Agriculture ยท GS3
Digital General Crop Estimation Survey
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The government highlighted the Digital General Crop Estimation Survey as part of technology-driven crop statistics.
Summary
Reliable area and yield estimates guide food policy, insurance, procurement, and market decisions. Digital collection can improve speed and auditability, but sampling quality and field verification remain essential.
PYQ pattern
UPSC may connect agricultural statistics with crop insurance, food security, remote sensing, and evidence-based policy.
Core notes
- Crop production estimates combine information on cultivated area and yield.
- Crop-cutting experiments estimate yield from sampled plots.
- Geotagging and digital forms can reduce transcription error and improve supervision.
- Remote sensing can complement, not automatically replace, ground observations.
Prelims lens
- Yield is output per unit area.
- A sample survey estimates a population characteristic from selected observations.
- Administrative data and survey data have different error structures.
MCQ 1
Crop-cutting experiments are mainly used to estimate:
- A. Crop yield
- B. Soil ownership
- C. Retail inflation only
- D. Groundwater law
Reveal answerHide answer
A
Output harvested from sampled plots supports yield estimation.
MCQ 2
Digital data collection automatically removes:
- A. Every sampling and measurement error
- B. Some transcription delays, while field-quality controls remain necessary
- C. The need for definitions
- D. The need for any ground observation
Reveal answerHide answer
B
Digital tools improve workflow but do not guarantee representative or accurate observations.
Mains
Question, 10 marks, 150 words: How can digital tools improve crop statistics without weakening statistical rigour?
Approach:
- Explain area, yield, and production estimates.
- Discuss geotagging, mobile collection, and remote sensing.
- Note sampling and verification risks.
- Suggest open methods and independent quality checks.