India worries about producing enough food. A significant share of what it produces is lost before anyone eats it.
Data year: 2022 study · Source: ICAR-CIPHET loss assessment · Updated: Roughly decadalIn preparation
India worries about producing enough food. A significant share of what it produces is lost before anyone eats it.
Losses happen at harvest, in storage, in transport and at the mandi — different crops fail at different points. Fruit and vegetables lose most in cold chain gaps; cereals lose most in storage.
Because the loss is distributed across many small steps, it rarely appears as a single headline. Assembling the published estimates by crop and by stage shows where a cold storage unit or a better sack would return the most food per rupee.
These are the indicators this topic is built from. Each names its source and the year that source refers to, because an official figure is not the same thing as a current one.
| Indicator | Unit | Source and year |
|---|---|---|
| Post-harvest loss by crop | % of production | ICAR-CIPHET loss assessmentData year: 2022 study |
| Loss by stage | % of production | ICAR-CIPHET loss assessmentData year: 2022 study |
| Cold storage capacity | tonnes, by state | NHB / MoFPIData year: 2024-25 |
| Municipal waste that is organic | % of waste | CPCBData year: 2023-24 |
A crop-by-stage matrix showing where in the chain each commodity is lost.
The brief above is written and the sources are identified. What remains is the data build. Until it publishes, no figure for this topic appears anywhere on DataSpeaks — we would rather show nothing than show a placeholder.
Where a source has not reported for a recent year, the gap is shown as a gap. We do not interpolate to make a series look continuous or a map look complete. Where two agencies publish different figures for the same thing, both appear and we say which is which.
Charts from this topic may be reused with credit to DataSpeaks and to the original source named above. The underlying data remains under its own licence — most Government of India datasets permit reuse with attribution, some survey microdata does not.
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