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Book (series)Assessment and collection of data on pre-harvest food grain losses due to pests and diseases 1983
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No results found.The manual is intended to serve as a guide to the statistical methodology for assessing and collecting data on pre-harvest foodgrain losses due to pests and diseases. It should be useful to those developing countries which plan to launch foodgrain losses reduction programmes but find themselves seriously handicapped because of lack of basic data. The manual will go a long way in assisting those who will be charged with the responsibility of planning and implementing surveys for estimating pre-harvest foodgrain losses due to pests and diseases. -
ArticleCombining farm and household surveys with modelling approaches to improve post-harvest loss estimates and reduce data collection costs 2022
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No results found.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. -
Book (stand-alone)Report on off-farm post-harvest loss assessment survey in Ethiopia 2023
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No results found.A study on post-harvest losses was conducted in Amhara, Oromiya and the Southern nation nationality and peoples regions of Ethiopia to pilot a methodology to produce national statistics of off-farm losses. The study was conducted by the Ethiopian Statistics Service (ESS), with technical and financial support from the Office of the Chief Statistician and the Statistics Division of the Food and Agriculture Organization of the United Nations (FAO).
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