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Book (stand-alone)Guidelines on the measurement of harvest and post-harvest losses
Findings from the field test on estimating harvest and post-harvest losses of fruits and vegetables in Mexico. Field test report
2020Also available in:
No results found.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). -
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. -
ProjectRwanda Post-Harvest Management Strategy - TCP/RWA/3801 2024
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No results found.The agriculture sector is the backbone of Rwanda’s economy, contributing over 30 percent to the country’s gross domestic product, generating over 70 percent of total export earnings and accounting for over 70 percent of employment. The transformation and commercialization of the sector are key to achieving national development objectives, as well as to ensuring resilient livelihoods, food security and nutrition in the country. However, the process of transforming subsistence-dominated agriculture to productive high-value and market-oriented farming is faced by several challenges. These include high post-harvest losses (PHL), a lack of appropriate and affordable technologies for smallholder farmers, inadequate access to markets, fragmented and discontinued post-harvest management (PHM) interventions, limited access to finance, a lack of rural infrastructure, and high production and transport costs.
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