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Influence of poverty concerns on demand for healthier processed foods: A field experiment in Mexico City

Marrit Van den Berg Jason Donovan (2023, [Artículo])

Living in poverty can present cognitive biases that exacerbate constraints to achieving healthier diets. Better diets could imply food choice upgrades within certain food categories, such as electing processed foods with an improved nutritional profile. This study evaluated the influence of monetary and health concerns on the willingness to pay (WTP) for healthier processed foods in a low-income section of Mexico City. We employed priming techniques from the scarcity literature, which are applied for the first time to healthier food purchasing behaviours in low-income settings. Our predictions are based on a dual system framework, with choices resulting from the interaction of deliberative and affective aspects. The WTP was elicited through a BDM mechanism with 423 participants. Results showed that induced poverty concerns reduced the valuations of one of the study's healthier food varieties by 0.17 standard deviations. The latter effect did not differ by income level. The WTP for a healthier bread product but one with relatively high sugar and fat content was reduced by induced poverty concerns only among certain consumers without bread purchasing restrictions (78% of the sample). Potential mechanisms were assessed through regression analysis and structural equation modelling. The relationship between poverty concerns and WTP was mediated by increased levels of stress. While we could not rule out impact on cognitive load, it was not deemed a mediator in this study. Our findings signal that improvements in economic and psychological well-being among low-income consumers may aid to increase their demand for healthier processed foods.

Healthier Diets Poverty Psychology Dual System Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DIET POVERTY PSYCHOLOGY STRESS WILLINGNESS TO PAY

Remote sensing of quality traits in cereal and arable production systems: A review

Zhenhai  Li xiuliang jin Gerald Blasch James Taylor (2024, [Artículo])

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Quality Traits Grain Protein CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING QUALITY GRAIN PROTEINS CEREALS PRODUCTION SYSTEMS