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Food loss estimation: SDG 12.3.1a data and modelling approach











Taglioni, C., Rosero Moncayo, J. & Fabi, C. 2023. Food loss estimation: SDG 12.3.1a data and modelling approach. FAO Statistics Working Paper Series, No. 23-39. Rome, FAO.




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    Combining farm and household surveys with modelling approaches to improve post-harvest loss estimates and reduce data collection costs 2022
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    While there is growing awareness of the issue of food losses at the political level, official post-harvest loss data for informing policymaking and reporting on SDG Indicator 12.3.1. (a) Food Loss Index is scarce. Representative sample-based surveys are necessary to obtain information on on-farm losses at the country level, but due to the issue’s complexity, a loss module covering several key questions is needed. One main strategy proposed by the 50x2030 Initiative for optimizing data collection is sub-sampling for some of the survey modules. This paper examines whether modelling approaches can be combined with sub-sampling to improve harvest and post-harvest loss estimates and allow for further sample and cost reduction. The paper first presents the loss models generated on four selected surveys conducted in Malawi, Zimbabwe, and Nigeria, which were built using the Classification and Regression Tree (CART) method. The performance of each model is assessed for different sizes of sub-samples to improve the sample-based estimates, either by model-based estimates or by model-based imputation. The research concludes that the model-based estimates improve the loss estimates of the sub-samples due to post-stratification implied in the CART method, whereby they can constitute a cost-effective complement to sub-sampling strategies, while model-based imputations should only be used on a reduced number of missing observations. The models perform best when the survey invests in obtaining more detailed on-farm loss data and considers some key variables identified as relevant for on-farm loss models. Sub-sampling allows for investment in more detailed questionnaires and some considerations are derived for its design.
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    The FAO approach to food loss concepts and estimation in the context of Sustainable Development Goal 12 Target 3 2016
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    FAO is undertaking a two-pronged approach to building a dedicated global database on post-harvest/slaughter food losses (up to the retail level), and providing country level support to measure, estimate or impute the pertinent data. Country-specific food loss indices will then be calculated, and geo-aggregated up to a global level index. These indices will measure and monitor progress against one of the two components of Sustainable Development Goal 12 Target 3 (denoted as SDG 12.3).

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