Climate & Agriculture 5: Risk of declining (tropical) crop yields has been known for over 25 years

Not just from theoretical thinking, but as calculated outcomes of pioneering climate-crop prediction models – with studies from the early nineties already offering broad patterns of expected changes in agricultural productivity in a warming world. These patterns have of course been fine-tuned by tireless research ever since but already look very familiar to those who follow today’s climate impact research. And that’s something we think deserves our attention…

The influence of climate change on the growing season in a typical temperate, subtropical, tropical and desert climate. Taken from climate model study from 1993.
climate model graph legend
The influence of climate change on the growing season in a typical temperate, subtropical, tropical and desert climate. Taken from climate model study from 1993.

We came to this topic, by stumbling upon a very interesting study – interesting firstly because it’s research into the specific topic we’re pursuing for this series, and also interesting because of the year it was published: 1993 – in the journal Climate Research (PDF). Authors are Rik Leemans and Allen Solomon, who then worked at the Dutch National Institute of Public Health and Environmental Protection (RIVM) and the US Environmental Protection Agency (EPA) respectively, and who are both still actively participating in current climate research. We’ll have a short chat below…

A longer growing season versus a longer dry season

The study investigates the length of the growing season for different crops and in different climate zones, following climate warming in the year 2050 from a business as usual emissions scenario that leads to a doubling of (preindustrial) atmospheric CO2 [let’s say a scenario that’s more or less in line with 3 degrees warming by the end of the 21st century].

Compared to the situation in 1970 the researchers find an improved outlook for higher latitudes regions (particularly Canada, Siberia), where agriculture may benefit from earlier onset of the growing season. In warmer regions (Mediterranean to tropical) however this isn’t always a benefit and net agricultural damage can follow from changing moisture availability:

“The warmer temperature in mid latitudes has significant impacts on the water balance. An enhanced dry period occurs during the summer season in arid northern Mexico, the south-western United States, southeast Asia, and southern France.”

“The growing season shifts only slightly in tropical regions. Most changes are moisture related. Despite increased evaporative demand, increased precipitation enhances the growing season in the western Sahelian region. Here agricultural production could increase, although conditions remain marginal.

“These moisture related changes should be treated cautiously, because AGCMs are notoriously inaccurate in simulating changes in precipitation. However, that changes in crop growing period properties are determined by future temperatures at high latitudes and by future precipitation at low latitudes is indeed a reliable implication of this simulation.”

It’s an interesting detail that this climate model projects Sahel greening for the western Sahel [stating these projections should be treated cautiously, read above!]. In current thinking this has shifted to eastern Sahel greening – see our special on that topic.

Overshoot and collapse graph from Limits to Growth
One of the first global system models to be put to the test was of course MIT’s World3 – that enabled creation of The Limits to Growth from 1972, arguably the most influential environmental report ever published, with very specific calculations about the unsustainable growth patterns of the global economy. These forecasts (the famous overshoot & collapse graphs for resources, pollution, industrial output per capita and food per capita, even human population) were met with scepticism first, but receive broad recognition by recalculating 21st-century academics for (sadly) having been remarkably right. All this is to say that a good model does not require a speedy computer. It requires a good design, made by good scientists. [If you’re interested in this history we strongly recommend(!) our recent video interview with Limits to Growth author Dennis Meadows, in which he talks about the current climate crisis and what ‘collapse’ means.]

It’s also good to note that newer research has suggested for the tropics heat stress may be more damaging than precipitation changes – and that also agriculture in temperate regions is not exempt from increasing heat-induced crop damage. This means also in colder regions temperature rise is not always your friend: an increasing growing season is beneficial, but hot summer extremes may still decrease productivity – also for instance in a country like Russia.

So we thought it would be nice to ask the lead author Rik Leemans how his thinking on these issues has progressed, drawing from his own (ongoing!) follow-up research on agricultural climate impacts over the last 25 years.

Remember, it was also Rik Leemans who helped us better understand heat stress impacts on (European) wheat yields in our previous piece of this series.

One of the first climate-crop prediction models showing impacts of climate change for various crops. Now do these calculations still stand? And what may have been missing back in 1993? We've asked the lead author:
One of the first climate-crop prediction models showing impacts of climate change for various global food staples. Now do these calculations still stand? And what may have been missing back in 1993? We’ve asked the lead author:

Q – Rolf: “In your study from 1993 you express the expectation that (on average) high-latitude agriculture will benefit from climate change, as the longer growing seasons will increase net productivity. Warmer regions however may face an opposite effect, where climate change may decrease net productivity. Do you still expect the same pattern?”

Q – and in addition: “In 1993 you wrote that (in lower-latitude regions) changes in moisture availability is the major threat from climate change that could lower net productivity. After 25 years of follow-up research, what do you now see as the major agricultural threat of climate change – and how do you weigh the effects of precipitation changes (or droughts & floods) compared to heat stress?”

A – Rik: “Yes, I think shifts in crop patterns are still relevant and that these require taking into account both temperature and precipitation patterns throughout the year. Our productivity model was very basic (photosynthesis minus respiration). Much more detailed crop models with local input can and are doing this much better today, especially on a local and regional scale. But these also demand much more data input and computing power.”

“A number of important factors have been added. In the latest IPCC report about agriculture for instance heat stress is regarded as a major crop-yield-lowering factor – we’ve seen this last summer for instance in the Netherlands.”

“Nowadays I always say that in the nineties we had to rely on model calculations, but today we can witness the broad lines of these calculations become reality. We then knew the order of magnitude and we are now adding ever more decimal numbers…”

That’s powerful. Thanks to Joseph Fourier we’ve known since 1822 about the Earth’s greenhouse effect. Thanks to John Tyndall we’ve known the specific heat absorbing properties of greenhouse gases like methane and CO2 since 1862. And we’ve known that our fossil CO2 emissions would cause global warming since Svante Arrhenius did that math, in 1896. It goes to show that the physical core of climate science was already established in the 19th century.

And building on that the more detailed fields of climate science have similarly impressive foundations, like the research of agricultural impacts. And all this time we’ve known that if we wanted to prevent this damage there’s just one solution: mitigation. That message never changed – but somehow that message never stuck.

© Rolf Schuttenhelm |

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