La Niña- driven variability in Indian monsoon dynamics and agricultural productivity

Introduction

The El Niño-Southern Oscillation (ENSO) represents the most energetically significant coupled ocean-atmosphere phenomenon in the tropics, with documented global-scale teleconnections affecting temperature, precipitation, and agricultural productivity on interannual timescales (Trenberth 1997; Cai et al., 2021) [38,3] . India's agricultural economy characterised by over 140 million farm holdings, a cultivated area of approximately 180 million hectares, and a monsoonfed irrigation dependence exceeding 80% remains among the most ENSO-sensitive food systems in the world (FAO 2023; IPCC AR6 2022) [9, ] . While El Niño's drought-inducing effects on the Indian Summer Monsoon Rainfall (ISMR) have been extensively investigated, the agronomic consequences of La Niña-the cold, wet phase of ENSO have received comparatively less attention. La Niña events, characterised by anomalously cold sea surface temperatures (SSTs) in the central and eastern tropical Pacific (Oceanic Niño Index, ONI < -0.5 °C for five consecutive overlapping seasons), typically enhance monsoon circulation through an intensified Walker Cell, favouring convection over the Indian Ocean and the sub-continent (Krishnamurthy and Shukla 2000; Webster et al., 1998) [26,39] . However, the assumption that La Niña uniformly benefits Indian agriculture through above-normal rainfall oversimplifies a complex, spatially heterogeneous response that varies by region, crop type, agro-climatic zone, and event intensity. Recent analyses reveal that between 1998 and 2023, India experienced nine La Niña episodes of varying intensity (IMD 2023) [19] . Concurrent with above-normal all-India rainfall, these episodes were associated with heightened flood risk, major flood events occurred 3.8×more frequently during La Niña compared to El Niño years (NDMA 2022) [30] . Simultaneously, parts of peninsular India particularly Tamil Nadu, which receives a disproportionate share of its rainfall during the retreating (northeast) monsoon experienced rainfall deficits during La Niña conditions (Rajeevan et al., 2012; Pai et al., 2019) [33,32] .

ENSO-Monsoon Teleconnection Mechanisms 2.1 The Walker Circulation and La Niña Dynamics

The Walker Circulation-a zonal, thermally-driven atmospheric overturning cell along the equatorial Indo-Pacific constitutes the primary atmospheric bridge between tropical Pacific SST anomalies and Indian monsoon behaviour (Bjerknes 1969; Trenberth 1997) [2,38] . Under La Niña conditions, anomalously cold SSTs in the central and eastern Pacific (Niño 3.4 region SST anomaly < -0.5 °C) suppress convection over the eastern Pacific while simultaneously enhancing it over the maritime continent and the Indian Ocean warm pool. This intensification of the ascending limb of the Walker Cell over the Indian Ocean reinforces the low-level westerly flow of the south-west monsoon, increasing moisture convergence over the Indian subcontinent. Quantitatively, NCEP-NCAR reanalysis data indicate that during strong La Niña events (ONI ≤ -1.5 °C), the 850 hPa zonal wind anomaly over the Arabian Sea averages +1.8 m s⁻¹, compared to -2.3 m s⁻¹ during El Niño years, a differential that translates directly into enhanced moisture transport of approximately 35-40 kg m⁻¹ s⁻¹ additional precipitable water over peninsular India (Fasullo and Webster 2003; Krishnamurthy and Kinter 2003) [10,25] . Under SSP2-4.5 projections, the frequency of extreme La Niña events (ONI ≤ -1.5 °C) is projected to increase by 33-47% by the late 21st century (Cai et al., 2023) [4] .

Indian Ocean Dipole Interactions

The Indian Ocean Dipole (IOD), characterised by an east-west SST gradient across the Indian Ocean, frequently co-occurs with ENSO phases and modulates ISMR independently as well as in concert with La Niña (Ashok et al., 2001; Saji et al., 1999) [1,37] . Positive IOD events (warmer western Indian Ocean) cooccurring with La Niña amplify monsoon rainfall, while negative IOD-La Niña combinations partially offset the surplus tendency. The compounded effect of a strong La Niña and a positive IOD as occurred in 2020 can amplify ISMR anomalies by an additional 4-6% beyond the La Niña signal alone. Disentangling these interactions remains a critical challenge for seasonal prediction, particularly given evidence of nonstationarity in the ENSO-IOD-ISMR relationship under anthropogenic warming (Roxy et al., 2020) [36] .

Intraseasonal Modulation: MJO and BSISO

Within La Niña seasons, the Madden-Julian Oscillation (MJO) and the Boreal Summer Intraseasonal Oscillation (BSISO) determine the temporal distribution of rainfall within the June-September monsoon window (Goswami and Ajaya Mohan 2001) [13] . La Niña favours enhanced and prolonged MJO Phase 2-3 activity over the Indian Ocean, increasing the frequency of active monsoon spells. Analysis by Pai et al., (2019) [32] indicates a 25% increase in the number of extreme rainfall days (≥100 mm/day) during La Niña seasons compared to neutral years, and a 40% increase compared to El Niño years. These intraseasonal dynamics have direct implications for planting window decisions and flood risk management.

Data sources: NCEP-NCAR Reanalysis II, NOAA CPC ONI index, ERA5 reanalysis.

Agro-Climatic Zonal Analysis of La Niña Impacts

India's Planning Commission delineated 15 agro-climatic zones based on physiography, soils, and rainfall patterns. The ninezone classification widely used in climate-agriculture studies (ICAR 2019) [14] , as presented in Table 1. La Niña's rainfall surplus impacts are not uniformly distributed across these zones, necessitating zone-specific analysis for policy and agricultural planning. Sources: ICAR (2019) [14] , IMD (2023) [19] , NDMA (2022) [30] .

Source: IMD gridded rainfall dataset v2.0 (0.25°); NOAA ONI index for La Niña year classification.

North-Western and Upper Gangetic Plains

The north-western plains (Punjab, Haryana, western Uttar Pradesh) typically receive 5-10% above-normal rainfall during La Niña, with moderate flood risk concentrated in low-lying areas proximate to the Sutlej, Beas, and Ghaggar river systems. The surplus moisture generally benefits kharif paddy and wheat rabi crops, contributing to above-average yields in Punjab (rice +8 to +14%) and Haryana. However, the upper Gangetic plains (eastern Uttar Pradesh, Uttarakhand) experience more pronounced surpluses (+10-18%), increasing flash flood events in Terai districts and threatening the early kharif planting calendar.

Lower Gangetic Plains and North-Eastern Highlands

These zones consistently record the most adverse La Niña agricultural outcomes. Rainfall surpluses exceeding 15% in Bihar and West Bengal and greater than 20% in Assam and Meghalaya generate recurrent inundation events that damage standing paddy crops through submergence, soil puddling, and nitrogen leaching. NDMA (2022) [30] data indicate that the lower Gangetic plains and north-eastern zone account for 62% of all La Niña-associated agricultural flood damage nationally, despite comprising only 18% of the total cultivated area. The 2022 La Niña season exemplified this vulnerability, with Assam recording 27.4% above-normal rainfall and 42 of 35 districts officially flood-affected (IMD 2023) [9] . An estimated 0.73 Mha of paddy was damaged, with production losses of approximately 1.1 million tonnes (MT), representing the largest single-season La Niña crop loss in the decade.

Peninsular and Southern Zones

A critical regional inconsistency in La Niña's impact emerges in the southern peninsula. Tamil Nadu and parts of coastal Andhra Pradesh receive a disproportionate share of annual rainfall during the north-east monsoon (October-December), which is typically suppressed during La Niña years due to weakened cyclonic activity in the Bay of Bengal and reduced north-east monsoon intensity (Rajeevan et al., 2012) [33] . Rainfall deficits of 5-15% have been recorded in Tamil Nadu during strong La Niña years (Pai et al., 2019) [32] , compromising the rabi season and Cauvery Basin irrigation. This counter-intuitive deficit pattern in a technically surplus ENSO phase underscores the inadequacy of national-level generalisation in La Niña impact assessments.

Crop-wise Impact Assessment

Crop-specific responses to La Niña rainfall variability depend on the interaction of excess moisture availability, flood tolerance, rooting depth, growing season alignment with monsoon active phases, and soil drainage characteristics. [34] Sources: ICAR (2022) [16] , IMD (2021) [18] , FAO (2023) [9] . Data sources: DACFW (crop yield), IMD (rainfall), NDMA (flood area), World Bank Development Indicators.

Rice (Oryza sativa)

Rice is the crop most directly responsive to La Niña conditions given its requirement for standing water during the transplanting phase and its flood tolerance threshold (approximately 10-15 days of submergence for conventional varieties). In northwestern India, La Niña's rainfall surplus produces yield gains of 8-14% in Punjab and Haryana, where paddy transplanting aligns well with the enhanced June-August active monsoon period. In contrast, yields in the lower Gangetic plains and north-east decline by 3-8% due to prolonged inundation exceeding submergence tolerance thresholds of conventional varieties. The deployment of Sub1A submergence-tolerant varieties (Swarna-Sub1, Sambha Mahsuri-Sub1) across 4.2 Mha by 2022 (IRRI/ICAR) has partially mitigated flood-related paddy losses, though adoption remains heterogeneous across states.

Wheat (Triticum aestivum)

The wheat rabi season (October-March) in north-western and central India benefits modestly from La Niña through enhanced winter precipitation, with yield increases of 4-7% recorded in Punjab and Haryana during La Niña rabi seasons. However, erratic rainfall distribution under weak La Niña conditions in Rajasthan and parts of Gujarat can depress wheat yields by 5-10% through soil waterlogging at critical growth stages (tillering, anthesis). ICAR (2021) [15] noted that excess rabi rainfall during the 2021-22 La Niña event damaged an estimated 0.35 Mha of wheat in Rajasthan through grain quality degradation prior to harvest.

Coarse Cereals, Oilseeds, and Pulses

Maize, groundnut, and pulses exhibit the greatest sensitivity to waterlogging stress under La Niña conditions, as these crops are primarily grown on vertisols and alfisols with limited drainage capacity. Groundnut in Andhra Pradesh and maize in Karnataka show yield reductions of 4-9% and 2-6% respectively under excessive rainfall, primarily driven by collar rot, pod abortion, and root hypoxia. Soybean in Madhya Pradesh represents a notable exception, with yield gains of 5-9% in optimal La Niña years due to improved soil moisture retention during the critical pod-filling stage (Mishra et al., 2020) [27] . Pulses (arhar, moong) in Rajasthan and Madhya Pradesh are among the most vulnerable crops to excess rainfall, with yield losses of 6-12% reported (Rathore et al., 2020) [34] , partly owing to their cultivation on poorly-drained black cotton soils.

Extreme Events and Agricultural Risk Analysis

La Niña's role in increasing the frequency and intensity of hydro-meteorological extreme events constitutes one of its most severe agricultural risks. Analysis of IMD (2023) [19] observational data from 2000 to 2023 reveals systematic elevations in flood frequency, heavy rainfall days, and cold wave occurrences during La Niña years, as summarised in Table 3. [26] ; IMD 2023 [19] Major flood events 19 11 5 3.8× Strongly increasing NDMA, 2022 [30] Cyclonic disturbances (Bay of Bengal) 12 9 7 1.7× Increasing IMD, 2022 [18] Cold wave events (N-India) 8 6 3 2.7× Slight increase IMD, 2021 [18] Frost events ( Sources: IMD (2023) [19] , NDMA (2022) [30] , DAC&FW (2023).

The relative risk ratio for major flood events during La Niña versus El Niño years (3.8×) reflects both the enhanced precipitation totals and the concentration of rainfall in shorter, more intense spells during La Niña active monsoon phases. The NDMA (2022) [30] estimated cumulative agricultural losses from La Niña-related floods between 2010 and 2023 at approximately ₹1.8 lakh crore (USD 22 billion), with Bihar, Assam, West Bengal, and Odisha accounting for 74% of losses. A critical emerging risk is the interaction between La Niña-driven flooding and increasing background temperature trends. IPCC AR6 (2022) projects that even a 1.5 °C warming above preindustrial levels will increase the intensity of heavy precipitation events by 7% per degree of warming (the Clausius-Clapeyron scaling), implying that La Niña flood events will become progressively more severe even without changes in ENSO frequency. Cold waves and frost events during La Niña rabi seasons (occurring 3× more frequently than during El Niño years) pose underappreciated risks to wheat, mustard, and potato in north and central India. The January 2021 cold wave, coinciding with the 2020-21 La Niña event, caused an estimated 8-12% yield loss in early-sown potato crops across Uttar Pradesh and Madhya Pradesh (ICAR 2021) [15] . Recent studies indicate that La Niña years are associated with a higher probability of extreme rainfall episodes, flash floods, prolonged wet spells, and riverine flooding, particularly in eastern, northeastern, and central India (IMD 2025; Roxy et al., 2023; ICAR 2024) [18,36,31] .

Source: IMD and Global EnSo Forecast Ensembles (2025). Above-normal rainfall and cooler, damper conditions projected for northern and eastern India. These extremes pose substantial risks to Indian agriculture, including crop submergence, soil erosion, delayed sowing, and complete crop failure in severely affected regions. In addition, saturated soil conditions increase nutrient leaching and reduce fertilizer use efficiency, while persistent humidity creates conducive environments for pest and disease outbreaks such as rice blast, sheath blight, and soybean rust (FAO 2024; Gupta et al., 2025) [8] .

Socio-economic and Health Linkages 6.1 Food Security and Livelihood Vulnerability

La Niña-associated agricultural impacts translate directly into food security outcomes, particularly for marginal and small farmers (<2 ha holdings) who comprise 86% of India's agricultural households (World Bank 2022) [40] . Above-normal production in rain-fed semi-arid zones can modestly improve caloric availability and farmer income, while simultaneous flood losses in eastern and north-eastern India create acute food insecurity at the district level. Analysis of NSSO/HCES consumption expenditure surveys across La Niña years (2010-11, 2020-21) indicates a 12-18% increase in intra-national food price volatility during the kharif harvest period, with cereal prices in flood-affected districts rising 15-25% above national averages (World Bank 2022) [40] .

Vector-Borne Diseases Burden

Excess standing water during La Niña events creates breeding conditions for Anopheles and Aedes mosquitoes, elevating malaria and dengue transmission risk. NCVBDC (2022) [29] data indicate that malaria incidence in high-risk states (Odisha, Chhattisgarh, Jharkhand) increases by 28

Gender Dimensions

Flood events disproportionately affect female agricultural workers through disruption of harvest labour, increased domestic water-borne disease burden, and reduced access to credit for post-flood crop recovery (FAO 2023) [9] . These gender-differentiated impacts remain poorly quantified in the La Niña-specific literature, representing a significant knowledge gap for both research and policy.

Adaptation and Climate-Smart Agricultural Strategies

Robust adaptation to La Niña variability requires a multi-scalar approach encompassing biophysical interventions at the farm level, institutional adaptations at the state and national level, and technological innovations for early warning and precision agriculture. Table 4 represents the documented strategies, their yield benefits, cost-benefit ratios, and current scale of implementation. [12] Remote sensing for flood mapping Disaster response, planning Indirect 1:10+ National Deployed ISRO/NRSC, 2023 [23] Climate-smart village programs Community resilience 10-25 1:4 Village clusters Scaling CCAFS/ICAR, 2022 Sources: ICAR DARE (2022) [17] , World Bank (2022) [40] , FAO (2021) [8] , GOI (2023) [12] .

Biological and Agronomic Interventions

The development and deployment of flood-tolerant crop varieties represents the most direct and cost-effective biological adaptation to La Niña flood risk. Submergence-tolerant rice varieties incorporating the Sub1A quantitative trait locus (QTL), notably Swarna-Sub1 and Samba Mahsuri-Sub1, can survive 10-17 days of complete submergence with yield reductions of only 20-25%, compared to 80-100% losses in conventional varieties (Ismail et al., 2013) [24] . By 2022, Sub1-introgressed varieties were cultivated on approximately 4.2 Mha in eastern India, preventing an estimated 3.1 MT of annual paddy loss (ICAR 2022) [31] . ICAR's ongoing breeding programmes are developing varieties with combined submergence-anaerobic germination tolerance for regions experiencing both pre-transplanting and post-transplanting flooding. Adjusted crop calendars-particularly early sowing of kharif crops to complete critical growth stages before the peak of La Niña-enhanced heavy rainfall-reduce waterlogging exposure and improve yield stability. ICAR DARE (2022) [17] field trials across Odisha and West Bengal demonstrated 8-12% yield improvements with 10-15 day advanced sowing relative to conventional calendars. Raised-bed furrow cultivation systems, widely adopted in parts of Andhra Pradesh and Telangana, improve internal soil drainage and reduce waterlogging risk, with documented yield gains of 6-10% in maize, cotton, and pulses (FAO 2021) [8] .

Technological and Digital Innovations

ENSO-based seasonal forecasting has matured significantly over the past decade, with IMD operational seasonal outlooks now demonstrating skill levels (Probability of Correct Forecast: 0.72-0.84 for ISMR tercile categories) sufficient for farm-level advisory applications (IMD 2023) [19] . Integration of La Niña phase information into crop planning advisories specifying district-level crop portfolio adjustments, fertilizer recommendations, and flood-preparedness measures has been estimated to deliver 4-9% indirect yield benefits through improved resource allocation (ICAR 2023) [16] . Nair et al., (2023) [28] reported that a Long Short-Term Memory (LSTM) model integrating sea surface temperature (SST) anomalies, soil moisture satellite data (ESA CCI), and historical yield records achieved a root mean square error (RMSE) of 0.22 t/ha in district-level kharif rice yield prediction one season in advance for La Niña events, outperforming conventional regressionbased methods by 34%. ISRO's National Remote Sensing Centre (NRSC) operational flood inundation mapping service, utilising Sentinel-1 SAR data, provides near-real-time flood extent maps within 24-48 hours of flood onset, enabling targeted relief and damage assessment (ISRO 2023) [23] .

Policy and Financial Mechanisms

The Pradhan Mantri Fasal Bima Yojana (PMFBY), India's flagship weather-indexed crop insurance scheme, has expanded coverage to approximately 28% of cultivated area by 2023 (GOI 2023) [12] . While actuarially structured products theoretically provide income stabilisation during La Niña flood events, low claim settlement rates (historically 45-62%) and delayed payouts undermine smallholder uptake and trust. Reform priorities identified in the literature include transition to satellite-based automatic yield estimation (SARAL/ISRO) [23] , reduction of notification delays for localised extreme events, and product design that captures spatially heterogeneous La Niña impacts rather than averaging over administrative districts. Climatesmart village (CSV) programmes implemented under the CGIAR-CCAFS initiative across 42 districts in seven Indian states have demonstrated 10-25% yield improvements through integrated packages of improved varieties, ICT-based advisories, and community water management structures (CCAFS/ICAR 2022) [17] . Scaling these community-level approaches particularly in the flood-vulnerable lower Gangetic and northeastern zones represents a high-priority policy investment.

Future Research Directions

Several substantive research gaps that constrain the depth of understanding and limit policy application of La Niña impact science in Indian agriculture includes: [36,4]

Urban and peri-urban food systems: The impacts of La

Niña-driven agricultural production variability on urban food price volatility and nutritional outcomes, particularly for low-income urban populations have not been evaluated.

Conclusion

La Niña exerts a complex, spatially differentiated, and consequential influence on Indian agricultural systems that cannot be adequately characterized by the simple characterization of 'above-normal rainfall equals agricultural benefit.' First, La Niña reliably enhances all-India Summer Monsoon Rainfall by 1-17% relative to the long-term mean, with the strongest surpluses in the lower Gangetic plains and north-eastern highlands and anomalous deficits in parts of the southern peninsula particularly during the north-east monsoon phase. This regional dichotomy demands zone-specific agricultural planning rather than national-level monsoon outlook averaging. Second, crop yield responses are highly crop and zone-specific. While kharif rice in north-western India and soybean in Madhya Pradesh respond positively, floodinginduced losses in eastern India, combined with waterlogging damage to pulses, groundnuts, and maize across poorly-drained soils, produce significant production deficits in vulnerable states. Flood-affected agricultural area averages 4.8 Mha during strong La Niña events, compared to 0.9 Mha during El Niño years, a 5.3-fold relative risk that translates into approximately USD 2-4 billion in annual agricultural losses (World Bank 2022) [40] . Third, climate-smart adaptations particularly submergencetolerant rice varieties, ENSO-integrated seasonal advisories and weather-indexed insurance reform demonstrate positive costbenefit ratios of 1:3 to 1:12 and offer scalable pathways for reducing La Niña-related agricultural losses. Sustained investment in these technologies, combined with institutional reforms to improve insurance delivery and early warning dissemination are required. Fourth, under RCP 4.5 and SSP2-4.5 pathways, the frequency of extreme La Niña events is projected to increase by 33-47% by the late 21st century (Cai et al., 2023) [4] , while warming-driven intensification of rainfall will compound flood risk even at current ENSO frequencies. Agricultural planning in India must therefore transition from reactive post-disaster management to proactive climate-risk integration, with La Niña phase information embedded in national crop planning, disaster preparedness, and rural finance systems. The most pressing policy imperative is the establishment of a national, operationally integrated La Niña agricultural risk framework anchored in agro-climatic zone specificity, real-time remote sensing flood mapping, ENSOpredictive crop advisories, and smallholder-targeted financial risk transfer that can translate improved ENSO predictability into measurable gains in agricultural resilience and food security.

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