Context:
Starting July 2025, the National Statistics Office (NSO) has increased the number of Crop Cutting Experiments (CCEs) conducted across India to improve the accuracy of crop yield estimates. The move is expected to bolster agricultural policy formulation, including export curbs, inflation control, and price management strategies.
Key Highlights:
Objective of the Initiative
- To enhance precision in crop yield estimation for kharif and rabi seasons of 2025–26.
- To provide data-backed insights for policy decisions such as:
- Export bans
- Stock limits
- Futures trading suspensions
What are Crop Cutting Experiments (CCEs)?
A Crop Cutting Experiment is a scientific sampling method used to estimate crop yield by harvesting and weighing a small, randomly selected portion of a crop field. This method ensures statistically reliable estimates of agricultural productivity.
Key Objectives of CCE
- Yield Estimation: Provides accurate per-hectare yield figures.
- Production Forecasting: Supports national and state-level crop output calculations.
- Policy Formulation: Informs decisions on food security, procurement, subsidies, and export-import strategies.
How CCE is Conducted
- Random Plot Selection
- Fields and plots are selected using random sampling techniques to avoid bias.
- Standardized Plot Size
- Each plot has a fixed size, typically depending on crop and region (e.g., 5m Ă— 5m for wheat).
- Harvesting & Weighing
- The selected area is harvested manually.
- Produce is threshed, cleaned, dried, and weighed to determine yield.
- Data Compilation & Analysis
- Yields from multiple CCEs are compiled.
- Statistical methods are applied to estimate average yields at the district, state, or national level.
Why the Expansion of CCEs?
- Current methods are outdated and do not cover newer, non-traditional crops like:
- Avocado, dragon fruit, kiwi, berries, etc.
- Existing estimates cover only 25–26 major crops such as rice, maize, jowar.
- Enhanced sample size aims to:
- Improve statistical confidence levels
- Capture regional and varietal diversity
- Address data gaps in horticulture
