Would You Rather: Earth Data with Daniel Jimenez
PERSPECTIVES

Would You Rather: Earth Data Edition – with Daniel Jimenez

Data is the life-blood of today’s world.

It’s informing everything from when to leave your house to make it to soccer practice on time, to guiding farmers on what and when to plant for best crop yields. In celebration of Earth Day 2018, we sat down with a notable climate scientist to find out what kind of Earth data he prefers.

Would You Rather: Earth Data Edition - Today’s guest: Daniel Jiménez, Data-Driven Farming Specialist from CIAT

Daniel is a scientist at the International Center for Tropical Agriculture (CIAT) where he coordinates the Data-Driven Agronomy Community of Practice of the CGIAR Platform for Big Data in Agriculture. Daniel and his team help farmers use big data to better understand environmental factors and guide best planting dates, right amount of pesticides and fertilizers in specifc contexts for optimal crop yield. Their work has been recognized with a number of honors including the 2014 UN Big Data Climate Challenge, 2015 World Bank Group Big Data Innovation Challenge and 2017 UN Momentum for Change award.

WOULD YOU RATHER BE STUCK IN A DRY DESERT OR A FROZEN TUNDRA?

DANIEL: (sigh and a laugh) Frozen tundra probably. I was born in tropical conditions, and this type of heat in the desert … I cannot take that much. I live in Cali (Colombia) and sometimes it gets so hot, you ask yourself ‘what is left to remove?’ (laughs). You know, what is too much?

WOULD YOU RATHER PLANT A FOREST OR REGROW A CORAL REEF?

DANIEL: I’d go for forest. Definitely grow a new forest. And I know why. I have an argonomic background, I’d know how to do it! (laughs)

Daniel Jimenez and Rolando Martinez investigate a farm field in Honduras.
Daniel Jimenez and Rolando Martinez investigate a farm field in Honduras.
WOULD YOU RATHER HAVE A 1000-MILE DEEP CORE SAMPLE OR 1000 YEARS OF ATMOSPHERIC DATA?

DANIEL: I would rather choose, and by far I think, 1000 years of atmospheric data. Because when we work in data science and the environment we always talk about variability and how your sample should represent as many conditions as possible. So in that sense, 1000 years of atmospheric data would be much more interesting, and much more actionable than just one single core sample, even one as difficult to get as 1000 miles deep.

WOULD YOU RATHER USE WIND, SOLAR OR HYDROPOWER?

DANIEL: Solar. In the conditions I live in, which are the tropics, I think solar is more efficient in all senses.

WOULD YOU RATHER HAVE ONE MILLION DATA POINTS FROM A SINGLE, WELL-PLACED SENSOR OR ONE DATA POINT FROM ONE MILLION RANDOMLY-PLACED SENSORS?

 DANIEL: I would have one data point from one million placed sensors. I would prioritize spatial over temporal variability.

Daniel and his team at CIAT.
Daniel and his team at CIAT.
WOULD YOU RATHER BE INSIDE LOOKING OUT OR OUTSIDE LOOKING IN?

DANIEL: (laughs) Dude, that is a difficult one. Um … outside looking in. I spend part of my time living in a temperate country with winter and … you know … I like to be outside!

WOULD YOU RATHER READ SCIENCE OR SCIENCE FICTION?

DANIEL: Science. Yes, you know it happens when you do a PhD, you know this stuff. You have to get used to reading science and you start to enjoy it actually.

WOULD YOU RATHER SWITCH 100 HOMES OR 100 VEHICLES TO CLEAN ENERGY?

DANIEL: I would rather switch 100 homes. I think we can make a huge difference at home in terms of renewable energy. With vehicles in that sense, we’re literally going somewhere. I mean you can see so many airports and cities that have been made better in that sense, and also I think in terms of transportation and vehicles, we should promote the switch in collective [public] transportation.

Earth Day Words of Wisdom from Daniel

“When we talk about data – atmospheric data, data and the environment and agriculture – I remember before I started my PhD, we complained about the lack of data in those days. Now we have a huge amount of information and sources that we have not yet explored the full extent of. That’s why we need more data scientists working on this. But the interpretation of the data is very important. We cannot leave data scientists to explore that data without an? interpretation that is guided by experts such as climate and environmentalists. We all need to work together.”

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