Crop Survey of HONNAVARA Hobli of Honnavar Taluk of Uttar Kannada District of Karnataka for Kharif Season 2018-19

The dataset consists of 15,413 individual crop survey records collected at village-level granularity across multiple districts in Karnataka. Each record represents a specific agricultural observation tied to a survey date and a unique Survey_id.\n\nA key feature of this dataset is the Crop_Extent field, which is recorded in a traditional land measurement format (likely acres–guntas–fractions), indicating highly localized agricultural reporting practices rather than standardized metric units. The dataset includes 57 distinct crop categories, with a dominant presence of 'Fallow' land (over 6,400 entries), suggesting significant portions of surveyed land were uncultivated during the observation period.\n\nThe dataset captures hierarchical geographic identifiers including district, taluk, hobli, and village levels, along with precise latitude and longitude coordinates, enabling spatial mapping down to micro-regions. Temporal information is partially structured: while CropSurveyDate is consistently populated (covering about 50 distinct dates), derived temporal fields such as Month and Weekname are sparsely filled (~10% coverage), indicating incomplete temporal enrichment.\n\nSeasonal classification is consistent across the dataset, with all records belonging to a single agricultural year (2018–2019) and sharing the same Year_code (116), suggesting a single-cycle survey dataset rather than multi-year longitudinal data.\n\nNotably, the Image_url field is entirely empty, implying either missing multimedia capture or exclusion during data extraction.

Data Quality

The dataset demonstrates strong structural consistency with no missing values in core identifiers (Survey_id, geographic codes, coordinates). However, several quality issues exist: (1) Cropname has minor missing values (~53 records), (2) Month and Weekname are highly incomplete (~90% missing), (3) Image_url is completely null, and (4) Crop_Extent is stored as a string in a non-standard unit format, limiting direct quantitative aggregation. Geographic coordinates are complete and appear valid, enhancing reliability for spatial analysis.

Use Cases

High-resolution crop distribution mapping, identification of fallow vs cultivated land patterns, micro-level agricultural planning, geospatial analytics using GIS tools, crop diversification studies, and government agricultural policy evaluation at district and village levels.

Limitations

The dataset lacks standardized measurement units for land extent, making numerical comparisons difficult without preprocessing. Temporal granularity is incomplete due to missing Month and Weekname values. Absence of multi-year data prevents trend analysis. Missing image data reduces validation capability. High prevalence of 'Fallow' category may skew crop distribution insights if not handled carefully.

Methodology

The data was likely collected through ground-level agricultural surveys conducted by field officers using structured forms and GPS-enabled devices. Observations were recorded per plot or farm location, capturing crop type and extent along with administrative identifiers. The presence of hierarchical codes suggests integration with government land and administrative registries.