The experience of Lesotho is described in Sephoko (2013). The Bureau of Statistics conducts annual agricultural production surveys and a sample census every 10 years. Data are collected for both rural and urban domains. Most of the agriculture in Lesotho is subsistence, with minimal commercial farming.
Lesotho has integrated non-agricultural and agricultural surveys that share the same MSF. The MSF becomes the basis for the sample selection for all surveys conducted by the Bureau of Statistics. The current MSF was constructed from the 2006 census of Population and Housing. Census EAs or groups of EAs were the PSUs for the first stage of sampling. The EAs are well-defined and cover the country’s total area, with no duplication or overlapping of units; hence, complete coverage is ensured. All PSUs and EAs are geo-referenced. The PSUs are stratified by major district and urban/rural.
PSUs are selected with probability proportionate to numbers of households. All households in the PSUs selected are listed and classified as urban or rural, and with categories such as “operating at least one field” or “presence of livestock”. One finding is that household characteristics change over time, which means that the PSU listings must be updated every two-three years.
The annual agricultural survey also provides estimations of crop yields; a maximum of fifteen fields under each principal crop are selected, with equal probability for each crop.
The selected PSUs are also subsampled for the Continuous Multi-Purpose Household Survey, which is conducted in parallel with the annual agricultural survey and with the same demographic information collected from both surveys. The annual agricultural survey provides estimates of crop yields after harvest. However, there is a need for early season forecasts. These crop forecasts are compiled by the Division of Agriculture and Food Security by the end of May each year, using subjective methods. Efforts are under way to implement an area frame to improve area estimates under each crop and forecast yield.
In summary, the Lesotho master frame developed using census EAs could become an area frame if the geo-referenced PSUs were overlaid onto a land cover map database.
8. RWANDA
Rwanda has conducted the Seasonal Agricultural Surveys (SAS) Programme based on probability sampling and estimation methods since 2013. The agricultural surveys implemented are based on Multiple Frame agricultural Surveys (MFS) that consist of an area sample survey combined with data from a list of special farms. The MFS takes into account the country’s relative advantages and the constraints (mainly in terms of permanent specialized staff, training and resources).
Rwanda had a wealth of sound materials which could serve as the basis to establish the SAS Programme, including excellent cartography material – in particular, two sets of digital cartography (ortho-photos with a resolution of 2 m and ortho-photos with a resolution of 25 cm) that would allow for the measurement of areas on the photos. Also, the availability of computer programs and instruments such as GISs, PDAs and GPS, satellite imagery, and powerful software for data entry, processing, analysis and dissemination provided valuable inputs. Another characteristic of Rwanda was the country’s relatively small total agricultural area.
The multiple frame sampling methods applied combined a sample of segments, selected from an area frame, with a complementary short list of special farms. The multiple frame estimates combine estimates from the area sample with estimates obtained from the list of special farms.
The area sample design consisted of a stratified probability sample of segments, with a replicated selection procedure. From the improved stratification, the total land of Rwanda was subdivided into 12 non-overlapping strata.
Among the 12 strata, only five were sampled, covering 17,596.20 km2; the other strata did not contain information that was relevant to the survey program. 84 percent of the intensive agriculture is found in the first and second strata. These are key strata for the purposes of area frame construction and sample selection.
The strata, PSUs, zones, and sample segments have identifiable physical boundaries (roads, paths, rivers, etc.) that can be located both in the field and on the cartographic materials used for their identification. For 2014, Seasons A, B, and C, the PSUs were delineated to have a total size between 100 and 200 hectares.
The sample design has segments of equal target size in each stratum. As a result of experience in data collection, it was concluded that segments of approximately 10 hectares in the sampling universe should be delineated (originally, segments of 20 hectares were constructed). This was done to reduce the size of the cluster in the survey design. However, for the Rangelands, due to the lack of physical boundaries for small areas, the segments selected are of 50 hectares.
The number of sample segments is determined by a large number of factors, e.g. the resources available, the precision of data required and the enumerator’s workload, and the required frequency of data collection. Five field data collection operations must be conducted for each agricultural year, to cover area and the yield of three seasons. Therefore, in view of the experience gained, it has been also concluded that the largest possible sample size should be less than 600 segments. Based on previous work, the total sample size was determined to be n = 540 segments. The complementary list of special farms ensures the inclusion of farms that make a significant contribution to the total estimate of certain important survey variables.
The special farms were defined as follows: growing crops on at least 10 hectares of land or any farmer raising 70 or more cattle, 350 goats and sheep, 140 pigs, 1,500 chicken or managing 50 beehives. The list of special farms is updated once a year and the updated list used for Season A. Then a total of 499 special farms was considered for the Phase 1 survey for survey season A; of these, only 20 had intersections with sample segments. The special farms are
treated separately in the survey design, because the data from listed farms is at the farm level, while the data from the farms selected in area frame segments is for the farm tract (land within the segment).
The multiple frame methods are considered to result in greater precision of the estimates of agricultural areas, main crop areas and other key variables of all multiple-purpose agricultural surveys, since the area sample component involves a practical procedure for the objective measurement of agricultural areas on the GIS. In addition, the area sample component may provide the means for selecting probability samples of fields necessary for the yield surveys that provide objective crop production and crop forecasting estimates.