Data Dictionary

Column Type Label Description
id int4 index
year text Year
state_name text State(s)
state_code text State Code
district_name text District(s)
district_code text District code
farm_size_class text Farm size class
farm_size_category text Farm size category
net_ir_a numeric Net Irrigated Area
grs_ir_a numeric Gross Irrigated Area
unir_netunir_a numeric Net Unirrigated Area
unir_grsunir_a numeric Gross Unirrigated Area
oa_t numeric Total Operated Area
ctl_tn numeric Total Number of Cattle
bffl_tn numeric Total Number of Buffaloes
op_hol_tn numeric Total Number of Operational Holdings
es_oh_ins_cr numeric Estimated Number of Operational Holdings That Took Institutional Credit
oh_cr_pacs numeric No. of Operational Holdings That Took Credit From Primary Agricultural Credit Society (PACS)
oh_cr_pldb numeric No. of Operational Holdings That Took Credit From Primary Land Development Bank/Branch of SLDB (PLDB/SLDB)
oh_cr_cbb numeric No. of Operational Holdings That Took Credit From CBB (Commercial Bank Branch)
oh_cr_rrbb numeric No. of Operational Holdings That Took Credit From RRBB (Regional Rural Bank Branch)
amt_ins_cr_sl numeric Amount Of Institutional Credit Taken for SL (Short Term Loans)
amt_ins_cr_ml numeric Amount Of Institutional Credit Taken for ML (Medium Term Loans)
amt_ins_cr_ll numeric Amount Of Institutional Credit Taken for LL (Long- Term Loans given by Commercial Banks/Regional Rural Banks)
amt_ins_cr_tot numeric Total Amount Of Institutional Credit Taken
hol_n_cs numeric No. of Holdings Used Certified Seed
hol_n_nv numeric No. of Holdings Used Notified Seed
hol_n_hs numeric No. of Holdings Used Hybrid Seed

Additional Information

Field Value
Data last updated June 5, 2024
Metadata last updated June 5, 2024
Created unknown
Format CSV
License Open Data Commons Attribution License
Created5 months ago
FrequencyQuinquennially
Size16,883
Additional infohttp://inputsurvey.dacnet.nic.in/
Data extraction pagehttp://inputsurvey.dacnet.nic.in/
Data insightsHere are the updated data insights that can be drawn from the Agricultural Inputs Consumption Survey Dataset for Meghalaya:1. **Irrigation and Water Resource Utilization**: Insights into irrigation practices, including net and gross irrigated areas, unirrigated sections, and water resource utilization, can inform strategies for optimizing water use and improving agricultural productivity.2. **Agricultural Practices and Credit Patterns**: Data on operated area, livestock count, credit usage patterns, and seed adoption trends can help identify areas for improvement in agricultural practices, credit accessibility, and seed adoption, ultimately informing policies to support sustainable agriculture.3. **Operational Holding Characteristics**: Categorization of holdings by size (marginal to large) and analysis of input consumption patterns can reveal opportunities to enhance agricultural productivity, efficiency, and sustainability across different operational holding sizes.
Data last updated2015-16
Data retreival date2018-19
Datastore activeTrue
District no11
GranularityDistrict
Has viewsTrue
Idf3c0ea37-9dd4-4d66-bed2-9a4cd9d92754
Idp readyTrue
MethodologyThe "Input Survey Composite" dataset, within the domain of Food and Agriculture, is collected and maintained by the Agricultural Census Division under the Department of Agriculture and Cooperation, Ministry of Agriculture. The data is gathered at the district level and is conducted on a quinquennial basis, meaning it occurs every five years. The methodology employed involves a comprehensive survey process wherein data pertaining to various aspects of food and agriculture within each district is systematically collected. This includes information on crop production, livestock, agricultural practices, and other relevant variables. The survey is likely to involve field visits, interviews, and possibly the use of specialized tools or technologies for data collection. The aim is to provide a detailed and accurate snapshot of the agricultural landscape in each district, enabling informed policy-making and resource allocation in the sector.
No indicators20
Package id0ee77590-fe5b-4d6d-91c3-baad545952a3
Position1
Skumoafw-input_survey_noncrop-dt-qq-meg
Stateactive
States uts no1
Url typeupload
Years covered2011-12
Methodology The "Input Survey Composite" dataset, within the domain of Food and Agriculture, is collected and maintained by the Agricultural Census Division under the Department of Agriculture and Cooperation, Ministry of Agriculture. The data is gathered at the district level and is conducted on a quinquennial basis, meaning it occurs every five years. The methodology employed involves a comprehensive survey process wherein data pertaining to various aspects of food and agriculture within each district is systematically collected. This includes information on crop production, livestock, agricultural practices, and other relevant variables. The survey is likely to involve field visits, interviews, and possibly the use of specialized tools or technologies for data collection. The aim is to provide a detailed and accurate snapshot of the agricultural landscape in each district, enabling informed policy-making and resource allocation in the sector.
Similar Resources
Granularity Level District
Data Extraction Page http://inputsurvey.dacnet.nic.in/
Data Retreival Date 2018-19
Data Last Updated 2015-16
Sku moafw-input_survey_noncrop-dt-qq-meg
Dataset Frequency
Years Covered 2011-12
No of States/UT(s) 1
No of Districts 11
No of Tehsils/blocks
No of Gram Panchayats
Additional Information http://inputsurvey.dacnet.nic.in/
Number of Indicators 20
Insights from the dataset Here are the updated data insights that can be drawn from the Agricultural Inputs Consumption Survey Dataset for Meghalaya:1. **Irrigation and Water Resource Utilization**: Insights into irrigation practices, including net and gross irrigated areas, unirrigated sections, and water resource utilization, can inform strategies for optimizing water use and improving agricultural productivity.2. **Agricultural Practices and Credit Patterns**: Data on operated area, livestock count, credit usage patterns, and seed adoption trends can help identify areas for improvement in agricultural practices, credit accessibility, and seed adoption, ultimately informing policies to support sustainable agriculture.3. **Operational Holding Characteristics**: Categorization of holdings by size (marginal to large) and analysis of input consumption patterns can reveal opportunities to enhance agricultural productivity, efficiency, and sustainability across different operational holding sizes.
IDP Ready Yes