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Guidelines on the measurement of harvest and post-harvest losses – Estimating fish and post-harvest loss measurement in Guyana

Field test report









​FAO. 2020. Guidelines on the measurement of harvest and post-harvest losses – Estimating fish and post-harvest loss measurement in Guyana. Field test report. Rome.


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    Findings from the field test on estimating harvest and post-harvest losses of fruits and vegetables in Mexico. Field test report
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    This technical report provides findings of field test conducted in identified states/districts/ municipalities/study area in Mexico on the basis of sampling methodology for estimation of post-harvest losses of horticultural crops (fruits and vegetables) developed by the team led by Dr. Tauqueer Ahmad, Head, Division of Sample Surveys, Indian Agricultural Statistics Research Institute, Institute of Indian Council of Agricultural Research (ICAR-IASRI) ICAR-IASRI, New Delhi, India. The Technical Report entitled “Findings from the field test conducted on estimating post-harvest losses of fruits and vegetables in Mexico” contains details of findings of the developed methodology implemented in Mexico, including challenges encountered and lessons learnt. It is expected that this report will help the users from different countries in designing surveys for measurement of post-harvest losses of horticultural crops (fruits and vegetables).
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    Estimating harvest and post-harvest losses in Zambia. Meat and milk. Field test report
    2020
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    This technical report provides findings of a field test conducted in identified districts / study area in Zambia on the basis of sampling methodology for estimation of harvest and post-harvest losses of animal products (meat and milk) developed by the team led by Dr. Tauqueer Ahmad, Head, Division of Sample Surveys, Indian Agricultural Statistics Research Institute, Institute of Indian Council of Agricultural Research (ICAR-IASRI), New Delhi, India. The Technical Report entitled “Findings from the field test conducted on estimating harvest and post-harvest losses in Zambia. Meat and milk” contains details of findings of the developed methodology implemented in Zambia including challenges encountered and lessons learnt. It is expected that this report will help the users from different countries in designing surveys for measurement of harvest and post-harvest losses of animal products (meat and milk).
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    Estimation of crop harvest and post-harvest losses in Malawi. Maize, rice and groundnuts. Field test report
    2020
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    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.

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