Owen Young, Kevin Kantono, Martin Waiguny, Li-Fan Hung and Nazimah Hamid
The purpose of this paper is to explore understanding of a graphic equivalent to mandatory nutrition information tables.
Abstract
Purpose
The purpose of this paper is to explore understanding of a graphic equivalent to mandatory nutrition information tables.
Design/methodology/approach
The horizontal bar graphic’s single number shows the per cent content of the dominant nutrient, marked “Most”, contrasting with “Least” at the origin. A separate bar for energy is expressed as percentage of 3,700 kJ, the energy in 100 g of fat. Six randomised table and equivalent graph images were shown to subjects who answered questions about the foods’ energy, dominant nutrient and per cent content, and relative abundance of seven mandated nutrients. One trial tested 40 food science students, another 100 online Australasian consumers. Scores were compared by the χ2 test. Liking of the two formats was compared by t-test.
Findings
Correct online consumer responses were: energy – 18 per cent (tables), 71 per cent (graphics); dominant nutrient – 81, 96 per cent; per cent dominant nutrient – 43, 82 per cent. All differences were highly significant. Relative abundance questions created a 7 nutrient × 6 food matrix (42 combinations) where tables were more accurately understood 14 times (3 significant) and graphics 28 times (12 significant). Responses in the student trial paralleled the consumer trial; differences were less marked but with similar statistical significances. Consumers liked the graphic more.
Practical implications
The graphic format was more understandable than the table format, and would be useful in internet-based applications.
Originality/value
The graphic format represents a huge advance in understanding of mandatory nutrient information.
Details
Keywords
Hui Tao, Hang Xiong, Liangzhi You and Fan Li
Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers'…
Abstract
Purpose
Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.
Design/methodology/approach
This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.
Findings
The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.
Practical implications
Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.
Originality/value
This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.