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Book (stand-alone)Guidelines on the measurement of harvest and post-harvest losses
Estimation of crop harvest and post-harvest losses in Malawi. Maize, rice and groundnuts. Field test report
2020Also available in:
No results found.A study was conducted in two Agriculture Development District (ADDs) of Malawi, Salima and Lilongwe, to pilot a new methodology for estimating on-farm harvest and post-harvest losses. The study was carried-out with technical support from the Global strategy to improve agricultural and rural statistics (GSARS) of the Food and Agricultural Organization of the United Nations (FAO). This pilot exercise principally aimed at strengthening the capacity of Malawi in generating reliable estimates on post-harvest losses. The data collection was carried out using a household questionnaire which was specifically developed for this exercise. The analysis of the results showed that a significant amount of farm produce is lost during harvesting, followed by threshing. The study also highlighted that on-time harvesting and use of chemicals are considered by farmers as the most effective strategies for preventing on-farm losses, even though farmers are not always in a position to implement these strategies. The authors recommend that a solid baseline on harvest and post-harvest losses be established by replicating on a larger scale this pilot survey for three consecutive years, to account for weather variation and other exogenous factors which may affect losses. The survey would benefit from the integration with existing country-wide data collection systems such as the Agricultural production estimates survey (APES) to ensure low operational costs and sustainability. It is also recommended that Computer assisted personal interviewing (CAPI) should be introduced for future exercises to improve on data quality and timeliness. -
BookletGuidelines on the measurement of harvest and post-harvest losses – Estimating fish and post-harvest loss measurement in Guyana
Field test report
2020Also available in:
No results found.The Food and Agriculture Organization of the United Nations (FAO) in collaboration with The Ministry of Agriculture in the Republic of Guyana organized and conducted a fish loss training/workshop. The training workshop held in Georgetown, Guyana from 25-29 November 2019. The main purpose of the training workshop was to test Fish Loss measurement tools and provide knowledge and values regarding global fish losses in the context of food security. This report presents details about the training workshop and experience gained from testing of the Guidelines on fish loss measurement. It includes situation analysis, Training Needs Assessment (TNA), selection of participants, goal and objectives, training description, preparation of training, delivery of training, and evaluation. In addition, some recommendations were provided for improving the Guidelines and future training workshops. The summative evaluation, based on candid opinion of trainees, suggest that the five (5) day training workshop was successful. -
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.
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