Data Dictionary

ID
id
Type
int4
Label
index

ID
year
Type
text
Label
Year

ID
state_name
Type
text
Label
State Name

ID
state_code
Type
text
Label
State Code

ID
district_name
Type
text
Label
District Name

ID
district_code
Type
text
Label
District Code

ID
land_use_metrics
Type
text
Label
Land Use Metrics

ID
area_classification
Type
text
Label
Area Classification

ID
area_sub_classification
Type
text
Label
Area Sub Classification

ID
area_in_hectares
Type
numeric
Label
Area (In Hectares)

Additional Information

Field Value
Data last updated February 26, 2026
Metadata last updated February 26, 2026
Created February 26, 2026
Format CSV
License Open Data Commons Attribution License
Data extraction pagehttps://data.desagri.gov.in/weblus/lus-grand-query-report
Data insightsThe Classification of Area dataset enables analysis of how land use patterns vary across regions and years. It helps identify changes in net sown area, cropping intensity, and the extent of multiple cropping practices. Policymakers can use this data to assess land utilization efficiency, monitor agricultural expansion or decline, and plan land and irrigation policies. Researchers can study relationships between land classification, productivity, and regional development. The Classification of Area dataset also supports evaluation of long-term land use trends and sustainability of agricultural practices.
Data last updated18-12-2024
Data retreival date10-12-2025
Datastore activeTrue
District no742
FrequencySeasonal
Gp no0
GranularityDistrict
Has viewsTrue
Id4c4cdf25-3103-42f7-bac2-96fd0563789f
Idp readyTrue
Indicators['Area In Hectares']
Lgd mappingyes
Mapping status100
MethodologyThe Land Use Statistics data are compiled from official secondary sources, like annual administrative land records maintained at the village level by revenue under the nine fold land classification system which includes categories such as forests, non-agricultural land, fallow land, net sown area, cropped area, etc. The classification and utilization metrics are aggregated up to district or state levels and published periodically by DES. It’s likely supplemented by remote sensing or survey inputs in some sub-domains, though the primary data comes from official land records and administrative returns.
No indicators5
Package id8742251a-5ca1-483d-936a-f68306663cc9
Position1
Similar resourcesadbda590-ac79-448f-95ff-2c455c538288,0fb99c18-a1f7-46f0-b40d-ddffca021319,ef174105-0886-45f2-bd0d-679c45e05845
Size21 MiB
Skumoafw-des_grand_query_reports-classification-of-area-dt-sn-qzf
Stateactive
States uts no36
Tehsil no0
Url typeupload
Years covered1998 - 2024
Methodology The Land Use Statistics data are compiled from official secondary sources, like annual administrative land records maintained at the village level by revenue under the nine fold land classification system which includes categories such as forests, non-agricultural land, fallow land, net sown area, cropped area, etc. The classification and utilization metrics are aggregated up to district or state levels and published periodically by DES. It’s likely supplemented by remote sensing or survey inputs in some sub-domains, though the primary data comes from official land records and administrative returns.
Indicators ['Area In Hectares']
Similar Resources
  1. Area Under Crops
  2. Crop Wise Irrigated Area
  3. Sources of Irrigation
Granularity Level District
Data Extraction Page https://data.desagri.gov.in/weblus/lus-grand-query-report
Data Retreival Date 10-12-2025
Data Last Updated 18-12-2024
Sku moafw-des_grand_query_reports-classification-of-area-dt-sn-qzf
Dataset Frequency Seasonal
Years Covered 1998 - 2024
No of States/UT(s) 36
No of Districts 742
No of Tehsils/blocks 0
No of Gram Panchayats 0
Additional Information
Number of Indicators 5
Insights from the dataset The Classification of Area dataset enables analysis of how land use patterns vary across regions and years. It helps identify changes in net sown area, cropping intensity, and the extent of multiple cropping practices. Policymakers can use this data to assess land utilization efficiency, monitor agricultural expansion or decline, and plan land and irrigation policies. Researchers can study relationships between land classification, productivity, and regional development. The Classification of Area dataset also supports evaluation of long-term land use trends and sustainability of agricultural practices.
IDP Ready Yes
LGD Mapping Yes
Mapping Status % 100
Geo Columns