CURRENT AND FUTURE ACTIVITIES TO IMPROVE STRATIFICATION FOR SEASONAL AGRICULTURE SURVEYS IN RWANDA

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1 CURRENT AND FUTURE ACTIVITIES TO IMPROVE STRATIFICATION FOR SEASONAL AGRICULTURE SURVEYS IN RWANDA Roselyne Ishimwe 1 *, Sebastian Manzi 1 National Institute of Statistics of Rwanda (NISR) * Corresponding author: ishiroste@gmail.com, During the last decade, Rwanda has impressively achieved a lot in poverty reduction, and agriculture sector played an important role. To continue the effort to eradicate poverty and hunger, policy makers and planners will continuously need accurate and timely statistical data from the agriculture sector. Since November 2012, the Government of Rwanda through the National Institute of Statistics of Rwanda (NISR) conducted the Seasonal Agriculture Survey (SAS) to collect up-to-date information for monitoring progress on agriculture programs and policies in Rwanda. Seasonal Agricultural Surveys (SAS) applies the Multiple Frame Survey design that uses a probability sample of segments selected from an area sampling frame. In Rwanda, area frame is constructed using ortho-photos images since its spatial resolution is enough to allow stratification as well as subsequent subdivision of strata into Primary Sampling Units (PSUs), which has recognizable permanent physical boundaries. Currently, strata are stratified using on-screen digitization of GIS software (ArcGIS). Land cover stratification provides for good estimates of cultivated areas. There are about 12 nonoverlapping land-use strata that are stratified based on the proportion of cultivated land, pre-dominance of certain crops, special agricultural practices, agrourban areas, physical factors or other land use characteristics. This paper describes present land use stratification currently in use as a base for sampling in seasonal agriculture surveys and identifies future needed improvements. KEY WORDS: Area frame, Geographic Information System, Primary Sampling Unit, Stratification. I. INTRODUCTION During the last decades, Rwanda has impressively achieved a lot in poverty reduction. This is attributed to the important role the agriculture sector played (NISR, 2013). To continue the effort to eradicate poverty and hunger, policy makers and planners will continuously need accurate and timely statistical data from the agricultural sector. In recognition of above achievements, the government of Rwanda through National Institute of Statistics of Rwanda (NISR) has been developing, using and analyzing area sampling frames as a vehicle for conducting agriculture surveys to gather information regarding crop acreage, cost of production, farm expenditures, crop yield and production, livestock inventories and other agricultural items. The area frame methods was designed in 2013 agricultural year, and has been in use ever since. An area frame for a land area such as a state or country consists of a collection or listing of all parcels of land for the area of interest (Cotter and Nealson, 1987). These land parcels can be defined based on factors such as ownership or based simply on easily identifiable boundaries as is done by NISR. The final stage-sampling units of an area frame are land areas called segments, which should not overlap and must cover the entire survey area. Area frames are critical to producing quality estimates, since they provide complete coverage with all land areas being represented in a probability survey with a known chance of selection (Cotter and Tomczak, 1994). This frame does not become outdated rapidly over time unless agriculture activities extend into areas not covered by the frame (FAO, 1996). Area frame construction involves many steps such as stratification, multi-step sampling, data collection and data analysis, which have been developed to provide statistical and cost effective data. This paper will briefly describe the stratification procedures specifically (strata definition, construction of primary and secondary sampling units, and digitization process) used to develop and sample area frame, which is now operational for Seasonal Agricultural Surveys (SAS) in Rwanda. This will be followed by a description of future needed improvements in stratification process. II. STRATIFICATION II.1 Materials used

2 The stratification process was developed for the entire country. Materials used in the process include: National Aerial Photography (Ortho Photo Images): Ortho photo images taken in 2008 are the primary stratification tool as it is currently used for boundary identification. These images cover the whole country and have a very high resolution of 25 centimeters which makes ground features identifiable from image by visual interpretation. Ancillary Data: These include national roads, forest coverage and national parks data. They were used during the frame construction process as additional layers to assist in isolating cultivated areas. II.2 land-use stratification Theoretically, Land-use stratification is the delineation of land areas into land-use categories whereby land-use strata are defined by the proportion of cultivated land, predominance of certain crops, special agricultural practices, average size of cultivated fields, agro-urban areas, or other land use characteristics (Cotter and Nealson, 1987). Although stratification is not efficient in estimating rare items or items concentrated in small geographic areas, ad-hoc stratification enables the provision of good estimates of crop areas. GIS tools are used in the whole process of land use stratification. The purpose of stratification is to reduce sampling variability by creating homogeneous groups of sampling units. Although certain parts of this process are highly subjective in nature, precision work is required of the personnel who are doing onscreen digitization of the aerial images to ensure that overlaps and omissions of land areas do not occur and that land is correctly stratified. During the stratification process, stratifiers divide the land into PSUs using identifiable physical boundaries (roads and rivers), then assigns them to a land-use strata.perhaps the most important concept conveyed during initial training of personnel is the idea of using quality physical boundaries. A quality boundary is a permanent or, at least, long-lasting geographic feature which is easily found and identifiable (Cotter and Tomczak, 1994). In Rwanda roads and rivers are commonly used during the stratification process as quality permanent boundaries. Rwanda is subdivided into 12 non-overlapping land-use strata. Table 1 presents the land-use stratification scheme generally followed along with the codes used during the stratification process. TABLE 1: LAND USE STRATIFICATION SCHEME Stratum Definition 1.1 Potential agriculture land (Season A & B) 1.2 Potential Agriculture land Season (A, B plus C) 2.1 Marshlands for other crops 2.2 Marshland potential for rice 3.0 Rangeland 4.0 Non crop land (Bare soil) 5.0 Urban area (Cities and towns) 6.0 Water (lakes and water bodies) 7.0 National Parks 8.0 Uncultivated marshlands 9.0 Forest 10.0 Tea plantation A typical SAS area frame employs one or more strata for land in intensive and/or extensive agriculture including marshlands for rice and other crops (stratum 1.1; 1.2, 2.2 and 2.1) and range land (3.0). Less frequently an area frame contains "crop specific" strata. This may occur due to crop intensification program where a high percentage of land in a particular district or a sector is dedicated to the production of a specific type of crop, such as rice in Rusizi and Gisagara Districts. Furthermore, each area frame uses non crop land (bare soil) and urban areas (Cities and towns) stratum (more than 50 homes per square meters). In addition, large bodies of water are separated into a water stratum plus national parks stratum and uncultivated marshlands stratum as well as forest stratum. Finally, there is a tea plantation stratum, SAS team decided to put tea in a separate stratum because it is a cash crop. This data are available in National Agricultural Export Development Board (NAEB). Nonetheless coffee is also a cash crop however it doesn t have its own defined strata because it is cultivated in small farms not in plantation therefore it is coved SAS.

3 Figure 1: Rwanda Land use stratification map Throughout the construction of primary and secondary sampling units (PSU s and SSU s), only strata 1.1; 1.2, 2.2; 2.1 and 3 were subjected to agricultural land sampling. Sample selection of SAS, was done in two stages as follows: a) In each Stratum, PSUs was selected using Probability Proportional to Size (PPS) sampling with area size between 180 ha and 200 ha. If for example stratum one is divided into large PSUs, sampling units of 20 hectares will be assigned to each PSU. But if a PSU had 200 hectares, it would be divided into eleven (20) sampling units of 10 hectares each. And if this PSU is selected, one of its 20 sampling units will be selected as the segment for data collection. b) For each selected PSU, one Second Stage Sampling unit (SSU) was selected; in this case segment was randomly selected. Once all of the PSUs in the country have been delineated and classified into strata, the PSU identification number was attached. The populations of PSU s were sampled by stratum, and the selected PSUs were further broken down into an average of 18 to 20 segments from which one segment was designated for data collection. Each segment has an average of 10 Ha (NISR, 2015). This way an entire frame need not be divided into segments, thus saving in labor costs.

4 Figure 2: Main steps in Area Frame Construction Distribution of Sampled Primary Sampling Units In the entire country, 540 PSUs were selected in the five main agricultural strata with probability proportionally to the size of each Stratum. Each selected PSU having a size of hectares was subdivided into Second Stage Sampling Units (SSUs) or segments of around 10 hectares each, following natural boundaries as explained earlier. Note that for Stratum 3 PSUs, a segment had a size of around 50 Hectares. As discussed earlier in each sampled PSU a segment is randomly selected thus 540 segments are sampled for SAS data collection. Table 2 below shows the distribution of SSUs in each of the five Strata. TABLE 2: SAMPLED SSU S BY STRATA Stratum Number of Selected SSUs

5 Figure 3: Location Map of sampled segments for data collection It is worth noting that a sample rotation scheme for segments is used in SAS to reduce respondent burden caused by repeated interviewing, avoid the expense of selecting a completely new area sample each year, and provide reliable measures of change in the production of agricultural commodities from year to year through the use of the ratio estimator. Therefore every three years a new area frame is constructed for SAS activities. III. FUTURE ACTIVITIES TO IMPROVE STRATIFICATION PROCESS FOR AGRICULTURE SURVEY III.1 Need for improvement in the stratification process As previously mentioned the stratification was basically based on the visual interpretation of the very high resolution aerial image taken in Humans are able to distinguish millions of colors, several shades of gray, and have a demonstrated ability to identify water, vegetation, and urban forms on several types of imagery however there are limited to distinguish small differences in color (Butler et al, 1988). If data are collected using 256 shades of gray, yet an analyst can only distinguish 8-10 (optimistically) of them, a great deal of information is potentially lost thus the interpreter is outpaced by the precision of the data. Computers, however, have no trouble distinguishing 256 shades of grayas each one is individually recognizable (Butler et al, 1988). An analyst using a computer has control over presentation of the data where data sets can be combined, compared, and contrasted with more ease and precision. In addition, since human interpretations are highly subjective, this implies they are not perfectly repeatable. Conversely, results generated by computer are usually repeatable. Lastly, when very large data are involved, the computer may be better suited to managing the large body of detailed data. Therefore there is need to process digital image processing in stratification as spectral signatures of the object under investigation (land use), which are rather confusing and cannot be easily distinguished by human visual interpretation only.

6 Furthermore, satellite images can be most conveniently used to delineate strata and primary survey units (PSUs) (Madana, 2002). The use of satellite images avoids the laborious and meticulous work involved in digitization, and allows for more precise area measurements therefore it constitutes a very important improvement and simplification of the stratification process. In addition the acquisition of current satellite images of a given large area is much cheaper than obtaining current aerial photos. Therefore with the cost efficiency of earth observation data approaches the free data policy that is being adopted for some medium high resolution images seems essential. Thus remote sensing application to agriculture statistics can be sustainable in the long term if their total cost can be budgeted without endangering the feasibility of surveys that cannot be substituted by satellite technology (Gallegoet al, 2010) which result in a great savings in time required for stratification. Figure 4: Figure 4: Low-level diagram of stratification process using satellite images As described by Carfagna and Gallego (2005) there are different ways to use remote sensing for agriculture statistics however at the beginning NISR is planning to use remote sensing techniques to update the current area frame (stratification, crop areas estimates and crop monitoring). High resolution satellite images will be used where supervised classification will be performed with the help of ground observation, secondary data (reclaimed wetlands, forest, land use master plan, agro ecological zones, Vision umurenge program (VUP) data) shall be collected from governmental and non governmental institutions and image characteristics. Visual interpretation and supervised classification of the satellite images will be treated as means of identifying different strata. All regions of the country have different cropping time therefore it is worth nothing that satellite image acquisition dates shall be based on the cropping season of the country as well as Rwanda agro ecological zones in order to avoid misclassification of some strata The steps followed are shown in Figure 4. The method of using satellite images processing techniques will reduce much effort involved in different steps of frame construction mainly the tremendous work involved in the digitalization, therefore strata and PSUs boundaries, and frame limits will be easily delineate by the stratifier; this will save cost and time of seasonal agricultural surveys. IV. Conclusion Considering the discussion of the above factors, it can be concluded that high-resolution satellite images will have good beneficial potential for improving stratification methods with respect to reduce the tremendous labor, cost and time that are actually used for stratification.

7 V. Acknowledgment The authors thank the National Institute of Statistics of Rwanda (NISR) and the government of Rwanda for providing all necessary materials and financial support to realize this project. VI. Reference Butler, MJA, Mouchot, MC, Barale, V & LeBlanc, C 1988, The Application of Remote Sensing Technology to Marine Fisheries: An Introductory Manual, FAO, Rome, Italy. Carfagna, E & Gallego, FJ 2005, 'Using Remote Sensing for Agricultural Statistics', International Statistical Review, vol 73, no. 3, pp Cotter, JJ & Nealson, J 1987, Area Frame design for agricultural surveys, National Agriculture Statistics Service, Washington, DC. Cotter, JJ & Tomaczak, CM 1994, 'An Image Analysis system to develop Area sampling Frames for Agricultural Surveys', Photogrammetric Engineering and Remote Sensing, pp FAO 1996, Multiple Frame Agricultural Surveys - Current surveys based on area and list sampling methods, FAO, Rome. FAO 2008, 'The State of Food and Agricultural Statistics Systems in Africa ', Food and Agriculture Organization of the United Nations Regional Office for Africa, Accra, Ghana. Gallego, J, Carfagna, E & Baruth, B 2010, 'Accuracy, Objectivity and Efficiency of Remote Sensing for Agricultural Statistics', in R Benedetti, M Bee, G Espa, F Piersimoni (eds.), Agricultural Survey Methods, John Wiley & Sons, LTD, Chichester, UK. Madana, MHBPH 2002, 'Improving Land Use Survey Method using High Resolution Satellite Imagery', ITC, The Netherland. National Institute of Statistics of Rwanda (NISR) 2015, 'Seasonal Agriculture Survey Report', Cotter, JJ & Tomaczak, MC 1994, 'An Image Analysis system to develop Area sampling Frames for Agricultural Surveys', Photogrammetric Engineering and Remote Sensing, vol 60, no. 3, pp