ImmunIGy: A Novel Pen-side Test for Checking Calf Immune Status, to increase the efficiency of beef production through supply chain feedback and improved management
ImmunIGy: A Novel Pen-side Test for Checking Calf Immune Status, to increase the efficiency of beef production through supply chain feedback and improved management
批准号:
10052523
负责人:
金额:
$20.63万
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
Serum concentration of IgG in neonatal calves is a direct marker of the level of passive transfer of maternal antibody from colostrum. There is substantial evidence that successful passive transfer is associated with decreased morbidity and mortality rates, improved growth rates, feed conversion and lifetime productive capacity, whether the calf is destined to produce milk or enter the beef supply chain. As such, successful passive transfer is a key driver of economic and environmental efficiency on both dairy and beef farms rearing calves sourced from dairy units.ImmunIGy is a pen-side diagnostic test for measuring IgG in calves. It is novel for 2 reasons: firstly it is a Lateral Flow Test which can be carried out by the farmer, pen-side, and secondly the sample can be obtained by the farmer using a lancet on an insensitive part of the calf's nose. Hence the result can be obtained cheaply in the space of a few minutes.The project aims are to ;(a) Confirm that the output of the ImmunIGy test correlates with the current gold standard for measuring IgG, which will confirm its value for use in calves less than a week of age. (b) Determine if the measurement of IgG at collection centres for beef calves from the dairy herd can be used to predict future performance, both alone and when used to improve the function of a pre-existing Calf Algorithm. Such testing can drive improved efficiency of beef production via direct feedback to farms supplying calves with insufficient maternal immunity, pricing differentials and via bespoke management of calves according to their performance prediction. (c) Incorporate the algorithm into an App which will allow calf rearers to test calves on entry, enter the data to predict calf performance and put in place differential management practices, such as separate groups or a different threshold of disease prevalence before implementing antibiotic therapy. (d) Train the participating farmers in the use of the test, the App and the data that they need to provide whilst receiving feedback on its usability.
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