Assessing the Geographic Representativeness of Farm Accountancy Data in Indian Agriculture: Need, Challenges, and Precautionary Measures under El Niño and Low Rainfall Conditions

July 7, 2026Iconic Research and Engineering Journals

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Key Points

This study aims to evaluate the geographic representativeness of farm accountancy data in Indian agriculture amid climatic challenges like El Niño and low rainfall.
Analyzed secondary data from the Agricultural Census, IMD, NABARD reports, and Directorate of Economics and Statistics.
Utilized descriptive statistics, representation indices, and hypothesis testing methods to assess data coverage.
Focused on drought-prone and rainfed regions to identify regional disparities.
Significant regional disparities were found in farm accountancy data, particularly in drought-prone areas.
The study emphasizes the necessity of climate-sensitive sampling frameworks for accurate data representation.
Recommendations include implementing digital data collection systems and spatially balanced survey designs to enhance reliability.

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Assesses farm accountancy data reliability in India under El Niño and low rainfall, indicating the need for improved methods.

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Patil et al. (2026) studied this question.

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