The influence of monsoon on Indian agriculture
The monsoon plays a pivotal role in shaping India’s agriculture landscape. Any deviation in timing or amount of rainfall can significantly impact millions of hectares of cropland. Two major ocean-atmosphere phenomena, the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), are key drives of monsoon variability. Understanding their impact is essential for reinsurers responsible for pricing agricultural risk and managing climate-driven portfolio volatility.
This study, conducted by Allianz Re, examines the historical correlations between ENSO and IOD events and Indian monsoon precipitation, as well as their combined effects on agricultural yields over the past seven decades. The findings offer valuable insights into the agriculture reinsurance industry which are outlined in the article below.
Understanding ENSO and IDO: The significance in Indian agriculture
The El-Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are crucial ocean-atmosphere phenomena that significantly impact weather patterns across the India subcontinent. ENSO involves periodic fluctuations in sea surface temperatures (SST) in the Pacific Ocean, primarily measured by the Niño-3.4 index. An El-Niño event is characterised by warmer-than-normal SSTs, while cooler temperatures indicate a La Niña. Conversely, the IOD measures the temperature gradient between the western and eastern equatorial Indian Ocean, with positive IOD indicating warmer waters in the west and cooler in the east, while a negative IOD showing the opposite patterns. These phenomena affect wind circulation, cloud formation, and precipitation levels in a diverse way across India.
Although ENSO and IOD have traditionally been used as indicators for predicting rainfall, their precise influence on India’s precipitation patterns and more critically, on agricultural yields – has been ambiguous. For reinsurers, understanding these dynamics is vital. It is essential to move beyond simplistic assumption such as “El Niño equals drought”.
Allianz Re conducted a comprehensive study covering the period from 1950 to 2023, including daily gridded rainfall, SST anomalies, and crop yield figures from 2012 to 2022. The study employed spatial analysis through QGIS to assign rainfall data to Indian states and aggregate it by cropping season. By analysing seasonal precipitation alongside SST anomalies and examining temporal shifts, the research aimed to uncover potential lags between ocean temperatures and rainfall responses, providing deeper insights into the complex relationship between these ocean-atmosphere phenomena and agricultural outcomes.
The complexity of rainfall patterns: Beyond the El Nino-drought paradigm
An insight from the study was the limited predictive value of the SST anomalies and IOD indices. Although a moderate interdependence was observed, neither index alone consistently correlated with the performance of the Indian monsoon. These findings challenges conventional beliefs that stronger El Niño events invariably lead to severe drought. In reality, several strong El Niño years did not result in poor monsoons, and negative IOD phases were not always associated with deficient rainfall. The vast regional diversity of India further complicates any predictions, underscoring the need for a more nuanced approach to interpreting climate signals in the context of Indian agriculture.
Despite these complexities, certain trends were observed. During the El-Niño events, precipitation in the Kharif season is typically lower than average, while La Niña events tend to bring higher-than-average rainfall. The Rabi season exhibits the opposite trend. Additionally, the impact of various ENSO phases varies significantly across different states, whereas the pattern is less distinct. The combined influence of ENSO and IOD remains inconclusive. In Kharif seasons, ENSO phases appear to be the primary driver of rainfall, with neutral IOD events intensifying drought conditions in northwestern states and mitigating them in northeastern states.
Beyond analysing rainfall patterns, the study delved into the impact of climate indicators on crop yields, where correlations became clearer. Yield data, particularly for sensitive crops like rice and pulses, demonstrated declines during El Niño years, even when rainfall volumes appeared normal. Conversely, La Niña years were frequently associated with increased yields. These findings underscore the importance of prioritising agricultural outcomes rather than relying solely on individual weather variables like rainfall. Crop yield responses are influenced not only by the comprehensive understanding of how climate phenomena affect agriculture productivity, emphasising the significance of moisture arriving at the right time and in the right amount.
The crucial role of timing in agriculture
Timing emerged as a pivotal factor in the study. A delayed onset or premature withdrawal of monsoon, irrespective of the total seasonal rainfall, can significantly impact planting and crop development. This temporal variability highlights the necessity for reinsurance products to consider not only the quantity of rainfall but also its distribution and alignment with crop calendars. For instance, a region might receive normal total rainfall but encounter dry spells during critical growth stages, resulting in yield stress and economic loss. Designing indices that account for rainfall variability within planting and flowering windows could provide a more accurate reflection of actual risk, thereby enhancing the effectiveness of reinsurance solutions.
Evaluating the reliability of ENSO forecasts
The study also addressed the accuracy of ENSO forecast, revealing that predictions from 2004 to 2023 have been reasonably reliable. Although accuracy diminishes with increased lead time, forecasts still maintain a commendable level of reliability. The ‘Average, dynamical models’ demonstrated the strongest between forecasted and actual SST anomalies, starting at 0.98 for one-month lead time and gradually decreasing as lead time extended. Even with a seven-month lead team, the correlation remained at 0.8. Despite the reliability, reinsurers should consider ENSO forecasts as one of several tools, complemented by historical scenario testing, soil moisture monitoring, and crop condition assessments, to ensure a comprehensive approach to risk management.
From signals to strategy: Leveraging climate data for reinsurers
The insights gleaned from this study hold significant implications for the agricultural reinsurance industry.
“Over the past decade, observational data has made it clear that both ENSO and IOD events have measurable impacts on the timing, distribution, and intensity of the Indian monsoon – factors that directly affect crop yields and claims volatility. As underwriters, we must move beyond historical averages and integrate dynamic climate indicators into pricing and portfolio selection,” said a leading agriculture insurance expert in India, Azad Mishra.
“Incorporating Earth observation data and seasonal forecasts into our underwriting models isn’t just innovation – it’s a necessary adaptation to a changing risk regime.”
One of the key takeaways from this study is the necessity to reassess traditional hazard triggers used in modelling and pricing. Historically, reinsurers have depended on simplified heuristics – such as associated El Niño events with drought and La Niña with flood – to predict seasonal risks. However, while precipitation levels are closely linked to ENSO phases, the impact on the yield is far more intricate, influenced by factors such as rainfall timing, temperature, and location.
Another significant consideration is the adoption of yield-based indices. Although rainfall has traditionally served as a proxy for crop performance, it fails to capture the full spectrum of variables affecting agricultural outcomes. This study reveals that yield data demonstrates a stronger and more consistent correlation with climate indicators than precipitation does. Consequently, index-based insurance products incorporating yield estimates may more accurately reflect true loss exposure. These indices can include modelled yields, satellite-derived vegetation indices, or ground-truth historical yield trends. It is crucial, however, that all index-based insurance product development includes a robust validation process to ensure the underlying indices accurately reflect yield loss.
Reinsurers should also focus beyond the total rainfall volume and pay closer attention to its timing and distribution. Even in years with average or above-average seasonal rainfall, delayed onset or erratic intra-seasonal patterns can severely disrupt crop cycles. Timing anomalies can delay sowing or reduce flowering, leading to significant yield losses. Integrating rainfall distribution metrics or phenology-aligned windows into risk assessment frameworks can improve the precision of underwriting and claims modelling.
Finally, reinsurers must transition toward dynamic, multi-factor models that incorporate a broad array of variables beyond climate indices. These can include soil moisture, crop calendars, regional agronomic practices, and socio-economic resilience factors. Advances in machine learning, remote sensing, and geospatial analytics offer powerful tools for constructing such models. These innovations not only improve risk accuracy but also facilitate better communication with clients, brokers, and regulators.
In summary, as climate variability continues to challenge agricultural stability, reinsurers must adapt. ENSO and IOD are influential but complex tools, best understood as part of a larger system influencing crop outcomes. By aligning risk assessment with real-world yield behavior and embracing data-driven strategies, the industry can better manage uncertainty and support a more resilient agricultural sector. For reinsurance specialists, the challenge is not only in interpreting these signals but in integrating them with local agronomic realities, yield patterns, and probabilistic forecasts. Future-ready risk models will rely less on rigid climate templates and more on adaptable, data-driven strategies. A
Sonia Rawal is head of P&C and agriculture client management and Peter Nagy is senior agricultural underwriter at Allianz SE Reinsurance.