Super El Niño Probability Rises to 63%, Sugar Prices Most Sensitive

Overview

Morgan Stanley has released a special report on Latin American agribusiness, focusing on the escalating risks posed by the 2026 El Niño event. NOAA has confirmed the formation of El Niño and has significantly raised the probability of it developing into an 'extremely strong event' from November 2026 to January 2027, from 37% to 63%. The report argues that current conditions are comparable to historical super-El Niños such as those in 1982/83, 1997/98, and 2015/16, but more closely resemble the gradual trajectory of 2023/24 rather than a definitive super-El Niño. The core analysis centers on El Niño’s differentiated effects on commodities like sugar, soybeans, and corn, as well as its structural implications for Latin American agricultural stocks (SMTO3, AGRO, RAIL3, SLCE3).

Core views

The report’s central thesis is that El Niño is evolving from ‘weather noise’ into an ‘investment signal.’ In terms of intensity, the Nino 3.4 index currently stands at around +0.7°C, and ocean–atmosphere coupling has been established; models indicate continued strengthening in the second half of the year. However, a ‘super-El Niño’ (exceeding +2.5°C) remains a tail risk, requiring triple confirmation: summer ocean–atmosphere feedback, observed warming of Nino 3.4, and model convergence. Historical analogies point to 1982/83, 1997/98, and 2015/16 as extreme-scenario benchmarks, while the 2023/24 experience shows that regional offsetting effects are not always reliable—in 2015/16, losses in Mato Grosso were largely offset by gains in southern Brazil and Argentina, but in 2023/24 the offset was much weaker, leaving a larger net shortfall. Commodity impacts are markedly divergent. Sugar is the clearest beneficiary: El Niño-induced drought in Asia (weakened Indian monsoon) will tighten global supply, and sugarcane crushing in Brazil’s central–southern region may also face execution challenges due to rainfall disruptions. Short-term ample Brazilian harvest supplies may cap prices, but upside risks for the 2027/28 season are clear. Soybeans present a more complex picture: Brazil’s Mato Grosso and MATOPIBA (the emerging grain belt spanning Maranhão, Tocantins, Piauí, and Bahia) face yield reduction risks, while Rio Grande do Sul and Argentina typically benefit; the investment signal hinges on the regional net balance. Corn is a timing play: if delays in Brazil’s soybean planting squeeze the second-crop (safrinha, which accounts for 80% of national production) window, or if the U.S. experiences heat and drought in July–August, prices could rise; otherwise, fundamentals will prevail. Soft commodities (palm oil, robusta coffee, cotton, fishmeal) are highly elastic, trigger-driven products that require confirmation from regional weather and production data. India, Thailand, Indonesia, Malaysia, and Vietnam are key observation points for sugar, palm oil, cotton, and robusta coffee.

Analysis framework

The report employs a transmission-chain analytical framework: ‘climate scenario → crop windows → regional yields → commodity prices → company exposures.’ Step one: Based on NOAA/CPC probability frameworks and multi-regional sea-surface temperature indices (Nino 3.4, Nino 4, Nino 1+2), the report assesses El Niño’s intensity trajectory, distinguishing between baseline scenarios (strong events) and tail-risk scenarios (super events). Step two: Climate forecasts are mapped onto specific crop windows: sugar focuses on the Indian monsoon (June–September) and Brazil’s central–southern crushing period; soybeans track Brazil’s planting season (October–December) and rainfall in key producing areas; corn looks at the safrinha planting window (January–February) and U.S. summer weather. Step three: Historical analog years are used to quantify regional yield elasticity, but the report emphasizes that analogies should guide probabilities rather than dictate outcomes—the same peak ONI value can yield different crop outcomes depending on rainfall distribution, planting rhythms, soil moisture, input costs, and export logistics. Step four: Commodity inferences are translated into company-level exposure analyses: Sao Martinho (SMTO3) benefits from rising sugar prices; Adecoagro (AGRO) profits from improved Argentine rainfall; Rumo (RAIL3) faces risks related to Mato Grosso’s export volumes and logistics rhythms; SLC Agricola (SLCE3) is exposed to drought risks in Brazil’s Midwest and central regions.

Methodology notes

• Cycle and Business Cycle FrameworkBusiness Cycle Turning Point Analysis

  El Niño–Southern Oscillation (ENSO) Cycle Analysis

  El Niño is not merely a weather event but a climate cycle with global ripple effects. The report overlays ENSO intensity indicators (e.g., Nino 3.4) with agricultural production cycles to identify how climatic turning points lead commodity price movements. At its core, this approach posits: climate anomalies → crop-window shocks → shifts in supply–demand balances → price reactions; the time lags and elasticities at each stage determine the reliability of investment signals.

• Industry/Industrial Analysis FrameworkSupply-demand framework

  Regional Yield Net-Balance Analysis

  For commodities like soybeans, whose production is geographically dispersed, a yield reduction in a single region is insufficient to signal bullishness; one must calculate the net balance across major producing areas. The report uses Brazil’s MT, MATOPIBA, RS, PR, and Argentina as analytical units, assessing regional heterogeneity under El Niño and avoiding extrapolating localized risks into global shortages.

• Industry/Industrial Analysis FrameworkUpstream–Midstream–Downstream Transmission Along the Value Chain

  Crop Calendar and Logistics Chain Matching Analysis

  Investment in agricultural commodities hinges not only on yields but also on whether the timing of those yields aligns with logistics, processing, and export capacity. The report pays particular attention to Brazil’s safrinha corn planting–harvest window, sugarcane crushing durations, and rail–port logistics capacities; these midstream links often serve as bottlenecks where climate risks translate into price volatility.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• Sao Martinho SA (SMTO3.SA)

  Direct beneficiary of rising sugar prices under El Niño

  Strengths

  High sugar-business weighting and strong sensitivity to sugar-price movements

  Weaknesses

  Brazil’s fuel policies may cap ethanol prices; a stronger real erodes export revenues

  Comparison

  Among peer sugar producers, its exposure is the purest

  Risks

  Sugar-price volatility, changes in Brazilian fuel policy, weather-related yield impacts

• Adecoagro S.A. (AGRO.N)

  Beneficiary of improved Argentine rainfall

  Strengths

  Argentine crops benefit from increased rainfall brought by El Niño; its fertilizer business (Profertil) is affected by urea prices and natural-gas costs

  Weaknesses

  Risk of worsening Argentine macroeconomic conditions; rising sugar-production costs in Brazil

  Comparison

  Greater regional diversification than purely Brazilian agricultural stocks

  Risks

  Argentine macroeconomics, natural-gas-price fluctuations, Brazilian fuel policy

• Rumo SA (RAIL3.SA)

  Faces risks related to Mato Grosso’s logistics volumes and rhythms

  Strengths

  Major agricultural logistics infrastructure operator in Brazil

  Weaknesses

  Crop yield reductions or delays directly affect transport volumes; execution risks with the LRV project

  Comparison

  Positively correlated with commodity prices, driven by yields rather than pricing

  Risks

  Brazilian crop yield reductions, adverse weather impacting export volumes, declining pricing power, competitive projects

• SLC Agricola S.A. (SLCE3.SA)

  Exposed to drought risks in Brazil’s Midwest and central regions

  Strengths

  Large-scale mechanized farming with relatively high operational efficiency

  Weaknesses

  Main production areas lie in the drought-prone MT/MATOPIBA belt; demand for cotton and feed grains is sluggish

  Comparison

  Higher weather-risk exposure than peer planting firms

  Risks

  Sluggish cotton demand and prices, declining feed-grain demand, crop failures, a strengthening real

Key data

• Probability of an Extremely Strong El Niño (November–January)63%A significant increase from the previous 37%, serving as a key monitoring indicator
• Current Nino 3.4 Anomaly+0.7°COcean–atmosphere coupling has been established, supporting El Niño projections
• Share of Brazil’s Second-Crop Corn80%A contraction of the safrinha window is the primary trigger for corn’s upside potential
• India’s Global Sugar Production RankingNo. 2Weakened monsoons will directly impact its production capacity
• Mato Grosso’s Share of Brazil’s Soybean Production29%As Brazil’s largest producing state, it faces historically significant yield-reduction risks under El Niño

Impact & implications

The report contends that El Niño’s impact on Latin American agriculture is structural rather than uniform. Sugar prices offer the clearest path to upside; short-term Brazilian supply abundance may suppress them, but the risk of Asian drought in 2027/28 will dominate. For soybeans, regional net effects must be observed, and simple long positions are inadvisable. Corn is a conditional trade, requiring confirmation from both delayed Brazilian planting and U.S. summer weather. Among relevant companies, Sao Martinho and Adecoagro are seen as potential beneficiaries, while Rumo and SLC Agricola face operational risks. The report specifically cautions that company-level exposures should be monitored through local data (export volumes, crop ratings, rainfall, planting progress) rather than ENSO headlines, to distinguish between commodity-price rallies and corporate operational hazards.

Risks

• El Niño’s intensity falls short of expectations, and the super-El Niño narrative fades
• Uneven rainfall distribution across Brazil’s crop-producing regions leads to regional offsetting effects, limiting net yield losses
• The Indian monsoon weakens less than anticipated, leaving sugar-supply risks unfulfilled
• Changes in Brazil’s fuel policy suppress ethanol prices, indirectly affecting sugar prices
• Further deterioration of Argentina’s macroeconomic conditions impacts Adecoagro’s operations
• Global demand weakness (especially for cotton and feed grains) offsets supply-side shocks

What to watch

• Whether Pacific trade winds remain persistently weak or reverse from June to August, confirming the strength of ocean–atmosphere coupling
• Observation of Nino 3.4 from August to September to see if it converges toward +2.0°C or higher
• Rainfall patterns during the monsoon season in India, Thailand, Indonesia, Malaysia, and Vietnam
• Sugarcane-crushing days, ATR (sugar content per ton of cane), and cane-age profiles in Brazil’s central–southern region
• Planting progress of soybeans in Mato Grosso/MATOPIBA and rainfall from January to March
• Export volumes from Mato Grosso handled by Rumo, along with corn and soybean transport volumes
• Crop ratings and rainfall data for Adecoagro’s holdings in Argentina
• Rainfall and planting progress in Brazil’s Midwest and central regions for SLC Agricola