
This dataset contains Odisha's agriculture statistics for 2023-24 in 70 tables: district-wise crop area, production and yield for all major crops, rainfall and weather, land use, irrigation, fertiliser, seed and machinery use, crop loans, insurance, procurement, and state-wise yield comparisons.
This dataset provides the agriculture statistics of Odisha, India, for the agricultural year 2023-24 (July 2023 to June 2024), taken from the state's annual publication Odisha Agriculture Statistics 2023-24. It contains 70 tables with about 18,000 rows. Most tables are district-wise for all 30 districts with a state total; some give state-level time series and comparisons with other Indian states and All-India. The tables cover: (1) crop area, production and yield by district, season (kharif and rabi) and crop for about 40 crops and crop groups, including paddy and rice (autumn, winter and summer), maize, ragi, millets, pulses, oilseeds, fibres, vegetables, spices and sugarcane, with HYV and local varieties where reported; (2) rainfall: monthly normal rainfall and rainy days, monthly and weekly rainfall for 2023, and the June-September monsoon from 1998 to 2023, plus monthly temperature and humidity by weather station; (3) land use, cultivated area by land type (high, medium and low land), cropping intensity, and area under fruits, plantation crops and flowers; (4) inputs and infrastructure: fertiliser (N, P, K) consumption, certified seed distribution, seed replacement rates, pesticides, farm machinery, electricity use in agriculture, irrigation potential by source and irrigated area by crop; (5) agricultural credit, crop insurance (2009 to 2023-24) and scale of finance by crop and district; (6) procurement of paddy, ragi, seed cotton, pulses and oilseeds, minimum support prices (2015-16 to 2024-25) and sugar factory output; (7) yields by agro-climatic zone (2018-19 to 2023-24), Odisha versus All-India productivity (1976-77 to 2023-24), state-wise yields of major crops (2006-07 to 2022-23) and the rice area, yield and production series from 1995-96; and (8) supporting data on operational holdings (Agriculture Census 2015-16), population and agricultural workers (Census 2011), marketable surplus, crop damage from disasters, driage ratios and conversion factors. Area is in thousand hectares, production in thousand tonnes (fibres in thousand bales) and yield in kg per hectare unless a column states otherwise. Each table is in tidy format with one header row and units in the column names, and district names are spelt the same way in every table. The data show, for example, that in 2023-24 Odisha had a gross cropped area of 9,144.56 thousand hectares on a net sown area of 5,592 thousand hectares (cropping intensity 164%), produced 17,482.88 thousand tonnes of paddy (11,538.70 thousand tonnes of rice) and 14,303.51 thousand tonnes of foodgrains, and received 1,386.5 mm of rainfall in 2023 against a normal of 1,451.2 mm. Values were checked against the totals printed in the publication; the few inconsistencies in the original report are kept as published and documented in the accompanying summary file. The figures are aggregates and contain no personal information. The dataset is useful for policymakers, agricultural planners, researchers and analysts studying crop production, input use and agricultural development in Odisha.
To provide Odisha's official agriculture statistics for 2023-24 in a clean, machine-readable form for analysis at district and state level. Use cases: 1. Policy & Governance: Plan crop programmes, input supply (seed, fertiliser, machinery), irrigation and procurement, and target support to districts with low yields or low irrigation coverage. 2. Academic Research: Study cropping patterns, crop diversification, yield gaps between districts and agro-climatic zones, rainfall and production, and how Odisha compares with other states and All-India. 3. Agribusiness & Finance: Estimate production and marketable surplus for supply-chain, storage and processing decisions, and use scale-of-finance, crop-loan and insurance data for agricultural credit and risk assessment. 4. AI & Predictive Modelling: Build models for crop yield prediction, rainfall-yield relationships, district-level production estimation and agricultural risk mapping using the combined crop, weather, land-use and input data.
Open Government License, India
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