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

• Author(s): Dr Amol Shivaji Patil; Chhaya A. Patil; Dr. Mangesh Subhash Phutane
• Paper ID: 1719522
• Page: 485-490
• Published Date: 07-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 10 Issue 1 July-2026

Abstract

Indian agriculture is characterized by substantial regional diversity in climatic conditions, farm structures, resource availability, and cropping systems. Farm Accountancy Data (FAD) plays a crucial role in measuring farm income, production costs, productivity, and economic sustainability, thereby supporting evidence-based agricultural policymaking. However, climatic disturbances such as El Niño events and low rainfall conditions often create significant disparities in agricultural performance across regions. Under such circumstances, the representativeness of farm-level accounting datasets becomes an important concern because non-representative samples may lead to inaccurate estimates of farm income, production costs, and policy outcomes. The present study examines the geographic representativeness of farm accountancy data in Indian agriculture under El Niño and deficient rainfall conditions. Secondary data collected from the Agricultural Census, India Meteorological Department (IMD), NABARD reports, and Directorate of Economics and Statistics were analyzed using descriptive statistics, representation indices, and hypothesis testing methods. The findings indicate considerable regional disparities in data coverage, particularly in drought-prone and rainfed regions. The study suggests the adoption of climate-sensitive sampling frameworks, digital data collection systems, and spatially balanced survey designs to improve the reliability of farm accountancy data under changing climatic conditions. The research contributes to the development of resilient agricultural information systems capable of supporting sustainable agricultural growth and effective policy interventions in India. [1][2][3]

Keywords

Farm Accountancy Data, Geographic Representativeness, El Niño, Climate Change, Low Rainfall, Agricultural Economics, India.

Citations

IRE Journals:
Dr Amol Shivaji Patil, Chhaya A. Patil, Dr. Mangesh Subhash Phutane "Assessing the Geographic Representativeness of Farm Accountancy Data in Indian Agriculture: Need, Challenges, and Precautionary Measures under El Niño and Low Rainfall Conditions" Iconic Research And Engineering Journals Volume 10 Issue 1 2026 Page 485-490

IEEE:
Dr Amol Shivaji Patil, Chhaya A. Patil, Dr. Mangesh Subhash Phutane "Assessing the Geographic Representativeness of Farm Accountancy Data in Indian Agriculture: Need, Challenges, and Precautionary Measures under El Niño and Low Rainfall Conditions" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026

APA:
Dr Amol Shivaji Patil, Chhaya A. Patil, Dr. Mangesh Subhash Phutane (2026). Assessing the Geographic Representativeness of Farm Accountancy Data in Indian Agriculture: Need, Challenges, and Precautionary Measures under El Niño and Low Rainfall Conditions. Iconic Research And Engineering Journals, 10(1).

MLA:
Dr Amol Shivaji Patil, Chhaya A. Patil, Dr. Mangesh Subhash Phutane "Assessing the Geographic Representativeness of Farm Accountancy Data in Indian Agriculture: Need, Challenges, and Precautionary Measures under El Niño and Low Rainfall Conditions" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026.

BibTeX

@article{1719522,
author = {Dr Amol Shivaji Patil, Chhaya A. Patil, Dr. Mangesh Subhash Phutane},
title = {Assessing the Geographic Representativeness of Farm Accountancy Data in Indian Agriculture: Need, Challenges, and Precautionary Measures under El Niño and Low Rainfall Conditions},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {485-490},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719522.pdf},
abstract = {Indian agriculture is characterized by substantial regional diversity in climatic conditions, farm structures, resource availability, and cropping systems. Farm Accountancy Data (FAD) plays a crucial role in measuring farm income, production costs, productivity, and economic sustainability, thereby supporting evidence-based agricultural policymaking. However, climatic disturbances such as El Niño events and low rainfall conditions often create significant disparities in agricultural performance across regions. Under such circumstances, the representativeness of farm-level accounting datasets becomes an important concern because non-representative samples may lead to inaccurate estimates of farm income, production costs, and policy outcomes. The present study examines the geographic representativeness of farm accountancy data in Indian agriculture under El Niño and deficient rainfall conditions. Secondary data collected from the Agricultural Census, India Meteorological Department (IMD), NABARD reports, and Directorate of Economics and Statistics were analyzed using descriptive statistics, representation indices, and hypothesis testing methods. The findings indicate considerable regional disparities in data coverage, particularly in drought-prone and rainfed regions. The study suggests the adoption of climate-sensitive sampling frameworks, digital data collection systems, and spatially balanced survey designs to improve the reliability of farm accountancy data under changing climatic conditions. The research contributes to the development of resilient agricultural information systems capable of supporting sustainable agricultural growth and effective policy interventions in India. [1][2][3]},
keywords = {Farm Accountancy Data, Geographic Representativeness, El Niño, Climate Change, Low Rainfall, Agricultural Economics, India.},
month = {July}
}