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Assessment and collection of data on post - harvested foodgrain losses













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    Book (series)
    Technical study
    Assessment and collection of data on pre-harvest food grain losses due to pests and diseases 1983
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    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.
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    Article
    Journal article
    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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    Book (stand-alone)
    Technical report
    Report on off-farm post-harvest loss assessment survey in Ethiopia 2023
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    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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    FAO Strategy on Climate Change 2022–2031 2022
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    Status of the World's Soil Resources: Main Report 2015
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    The SWSR is a reference document on the status of global soil resources that provides regional assessments of soil change. The information is based on peer-reviewed scientific literature, complemented with expert knowledge and project outputs. It provides a description and a ranking of ten major soil threats that endanger ecosystem functions, goods and services globally and in each region separately. Additionally, it describes direct and indirect pressures on soils and ways and means to combat s oil degradation. The report contains a Synthesis report for policy makers that summarizes its findings, conclusions and recommendations.

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    Booklet
    Corporate general interest
    Emissions due to agriculture
    Global, regional and country trends 2000–2018
    2021
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    The FAOSTAT emissions database is composed of several data domains covering the categories of the IPCC Agriculture, Forestry and Other Land Use (AFOLU) sector of the national GHG inventory. Energy use in agriculture is additionally included as relevant to emissions from agriculture as an economic production sector under the ISIC A statistical classification, though recognizing that, in terms of IPCC, they are instead part of the Energy sector of the national GHG inventory. FAO emissions estimates are available over the period 1961–2018 for agriculture production processes from crop and livestock activities. Land use emissions and removals are generally available only for the period 1990–2019. This analytical brief focuses on overall trends over the period 2000–2018.