Towards Building a Data-Driven Framework for Climate Neutral Smart Dairy Farming Practices

Taneja, Mohit and Jalodia, Nikita and Dezfouli, Behnam (2021) Towards Building a Data-Driven Framework for Climate Neutral Smart Dairy Farming Practices. In: 2021 11th IEEE Global Humanitarian Technology Conference, GHTC 2021 :. 2021 11th IEEE Global Humanitarian Technology Conference, GHTC 2021 . Institute of Electrical and Electronics Engineers Inc., USA, pp. 213-218. ISBN 9781665433723

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Abstract

Climate change is one of the biggest challenges facing agriculture as we must produce food to feed a growing global population. According to Food and Agriculture Organization (FAO) of the United Nations, the world population is expected to be 9.2 billion by 2050, and consequently the food demand will be increased by 70%. To cope with these increasing demands, the food production also needs to increase by at least 50-70 % to its current capacity by 2050. Improving production efficiency on farms and minimizing greenhouse gas (GHG) emissions is an ongoing and active research area. The article is a work-in-progress presentation of our ongoing efforts in the direction of building an all-encompassing framework towards less GHG intensive climate-neutral farming practices. The dairy sector within agriculture is being used for validation and experimentation, with cows as the centrepiece, and the farmers as the stakeholders that will be the ultimate actors for sustainable environmental impact from the improved farming practices in place. The work also focuses on measuring the behavioural changes of farmer(s) for adoption of technology. The envisioned end goal is to build an overarching toolkit and framework for effective knowledge sharing. The article starts with a brief contextual introduction, followed by key considerations, challenges and identified research questions that need to be addressed and evaluated at a wider level in the research community to build the desired framework at a global scale.

Item Type:	Book Section
Additional Information:	Funding Information: This work has emanated from research funded by (i) NGI Explorers Fellowship Grant (grant agreement no. 825183), (ii) CISCO Research Gift Fund, (iii) SFI and DAFM on behalf of the Government of Ireland to the VistaMilk SFI Research Centre (16/RC/3835). Publisher Copyright: © 2021 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1706
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:17
Last Modified:	07 Jun 2023 18:40
URI:	http://repository-testing.wit.ie/id/eprint/5125

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