Simone Piras

Model selection and averaging in the assessment of the drivers of household food waste to reduce the probability of false positives

Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socio-economic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.

Moldovan family farms: social buffer or economic driver? A survey-based assessment

After obtaining independence from the USSR in 1991, the Republic of Moldova carried out an insider privatization of the land belonging to former Soviet collective farms. As a result, almost 900,000 small family farms emerged, the majority of whom are still active today. Although they play an important socioeconomic role, policy makers neglect them as a residual, shrinking phenomenon. By adopting the theoretical perspective of peasant economics, this dissertation aims at assessing the health status of these farms over ten years after the land reform, and their evolution over time. Data from an original mixed quantitative and qualitative survey carried out on a sample of 126 farms in spring 2015, and the databases of the Household Budget Survey for the period 2006-2013 are used. The main drivers of farmers’ livelihood choices are identified by means of a 31-item Likert scale, and a comprehensive picture of the typical family farm is drawn. Farms are then grouped according to land size, level of commercialization and location, and their evolution over time is analyzed by means of Markov transition chains and multinomial logistic regressions. A focus on production strategies follows. Finally, the impact of agriculture on poverty levels and the implications of alternative livelihood choices are assessed by means of counterfactual incomes and life levels calculated through propensity score matching. It emerges that families were allocated land plots without the tools for working them. Therefore, they adopt low-input, labour-intensive production strategies and are mainly subsistence-oriented. Farm income, although small, plays a key role in relieving vulnerable people from poverty, so that land is a fundamental social buffer. Moreover, home food production is important for social and self-appraisal. For these reasons, an agricultural development strategy based on farm intensification rather than growth and on leasing rather than sale of land is proposed.