<?xml version='1.0' encoding='UTF-8'?>
<codeBook version="1.2.2" ID="RWA_2012_RSAS_v01_M_v01_A_OCS" xml-lang="en" xmlns="http://www.icpsr.umich.edu/DDI" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.icpsr.umich.edu/DDI http://www.icpsr.umich.edu/DDI/Version1-2-2.xsd">
  <docDscr>
    <citation>
      <titlStmt>
        <titl>
          RWA-NISR-RSAS-2013-V1
        </titl>
        <IDNo>
          DDI_RWA_2012_RSAS_v01_M_v01_A_OCS_FAO
        </IDNo>
      </titlStmt>
      <prodStmt>
        <producer abbr="NISR" affiliation="Ministry of Finance and Economic Planning" role="Documentation of the study">
          National Institute of Statistics of Rwanda
        </producer>
        <producer/>
        <software version="4.0.10" date="2018-05-02">
          Nesstar Publisher
        </software>
      </prodStmt>
      <verStmt>
        <version>
          Version 01 (November 2019). This version is identical to FAO Version (RWA_2013_RSAS_v01_EN_M_v01_A_OCS_v01) except for the following fields that were edited by the World Bank: sampling procedure, questionnaires, notes on data collection, supervision, data entry and editing, other forms of data appraisal, and the citation requirement. Additionally the year of the ID was changed to 2012 to reflect the year data collection started.
        </version>
      </verStmt>
      <holdings URI="http://microdata.statistics.gov.rw/"/>
    </citation>
  </docDscr>
  <stdyDscr>
    <citation>
      <titlStmt>
        <titl>
          Seasonal Agriculture Survey 2013
        </titl>
        <altTitl>
          RSAS 2013
        </altTitl>
        <IDNo>
          RWA_2012_RSAS_v01_M_v01_A_OCS
        </IDNo>
      </titlStmt>
      <rspStmt>
        <AuthEnty affiliation="Ministry of Finance and Economic Planning">
          National Institute of Statistics of Rwanda
        </AuthEnty>
        <othId role="Survey campaign on mobilisation" affiliation="Ministry of Local Governance">
          <p>
            Local Government
          </p>
        </othId>
      </rspStmt>
      <prodStmt>
        <producer abbr="MINAGRI" affiliation="Government of Rwanda" role="Technical partner">
          Ministry of Agriculture and Animal Resources
        </producer>
        <producer abbr="NAEB" affiliation="Government of Rwanda" role="Technical partner">
          National Agriculture Export Board
        </producer>
        <producer abbr="RAB" affiliation="Government of Rwanda" role="Technical partner">
          Rwanda Agricultural Board
        </producer>
        <producer abbr="RNRA" affiliation="Government of Rwanda" role="Technical partner">
          Rwanda Natural Resources Authority
        </producer>
        <producer abbr="REMA" affiliation="Government of Rwanda" role="Technical partner">
          Rwanda Environmental Management Authority
        </producer>
        <producer abbr="BNR" affiliation="Government of Rwanda" role="Technical partner">
          National Bank of Rwanda
        </producer>
        <copyright>
          (c)2015, National Institute of Statistics of Rwanda
        </copyright>
        <software version="4.0.10" date="2018-05-02">
          Nesstar Publisher
        </software>
        <fundAg abbr="GoR" role="Funder">
          The Government of Rwanda
        </fundAg>
        <fundAg abbr="WB" role="Funding partner">
          The World Bank
        </fundAg>
        <fundAg role="Funding partner">
          Uk aid
        </fundAg>
        <fundAg abbr="EU" role="Funding partner">
          European Union
        </fundAg>
      </prodStmt>
      <distStmt>
        <contact affiliation="NISR" URI="www.statistics.gov.rw" email="rwanda.nada@statistics.gov.rw">
          Data Portals Management Officer
        </contact>
        <contact affiliation="NISR" URI="www.statistics.gov.rw" email="info@statistics.gov.rw">
          Public Relations Officer
        </contact>
      </distStmt>
      <serStmt>
        <serName>
          Agricultural Survey [ag/oth]
        </serName>
        <serInfo>
          <![CDATA[The Rwanda Seasonal Agriculture Survey (RSAS) is a study conducted annually by the National Institute of Statistics of Rwanda (NISR) from November to September to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda.

The RSAS 2013 covered three agricultural seasons: 
- Agricultural Season A: starts in September of one calendar year and ends in February of the following calendar year; 
- Agricultural Season B: starts in March and ends in July of the same calendar year; and 
- Agricultural Season C starts in August and ends with September of the same calendar year.]]>
        </serInfo>
      </serStmt>
      <verStmt>
        <version date="2015-06-26">
          <![CDATA[- v01:  Edited anonymized dataset for public use]]>
        </version>
      </verStmt>
    </citation>
    <stdyInfo>
      <abstract>
        <![CDATA[The main objective of the new agricultural statistics program is to provide timely, accurate, credible and comprehensive agricultural statistics that would not only describe the structure of agriculture in Rwanda in terms of land use, crop production and livestock and can be used for food and agriculture policy formulation and planning; but also which can be used for the compilation of national accounts statistics.  

In this regard, the National Institute of Statistics of Rwanda (NISR) conducted the Seasonal Agriculture Survey (SAS) from November 2012 to September 2013 to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda, including the Economic Development and Poverty Reduction Strategy (EDPRS), the Millennium Development Goals (MDGs) and Vision 2020. This 2013 RSAS covered three agricultural seasons (A, B and C) and provides data on background characteristics of the agricultural operators, farm characteristics (area, yield and production), agricultural practices, agricultural equipment, use of crop production by agricultural operators and by large scale farmers.]]>
      </abstract>
      <sumDscr>
        <collDate date="2012-11" event="start" cycle="Season A"/>
        <collDate date="2013-02" event="end" cycle="Season A"/>
        <collDate date="2013-03" event="start" cycle="Season B"/>
        <collDate date="2013-07" event="end" cycle="Season B"/>
        <collDate date="2013-08" event="start" cycle="Season C"/>
        <collDate date="2013-09" event="end" cycle="Season C"/>
        <nation abbr="RWA">
          Rwanda
        </nation>
        <geogCover>
          National coverage
        </geogCover>
        <anlyUnit>
          Agricultural holdings
        </anlyUnit>
        <universe>
          The RSAS 2013 targeted agricultural operators and large scale farmers operating in Rwanda.
        </universe>
        <dataKind>
          Sample survey data [ssd]
        </dataKind>
      </sumDscr>
      <notes>
        The scope of 2013 Rwanda Seasonal Agriculture Survey (RSAS) concerned demographic and social characteristics of agricultural operators and large scale farmers, as well as, their farm characteristics (area, yield and production; agricultural practices; small agricultural equipment; and use of crop production).
      </notes>
    </stdyInfo>
    <method>
      <dataColl>
        <dataCollector abbr="NISR" affiliation="Ministry of Finance and Economic Planning">
          National Institute of Statistics of Rwanda
        </dataCollector>
        <sampProc>
          <![CDATA[The sample was composed of two categories of respondents: Agricultural Operators and Large Scale Farmers (LSF). For the category of Agricultural Operators, the 2013 RSAS benefited from a dual frame sampling design called Multiple Frame Survey (MFS). For the category of LSF, everyone has been enumerated.

The 2013 Rwanda Seasonal Agricultural Survey (RSAS) used imagery from Rwanda Natural Resources Authority (RNRA) with a very high resolution of 25 centimeters. During the construction of the area sampling frame, the entire land area of Rwanda was subdivided into 10 non-overlapping land-use strata defined by proportion of cultivated land or other land-use characteristics. Three of these strata were chosen to be used for the survey since they were composed of agricultural land. Thereafter, agricultural land strata were delineated into segments within the Primary Sampling Units (PSU) with identifiable physical boundaries. For agricultural operators, the segments were the Second Stage Sampling (SSU) units.

The 2013 RSAS covered 327 segments, spread throughout the country during the two main agricultural seasons (A and B) and 251 segments during the Season C both in mountains and marshlands areas. 

The survey covered 15,441 Agricultural Operators and 562 LSF in season A; 15,730 Agricultural Operators and 503 LSF in Season B; and 1,412 Agricultural Operators in Season C. In Season C, LSF were not covered. Each selected PSU having a size of 200 - 400 hectares was subdivided into Second Stage Sampling Units (SSUs) of around 20 hectares each, following natural boundaries. Note that for stratum 3 PSUs, a segment had a size of around 50 hectares. In every selected PSU, one SSU (or segment) was randomly selected for data collection purposes. 

It is important to note that in each of agricultural season A and B, data collection was undertaken in two phases. Phase I was mainly used to collect data on demographic and social characteristics of interviewees, area under crops, crops planted, rainfall, livestock, etc. Phase II was mainly devoted to the collection of data on yield and production of crops. 

Regarding the selection of respondents, from the list of LSF in Phase I of Season A or Season B, all 562 LSF were enumerated. The LSF were engaged in either Crop farming activities only or Livestock farming activities only or both Crop and Livestock farming activities. For Agricultural Operators (being the Small Scale Farmers within the segment), every selected segment was firstly screened using the screening form. That means enumerators accounted for every plot inside the segment. All tracts were either agricultural (cultivated land and fallow land) or non-agricultural land (water, forests, roads, rocky and bare soils and buildings). 

During Phase I, a complete enumeration of all farmers having agricultural land and operating within the selected segment was undertaken by using a farm questionnaire. In Phase II, 25% of the Agricultural Operators undertaking either Crop farming activities only or both Crop and Livestock farming were selected and interviewed using a farm questionnaire for this phase. In Phase II, Season A, a sample of 1,799 Agricultural Operators was selected using the method of Probability Proportional to Size (PPS) in each district. 

In Phase II, Season B, a sample of 1,941 Agricultural Operators was selected as follows: 
a) 1,545 Agricultural Operators were selected of which 1500 were from Strata 1 and 2 selected at district level, and 45 Agricultural Operators were from Stratum 3 selected at country level (they were mainly from Nyagatare, Gatsibo and Kayonza districts). Again the PPS method was used, area under crops being the measure of size in each district; also
b) 500 Agricultural Operators were selected using area under crops in each district. Due to the previous selection explained in a) above only 396 were retained due to the removal of duplicates. This second sample gave weight to major crops and thus increased representativeness of crop yield in the districts.      

In Season C, a screening form was used to undertake a complete enumeration and account for every plot inside the segment on which land use was taking place. From a list of Agricultural Operators having agricultural land and cultivating Season C Crops:
a) A 10% sample of operators in Marshlands was selected for data collection that combined farm inputs, expenditure and production questions;
b) A complete enumeration in Mountain sites was undertaken for data collection that combined farm inputs, expenditure and production questions.]]>
        </sampProc>
        <collMode>
          Face-to-face paper [f2f]
        </collMode>
        <resInstru>
          <![CDATA[There were two types of questionnaires used for this survey namely screening questionnaire and farm questionnaires. A screening questionnaire was used to collect information that enabled identification of an Agricultural Operator or Large Scale Farmer and his or her land use.

The following were the two farm questionnaires administered:
a) Phase I Farm Questionnaire was used to collect data on characteristics of Agricultural Operators, crop identification and area, inputs (seeds, fertilizers, labor, …) for Agricultural Operators and large scale farmers.
b) Phase 2 Farm Questionnaire was used in the collection of data on crop production and use of production. 

It is important to mention thatl these Farm Questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted  by the team leader to check if the questionnaires had been well completed by the enumerators. For season C, after  screening, an  interview was conducted for each selected tract/Agricultural Operator using one consolidated farm questionnaire.

Questionnares are provided as external resources.]]>
        </resInstru>
        <sources/>
        <collSitu>
          <![CDATA[The survey used 120 enumerators in 40 field teams and 43 team leaders giving a ratio of one team leader to 3 enumerators. All field work staff in 2013 SAS had a degree in Agronomy Science and were trained before the start of data collection. A higher level supervision staff from NISR and MINAGRI visited the field teams during each phase of data collection to ensure quality control. 

Enumerators and team leaders had adequate materials composed of enumerator's instruction manual, screening questionnaire, farm questionnaires, measuring tapes, ruler, pens, pencils, calculator, weighing scales, global positioning system (GPS), personal data system (PDA), maps, rain coats, boots, umbrella, first aid equipment, etc. Each team was assigned a vehicle. 

Before proceeding to the field, enumerators and their team leaders checked that they had all necessary materials required for their field work. All staff were required to arrive early in the field (segment or LSF). The next step was the segment delineation and to take geographical coordinates of the identified landmarks to allow supervisors to check if the segment was delineated appropriately and to ensure the collected data was related to the plots inside the segment or LSF.]]>
        </collSitu>
        <actMin>
          <![CDATA[The survey used 120 enumerators organised around 40 field teams and 40 team leaders giving a ratio of 1 team leader to 3 enumerators.

At the bottom of the hierarchy, there are enumerators who would be assisted by a team leader also known as a controller. His/ her main function is to introduce the enumerators to the various key people from the sector to the villages leaders up to operators in the Secondary Sampling Unit (known as the segment), and assist enumerators during the entire course of the survey.

A higher level supervision staff from NISR and MINAGRI visited the field teams during each phase of data collection to ensure quality control. The responsibility of a team leader include managing the interviewers to ensure successful completion and quality of data collected in a given time period for the fieldwork. He/she was expected to record information about the fieldwork by completing the fieldwork forms, which track the status of completion of the work in the field, document problems in the field and solutions taken to resolve these problems, and track the data entry process. 

Specifically, his/her tasks included: 
1. Introduce the survey and interviewers at the local level where the survey is administered. 
2. Review questionnaires and check that it has been correctly filled in. 
3. Monitor and attend some interviews and make comments on the worker's performance. 
4. Meet frequently with each member of the group to discuss, improve and organize work. 
5. Check the availability of all the necessary items before going into the field. 
6. Help workers solve the problems they encounter in dealing with respondents who are not responsive to questions or refuse to be interviewed. 
7. Manage the team's work schedule, including tracking questionnaires completed in the field, questionnaires assigned to the data entry team, and questionnaires that require correction by interviewers. 
8. Make sure all the big farmers are identified and surveyed. 
9. Communicate with NISR/MINAGRI staff, regarding field issues, as necessary. 
10. He/she was responsible for helping the interviewers identify the segments and tracts that have been allocated to them, resolving any problems with reluctant operators, observing interviews, and making checks by visiting the operators after the survey to verify data.]]>
        </actMin>
        <weight>
          The sample weights were calculated for each district considering the total number of segments in each district and the sample size in the specific districts.
        </weight>
        <cleanOps>
          Data editing took place at different stages. First, the filled questionnaires were repatriated at NISR for office editing and coding before data entry started. Data entry of the completed and checked questionnaires was undertaken at NISR offices by 20 trained staff members using CSPro software. To ensure appropriate matching of data in questionnaires and plot area measurements from the GIS unit, a LOOKUP file was integrated in the CSPro data entry program to confirm the identification of each Agricultural Operator/LSF before starting data entry. Thereafter, data was entered in computers, edited and summarized in tables using SPSS software.
        </cleanOps>
      </dataColl>
      <anlyInfo>
        <respRate>
          The response rate for the Seasonal Agriculture Survey is 98%.
        </respRate>
        <dataAppr>
          All farm questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been completed successfully by enumerators. Additionally, in most cases, questionnaires completed by one enumerator were peer-reviewed by another enumerator before being checked by the team leader.
        </dataAppr>
      </anlyInfo>
    </method>
    <dataAccs>
      <useStmt>
        <confDec required="yes">
          This is edited data file for public use.
        </confDec>
        <contact affiliation="Ministry of Finance and Economic Planning" URI="http://microdata.statistics.gov.rw/" email="info@statistics.gov.rw">
          National Institute of Statistics of Rwanda
        </contact>
        <citReq>
          <![CDATA[Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download.

Example:

National Institute of Statistics of Rwanda. Rwanda Seasonal Agriculture Survey 2013. Ref. RWA_2012_RSAS_v01_M_v01_A_OCS. Dataset downloaded from [url] on [date].]]>
        </citReq>
        <conditions>
          To access this statistical data, the user agrees to NISR microdata access terms and conditions.
        </conditions>
      </useStmt>
    </dataAccs>
  </stdyDscr>
  <dataDscr/>
</codeBook>
