No abstract

Feeding a growing global population requires improving agricultural production in the face of multidimensional challenges; and digital agriculture is increasingly seen as a strategy for better decision making. Agriculture and agricultural supply chains are increasingly reliant on data, including its access and provision from the farm to the consumer. Far-reaching data provision inevitably needs the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) that offer data originators and depository custodians with a set of guidelines to safeguard a progressive data availability and reusability. Through a systematic literature review it is apparent that although FAIR data principles can play a key role in achieving sustainable agricultural operational and business performance, there are few published studies on how they have been adopted and used. The investigation examines: (1) how FAIR data assimilate with the sustainability framework; and (2) whether the use of FAIR data by the agriculture industry, has an impact on agricultural performance. The work identifies a social science research gap and suggests a method to guide agriculture practitioners in identifying the specific barriers in making their data FAIR. By troubleshooting the barriers, the value propositions of adopting FAIR data in agriculture can be better understood and addressed.

Feeding the growing global population while improving the Earth’s economic, environmental, and social values is a challenge recognised in both the United Nations Sustainable Development Goals and the United Nations Framework Convention on Climate Change. Sustaining global agricultural performance requires regular revision of current farming models, attitudes, and practices. In systematically reviewing the international literature through the lens of the sustainability framework, this paper specifically identifies precision conservation agriculture (PCA), digital agriculture (DA), and resilient agriculture (RA) practices as being of value in meeting future challenges. Each of these adaptations carries significantly positive relationships with sustaining agricultural performance, as well as positively mediating and/or moderating each other. While it is clear from the literature that adopting PCA, DA, and RA would substantially improve the sustainability of agricultural performance, the uptake of these adaptations generally lags. More in-depth social science research is required to understand the value propositions that would encourage uptake of these adaptations and the barriers that prevent them. Recommendations are made to explore the specific knowledge gap that needs to be understood to motivate agriculture practitioners to adopt these changes in practice.

Malnutrition implies disparity of nutrients and energy in corporal status among children and grownups. Malnutrition abolition, in its all forms, is designated as second Sustainable Development Goal (SDGs) of United Nations (WHO, 2017). Occurrence of under nutrition (stunting, wasting and underweight) produces more chances of sickness and decease among children (UNICEF, 2019). A large number of reports discussed the aspects of malnutrition, guardians and caretakers, and applied various methods and data packages on different age classes, such as less than 5 years old, 5 to 10 years old, and 5 to 12 years old. In this study, researcher used the quantitative data techniques and the domain of present study consisted of high-risk union councils of Faisalabad. Four union councils; 209,210,211 and 212 were selected. Convenient Sampling Technique (CST) was used for the selection of respondents as well as localities for the present study. Sample size was 200 children from four union councils i.e. 50 children from each union council were selected. In this study, a significant (χ2 = 11.21, p = .024) association among Mother’s level of education and stunting among children, non-significant (χ2 = 7.14, p = .129) association among Mother’s level of education and control over wasting among children and a non-significant (χ2 = 7.08, p = .132) association among Mother’s level of education and weight for age of the children were considered.