Former Director, Mahalanobis National Crop Forecast Centre (MNCFC), Ministry of Agriculture & Farmers Welfare; Former Group Director, Agricultural Sciences & Applications, NRSC-ISRO
Editor’s Note
This article is part of TheNews21’s Expert Policy Commentary series featuring domain specialists and former institutional leaders writing on key governance, technology, and public policy issues shaping India’s future.
The purpose of this article is to encourage technology professionals and policymakers to think seriously about developing a cutting-edge system for generating more accurate and timely crop estimates — a long-overdue necessity for the country. All stakeholders in the agricultural sector, including input suppliers, traders, exporters, insurers, and policymakers, require accurate, timely, and dependable crop estimates. Policy decisions are strongly influenced by the quality of crop production data, as these decisions have cascading effects throughout the agricultural economy, ultimately impacting farmers.
Crop production estimates determine the performance of the farm sector. Therefore, the crop estimation system in the country is one of the most critical components of the agricultural economy, and it must produce data of the highest standards. The longstanding need to improve India’s crop estimation system — to generate more accurate and credible estimates of acreage and crop yields — is evident from the recurring debates and reports appearing in newspapers almost every year. Concerns over underestimation or overestimation of crop production, especially food grain output, often result in last-minute policy and trading adjustments.
The Department of Agriculture and Farmers Welfare (DAFW), under the Ministry of Agriculture & Farmers Welfare, Government of India, has undertaken several initiatives over time to improve crop estimation procedures in coordination with other ministries and state governments.
Recent initiatives include:
- Digital General Crop Estimation Surveys (DGCES) for improving yield measurements through Crop Cutting Experiments (CCEs);
- YES-TECH for technology-driven yield estimation under Pradhan Mantri Fasal Bima Yojana (PMFBY); and
- Digital Crop Survey initiatives using smartphones for field-level crop data collection under the Agri Stack framework.
The department has also implemented satellite-based crop mapping with support from ISRO and other agencies for more than three decades, though its coverage remains limited to select major crops. Harmonisation of satellite-derived crop acreage estimates with conventional Patwari-based systems continues in a limited and ad hoc manner due to the absence of standardised procedures.
The Ministry of Statistics and Programme Implementation (MOSPI), the nodal ministry responsible for crop survey design and implementation, has also observed the need to integrate traditional and modern data sources to strengthen India’s agricultural statistical system. Therefore, modernising crop statistics using inclusive, technology-driven approaches should be treated as a national priority.
Strengthening the Conventional System
In the present era of digital and remote sensing technologies, multiple new datasets related to crop conditions can be generated and analysed rapidly using advanced analytical methods.
Three broad strategies are essential for building a robust crop estimation system:
- Strengthening the conventional system;
- Expanding remote sensing-based surveys; and
- Harmonising estimates from different methodologies into unified estimates.
The traditional crop survey mechanism, known as the General Crop Estimation Survey (GCES), was developed by the National Sample Survey Organisation (NSSO) under MOSPI. GCES-derived estimates continue to remain the primary basis for final crop estimation. Crop acreage estimation involves field inspections and manual recording of crop data by Patwaris across selected sample villages. These estimates are then aggregated statistically at district and state levels.
Crop yield estimation is based on Crop Cutting Experiments (CCEs), where field-level physical measurements are conducted through multistage stratified random sampling. However, the sampling methodology for selecting CCE fields was developed over 50 years ago. Since then, crop yield variability has changed substantially due to changing cultivation practices, climate variability, and technological shifts. It remains uncertain whether the current sampling methodology adequately represents present-day field-level variability.
Furthermore, the current system does not incorporate crop health variability into field selection because of historical data limitations. Crop health serves as a proxy for expected yield, and the exclusion of such variability introduces potential sampling errors.
The Smart Sampling Technique (SST), which uses satellite and weather datasets to generate yield-proxy indices for selecting CCE fields, is already being implemented under PMFBY. Initially introduced in Odisha during 2017–18, SST improved the distribution of CCE fields across insurance units and aligned field selection more closely with crop variability. Its adoption within GCES can significantly improve sampling efficiency.
Non-sampling errors also remain a serious concern. These include:
- discrepancies in locating CCE fields,
- yield measurement errors,
- errors in dry weight ratios,
- and data tabulation inaccuracies.
The shortage of trained personnel and lack of institutional accountability have further weakened the effectiveness of the existing GCES framework. The Digital General Crop Estimation Survey (DGCES), introduced by the Directorate of Economics and Statistics (DES), represents an important step toward improving transparency by recording CCE procedures through mobile applications. However, smartphone-based recording alone cannot fully digitise the GCES process. The use of smartphones for CCE recording was first introduced under PMFBY in 2016 through the CCE Agri App to improve transparency in Gram Panchayat-level yield estimation.
Combining SST-based scientific field selection with smartphone-enabled yield recording can substantially improve the quality and reliability of crop yield estimates. The recent decision of the National Statistics Office to increase the number of CCEs from 2025 onward is encouraging. However, improvements in field selection methodology and digital validation mechanisms must accompany this expansion. Location-specific CCE data can become an extremely valuable asset for validating yield-proxy datasets and strengthening future systems.
India should also institutionalise “gold standard” crop yield measurements at benchmark locations every season. Such high-quality reference datasets can create significant opportunities for developing advanced digital estimation technologies in the future.
Expanding Satellite-Based Surveys
Remote sensing-based crop inventory systems have existed in India for more than three decades. Yet, satellite-derived crop acreage estimates remain inadequately harmonised with conventional estimates. As a result, satellite-based crop mapping continues to function largely as a parallel exercise. Although satellite mapping agencies frequently claim high mapping accuracies, the actual operational integration of these datasets into policy systems remains limited. User departments often avoid providing objective feedback regarding the strengths and limitations of satellite-derived crop maps. This combination of overclaiming by implementing agencies and limited institutional feedback has slowed the evolution of satellite-based crop mapping systems despite advances in satellite datasets and analytics.
Another major limitation is the absence of a publicly accessible crop-layer repository or ground-truth data library in India. The United States Department of Agriculture (USDA), through the National Agricultural Statistics Service, began satellite-based crop mapping in 1997 and made cropland data layers publicly available from 2008 onward. India’s lack of such an open database has significantly constrained innovation in crop mapping methodologies. There is therefore an urgent need to overhaul India’s satellite-based crop mapping ecosystem to improve granularity, accuracy, transparency, and timeliness.
Harmonising Multiple Estimation Systems
Combining crop estimates generated through different systems into unified estimates remains another major challenge. Although DAFW has developed the Unified Portal for Agriculture Statistics (UPAG), including datasets related to crop area, yield, and production, standardised procedures for integrating satellite-derived estimates with conventional estimates are still absent. Similarly, Digital Crop Survey (DCS) systems being implemented under the Agri Stack framework are expected to capture parcel-level crop information. However, their estimates are yet to be fully validated and integrated into larger crop estimation frameworks. Developing scalable methodologies for harmonising multiple estimation systems remains an important research and policy challenge for the future.
Conclusion
India’s crop estimation system requires urgent modernisation to improve the reliability, transparency, and credibility of crop estimates.
The country already possesses:
- advanced remote sensing datasets,
- digital field-survey tools,
- powerful analytical capabilities,
- and strong institutional expertise.
The challenge lies in integrating these capabilities into a continuously evolving national crop estimation framework. The long-term economic and policy benefits of such a transformation would far outweigh the costs. India must also establish benchmark “gold standard” yield datasets to support future innovations in crop estimation technologies. States willing to adopt modern systems proactively can become models for the rest of the country and help drive nationwide transformation in agricultural statistics.
(The views expressed are personal.)
