{"type":"survey","doc_desc":{"idno":"DDI_ LKA_2023_SLPFS_v01_M_v01_A_ESS_FAO","producers":[{"name":"Agrifood Economics and Policy Division","abbr":"ESA","affiliation":"Food and Agriculture Organization of the United Nations","role":"Metadata producer"},{"name":"Statistics Division","abbr":"ESS","affiliation":"Food and Agriculture Organization of the United Nations","role":"Metadata adapted for FAM"},{"name":"Development Data Group","abbr":"DECDG","affiliation":"World Bank Group","role":"Metadata adapted for World Bank Microdata Library"}],"version_statement":{"version":"Identical to a metadata (LKA_2023_SLPFS_v01_M_v01_A_ESS) published on FAO microdata repository (https:\/\/microdata.fao.org\/index.php\/catalog). Some of the metadata fields have been edited."}},"study_desc":{"title_statement":{"idno":"LKA_2023_SLPFS_v01_M_v01_A_ESS","title":"Paddy Farmers Survey 2023","alternate_title":"SLPFS 2023"},"authoring_entity":[{"name":"Agrifood Economics and Policy Division (ESA)","affiliation":"Food and Agriculture Organization of the United Nations"}],"production_statement":{"producers":[{"name":"Hector Kobbekaduwa","abbr":"","affiliation":"Agrarian Research and Training Institute (Sri Lanka)","role":"Data Collector"}]},"series_statement":{"series_name":"Agricultural Survey [ag\/oth]","series_info":"Panel data were collected through three surveys: (i) a baseline in October-November 2022, (ii) a midline in October-December 2023, and (iii) an endline in April-May 2024."},"study_info":{"abstract":"The Sri Lanka Paddy Farmers Survey 2022-2024 collects panel data on paddy farmers in Sri Lanka, covering comprehensive information on demographics, risk management strategies, agricultural production, market engagement, and food security.","coll_dates":[{"start":"2023-10","end":"2023-12","cycle":"Midline"}],"nation":[{"name":"Sri Lanka","abbreviation":"LKA"}],"geog_coverage":"Rural Areas in Sri Lanka","analysis_unit":"Households","data_kind":"Sample survey data [ssd]","notes":"- Household Characteristics: Identification and demographic details.\n- Food Insecurity\n- Farm Land Use and Management: Details on plot characteristics, land preparation, water management, and crop production.\n- Fertilizer Acquisition: Information on types and sources of fertilizers used.\n- Seed and Planting Material Acquisition: Data on seed types and sources.\n- Crop Sales: Details on the sale of crops produced.\n- Livestock, Poultry, and Fish Farming: Information on non-crop farming activities.\n- Off-Farm Income and Remittances: Data on household income from non-farming sources.\n- Loans\/Credit\/Insurance: Information on financial instruments used by households.\n- Agricultural Information and Services: Access to agricultural advice and services.\n- Household Assets and Dwelling Characteristics: Assessment of household wealth and living conditions."},"method":{"data_collection":{"data_collectors":[{"name":"Hector Kobbekaduwa Agrarian Research and Training Institute (Sri Lanka)","abbr":"","role":"","affiliation":""}],"sampling_procedure":"This survey includes 2200 paddy farmers enrolled in 220 farmer organizations in Sri Lanka.  The sample is nationally representative of the paddy farmers enrolled in the farmer organizations (more than 90% of the total paddy farmers in the country). The sampling strategy is a multi-stage stratified cluster sampling:\n- First Stage: The total of 25 districts in Sri Lanka are stratified based on agro-ecological zones and randomly sampled with a probability proportional to the share of land devoted to paddy fields over the total extent of the district.\n- Second Stage: Within each of the 10 sampled districts, farmer organizations are stratified based on irrigation scheme, and then 220 are randomly selected.\n- Third Stage: For each farmer organization, paddy farmers are randomly selected with the sampling weight proportional to the number of farmers in each farmer organization. The final stage produces 2200 households in total.","coll_mode":["Computer Assisted Personal Interview [capi]"],"research_instrument":"The data collection process for the Sri Lanka Paddy Farmers Survey 2022-2024 involved a comprehensive Household Survey Questionnaire, meticulously crafted by the Environmental and Water Resources Management Division of HARTI in collaboration with FAO. Drawing from previous surveys, the questionnaire was specifically tailored to meet the project's objectives. The design process included stakeholder consultations, ensuring that all relevant topics, such as household demographics, land use, crop management, and income, were comprehensively addressed.","sources":[{"name":"","origin":"","characteristics":""}],"coll_situation":"During the data collection for the survey on Sri Lanka Paddy Farmers Survey 2022-2024, enumerators underwent a thorough training process that included familiarization with the survey tools and methods. Interviews were conducted in a manner that emphasized consistency and neutrality, and on average, each interview took approximately 120 minutes. Supervisors played a critical role in ensuring data quality by reviewing completed questionnaires and addressing any issues encountered in the field. Corrective actions were taken when necessary, including the rejection of incomplete questionnaires and the need for re-interviews.","weight":"Sample weights were calculated for the household data to ensure representativeness and account for the sampling design. The following weighting variables are used:\n\n- First Stage Weight:\nStage 1 Probability = 10\/25\u00d7Total Districts in Each Zone\u00d7(Selected District's Paddy Area (km?^2))\/(Total Area (km?^2))\n\nHere, 10\/25\u00d7Total Districts in Each Zone  indicates that 40% of districts are selected within each agro-ecological zone. For instance, if a zone (e.g., Wet Zone) has 5 districts, 2 districts are selected (which is 40%). The selection of specific districts within each zone is weighted by the paddy cultivation area. This means that districts with larger paddy areas have a higher probability of being selected.\n\n- Second Stage Weight: \nStage 2 Probability = 25\/(Total FO in Each District)\u00d7Total FO in Each Strata in Each District\u00d7(Members in Sampled FO)\/(Total Members in FO within District)\u00d722\/25\n\nHere, 25\/(Total FO in Each District) represents the target of selecting 25 Farmer Organizations (FOs) within each district (oversampled the FO because the list that was given to us is not updated). The FOs are further stratified by irrigation type (e.g., rainfed, minor, and major irrigation schemes), and a proportionate share of FOs is selected from each stratum. For example, in each district, if 7.2% of FOs should come from each stratum, this percentage guides the selection process within the strata. Within each stratum, FOs with a higher number of members are given a greater probability of being selected. This ensures that the sampling reflects the distribution of member sizes across FOs. There is 220 FO sampled altogether.\n\n- Third Stage Weight:\nStage 3 Probability =  11\/(Members in Sampled FO)\u00d710\/11\n\nWe use 3 FO as replacement and select only 22 FO to sample in this stage. The third stage involves selecting individual members (farmers) from each sampled FO. The probability here is straightforward: selecting 11 members out of the total members in each sampled FO. But only 10 have been interviewed and 1 HH per FO is used for replacement purpose. This stage ensures that the selection of individual farmers is representative of the FO\u2019s membership size. There are 2200 members (i.e., farming HHs) selected altogether from 220 FOs.\n\n- Final Weight:\nFinal weight at HH-Level =1\/(Stage 1 Probability \u00d7 Stage 2 Probability \u00d7 Stage 3 Probability)\n\nThe sum of these final weights at the HH-level is 2.8 million, which almost equal to 1.8 million, i.e., total paddy farmer population in Sri Lanka.","cleaning_operations":"The microdata files for the Sri Lanka Paddy Farmers Survey 2022-2024 were carefully processed to ensure they met several critical data quality and privacy standards. Specifically, the files were stripped of any variables that could directly identify a data subject, such as names, phone numbers, ID numbers, addresses, or geo-references. Sensitive information that could potentially cause harm, such as HIV status, was also excluded. All categorical variables were properly labeled, and missing values were clearly coded and labeled. Each dataset included a unique identifier or a combination of variables that uniquely identified every record. Numerical variables were checked to ensure they fell within realistic thresholds, and any variables with all missing values were removed. Additionally, if the dataset had a hierarchical structure, the relationships between datasets were clarified with unique identification variables to facilitate accurate data merging. If any weighting factors were required but missing, an explanation was provided along with guidance on the appropriate use of the dataset."}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"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). 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