How El Nino and La Nina Split the Tea Harvest

The tidy answer, that an El Nino year is bad for tea and a La Nina year is good, or the other way round, is wrong in both directions. The El Nino-Southern Oscillation is a single cycle in the tropical Pacific, but the tea map is spread across East Africa, South Asia, the Indian Ocean, and East Asia, and one phase of that cycle arrives in those places as entirely different weather. The same El Nino can put record volumes of green leaf on the ground in Kenya's Rift Valley while raising drought risk over Sri Lanka's high-grown districts in the same months. So an El Nino year is not one story about the tea harvest. It is several regional stories running at once, and some of them run in opposite directions.

ENSO and long-run climate change are two different problems, worth separating cleanly. ENSO is an oscillation on a rough three-to-seven-year period: it swings one way, swings back, and returns. Long-run warming is a trend, which by definition does not return. The two get filed together because both reach the trade through rainfall and temperature, but they behave differently and they call for different responses. The structural picture, suitability loss, picking-window compression, the adaptation levers and what they cost, belongs to Climate and the Tea Supply. This piece is the cyclical companion to it: what the short-run swing actually does to a season's harvest, and how much of that a reader can usefully anticipate.

A change in the harvest is not a change in the price a grower receives. A shortfall only moves the grower's price the way a simple shortage story predicts if it clears through the global auction alongside every other producer's supply that week, and it frequently does not. That mechanism is set out in full in the climate guide and applies unchanged here. Everything that follows is about volume, which is the part ENSO touches directly.

Kenya, 2015 and 2016: both halves inside one calendar year

Kenya supplied the cleanest single-country case study the record contains, because it ran the full cycle inside about fourteen months.

The El Nino of 2015 brought heavy rain to Kenya's Central and Rift Valley growing regions from October to December, and output in those regions rose by more than a third¹. The effect carried straight into the new year. Kenya harvested 50.3 million kilograms (about 111 million pounds) of tea in January 2016, the highest monthly yield in the country's history, and 43.9 million kilograms (about 97 million pounds) in February, the highest figure ever recorded for that month. For a country whose tea export earnings had reached $1.25 billion in 2015, and which is the world's leading exporter by volume, two consecutive record months add up to a measurable jolt of global supply, not a local curiosity.

The arithmetic of the rest of the year is where the reversal shows. Kenya's first eight months of 2016 totalled 308 million kilograms (about 679 million pounds). Subtract the two record months and the remaining six months average roughly 35.6 million kilograms each, against a January that alone ran above 50 million. The season did not just stop being exceptional. It fell well below its own opening pace.

The proximate causes were sequential rather than single. June 2016 production came in 970 metric tons below May, which cold weather explains adequately and which is, in isolation, a small number: less than three percent of a normal month's output. The more telling detail in the same period is operational. Factories were running only three to four days a week. A factory's week is a better read on how much leaf is actually arriving at the gate than a monthly tonnage figure is, because a factory does not idle two days out of five over a rounding error.

Then, by September 2016, El Nino had ended and La Nina had taken over, and conditions turned abruptly. "The tea leaves are becoming dry and falling off," Johnson Irungu, director of crops at Kenya's Ministry of Agriculture, said of the change. Twelve months separated the record and that sentence, and the same oscillation produced both.

One event, opposite signs, across the Indian Ocean

The Kenyan case is legible because it is one country. Widen the frame and the picture stops resolving into a single direction.

While El Nino was raising rainfall over Kenya's tea belt, it was pushing Sri Lanka the other way. The documented pattern there is that El Nino tends to weaken the Southwest Monsoon, the May to September rains that are the main wet season for the island's western, southern, and central agricultural zones, which include key tea districts. Weaker Southwest Monsoon means higher drought risk exactly where much of the crop grows. At the same time, El Nino can strengthen the Northeast Monsoon of December to February over the north and east, raising flood risk there instead. Within one small island, in one event, one growing region can be drying out while another is taking on too much water.

There is district-level evidence for the production consequence. Researchers presented findings at the 2018 American Geophysical Union Fall Meeting showing that, using district-level production and price data from 1994 to 2016 alongside rainfall records back to 1960³, El Nino was associated with a first-quarter production drop in Sri Lanka's high-grown regions, a statistically significant negative correlation, with a similar negative association appearing in the mid-grown regions in the second and third quarters. That is a conference presentation rather than a peer-reviewed journal result, and it should be weighted accordingly, but the direction it reports is the opposite of what the same phase delivered to Kenya.

The practical consequence for anyone reading an ENSO advisory is that the phase alone tells you nothing until you also specify which growing region you are asking about and which season within that region. A forecast that says "El Nino" says nothing about tea supply until it is localised.

Does the correlation actually forecast anything?

That question has been tested on a long series.

A 2020 study in the Journal of Applied Meteorology and Climatology, by E.E. Raj, Rajagopal Raj Kumar, and K.V. Ramesh, examined ENSO-related rainfall variability and its effect on south Indian tea crop yield across the Nilgiris and the wider Tamil Nadu belt for the period 1971 to 2015, forty-five years of data. It found a positive correlation between sea-surface-temperature anomalies, the physical signature of an El Nino or La Nina event, and tea production anomalies in south India, while concluding that there is limited predictability of tea production on the basis of ENSO phase alone².

Both halves of that sentence matter, and they are usually reported separately by people who want one of them. A positive correlation between sea-surface temperature and production is there in the data. It is not strong enough to forecast a season's output from the phase, and the paper's own results say so directly: production came out less responsive to ENSO phase than rainfall did, and not every measure of the relationship cleared statistical significance. A correlation can survive in the abstract and still leave most of a given season's variance to everything else: local rainfall distribution within the season, temperature at the wrong week, soil moisture carried over from the previous year, labour availability, pruning cycles, and the ordinary noise of an agricultural system.

The implication for a reader tempted to treat a seasonal ENSO outlook as a trading signal on tea volumes is direct. The signal exists but it is thin, it is regionally specific, and it is being read from a public forecast that every other participant can also read. An ENSO phase raises or lowers the odds of a good or bad season in a particular region. It does not deliver one, and a position sized as though it did is sized on a correlation the study that measured it explicitly declined to call predictive.

What the tail looks like when it lands

The odds framing is not a reason to treat weather as unimportant, because the bad outcomes, when they do land, are large.

Tanzania's 1992 drought is the cleanest illustration of scale. Overall tea yield fell by a third, from 5,900 kilograms per hectare to 3,900⁴ (about 5,270 to 3,480 pounds per acre). Yield on young plants fell by nearly 60 percent, from 4,720 kilograms per hectare to 1,960 (about 4,215 to 1,750 pounds per acre). Mature bushes absorb a drought and recover. A young field is a multi-year capital investment that has not yet started paying back, and losing most of its output, or the plants themselves, moves the damage from one season into the next several. The same source reports that in Kenya, drought-induced oxidative stress has been found to reduce yield by 14 to 19 percent and to raise plant fatality by 6 to 19 percent, which is the mechanism behind the headline numbers rather than a separate finding.

Sri Lanka's record shows the same order of magnitude on the national scale. The island's tea output fell 26 percent during the severe drought of 1992, a year that fell inside a confirmed moderate-to-strong El Nino event on the standard climate records, though the drought's attribution to that event is context here rather than a claim the trade reporting itself makes. Some Sri Lankan regions saw seasonal rainfall drop by 60 to 70 percent during 2016. And in 2021, after four months of drought, the Planters' Association of Ceylon forecast a 40 percent production decrease.

Set those droughts against the Kenyan record months. The distribution of outcomes is wide in both directions, the tails are severe, and ENSO shifts the probability of ending up in one of them without determining which, a risk-management fact rather than a forecasting one.

The cyclical piece, not the structural one

Everything above is about a cycle that comes back. The separate question, whether the whole map is moving underneath the cycle, is the subject of Climate and the Tea Supply, which covers the long-run suitability loss, the compression of the picking window, the three adaptation levers and who pays for each, and the price-transmission mechanism that determines whether any of this reaches the grower's cheque at all. A warming trend and an interannual oscillation are different problems, and a garden manager facing both needs to know which one is producing this season's number.

What the oscillation offers a reader is narrower than the coverage of it usually suggests, and worth stating plainly: across 1971 to 2015, south Indian tea production moved with Pacific sea-surface temperatures often enough to measure, and not often enough to trade on.