Related items
Showing items related by metadata.
-
Book (series)Assessment and collection of data on post - harvested foodgrain losses 1992
Also available in:
No results found.The manual is intended to serve as a guide to the statistical methodology for assessing and collecting data on post-havest foodgrain losses. It should be useful to countries which plan to lunch foodgrain losses. It should be useful to those countries which plan to lunch foodgrain losses reduction programmes but find themselves seriously handicapped because of lack of basic data. -
Brochure, flyer, fact-sheetNear real-time data collection for pests and diseases outbreak
Part of the Laos Climate Services for Agriculture, LaCSA
2022Also available in:
The leaflet presents the tool used in the Laos Climate Services for Agriculture (LaCSA) to collect pest and disease data in near-real time. The real-time pest and disease outbreak data are collected by PPC since many years. Under the collaboration with FAO, the data are now used as part of the LaCSA to produce climate services for farmers at weekly and monthly intervals. Near real-time pests and diseases input forms and incidence reports are also presented. In addition, the leaflet also explains how the new tool helps with strategic pests and disease planning and outbreak management, to improve the capacities of farmers at the local level, and to leverage proactive farming practices. Finally, the leaflet explains how government entities and national and local NGOs who work in agriculture promotion can use this information for capacity building and rural development programs. -
ArticleCombining farm and household surveys with modelling approaches to improve post-harvest loss estimates and reduce data collection costs 2022
Also available in:
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.
Users also downloaded
Showing related downloaded files
No results found.