Time-travelling to food security

The category Cool initiatives is a space to briefly summarise and highlight projects and programmes in agriculture that work to increase food security and that I find cool. From implementation of important theories, such as participative action research, to the use of novel tools to support old traditions, cool can mean different things.

Imagine that you are a Colombian quinoa farmer. Things are going well right now but you’ve heard about climate change and you’re concerned because you don’t know how your soil and rainfall patterns are going to be like in let’s say 2030. You would like to peek into the future conditions of your farm today in order to prepare and adapt.

Well, put your aluminium foil helmet on and let’s travel to your future climate, TODAY!

Climate Analogues is a time-travel machine that helps us answer these types of questions:


Build your own question and time-travel. Figure built based on Ramírez-Villegas, et al. (2011).

For the hypothetical case mentioned at the start where you’re a Colombian quinoa farmer, the question we would ask is:

Where can I find sites that are at present analogous to my quinoa farm in Colombia in the future (2030)?

The tool finds sites that are statistically similar according to your input. This time-travel machine, developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCFAS), lets you further specify your question by allowing you to choose from a set of conditions that may be of particular interest to you such as search range (i.e. the whole world or a set of locations), climate variables (i.e. temperature, precipitation, soils), period or year (i.e. historical, present, or future), climate scenario, time step (i.e. days, months or seasons) and weight (i.e. the relevance you give to certain conditions over others) (Ramírez-Villegas, et al., 2011).

I’ve italicised statistically in the last paragraph for a reason. Climate Analogues is coded in R. High-five if you know how to use R. If you don’t however, R is a free software for statistical analysis. This means that all answers to your questions come with fine print, disclaimers otherwise known as uncertainties. If you want to learn about the mathematical explanation of how the answers are retrieved, here is the very approachable working paper this post is based on.

Let’s use the tool together for our example, ready?

Step 1. Pick your reference site. Since in our example you are a quinoa farmer like Cristela, I’ve selected that region. For the search range, I’ve selected ‘Colombia’ because we want the analogous site to be close by so we can go visit the farmers who are currently living where our future climate would be.

Selecting the ‘Backward’ option means that we want to find sites that currently resemble what our quinoa farm will be like in the future.

Step 2. Travel to your future climate? Be the future climate of other sites? Find similar sites at present? This tool is 2 cool 4 school.

Now it’s time to put our preferences in place. What do we want to know about our future climates?

Step 3. I’ve left the predetermined options in place: monthly mean temperature and monthly precipitation are given equal importance.

Now we let the tool do it’s magic a.k.a statistical analysis.

Step 4. The results. The higher the decimal means the more similarity according to our input.

I’m not surprised with the results. Here’s a quick mini-geography lesson for you. Colombia is one of the most biodiverse countries in the world. Many factors are responsible for this diversity and one of the most important ones is altitude. The Andean Mountain range stretches through South America creating a wide range of thermal floors, the region coloured with light green and yellow (0.4 and 0.5 similarity) is on the Andes Mountains. This means that within a few hours of travel we can visit our future climates and learn from farmers currently living there.

Now, it’s your turn. Where are you from? Are you ready to travel into the future and explore your future sites? Here’s the link for the tool. Go on! Use it! Impress your real life or imaginary fellow farmers. Use it to make new friends overseas and visit them: “Hey, did you know that your site is like mine but in 10 years? I’d love to visit and know what my future will be!”

It is important to acknowledge that this tool has limitations as any other tool really. Some are specific to Climate Analogues, to name a few: the sites are retrieved according to a ‘threshold’ that the user defines, the calculations are slow and time-consuming and others are regarding the data – the answer will be as relevant as the quality of information available to input.

However, in my opinion the most important limitations are in the hands of the user, meaning us. How well do we use and understand the tool? Do we recognise that the answer takes into account certain things and leaves out other factors such socio-economic and political conditions? Critical interpretation of the results is, well, critical.

I would love to hear about what your future climates are. Did you find regions that are close by? What questions would you ask the farmers currently living there?

References (because giving credit is everything):

Ramírez-Villegas, J., Lau, C., Köhler, A.-K., Signer, J., Jarvis, A., Arnell, N., et al. (2011). Climate Analogues: Finding tomorrow’s agriculture today. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).


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