. Fulfilling U.S. consumer needs with U.S. grown sheep and goats means job creation and internal food security.
In this project, machine learning models with computer vision and multifactorial herd qualities will be studied to significantly improve sheep and goat breeding success rates. Key identified features in the embryo will be used as a baseline that will enable evaluation of a variety of intrinsic and extrinsic factors related to the ewe during the gestational period to better understand environmental factors related to pregnancy failure and success.
This analysis will produce a comprehensive embryo and animal health analysis that can be used in sheep and goat embryoology laboratories to enable veterinarians, embryologists, and producers to improve breeding success rates. The resulting user interface incorporates the data collection and processing with herd breeding management to serve as a minimum viable product for immediate wider industry adoption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.- Subawards are planned for this award.
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Jonesboro,
Arkansas
72401-0477
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the End Date has been extended from 03/31/25 to 10/31/25 and the total obligations have increased 7% from $274,996 to $294,996.
Semen And Embryo Advanced Reproductive Technologies (Smart) was awarded
Project Grant 2415628
worth $294,996
from National Science Foundation in July 2024 with work to be completed primarily in Jonesboro Arkansas United States.
The grant
has a duration of 1 year 3 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Improving Domestic Small Ruminant Reproduction Through Computer Assisted Embryo Analysis
Abstract
The broader/commercial impact of this Small Business Innovation Research (or Small Business Technology Transfer) Phase I project will be to accelerate the quality and growth of U.S. sheep and goat production through improved embryo transfer rates. The United States is forced to import 1.5 billion USD yearly of small ruminant protein to fulfill national demand. Embryo transfer use to improve domestic herds is currently limited due to low success rates and almost exclusively used in a small margin of elite herds. By improving the success of this technology and lowering the cost, it will democratize it for wider industry use. Accelerating national sheep and inventory numbers through more productive and prolific animals will significantly bolster the health, safety, and welfare of the American populace through increased access to economical, lean protein. More animals entering the food supply means job creation and an expansion of tax revenue through increased demand for animal feedstuffs, routine animal care, veterinarian services, transportation, animal processing, and distribution of value-added products. Fulfilling U.S. consumer needs with U.S. grown sheep and goats means job creation and internal food security.
In this project, machine learning models with computer vision and multifactorial herd qualities will be studied to significantly improve sheep and goat breeding success rates. Key identified features in the embryo will be used as a baseline that will enable evaluation of a variety of intrinsic and extrinsic factors related to the ewe during the gestational period to better understand environmental factors related to pregnancy failure and success. This analysis will produce a comprehensive embryo and animal health analysis that can be used in sheep and goat embryology laboratories to enable veterinarians, embryologists, and producers to improve breeding success rates. The resulting user interface incorporates the data collection and processing with herd breeding management to serve as a minimum viable product for immediate wider industry adoption.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
BT
Solicitation Number
NSF 23-515
Status
(Ongoing)
Last Modified 5/5/25
Period of Performance
7/1/24
Start Date
10/31/25
End Date
Funding Split
$295.0K
Federal Obligation
$0.0
Non-Federal Obligation
$295.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2415628
Additional Detail
Award ID FAIN
2415628
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
RYFZRBZJU6L1
Awardee CAGE
94XX1
Performance District
AR-01
Senators
John Boozman
Tom Cotton
Tom Cotton
Modified: 5/5/25