Input Survey - Non Crop
عنوان الموقع URL: https://ckan.meghalayadataportal.com/dataset/0ee77590-fe5b-4d6d-91c3-baad545952a3/resource/f3c0ea37-9dd4-4d66-bed2-9a4cd9d92754/download/input-survey-non-crop.csv
The Input Survey - Non Crop dataset for Meghalaya offers detailed insights into the utilization of agricultural resources across different farm sizes, aiding in production planning, imports, and fertilizer distribution. With variables such as farm size, irrigation data, livestock count, credit usage, and seed type, this dataset serves as a valuable resource for informed decision-making in agricultural policy formulation.
قاموس البيانات الوصفي
| العمود | النوع | عنوان الحقل | وصف |
|---|---|---|---|
| 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 |
معلومات إضافية
| حقل | القيمة |
|---|---|
| آخر تحديث للبيانات | 5 يونيو 2024 |
| آخر تحديث للبيانات الوصفية | 5 يونيو 2024 |
| أنشئت | غير معروف |
| تنسيق | CSV |
| الترخيص | Open Data Commons Attribution License |
| Frequency | Quinquennially |
| Size | 16,883 |
| Additional info | http://inputsurvey.dacnet.nic.in/ |
| Data extraction page | http://inputsurvey.dacnet.nic.in/ |
| Data insights | 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. |
| Data last updated | 2015-16 |
| Data retreival date | 2018-19 |
| Datastore active | True |
| District no | 11 |
| Granularity | District |
| Has views | True |
| Id | f3c0ea37-9dd4-4d66-bed2-9a4cd9d92754 |
| Idp ready | True |
| 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. |
| No indicators | 20 |
| Package id | 0ee77590-fe5b-4d6d-91c3-baad545952a3 |
| Position | 1 |
| Sku | moafw-input_survey_noncrop-dt-qq-meg |
| State | active |
| States uts no | 1 |
| Url type | upload |
| Years covered | 2011-12 |
| أنشئت | قبل سنة واحدة |
| 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 | |
| معلومات إضافية | 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 |