NAM_2018_PHL_v01_M_v01_A_OCS
Post Harvest Losses 2018
Pilot Survey
Name | Country code |
---|---|
Namibia | NAM |
Agricultural Survey [ag/oth]
National Statistical Agency (NSA) of Namibia received technical assistance from the Global Strategy for improving Agricultural and Rural Statistics (GSARS) to measure post harvest losses for 2 crops: Maize and Millet. This is a pilot survey conducted in 2018.
Sample survey data [ssd]
Households
The scope of the survey include:
Regional coverage
Agricultural households in the Kavango West region
Name | Affiliation |
---|---|
Global Strategy for Improving Agricultural and Rural Statistics | Food and Agricultural Organization |
Namibia Statistics Agency | Government of Namibia |
Name | Affiliation | Role |
---|---|---|
Ministry of Agriculture, Water and Forestry | Collaborator | |
Agro-Marketing and Trade Agency | Collaborator | |
Global Strategy for improving Agricultural and Rural Statistics | FAO | Technical Assistance |
African Development Bank | Technical Assistance |
The PHL pilot study mainly followed the National Census of Agriculture (NCA) 2013/14 methodology. The NCA 2013/14 used a stratified two stage cluster sample design. At the first stage, primary sampling units (PSUs) were selected with Probability Proportional to Size (PPS) from the sampling frame based on the Enumeration Areas of 2011 Population and Housing Census. The size measure of a PSU in the sampling frame was the number of agricultural households which was derived from the questions included in 2011 Population and Housing Census as per the FAO recommendations.
The list of agricultural households was prepared through the listing process within a selected PSU to compile the sampling frame for agricultural households which was selected systematically.
A third stage of sampling was also conducted to select plots which contained the two main crops, maize, and millet for objective measurement as described below.
A list of plots planted with maize or millet in each sampled PSU was created. Then, one plot was randomly selected from the two main crops of the holder. An area was then marked within the selected plot according to the FAO guidelines and the matured crop inside this marked area was cut and weighed when the crop was wet and dry.
Crop cutting enable estimation of the yield of a crop and the losses during harvesting, threshing/shelling, and cleaning/winnowing. This was done through processing the produce of sub-plots in selected fields. Interviewers did the crop cutting manually according to the techniques used by the farmer. After the manual harvesting was done, the second team of supervisors entered the field and collected all fallen ears/cobs, grains and weighed them after which the information was recorded. These figures are used to estimate the average yields of each of the crops.
The weight was calculated based on the sampling design; with the application of a 2 stage sampling weight calculation.
Start | End |
---|---|
2018-05-28 | 2018-08-30 |
The dataset received by the Office of Chief Statistician (OCS) team was already cleaned by Aliou Mballo directly with NSA. During the cleaning process, all direct identifiers were removed. Furthermore, the declaration, phyiscal measurement, and storage data for the second crops, were transposed from wide to long. So instead of the farmer declaration variables of the second crop captured by the variables titled from “D6” to “D10-6” in the questionnaire being in their own columns, there is a second row in the dataset containing data from sections C, D, E and G containing data for the second crop, spread across columns “crop_code” to “D5-6”. The same logic applies to the physical measurements and storage data.
The sections CDEG dataset contains data for some crops which do not correspond to records in the Section C dataset on agricultural practices. This is due to a mistake amongst some enumerators which filled in directly Section D for some crops and skipped agricultural practices. This is especially prevelant for measurement data for maize. The data from the lab was not received in time for the project deadline. Accordingly, section “H_Storage_Lab” from the questionnaire was not available to be included in the dataset.
Is signing of a confidentiality declaration required? | Confidentiality declaration text |
---|---|
yes | The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO. |
Micro datasets disseminated by FAO shall only be allowed for research and statistical purposes. Any user which requests access working for a commercial company will not be granted access to any micro dataset regardless of their specified purpose. Users requesting access to any datasets must agree to the following minimal conditions:
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Name | Affiliation | |
---|---|---|
Aliou Mballo | Food and Agriculture Organization | Aliou.Mballo@fao.org |
DDI_NAM_2018_PHL_v01_M_v01_A_OCS
Name | Affiliation | Role |
---|---|---|
Office of the Chief Statistician | Food and Agricultural Organization | Metadata Producer |
Development Economics Data Group | The World Bank | Metadata adapted for World Bank Microdata Library |
2023-01-31
Version 01 (January 2023): This metadata was downloaded from the FAO website (https://microdata.fao.org/index.php/catalog) and it is identical to FAO version (NAM_2018_PHL_v01_EN_M_v01_A_OCS). The following two metadata fields were edited - Document ID and Survey ID.
This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here.