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- [Nelson] And on that note,

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I'd actually like to introduce John Hayes

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from our Geography Department.

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Take it away, John.

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- Thank you, Nelson. (coughs)

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Good morning and thank
you all for coming today.

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The Geography and Sustainability
Department is honored

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to host the visit of Dr. Molly Brown

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to the 43rd Annual Darwin
Festival of Salem State.

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Dr. Brown is a Research Professor

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in the Department of Geographical Sciences

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at the University of Maryland.

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She received her Bachelor
of Science degree in Biology

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from Tufts University

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and her master's and PhD in Geography

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from the University of Maryland.

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Dr. Brown worked at the
NASA Space Flight Center

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in the Biospheric Sciences
branch for over 15 years.

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She's published over 120 journal articles

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and book chapters and two books.

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The Geography and
Sustainability Department

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of Salem State has been
hosting an invited speaker

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at the Darwin Festival
since February of 2002.

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And we are very pleased to have Dr. Brown

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with us today to continue this tradition.

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We would also like to thank
the Charles Albert Reid Trust

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for sponsoring our invited
speakers over the past 20 years.

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And we as especially thank
the Biology Department

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and the Darwin Festival Planning Committee

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for giving the Geography and
Sustainability Department,

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the opportunity to host
a speaker every year.

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We really do appreciate it.

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Dr. Brown's research
interests intersect a number

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of interdisciplinary fields including

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climate change science, crop production,

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remote sensing and
digital image processing,

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mathematical modeling, and food security

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and insecurity and nutrition.

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Today, she will describe data sets

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that she has used to
measure climate extremes,

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explain and define what food security is,

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and to share data sets that demonstrate

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the impact of climate change
on food security and children.

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Another part of Dr Brown's life is,

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she's also the Chief Science
Officer at 6th Grain,

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a company that develops software solutions

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with data gained from
remote sensing in the field.

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She, today is gonna
present her work regarding

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climate extremes, food security,

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and the nutrition of
children in Africa and Asia.

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Again, we are thrilled that
Dr. Brown is with us today

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to speak on the topic,

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"Climate change and Food Security,

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Evidence that Connects
Climate Change of Temperature,

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Rainfall, and Vegetation Dynamics

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to Nutrition and Human Health Outcomes."

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So thank you, Dr. Brown,

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looking forward to your talk, take care.

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(paper rustling)

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- Okay.

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So I assume you all can see my screen.

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Yeah, so thank you very much

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for attending this lecture.

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And as the introduction said,

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I'm going to be talking about
human health and nutrition,

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and we're going to be
using satellite data.

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And you know, satellite
data is an amazing tool

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that we can use to look back in time

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and to connect what's happening with,

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what's happening with people,

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with what was happening with the climate

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when those people were spoken to.

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A little bit about me before I start.

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I grew up on a Connecticut dairy farm,

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and this really has changed
how I've looked at the world

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because I've been fascinated
by food and food production

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and growing things

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and understanding how the
weather affects that process.

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As you heard,

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I also studied biology
and environmental studies,

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and then I went into the
Peace Corps in Senegal,

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where I worked on environmental education

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and was continually amazed
by how incredible it was

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that people could grow food
in a semi-arid ecosystem,

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so profoundly different than that,

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where we all have lived in New England.

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After studying geography,

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I went and became a civil
servant and then in 2015,

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I left NASA Goddard

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so that I can focus on more social

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and economic aspects of food security

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than what I was able
to look at, at Goddard.

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So today, I'm gonna be
speaking a little bit about,

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first, the conceptual frameworks.

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How do we link climate and food security?

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And then I'll be talking a little bit

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about what the data sets are that measure

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climate in the past, the present,

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and how we can see into the future.

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And then, I will be defining food security

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and speak in a little bit,

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giving you a little bit of background

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about how the food security
indicators can be used

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to connect to food security,

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according to the literature.

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And then right when
you're totally exhausted,

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I will give a bit results
from three different papers,

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one Niles et al in 2020,

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and drought and stunting
with Cooper et al in 2019.

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And then finally, a
paper about birth weight,

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children's birth weight,
temperature, and precipitation.

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The overall message just to
give you the punchline here

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start at the beginning is
that temperature changes

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and temperature increases
will have a profound effect

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on food, food security,

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malnutrition, and children's birth weight.

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And that is really what I
wanna convince you all of

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before the end of this, the talk.

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(keyboard clanks)

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Okay, so what is food security?

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"Food security exists when all people,

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at all times, have physical, social,

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and economic access to sufficient, safe,

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nutritious foods that
meets their dietary needs

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and food preferences for an
active and healthy life."

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So it's not enough to be merely food.

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It has to be food that you want to eat,

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that your family wants to eat, et cetera.

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So there are three,

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there are four linked
concepts of food security.

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There's availability, access, utilization,

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and the stability of these.

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Availability is whether or
not food is actually present.

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Oops, excuse me,

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present in the place you are.

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And we can see availability
looking at food production,

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distribution exchange, using
satellite data as proxies.

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We can use satellite
observations of crop area

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and crop health to figure out

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whether or not a particular
area has sufficient food

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or if they're having a big
reduction in availability.

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Then there's access.

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Access is about food affordability,

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allocation, and preference.

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There's not enough to
have food where you are

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if you can't afford to buy it.

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So it's basically, the whole foods effect.

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You go into whole foods, you
can only afford a candy bar.

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It's not exactly sufficient.

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And then we can use data sets on prices,

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trade, household resources,

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and wealth to understand
access variations.

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And then finally, there's utilization.

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You can think of utilization
as more at the individual.

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Whereas availability is at the region

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and access is at the household.

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Utilization is at the individual.

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Are you well enough to use the food

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that you consume to have
an active and healthy life?

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If you're too ill,

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or if the food isn't safe,
then you want food secure.

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So, and we often use
proxies of healthcare, food,

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and status nutrition levels.

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And then finally, that
yellow dot in the middle,

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these all have to be stable.

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It's not enough to have
all three of these aspects

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nine months of the year,

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and then be starving
three months of the year.

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It's just in, or it's just not sufficient.

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Okay, so when we're connecting
climate change to help,

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what we do is we often use
this complex set of parameters.

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(claps) And I know it looks
very hard and complicated,

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but I wanted to just give you some idea

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about the different aspects

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to the impact of extreme temperatures

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or precipitation extremes,
like droughts or floods.

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So here we have internal
socioeconomic vulnerability.

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This is your culture,
your social networks.

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If you can be vulnerable to climate change

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because of economic considerations.

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And then there's internal
biophysical vulnerability,

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your age, your sex,
your nutritional status,

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your personal, your health, right?

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So this is like, so if you are disabled,

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you're more vulnerable to extremes

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than if you were a healthy
20-year-old, right?

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And then you have external
socioeconomic vulnerability,

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your income, your food
affordability, social aid.

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And then external,

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is your house located in a flood plain?

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If your house is located in a flood plain,

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doesn't matter if you're
a healthy 20-year-old,

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if your house has been flooded,

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you're still vulnerable, right?

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So you need to think about
the impact of temperature

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and climate extremes
across all these factors.

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So even if you're physically safe,

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maybe you're economically not safe, right?

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Another thing we might think
about is like the difference

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between twins, a 13-year-old
girl and a 13-year-old boy,

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they have very different vulnerabilities

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because they have different social roles,

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different cultures, they
do different things.

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All of those things affect your
ability to adapt to extremes

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and your exposure.

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(keyboard clanks)

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Okay, so let's go back

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and talk about the
biophysical side for a minute

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now that I've confused you
about the socioeconomic.

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So on the biophysical side,

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we can really see amazing stuff right back

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to the early '80s

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about temperature, rainfall, clouds,

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humidity, soil, moisture, et cetera.

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But today is a theme is temperature.

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So I really wanted to focus on temperature

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because most people think
about climate extremes

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that affect food security
regarding drought.

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They think, ah, you have a drought,

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you're gonna have loss of crops,

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and therefore you're
gonna have food deficits

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and therefore food insecurity.

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And that's true.

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But recently in the work
that I've been doing,

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we've been finding

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that temperature is
really the driving factor.

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It might be that temperature's
driving the droughts

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or I'm not exactly sure,
(dogs barking)

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but it's quite,

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sorry about the dogs.

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You can really see these big
trends in temperature changes

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over the last,

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from 1880 to 2019.

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But I wanted to bring this
home a little bit more.

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So there's this great tool
that shows the number of days

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between birth today and when you're 80.

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So here, if you happen to have been born

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in Hartford, Connecticut in 1969,

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then the average number of days

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00:12:20,580 --> 00:12:23,550
at or above 90 degrees would be eight.

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By the time 2017 rolls around,
it would be 10 days over 90.

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However, by the time this
per theoretical person is 80

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and they still live in
Hartford, Connecticut,

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it would be 25.

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So you can see that between 8 and 25,

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this really is driving a lot
of choices that you're making

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in the kind of places that you're living,

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but it isn't hugely transformative.

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There's still a lot more heat waves.

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And a lot of this warming that
we expect is in the future

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between 2017 and 2049,

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there's a much bigger change
than between '69 and 2017.

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Now, if a person was
born in Dakar, Senegal,

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right about 1969 or 1970,

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there was 105 days over 90 degrees.

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This is not all that surprising.

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It's pretty warm there,

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but 105, so that's one third of the year.

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By the time 2017 has happened,

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significant warming over
double the number of days,

261
00:13:38,030 --> 00:13:42,470
226 days at 90 degrees or more.

262
00:13:42,470 --> 00:13:44,980
And you see that

263
00:13:44,980 --> 00:13:49,170
most of this significant
warming has happened in the past

264
00:13:49,170 --> 00:13:50,830
instead of in the future,

265
00:13:50,830 --> 00:13:52,650
which means that the vulnerability

266
00:13:52,650 --> 00:13:55,430
that people in tropical
areas are experiencing

267
00:13:55,430 --> 00:13:58,470
the food security caused by increasing

268
00:13:58,470 --> 00:14:02,100
temperatures has already happened.

269
00:14:02,100 --> 00:14:03,387
We can see that now,

270
00:14:03,387 --> 00:14:06,060
and we don't need to wait until 2050

271
00:14:06,060 --> 00:14:08,630
to see the impacts of climate change.

272
00:14:08,630 --> 00:14:12,350
And this insight is driving
a lot of the literature

273
00:14:12,350 --> 00:14:14,450
and my research in particular,

274
00:14:14,450 --> 00:14:15,930
that in tropical areas

275
00:14:15,930 --> 00:14:20,040
that are already on the
high end of the cusp.

276
00:14:20,040 --> 00:14:22,070
As the climate has been warming,

277
00:14:22,070 --> 00:14:23,400
they're already experiencing

278
00:14:23,400 --> 00:14:26,420
a great deal of change. (sniffs)

279
00:14:26,420 --> 00:14:29,910
Okay, so when we're
measuring food security,

280
00:14:29,910 --> 00:14:33,060
there's a number of ways
that we can show the impact

281
00:14:33,060 --> 00:14:35,830
of this change on outcome.

282
00:14:35,830 --> 00:14:38,020
One of of the big ways is through surveys,

283
00:14:38,020 --> 00:14:41,280
these things called
Demographic and Health Surveys.

284
00:14:41,280 --> 00:14:43,130
Currently, the most common,

285
00:14:43,130 --> 00:14:46,330
reliable data source are these DHS.

286
00:14:46,330 --> 00:14:50,070
And it's really about
looking at the individual

287
00:14:50,070 --> 00:14:52,520
instead of using average statistics.

288
00:14:52,520 --> 00:14:56,500
I'm sure you all have
seen the FAO saying 35%

289
00:14:56,500 --> 00:14:59,190
of this and that have
experienced food insecurity.

290
00:14:59,190 --> 00:15:01,540
It's very difficult to relate,

291
00:15:01,540 --> 00:15:05,420
like the average in Mauritania
or the average in Russia,

292
00:15:05,420 --> 00:15:07,820
such a gigantic area to the-

293
00:15:07,820 --> 00:15:11,160
what is happening with the
food security of an individual

294
00:15:11,160 --> 00:15:15,320
or even a community or
even a neighborhood,

295
00:15:15,320 --> 00:15:17,580
because it's just too average,

296
00:15:17,580 --> 00:15:21,120
'cause you're averaging
dry places and wet places,

297
00:15:21,120 --> 00:15:24,120
cold places and hot places.

298
00:15:24,120 --> 00:15:27,740
And so you get this
milk-toast number, right?

299
00:15:27,740 --> 00:15:31,690
So by having individual observation,

300
00:15:31,690 --> 00:15:36,100
we're able to really see the geospatial,

301
00:15:36,100 --> 00:15:38,680
connecting that geospatial data

302
00:15:38,680 --> 00:15:40,530
and the changes we see in the satellites

303
00:15:40,530 --> 00:15:43,180
to the changes we're seeing on the ground.

304
00:15:43,180 --> 00:15:46,230
The DHS data is extensively available.

305
00:15:46,230 --> 00:15:50,310
This, you can find the
data at dhsprogram.com

306
00:15:50,310 --> 00:15:54,020
and it's funded by your
tax dollars and mine.

307
00:15:54,020 --> 00:15:56,520
So it started in 1985.

308
00:15:56,520 --> 00:15:58,470
It's continuing to go on.

309
00:15:58,470 --> 00:16:01,390
It covers an extensive
amount of the world.

310
00:16:01,390 --> 00:16:05,520
And so we're able to
use this data to gather

311
00:16:05,520 --> 00:16:09,130
an understanding of how
individual observation

312
00:16:09,130 --> 00:16:11,240
of nutrition outcomes,

313
00:16:11,240 --> 00:16:15,810
which is a direct measure of
food security can be connected

314
00:16:15,810 --> 00:16:17,700
to extreme temperatures.

315
00:16:17,700 --> 00:16:19,890
So this is really the
foundation of the work

316
00:16:19,890 --> 00:16:22,150
that I'm presenting today.

317
00:16:22,150 --> 00:16:24,590
When we're looking at the individuals,

318
00:16:24,590 --> 00:16:27,990
you have to connect them to a population.

319
00:16:27,990 --> 00:16:31,300
So if you look at this
figure on the right,

320
00:16:31,300 --> 00:16:35,270
you can see that what we
do is we take a huge number

321
00:16:35,270 --> 00:16:38,720
of individuals and we plot them on a graph

322
00:16:38,720 --> 00:16:42,683
and what we expect in a healthy community,

323
00:16:43,690 --> 00:16:45,280
in a perfectly healthy community,

324
00:16:45,280 --> 00:16:47,160
you would have the average,

325
00:16:47,160 --> 00:16:50,290
the peak here on this line would be zero.

326
00:16:50,290 --> 00:16:51,810
You would have some skinny people,

327
00:16:51,810 --> 00:16:54,280
some larger people,

328
00:16:54,280 --> 00:16:56,590
but you basically have this distribution.

329
00:16:56,590 --> 00:16:59,080
In a community with food insecurity,

330
00:16:59,080 --> 00:17:02,120
this entire area is shifted to the left.

331
00:17:02,120 --> 00:17:05,920
And one experiencing an obesity epidemic,

332
00:17:05,920 --> 00:17:10,190
like some places that
we would, that we know,

333
00:17:10,190 --> 00:17:12,710
this whole thing would
be shifted to the right.

334
00:17:12,710 --> 00:17:17,230
So many of the communities that I study,

335
00:17:17,230 --> 00:17:20,420
this distribution of children

336
00:17:20,420 --> 00:17:22,890
under the age of five
is shifted to the left.

337
00:17:22,890 --> 00:17:26,100
And you can see this figure on the bottom,

338
00:17:26,100 --> 00:17:28,870
the light gray areas are normal.

339
00:17:28,870 --> 00:17:30,110
And then you see

340
00:17:32,160 --> 00:17:34,430
these dark gray areas show

341
00:17:34,430 --> 00:17:36,800
this distribution has shifted to the left.

342
00:17:36,800 --> 00:17:39,010
And so this negative three,

343
00:17:39,010 --> 00:17:41,140
that's this number here.

344
00:17:41,140 --> 00:17:43,660
And so this shifting to the left,

345
00:17:43,660 --> 00:17:46,740
this is talking about the
center of this distribution.

346
00:17:46,740 --> 00:17:49,410
We have very, very malnourished people

347
00:17:49,410 --> 00:17:51,020
and less malnourished people,

348
00:17:51,020 --> 00:17:53,970
but what we want is to
have that distribution

349
00:17:53,970 --> 00:17:57,130
right in the center
instead of on the left.

350
00:17:57,130 --> 00:18:00,120
So there are a few measures
of nutrition outcomes.

351
00:18:00,120 --> 00:18:01,410
There's stunting,

352
00:18:01,410 --> 00:18:03,580
which is what we're talking about here,

353
00:18:03,580 --> 00:18:07,640
which is basically when
you're too short for your age.

354
00:18:07,640 --> 00:18:09,900
Wasting is when you're
too skinny for your age,

355
00:18:09,900 --> 00:18:11,710
but your height is average.

356
00:18:11,710 --> 00:18:15,200
Underweight, which means that your weight

357
00:18:15,200 --> 00:18:18,178
for your age is low

358
00:18:18,178 --> 00:18:21,210
and there's finally also low birth weight.

359
00:18:21,210 --> 00:18:23,350
So I'm gonna be talking
about these stunting,

360
00:18:23,350 --> 00:18:25,000
wasting and underweight,

361
00:18:25,000 --> 00:18:26,920
and essentially, they're all proxies

362
00:18:26,920 --> 00:18:29,310
for your food security status

363
00:18:29,310 --> 00:18:31,190
for one of the most vulnerable groups,

364
00:18:31,190 --> 00:18:32,933
which is children under five.

365
00:18:34,610 --> 00:18:36,180
Okay, so if we were live,

366
00:18:36,180 --> 00:18:38,800
you could then ask me whether
or not I'm going too fast,

367
00:18:38,800 --> 00:18:40,070
but because we're not live,

368
00:18:40,070 --> 00:18:43,060
you can ask me to repeat
any of this later.

369
00:18:43,060 --> 00:18:46,610
Okay, so when you're linking
these individual observations

370
00:18:46,610 --> 00:18:48,160
to the environment,

371
00:18:48,160 --> 00:18:50,533
what we need to do is to,

372
00:18:52,170 --> 00:18:54,990
we need to connect the
geospatial coordinate

373
00:18:54,990 --> 00:18:58,417
or the latitude, longitude
point to the satellite data.

374
00:18:58,417 --> 00:19:03,110
And we do that by averaging

375
00:19:03,110 --> 00:19:05,560
the satellite data over to small area,

376
00:19:05,560 --> 00:19:06,930
but not the whole continent.

377
00:19:06,930 --> 00:19:11,130
So 10 by 10 or 5 by 5 kilometer area

378
00:19:11,130 --> 00:19:14,320
where we have this
latitude, longitude point,

379
00:19:14,320 --> 00:19:17,890
which has been displaced so
that you cannot find the village

380
00:19:17,890 --> 00:19:21,010
or the community or the
household on the map.

381
00:19:21,010 --> 00:19:23,350
But we know there's been some displacement

382
00:19:23,350 --> 00:19:26,550
to protect confidentiality,

383
00:19:26,550 --> 00:19:28,210
but it works pretty good

384
00:19:28,210 --> 00:19:33,210
because we're able to
show the mean temperature

385
00:19:33,750 --> 00:19:37,053
and precipitation for that spatial area,

386
00:19:38,220 --> 00:19:41,430
but only for this little area inside.

387
00:19:41,430 --> 00:19:45,990
So we do a very careful
connecting of this location,

388
00:19:45,990 --> 00:19:49,810
where the child or that group
of households is located

389
00:19:49,810 --> 00:19:51,608
to the place on the map.

390
00:19:51,608 --> 00:19:52,441
(keyboard clans)

391
00:19:52,441 --> 00:19:56,700
And we carefully connect
the time of the observation

392
00:19:56,700 --> 00:19:58,940
of the child to the time.

393
00:19:58,940 --> 00:20:01,130
So not only do we connect the space,

394
00:20:01,130 --> 00:20:02,700
but we also connect the time.

395
00:20:02,700 --> 00:20:05,300
And essentially what we do is we say,

396
00:20:05,300 --> 00:20:08,980
okay, so if your five, four
and a half-year-old child,

397
00:20:08,980 --> 00:20:12,700
five-year-old child is
measured here in 2011,

398
00:20:12,700 --> 00:20:17,650
we then connect that the satellite data

399
00:20:17,650 --> 00:20:21,400
from either a short or a long term period.

400
00:20:21,400 --> 00:20:23,504
But not just any satellite data,

401
00:20:23,504 --> 00:20:25,110
(hand thuds) we don't say,

402
00:20:25,110 --> 00:20:29,130
for example, connect in
Salem State in Massachusetts,

403
00:20:29,130 --> 00:20:32,970
we do not take the
January temperature data

404
00:20:32,970 --> 00:20:35,260
and try to connect it to the food security

405
00:20:35,260 --> 00:20:37,980
of a child living in Salem, right?

406
00:20:37,980 --> 00:20:40,180
Because obviously
nobody's growing anything

407
00:20:40,180 --> 00:20:41,450
in January in Salem.

408
00:20:41,450 --> 00:20:45,310
We're all inside hoping
the snow goes away, right?

409
00:20:45,310 --> 00:20:49,400
So we need to be very
careful about taking-

410
00:20:49,400 --> 00:20:52,550
not only the space, like where Salem is.

411
00:20:52,550 --> 00:20:56,760
You're not averaging Montana
with Salem, with Massachusetts.

412
00:20:56,760 --> 00:20:59,180
You're taking only that one location.

413
00:20:59,180 --> 00:21:01,220
And you're only taking July,

414
00:21:01,220 --> 00:21:05,600
August and September
growing period for Salem

415
00:21:05,600 --> 00:21:08,270
to connect to the outcomes, right?

416
00:21:08,270 --> 00:21:09,710
So here's a little graph

417
00:21:09,710 --> 00:21:12,870
that shows if your
child was observed here,

418
00:21:12,870 --> 00:21:17,040
you wanna only take the
relevant growing season

419
00:21:17,040 --> 00:21:19,540
for just one year for wasting

420
00:21:19,540 --> 00:21:22,220
or for many years for stunting.

421
00:21:22,220 --> 00:21:26,390
The years that matter
before the child was born,

422
00:21:26,390 --> 00:21:28,603
when they were in utero,

423
00:21:29,500 --> 00:21:31,630
all the way through to the year

424
00:21:31,630 --> 00:21:34,460
when the child was observed, right.

425
00:21:34,460 --> 00:21:36,820
But not the off season,

426
00:21:36,820 --> 00:21:38,720
but only when the food is being grown.

427
00:21:38,720 --> 00:21:43,060
So then you can connect the
potential impact of temperature

428
00:21:43,060 --> 00:21:47,093
and precipitation
variability to the outcome.

429
00:21:48,340 --> 00:21:51,460
Okay, so this is all explained
in that paper in 2014,

430
00:21:51,460 --> 00:21:54,940
if you want to see more about this.

431
00:21:54,940 --> 00:21:57,210
Okay, so let's see.

432
00:21:57,210 --> 00:22:00,280
So now we're gonna get into
the brief literature review.

433
00:22:00,280 --> 00:22:04,040
So you know why I'm making
the humongous conclusions

434
00:22:04,040 --> 00:22:07,060
that I'm making in the last three papers.

435
00:22:07,060 --> 00:22:10,050
So we're getting there and it's yeah,

436
00:22:10,050 --> 00:22:14,510
we have maybe 20 minutes more
before I'll stop talking.

437
00:22:14,510 --> 00:22:17,190
Okay, so this literature review looked

438
00:22:17,190 --> 00:22:19,163
at 90 different papers.

439
00:22:20,130 --> 00:22:21,750
We published it in 2020,

440
00:22:21,750 --> 00:22:24,050
and we basically looked at papers

441
00:22:24,050 --> 00:22:28,320
that provided quantitative outcomes.

442
00:22:28,320 --> 00:22:31,190
And we were characterizing these papers

443
00:22:31,190 --> 00:22:33,400
and each of the factors within each paper

444
00:22:33,400 --> 00:22:36,290
as either a risk for food insecurity,

445
00:22:36,290 --> 00:22:40,270
a mitigating factor or
an inconclusive factor.

446
00:22:40,270 --> 00:22:42,820
And basically, what we did is we looked

447
00:22:42,820 --> 00:22:45,170
at four different geographic levels,

448
00:22:45,170 --> 00:22:47,560
the child, the household,

449
00:22:47,560 --> 00:22:50,410
the region community, and the country.

450
00:22:50,410 --> 00:22:52,450
At the child level,

451
00:22:52,450 --> 00:22:57,450
the factors that these 90
papers called out were whether

452
00:22:57,700 --> 00:23:00,350
or not you were male
or female, you're sex,

453
00:23:00,350 --> 00:23:02,370
your age, the young, you know,

454
00:23:02,370 --> 00:23:05,120
if anyone has tried to
feed a two-year-old knows

455
00:23:05,120 --> 00:23:06,530
how darn hard it is,

456
00:23:06,530 --> 00:23:07,610
but when they're six,

457
00:23:07,610 --> 00:23:09,200
they'll eat anything
you put in front of them

458
00:23:09,200 --> 00:23:12,180
or whatever it is this
for your particular child.

459
00:23:12,180 --> 00:23:14,840
Your age, your birth order,

460
00:23:14,840 --> 00:23:17,370
whether or not you've
been a multiple birth,

461
00:23:17,370 --> 00:23:21,490
short birth interval, if you were early,

462
00:23:21,490 --> 00:23:24,360
came early, vitamin A supplements,

463
00:23:24,360 --> 00:23:27,900
so each of these parameters
are either a risk

464
00:23:27,900 --> 00:23:31,230
or a mitigating, or they
could be in inclusive.

465
00:23:31,230 --> 00:23:36,230
Inconclusive is where a factor
appears in multiple studies,

466
00:23:36,230 --> 00:23:40,840
but they are not consistently
either positive or negative.

467
00:23:40,840 --> 00:23:43,740
And this means that we just
don't know what impact they have

468
00:23:43,740 --> 00:23:47,350
or maybe they get observed
and it's just confusing.

469
00:23:47,350 --> 00:23:50,740
So for example, birth order
is a risk for stunting,

470
00:23:50,740 --> 00:23:52,663
but not for wasting or underweight.

471
00:23:55,470 --> 00:23:58,410
Very important that here we can see

472
00:23:58,410 --> 00:24:02,210
that if you were born in a hospital,

473
00:24:02,210 --> 00:24:04,530
professional birth, we call it,

474
00:24:04,530 --> 00:24:08,170
it is quite a big mitigating
factor for stunting.

475
00:24:08,170 --> 00:24:09,250
Same with vitamin A.

476
00:24:09,250 --> 00:24:11,820
This means when you get sick later on,

477
00:24:11,820 --> 00:24:12,830
if you're not doing well,

478
00:24:12,830 --> 00:24:15,820
your mother brings you to
the hospital or to the doctor

479
00:24:15,820 --> 00:24:18,450
and you get looked at and you get,

480
00:24:18,450 --> 00:24:20,740
and this is a huge mitigating factor.

481
00:24:20,740 --> 00:24:22,870
So healthcare matters, right?

482
00:24:22,870 --> 00:24:24,080
We all know that.

483
00:24:24,080 --> 00:24:25,980
So at the individual level,

484
00:24:25,980 --> 00:24:27,390
these are the parameters.

485
00:24:27,390 --> 00:24:29,900
Now, when we go onto the household level,

486
00:24:29,900 --> 00:24:32,570
remember that big, complicated,
colorful figure I showed

487
00:24:32,570 --> 00:24:33,680
at the beginning,

488
00:24:33,680 --> 00:24:36,570
these are where the socioeconomics

489
00:24:36,570 --> 00:24:39,210
of your household really matter.

490
00:24:39,210 --> 00:24:41,993
So if your parents have had education,

491
00:24:44,731 --> 00:24:47,580
your mother's weight
and the mother's height,

492
00:24:47,580 --> 00:24:49,670
wealth and assets, obviously,

493
00:24:49,670 --> 00:24:51,680
if you're more wealthy,

494
00:24:51,680 --> 00:24:54,350
you get more to eat and
you're much less likely

495
00:24:54,350 --> 00:24:57,640
to be wasted, stunted or underweight.

496
00:24:57,640 --> 00:25:00,110
Also, we have rural indigenous

497
00:25:00,110 --> 00:25:04,463
altitude being isolated from town,

498
00:25:05,360 --> 00:25:07,620
more rural than urban, right?

499
00:25:07,620 --> 00:25:10,750
There's the big risk factor for stunting

500
00:25:10,750 --> 00:25:14,620
and rural is also a risk
factor for underweight.

501
00:25:14,620 --> 00:25:18,559
There are lot of
inconclusive factors in here.

502
00:25:18,559 --> 00:25:19,680
It's very complicated.

503
00:25:19,680 --> 00:25:23,310
So a lot of these things were
not conclusive all the time.

504
00:25:23,310 --> 00:25:24,930
For example, household size,

505
00:25:24,930 --> 00:25:27,650
female head, dependency ratio,

506
00:25:27,650 --> 00:25:30,270
none of that was really conclusive.

507
00:25:30,270 --> 00:25:34,060
Okay, so now we're getting into
the region of the community

508
00:25:34,060 --> 00:25:36,560
and here is where we get into

509
00:25:36,560 --> 00:25:39,130
our great remote sensing observations,

510
00:25:39,130 --> 00:25:41,950
rainfall, growing season rainfall,

511
00:25:41,950 --> 00:25:45,420
extreme temperature, drought,
and vegetation quality.

512
00:25:45,420 --> 00:25:49,023
All of these are important for,

513
00:25:50,530 --> 00:25:52,140
the excessive rainfall,

514
00:25:52,140 --> 00:25:55,230
extreme temperature or heat waves,

515
00:25:55,230 --> 00:25:58,380
and drought all are risk
factors for underweight,

516
00:25:58,380 --> 00:26:00,510
stunting and under nutrition,

517
00:26:00,510 --> 00:26:02,060
and finally, conflict.

518
00:26:02,060 --> 00:26:07,060
Conflict in the region,
days, born during conflict,

519
00:26:07,340 --> 00:26:10,940
these are all stunting risk factors.

520
00:26:10,940 --> 00:26:14,123
And then finally, the GDP
country level parameters,

521
00:26:14,123 --> 00:26:15,970
they're all mitigating.

522
00:26:15,970 --> 00:26:17,640
Your economy is doing better,

523
00:26:17,640 --> 00:26:21,163
much less likely to be
stunted or underweight.

524
00:26:22,330 --> 00:26:27,330
Okay, so moving on the results

525
00:26:27,420 --> 00:26:31,340
from our three papers as
you recall from my outline.

526
00:26:31,340 --> 00:26:34,550
First, I'm gonna talk a little
bit about diet diversity.

527
00:26:34,550 --> 00:26:36,730
Now, diet diversity is
one of those variables

528
00:26:36,730 --> 00:26:39,890
that is collected across
a lot of communities.

529
00:26:39,890 --> 00:26:44,700
It is essentially a measure
from the previous 24 hours

530
00:26:44,700 --> 00:26:47,870
where you ask the woman
that you're interviewing,

531
00:26:47,870 --> 00:26:49,880
'cause you don't interview the children.

532
00:26:49,880 --> 00:26:51,630
You ask the woman you're interviewing,

533
00:26:51,630 --> 00:26:56,500
how many of these food
groups did your child eat

534
00:26:56,500 --> 00:26:59,220
in the 24 hours previous, right?

535
00:26:59,220 --> 00:27:02,690
So we have breast milk, grains,

536
00:27:02,690 --> 00:27:06,750
fruit and tubers, legumes,
dairy products, flesh foods,

537
00:27:06,750 --> 00:27:09,410
which is otherwise known as meat, eggs,

538
00:27:09,410 --> 00:27:10,670
vitamin A rich foods,

539
00:27:10,670 --> 00:27:14,980
and that is basically things
like carrots or orange, squash

540
00:27:14,980 --> 00:27:16,840
and other fruits and vegetables.

541
00:27:16,840 --> 00:27:19,370
Now this is not a measure of quantity.

542
00:27:19,370 --> 00:27:21,750
It is only a measure of items.

543
00:27:21,750 --> 00:27:23,403
So if you have-

544
00:27:24,680 --> 00:27:26,470
if you eat a quarter of a banana,

545
00:27:26,470 --> 00:27:28,450
you get a tick for number eight.

546
00:27:28,450 --> 00:27:31,190
If you eat three bites of your breakfast,

547
00:27:31,190 --> 00:27:33,430
you get a tick for number two.

548
00:27:33,430 --> 00:27:34,263
You know what I mean?

549
00:27:34,263 --> 00:27:35,460
So it's not about how much,

550
00:27:35,460 --> 00:27:37,700
it's just about whether, how many.

551
00:27:37,700 --> 00:27:42,180
And this allows us to
compare a number of factors

552
00:27:42,180 --> 00:27:46,790
about how likely it is you're
gonna have outcome problems.

553
00:27:46,790 --> 00:27:49,900
So in this little map,

554
00:27:49,900 --> 00:27:53,100
it shows where we had 107,000 children

555
00:27:53,100 --> 00:27:55,970
in 19 low and middle income countries,

556
00:27:55,970 --> 00:27:58,003
across six global regions.

557
00:28:02,380 --> 00:28:06,200
You can see that the very
blue areas have the lowest

558
00:28:06,200 --> 00:28:11,200
diet diversity and that's
Niger in Nigeria here.

559
00:28:11,740 --> 00:28:14,940
This country here is
Nigeria with all that data

560
00:28:14,940 --> 00:28:18,650
and the higher yellow ones of
the highest diet diversity,

561
00:28:18,650 --> 00:28:21,940
which are in South America and in Asia.

562
00:28:21,940 --> 00:28:24,540
Okay, so when the results show

563
00:28:24,540 --> 00:28:27,560
that when you throw all of
these incredibly diverse things

564
00:28:27,560 --> 00:28:29,500
into one model,

565
00:28:29,500 --> 00:28:32,260
you get the obvious results,

566
00:28:32,260 --> 00:28:36,740
which are wealth matters
and age matters, right?

567
00:28:36,740 --> 00:28:41,380
So if the child age, the older the child,

568
00:28:41,380 --> 00:28:44,520
the less likely you're
going to have impacts

569
00:28:44,520 --> 00:28:45,460
in diet diversity.

570
00:28:45,460 --> 00:28:47,150
And then this figure,

571
00:28:47,150 --> 00:28:49,300
the elements to the right are mitigating

572
00:28:49,300 --> 00:28:52,980
and the elements to the left
are risk factors, right?

573
00:28:52,980 --> 00:28:57,663
So using that concept
that we had previously.

574
00:28:59,460 --> 00:29:03,530
So here on the right,
the wealthier you are,

575
00:29:03,530 --> 00:29:05,000
the much less likely

576
00:29:05,000 --> 00:29:07,900
that you're gonna have low diet diversity,

577
00:29:07,900 --> 00:29:08,800
but the poorer you are.

578
00:29:08,800 --> 00:29:11,300
So this is like obvious, right?

579
00:29:11,300 --> 00:29:12,700
One of the things we brought in

580
00:29:12,700 --> 00:29:16,993
to this paper were tree
cover, livestock density,

581
00:29:18,070 --> 00:29:19,140
distance to urban,

582
00:29:19,140 --> 00:29:22,560
so these are very similar
parameters, right.

583
00:29:22,560 --> 00:29:26,290
Now, when you separate
out all the regions,

584
00:29:26,290 --> 00:29:29,170
we see a much more complex figure,

585
00:29:29,170 --> 00:29:30,400
but we still see

586
00:29:30,400 --> 00:29:33,693
that the economic variables
are very important.

587
00:29:35,000 --> 00:29:39,320
Basically, the mitigating factors

588
00:29:39,320 --> 00:29:43,960
such as child age, education,

589
00:29:43,960 --> 00:29:47,870
improved services, hygiene services,

590
00:29:47,870 --> 00:29:52,290
also poor households
have less diet diversity.

591
00:29:52,290 --> 00:29:55,650
But here we begin to see
the temperature popping out

592
00:29:55,650 --> 00:29:57,777
'cause remember this is supposed
to be about temperature?

593
00:29:57,777 --> 00:29:59,150
And you're like, really?

594
00:29:59,150 --> 00:30:01,540
I haven't heard anything
about temperature recently.

595
00:30:01,540 --> 00:30:03,280
So here's temperature.

596
00:30:03,280 --> 00:30:07,510
You can see that the
temperature anomalies,

597
00:30:07,510 --> 00:30:11,230
how the current temperature
is affecting the results.

598
00:30:11,230 --> 00:30:16,050
The higher the temperature,
the greater likelihood

599
00:30:16,050 --> 00:30:19,180
that you're gonna have low diet diversity.

600
00:30:19,180 --> 00:30:21,840
And in the next slide, I just
pull out out West Africa,

601
00:30:21,840 --> 00:30:24,650
which is the poorest
of the African regions.

602
00:30:24,650 --> 00:30:27,680
And here we see a very high impact

603
00:30:27,680 --> 00:30:32,243
of long term average temperature
versus diet diversity.

604
00:30:33,610 --> 00:30:37,560
And this high temperature
anomaly outweighs

605
00:30:37,560 --> 00:30:40,740
all the positive relationships
between diet diversity

606
00:30:40,740 --> 00:30:44,050
and education, improve toilets,

607
00:30:44,050 --> 00:30:47,510
access to improve water and wealth.

608
00:30:47,510 --> 00:30:48,933
This is really important

609
00:30:48,933 --> 00:30:53,070
because it means that if
you live in West Africa

610
00:30:53,070 --> 00:30:54,220
and you're really wealthy,

611
00:30:54,220 --> 00:30:58,580
you're still gonna have hard
time getting sufficient,

612
00:30:58,580 --> 00:31:01,770
diverse diet to feed your children, right?

613
00:31:01,770 --> 00:31:05,100
So all of that investment
that we've been making

614
00:31:05,100 --> 00:31:09,690
in improving people's incomes
are really going to be swamped

615
00:31:09,690 --> 00:31:11,500
by the increasing temperature

616
00:31:11,500 --> 00:31:15,513
and the already observed
increases in temperature.

617
00:31:16,360 --> 00:31:19,010
Okay, so this is on diet diversity.

618
00:31:19,010 --> 00:31:22,060
How does it play out when
it comes to stunting?

619
00:31:22,060 --> 00:31:25,950
Now here, we see a study by Cooper et al

620
00:31:25,950 --> 00:31:29,570
in the proceedings of the
National Academy of Sciences

621
00:31:29,570 --> 00:31:31,880
about this standardized precipitation

622
00:31:31,880 --> 00:31:33,960
and evapotranspiration index.

623
00:31:33,960 --> 00:31:37,920
Here, we combine precipitation
and temperature together

624
00:31:37,920 --> 00:31:42,920
to look at 580,000
observations of children

625
00:31:43,210 --> 00:31:44,700
who were stunted.

626
00:31:44,700 --> 00:31:45,970
Now, remember what stunting is.

627
00:31:45,970 --> 00:31:50,270
Stunting is where if you
are particularly short

628
00:31:50,270 --> 00:31:51,330
for your age,

629
00:31:51,330 --> 00:31:53,830
which means you've had multiple incidences

630
00:31:53,830 --> 00:31:58,830
of inadequate feeding in
the previous life cycle.

631
00:31:59,250 --> 00:32:03,330
And so, and what this figure here shows

632
00:32:03,330 --> 00:32:06,640
that during the 24-month

633
00:32:06,640 --> 00:32:11,080
previous precipitation anomaly

634
00:32:11,080 --> 00:32:15,130
and the residual stunting indicators

635
00:32:15,130 --> 00:32:17,500
during periods of normal rainfall,

636
00:32:17,500 --> 00:32:19,130
which is this part in the middle,

637
00:32:19,130 --> 00:32:22,400
children were typically
taller than household

638
00:32:22,400 --> 00:32:25,580
and individual factors
would otherwise predict.

639
00:32:25,580 --> 00:32:28,340
In other words, normality is awesome.

640
00:32:28,340 --> 00:32:31,610
It means that people can
do what they usually do.

641
00:32:31,610 --> 00:32:33,740
They're able to feed their kids.

642
00:32:33,740 --> 00:32:36,090
Everything is ticking along.

643
00:32:36,090 --> 00:32:39,360
When it's particularly wet out,

644
00:32:39,360 --> 00:32:44,360
we see that severe wetness,
children were typically shorter

645
00:32:45,250 --> 00:32:48,860
and severe dryness, particularly shorter.

646
00:32:48,860 --> 00:32:53,490
And the reason why you might
get additional stunting

647
00:32:53,490 --> 00:32:56,690
during wet period is because
the lack of healthcare

648
00:32:56,690 --> 00:33:01,670
and you get more diseases
and more diarrheal events.

649
00:33:01,670 --> 00:33:02,910
And really wet periods,

650
00:33:02,910 --> 00:33:07,910
you get more severe lack
of food availability.

651
00:33:08,610 --> 00:33:11,040
So there's different
mechanisms through which,

652
00:33:11,040 --> 00:33:14,060
but essentially normal weather is better

653
00:33:14,060 --> 00:33:16,060
for children's outcomes,

654
00:33:16,060 --> 00:33:18,960
which is why climate change
is such a big threat.

655
00:33:18,960 --> 00:33:20,850
Okay, so very briefly,

656
00:33:20,850 --> 00:33:22,950
because I know you guys are getting tired.

657
00:33:24,260 --> 00:33:28,640
In this paper, we also did
this sort of mitigating

658
00:33:28,640 --> 00:33:32,950
and this mitigating on the right

659
00:33:32,950 --> 00:33:35,950
and risk factors on the left.

660
00:33:35,950 --> 00:33:39,120
And we found that
mitigating factors improve

661
00:33:39,120 --> 00:33:41,220
diversity of agriculture,

662
00:33:41,220 --> 00:33:42,700
government effectiveness.

663
00:33:42,700 --> 00:33:44,650
This just falls out of that other paper

664
00:33:44,650 --> 00:33:46,880
that we published,

665
00:33:46,880 --> 00:33:49,920
although this one was
before that 2020 paper.

666
00:33:49,920 --> 00:33:53,120
But political stability in
the absence of violence,

667
00:33:53,120 --> 00:33:54,810
these are all mitigating factors,

668
00:33:54,810 --> 00:33:57,620
which improve the ability of families

669
00:33:57,620 --> 00:34:00,180
to raise healthy children.

670
00:34:00,180 --> 00:34:02,590
On the other side is
topographic roughness,

671
00:34:02,590 --> 00:34:04,180
otherwise known as elevation.

672
00:34:04,180 --> 00:34:08,583
Do you live far away and
very distant from town?

673
00:34:09,630 --> 00:34:10,980
Percent of bare cover.

674
00:34:10,980 --> 00:34:13,590
Are you near a big desert

675
00:34:13,590 --> 00:34:17,850
or have you really overgrazed?

676
00:34:17,850 --> 00:34:20,090
And then here's the big one here,

677
00:34:20,090 --> 00:34:23,053
average monthly maximum temperature.

678
00:34:23,910 --> 00:34:26,960
Again, temperatures
just falls out of this.

679
00:34:26,960 --> 00:34:28,310
And if you have more people,

680
00:34:28,310 --> 00:34:30,689
that temperature is even more-

681
00:34:30,689 --> 00:34:34,923
big risk factor for stunting.

682
00:34:36,470 --> 00:34:40,050
So temperature again, comes
out as a very important

683
00:34:40,050 --> 00:34:45,050
and very strong driver of food insecurity.

684
00:34:45,180 --> 00:34:47,460
And then here's a map,
which I'm not gonna explain,

685
00:34:47,460 --> 00:34:49,410
'cause I don't wanna talk for too long.

686
00:34:49,410 --> 00:34:51,940
The last study that
I'm going to be talking

687
00:34:51,940 --> 00:34:55,870
about here is this Grace
et al paper in 2015,

688
00:34:55,870 --> 00:34:57,460
a little bit earlier

689
00:34:57,460 --> 00:35:00,390
and here the dependent
variables birth weight.

690
00:35:00,390 --> 00:35:02,330
So this one is in interesting.

691
00:35:02,330 --> 00:35:06,510
It has 66,000 children in it.

692
00:35:06,510 --> 00:35:11,280
It has a much longer timeframe
from 1986 through 2010.

693
00:35:11,280 --> 00:35:15,590
And we were interested in
looking at birth weight

694
00:35:15,590 --> 00:35:18,380
because of the combined factor

695
00:35:18,380 --> 00:35:22,270
between mother's health and birth weight.

696
00:35:22,270 --> 00:35:26,810
And the livelihood zones here are used

697
00:35:26,810 --> 00:35:28,870
to mediate the outcome.

698
00:35:28,870 --> 00:35:31,280
And there's a lot of
results in this paper,

699
00:35:31,280 --> 00:35:32,940
but the ones that I wanted to bring

700
00:35:32,940 --> 00:35:35,460
to your attention were again,

701
00:35:35,460 --> 00:35:38,540
about temperature and precipitation.

702
00:35:38,540 --> 00:35:41,130
What we can see here is that

703
00:35:41,130 --> 00:35:43,290
the impact of mediating factors,

704
00:35:43,290 --> 00:35:47,130
this one, the mitigating is above the zero

705
00:35:47,130 --> 00:35:50,008
and the risk factors are below the zero

706
00:35:50,008 --> 00:35:51,460
instead of left and right,

707
00:35:51,460 --> 00:35:54,440
but we can see that only
an increase in education

708
00:35:54,440 --> 00:35:57,830
and an electricity affect birth weight.

709
00:35:57,830 --> 00:36:02,610
When you combine improved education

710
00:36:02,610 --> 00:36:04,430
and improved access to electricity

711
00:36:04,430 --> 00:36:09,160
with temperature increases
and precipitation decreases,

712
00:36:09,160 --> 00:36:13,139
you see that these
factors totally overwhelm

713
00:36:13,139 --> 00:36:17,520
this investment in development factors.

714
00:36:17,520 --> 00:36:20,210
So again, temperature increases

715
00:36:20,210 --> 00:36:25,210
really have a very large
impact on these poor tropical

716
00:36:26,820 --> 00:36:30,720
and marginal communities

717
00:36:30,720 --> 00:36:35,720
that are otherwise
experiencing food insecurity.

718
00:36:36,700 --> 00:36:39,270
And we expect that the
impact of climate change

719
00:36:39,270 --> 00:36:42,580
on birth weight and low birth
weight will depend of course,

720
00:36:42,580 --> 00:36:45,550
on where a mother lives, right?

721
00:36:45,550 --> 00:36:48,870
And along with the mediating
impacts such as income,

722
00:36:48,870 --> 00:36:51,600
education and diverse and electricity,

723
00:36:51,600 --> 00:36:56,600
but really after spending 50
years investing in education

724
00:36:57,950 --> 00:37:00,900
and access to electricity,

725
00:37:00,900 --> 00:37:03,980
changes in temperature can overwhelm

726
00:37:05,200 --> 00:37:08,190
those health benefits.

727
00:37:08,190 --> 00:37:13,190
So to conclude, widespread
warming is consistently predicted

728
00:37:14,140 --> 00:37:15,590
by climate change models,

729
00:37:15,590 --> 00:37:20,180
far more consistently than precipitation.

730
00:37:20,180 --> 00:37:24,700
Temperature increases may pose
the greatest overall threat.

731
00:37:24,700 --> 00:37:26,650
Temperature impacts are affected

732
00:37:26,650 --> 00:37:30,880
by timing of exposure and
intensity and duration,

733
00:37:30,880 --> 00:37:34,420
but we really need better
predictive models and responses

734
00:37:34,420 --> 00:37:39,040
to changes in temperature and heat waves

735
00:37:39,040 --> 00:37:40,663
than we already,

736
00:37:42,320 --> 00:37:44,220
than we already have now.

737
00:37:44,220 --> 00:37:48,610
So a lot of this work is focused
on improving the prediction

738
00:37:48,610 --> 00:37:50,240
because temperature is one of those things

739
00:37:50,240 --> 00:37:52,170
that we can predict

740
00:37:52,170 --> 00:37:55,180
and we can predict changes through time,

741
00:37:55,180 --> 00:37:59,330
much better than rainfall.

742
00:37:59,330 --> 00:38:01,650
And that is I think all I have,

743
00:38:01,650 --> 00:38:02,483
I have one more slide,

744
00:38:02,483 --> 00:38:03,773
but I'm, yeah.

745
00:38:05,120 --> 00:38:09,105
Okay, I'm ready for questions.

746
00:38:09,105 --> 00:38:13,140
(hand thudding) Let me stop sharing, yes.

747
00:38:13,140 --> 00:38:15,830
- Thank you very much,
that's a wonderful talk.

748
00:38:15,830 --> 00:38:18,670
I'm amazed at how much you can get

749
00:38:18,670 --> 00:38:22,793
from satellite views. (laughs)

750
00:38:24,524 --> 00:38:25,441
but we are,

751
00:38:27,290 --> 00:38:28,210
there's a lot of talk

752
00:38:28,210 --> 00:38:32,363
about how to reduce the
temperature of the earth,

753
00:38:33,730 --> 00:38:36,580
but this is happening already.

754
00:38:36,580 --> 00:38:37,860
- Correct, yeah.

755
00:38:37,860 --> 00:38:41,710
- Do you see changes in
people's habits trying

756
00:38:41,710 --> 00:38:46,233
to deal with that in areas
that you've been studying?

757
00:38:50,490 --> 00:38:54,743
- Sadly, the places with the
biggest impact have the least,

758
00:38:55,600 --> 00:38:57,810
the places that experienced

759
00:38:57,810 --> 00:39:01,510
the biggest health consequences
have the least ability

760
00:39:01,510 --> 00:39:03,880
to change the trajectory

761
00:39:03,880 --> 00:39:08,200
because those places have
the least use of energy,

762
00:39:08,200 --> 00:39:09,840
they're not flying around,

763
00:39:09,840 --> 00:39:13,810
they're not heating and
cooling their houses,

764
00:39:13,810 --> 00:39:18,410
they really have a tiny
impact on climate change.

765
00:39:18,410 --> 00:39:21,750
So sadly the haves,

766
00:39:21,750 --> 00:39:24,860
which are Europe, China, India,

767
00:39:24,860 --> 00:39:29,070
and most importantly, us
are affecting the have-nots,

768
00:39:29,070 --> 00:39:31,590
which are Africa and Asia.

769
00:39:31,590 --> 00:39:34,520
So it really is up to us to make sure

770
00:39:34,520 --> 00:39:38,053
that those places do not
suffer the consequences.

771
00:39:39,360 --> 00:39:40,683
- Thank you.
- Uh-huh.

772
00:39:43,300 --> 00:39:44,133
- [Nelson] Dr. Gordon?

773
00:39:44,133 --> 00:39:46,040
- Good morning, Dr. Brown.

774
00:39:46,040 --> 00:39:47,190
I'm Ethel Gordon,

775
00:39:47,190 --> 00:39:50,890
I'm a Biology faculty and
a member of the committee.

776
00:39:50,890 --> 00:39:54,970
And I would like to ask
you one question relating

777
00:39:54,970 --> 00:39:59,970
to your information you
presented on stunting and drought

778
00:40:01,660 --> 00:40:04,480
and you compared wet and dry seasons.

779
00:40:04,480 --> 00:40:06,610
I was wondering if you had actually,

780
00:40:06,610 --> 00:40:09,920
any data or measurements that correspond

781
00:40:09,920 --> 00:40:12,830
to were there more diseases, et cetera,

782
00:40:12,830 --> 00:40:15,303
during either of those periods?

783
00:40:16,390 --> 00:40:20,280
You assumed or you
mentioned a correlation,

784
00:40:20,280 --> 00:40:24,550
but I was wondering if there
actually were studies saying

785
00:40:24,550 --> 00:40:26,850
what occurred during what seasons

786
00:40:26,850 --> 00:40:28,860
or what occurred during dry seasons

787
00:40:28,860 --> 00:40:30,443
that would affect stunting.

788
00:40:31,420 --> 00:40:32,760
- Right, okay.

789
00:40:32,760 --> 00:40:35,800
So complicated question.

790
00:40:35,800 --> 00:40:39,330
So what happens is that

791
00:40:40,410 --> 00:40:43,900
these demographic and
health survey technicians,

792
00:40:43,900 --> 00:40:46,840
they go and they talk with a family.

793
00:40:46,840 --> 00:40:47,900
So they knock on your door

794
00:40:47,900 --> 00:40:50,320
just sort of like a census worker, right?

795
00:40:50,320 --> 00:40:52,540
They knock on the door of the household

796
00:40:52,540 --> 00:40:55,550
and they record in the survey,

797
00:40:55,550 --> 00:41:00,500
the time, the date and the
location of that household.

798
00:41:00,500 --> 00:41:02,340
So then what we do is,

799
00:41:02,340 --> 00:41:04,970
then they measure the height

800
00:41:04,970 --> 00:41:07,330
and the weight and they ask the birthdate

801
00:41:07,330 --> 00:41:10,320
of all the children in
the household, right?

802
00:41:10,320 --> 00:41:13,920
So now we have observations of the child

803
00:41:13,920 --> 00:41:15,830
and the time and the date.

804
00:41:15,830 --> 00:41:18,760
So if that child, and
then they ask the mother,

805
00:41:18,760 --> 00:41:21,580
has this child experienced diarrhea

806
00:41:21,580 --> 00:41:24,160
in the last (claps) 24 or 48 hours,

807
00:41:24,160 --> 00:41:26,500
depending on the place, right.

808
00:41:26,500 --> 00:41:28,770
And no one knows more than the mom,

809
00:41:28,770 --> 00:41:31,240
whether or not a two-year-old
has diarrhea, right?

810
00:41:31,240 --> 00:41:32,900
Because clearly,

811
00:41:32,900 --> 00:41:34,760
so the mom says yes or no.

812
00:41:34,760 --> 00:41:39,090
And so we have those observations

813
00:41:39,090 --> 00:41:43,310
to know whether or not a
diarrheal disease is happening.

814
00:41:43,310 --> 00:41:48,310
And then, we can connect the
disease to the wasting, right.

815
00:41:50,290 --> 00:41:51,660
Here comes the rub.

816
00:41:51,660 --> 00:41:54,640
Remember I said 108,000 children.

817
00:41:54,640 --> 00:41:56,070
My N is great.

818
00:41:56,070 --> 00:41:57,490
That's only with stunting.

819
00:41:57,490 --> 00:42:00,830
Stunting is a well child
who has been malnourished

820
00:42:00,830 --> 00:42:02,433
over their entire lifetime.

821
00:42:03,960 --> 00:42:06,920
Poor height, very skinny, right.

822
00:42:06,920 --> 00:42:10,510
Wasted kid is a perfectly
normal kid who has been sick

823
00:42:10,510 --> 00:42:14,140
in the last week who's lost weight, right?

824
00:42:14,140 --> 00:42:17,780
So that means that we have
extremely small number

825
00:42:17,780 --> 00:42:21,320
of wasted observations
compared to stunted.

826
00:42:21,320 --> 00:42:23,600
So although from a satellite perspective,

827
00:42:23,600 --> 00:42:26,050
I'd much prefer to look at wasted kids

828
00:42:26,050 --> 00:42:28,630
because you have a much
more direct connection.

829
00:42:28,630 --> 00:42:31,170
It's raining, it's flooding,

830
00:42:31,170 --> 00:42:34,610
you get contamination of
water from up to down.

831
00:42:34,610 --> 00:42:37,810
You have diarrheal, it's
like cause and effect.

832
00:42:37,810 --> 00:42:39,020
We got it.

833
00:42:39,020 --> 00:42:40,230
Sadly, (claps)

834
00:42:40,230 --> 00:42:43,590
you only have like 400
kids in the whole country

835
00:42:43,590 --> 00:42:45,690
that you catch right at that point.

836
00:42:45,690 --> 00:42:48,550
Whereas you might have 47,000 children

837
00:42:48,550 --> 00:42:50,020
who are stunted, right?

838
00:42:50,020 --> 00:42:54,380
So this is why a lot of
these studies used stunting

839
00:42:54,380 --> 00:42:57,320
because the observations
are far more robust,

840
00:42:57,320 --> 00:42:58,890
far more dynamic.

841
00:42:58,890 --> 00:43:01,720
But the answer to that long- is yes.

842
00:43:01,720 --> 00:43:02,590
Yes, we do.
(Nelson laughs)

843
00:43:02,590 --> 00:43:04,560
We have way too much
information about all,

844
00:43:04,560 --> 00:43:07,780
there's literally 400 questions
on any of these surveys.

845
00:43:07,780 --> 00:43:09,260
So we know a lot about,

846
00:43:09,260 --> 00:43:12,153
they also take blood samples
and all sorts of other things.

847
00:43:13,213 --> 00:43:14,100
So-
- Thank you very much for,

848
00:43:14,100 --> 00:43:15,620
thank you for your answer.

849
00:43:15,620 --> 00:43:18,623
I see we have a number of other questions.

850
00:43:20,443 --> 00:43:25,300
So let's go to this one from our audience.

851
00:43:25,300 --> 00:43:29,523
Would you say natural selection
plays a pod in all of this?

852
00:43:32,484 --> 00:43:35,390
- (exhales sharply) That is
sadly beyond my expertise,

853
00:43:35,390 --> 00:43:40,183
but in general, humanity is,

854
00:43:41,960 --> 00:43:42,840
how shall we put it?

855
00:43:42,840 --> 00:43:44,810
We're gigantically adaptable.

856
00:43:44,810 --> 00:43:46,940
And because we have culture,

857
00:43:46,940 --> 00:43:49,810
culture plays a big role.

858
00:43:49,810 --> 00:43:54,480
And in general, we do not consider

859
00:43:56,210 --> 00:43:59,560
natural selection to
be the primary driver.

860
00:43:59,560 --> 00:44:01,890
I think it's far more structural,

861
00:44:01,890 --> 00:44:02,723
if you know what I mean?

862
00:44:02,723 --> 00:44:05,620
Like I just happened to have
been born in Connecticut

863
00:44:05,620 --> 00:44:08,090
and not in Malawi or wherever, right.

864
00:44:08,090 --> 00:44:13,090
So I think my personal answer would be no,

865
00:44:14,220 --> 00:44:17,550
but I'm also not an
evolutionary biologist.

866
00:44:17,550 --> 00:44:21,780
So it's really, I'm speculating here.

867
00:44:21,780 --> 00:44:25,862
Very little evidence to
support my answer. (laughs)

868
00:44:25,862 --> 00:44:26,840
- Thank you.

869
00:44:26,840 --> 00:44:29,339
I'd like to ask a question.

870
00:44:29,339 --> 00:44:30,172
- Sure.

871
00:44:30,172 --> 00:44:34,440
- Since the extremes of
climate change are already

872
00:44:34,440 --> 00:44:35,390
happening right now.

873
00:44:35,390 --> 00:44:37,690
- Yeah.
- And the food security

874
00:44:37,690 --> 00:44:40,790
of individuals throughout
populations are being affected,

875
00:44:40,790 --> 00:44:44,690
what exactly is being done
right now to improve this issue?

876
00:44:44,690 --> 00:44:46,770
And we've had several people ask,

877
00:44:46,770 --> 00:44:49,363
what can individuals do to help?

878
00:44:50,320 --> 00:44:52,393
- Okay, so.

879
00:44:55,070 --> 00:44:55,903
Right,

880
00:44:58,110 --> 00:45:03,110
so what is happening right
now is people are investing

881
00:45:03,110 --> 00:45:08,110
in improved agricultural technology use

882
00:45:09,090 --> 00:45:10,060
in these regions.

883
00:45:10,060 --> 00:45:13,590
So this is why I actually am,

884
00:45:13,590 --> 00:45:15,215
I spend a lot of my effort,

885
00:45:15,215 --> 00:45:19,890
I think someone mentioned that
I not only am do research,

886
00:45:19,890 --> 00:45:22,130
but I also invest in a company

887
00:45:22,130 --> 00:45:27,130
because I believe that agriculture
is primarily a, you know,

888
00:45:27,530 --> 00:45:30,220
no one does farming for their health.

889
00:45:30,220 --> 00:45:32,190
It's really hard work.

890
00:45:32,190 --> 00:45:36,240
So we all try to make a living
or make money being farmers

891
00:45:36,240 --> 00:45:40,233
and particularly countries
in Africa who are,

892
00:45:41,100 --> 00:45:46,100
80% of the people there actually
grow things for a living

893
00:45:46,240 --> 00:45:50,593
or they work in a food related,

894
00:45:51,460 --> 00:45:54,150
like they drive trucks
to move food around.

895
00:45:54,150 --> 00:45:55,240
It's not direct,

896
00:45:55,240 --> 00:45:57,850
but far greater percentage

897
00:45:57,850 --> 00:46:00,780
in these food insecure countries, right?

898
00:46:00,780 --> 00:46:04,980
So given that we need to invest
in agricultural technology

899
00:46:04,980 --> 00:46:07,500
and agricultural development.

900
00:46:07,500 --> 00:46:12,500
So I think what can people do
individually is pay attention

901
00:46:12,610 --> 00:46:15,100
to your carbon footprint basically.

902
00:46:15,100 --> 00:46:18,883
Try not to eat so much meat, red meat.

903
00:46:19,740 --> 00:46:22,850
Think about what you're
doing and who you vote for

904
00:46:22,850 --> 00:46:25,880
and whether or not the
U.S. is paying attention

905
00:46:25,880 --> 00:46:27,840
to the climate problem.

906
00:46:27,840 --> 00:46:31,210
Carbon, you know, the atmosphere
is not a big waste dump.

907
00:46:31,210 --> 00:46:33,400
It's like in 1885,

908
00:46:33,400 --> 00:46:36,170
you could take your garbage
and just chuck it out the door.

909
00:46:36,170 --> 00:46:37,340
We don't do that now.

910
00:46:37,340 --> 00:46:41,450
And we're not gonna be
dumping our CO2 gas pollution

911
00:46:41,450 --> 00:46:44,010
into the atmosphere any longer

912
00:46:44,010 --> 00:46:45,570
and just a few more years.

913
00:46:45,570 --> 00:46:49,350
So in my opinion, it's being active,

914
00:46:49,350 --> 00:46:51,270
caring about this issue

915
00:46:51,270 --> 00:46:55,700
and moving forward with our
climate policy here in the U.S.

916
00:46:55,700 --> 00:47:00,093
And pressuring our brother
in abroad to do so as well.

917
00:47:02,500 --> 00:47:03,410
Yeah.

918
00:47:03,410 --> 00:47:04,840
- Thank you for your response.

919
00:47:04,840 --> 00:47:06,793
I see we have other questions.

920
00:47:09,750 --> 00:47:13,200
One is, have you done any
research on the colder climates

921
00:47:13,200 --> 00:47:16,060
and birth growth or success,

922
00:47:16,060 --> 00:47:21,060
such as Greenland and how
do they compare? (chuckles)

923
00:47:21,170 --> 00:47:22,020
- Right.

924
00:47:22,020 --> 00:47:24,730
So I would love to do that.

925
00:47:24,730 --> 00:47:26,880
The tricky part,

926
00:47:26,880 --> 00:47:28,610
okay, so there's several tricky parts.

927
00:47:28,610 --> 00:47:31,940
First, Greenland and United States

928
00:47:31,940 --> 00:47:34,750
and all of Europe actually
have very poor data

929
00:47:34,750 --> 00:47:37,250
on child health outcomes

930
00:47:37,250 --> 00:47:40,550
because of issues having
to do with privacy.

931
00:47:40,550 --> 00:47:42,750
I would love to study, for example,

932
00:47:42,750 --> 00:47:47,250
United States, compare
Alaska to Washington state,

933
00:47:47,250 --> 00:47:48,960
but it's very, very hard to do that

934
00:47:48,960 --> 00:47:51,590
because the data is difficult to get.

935
00:47:51,590 --> 00:47:53,870
It's in the private domain

936
00:47:53,870 --> 00:47:57,970
because our healthcare
system is private, sorry.

937
00:47:57,970 --> 00:48:01,320
And I cannot study Greenland or Iceland

938
00:48:01,320 --> 00:48:02,270
or any of those countries

939
00:48:02,270 --> 00:48:05,940
because you need to be local
in order to access the data.

940
00:48:05,940 --> 00:48:08,090
So no data.

941
00:48:08,090 --> 00:48:11,450
However, there are a number of DHS surveys

942
00:48:11,450 --> 00:48:14,400
in places like Kazakhstan, Tajikistan,

943
00:48:14,400 --> 00:48:16,730
Turkmenistan, the -stans,

944
00:48:16,730 --> 00:48:17,880
which are in the Middle East,

945
00:48:17,880 --> 00:48:22,490
between Russia and Europe
and north of Iran and Iraq.

946
00:48:22,490 --> 00:48:27,360
So they are cold, wet, winter,

947
00:48:27,360 --> 00:48:29,853
dry, hot, summer places.

948
00:48:30,810 --> 00:48:33,700
Also Nepal has great data from this.

949
00:48:33,700 --> 00:48:36,600
And so I've written a
few papers about Nepal.

950
00:48:36,600 --> 00:48:40,170
And the bottom line is this
is where all this elevation.

951
00:48:40,170 --> 00:48:43,350
Remember that parameter you
saw in all pop out, elevation,

952
00:48:43,350 --> 00:48:44,660
you're like really?

953
00:48:44,660 --> 00:48:46,330
Where does elevation come into it?

954
00:48:46,330 --> 00:48:48,070
Not a lot of elevation, right,

955
00:48:48,070 --> 00:48:51,530
in Africa, it's pretty flat place.

956
00:48:51,530 --> 00:48:54,510
It all comes from Nepal and India

957
00:48:54,510 --> 00:48:57,210
and those -stans.

958
00:48:57,210 --> 00:49:01,340
So essentially if you live in
town in a place like Nepal,

959
00:49:01,340 --> 00:49:03,780
you're much better off
than if you're growing food

960
00:49:03,780 --> 00:49:07,800
and you live far from town

961
00:49:07,800 --> 00:49:11,520
because it's all about
access to markets, et cetera.

962
00:49:11,520 --> 00:49:15,950
So I can send you a paper if you email me

963
00:49:17,960 --> 00:49:21,340
by Shively et al about that Nepal thing.

964
00:49:21,340 --> 00:49:23,823
Nepal is a great place to study elevation.

965
00:49:25,736 --> 00:49:26,569
(computer dings)

966
00:49:26,569 --> 00:49:27,402
in cold.
- I'm really,

967
00:49:27,402 --> 00:49:29,415
could I ask a question, Nelson?

968
00:49:29,415 --> 00:49:30,317
- [Nelson] Absolutely.

969
00:49:30,317 --> 00:49:31,930
- Hi,

970
00:49:31,930 --> 00:49:34,943
the diet diversity on the survey, Molly.

971
00:49:36,030 --> 00:49:37,770
That 24-hour look

972
00:49:37,770 --> 00:49:40,460
at just the past 24 hours.
- Yes.

973
00:49:40,460 --> 00:49:44,210
- Does the survey include
like 48 hours too?

974
00:49:44,210 --> 00:49:45,043
Or is it,

975
00:49:45,043 --> 00:49:50,043
or is that, not just the 24-hour window?

976
00:49:50,070 --> 00:49:52,120
- Yes, so they ask us 24 hours.

977
00:49:52,120 --> 00:49:55,600
So if you know anything
about these kinds of surveys,

978
00:49:55,600 --> 00:49:57,020
there's been a huge,

979
00:49:57,020 --> 00:50:00,100
we would love, what I would really want,

980
00:50:00,100 --> 00:50:03,210
if I could just design
myself my own survey,

981
00:50:03,210 --> 00:50:08,050
I would like to know exactly
what all these kids ate

982
00:50:08,050 --> 00:50:11,210
and what they cost and
whether their mother got it,

983
00:50:11,210 --> 00:50:14,910
did they grow it or did they
buy it or where they got it

984
00:50:14,910 --> 00:50:15,980
and where they help, you know,

985
00:50:15,980 --> 00:50:17,150
what are all the different,

986
00:50:17,150 --> 00:50:19,460
sadly, no one can remember.

987
00:50:19,460 --> 00:50:22,010
So this is called consumption surveys.

988
00:50:22,010 --> 00:50:23,960
They're very, very inaccurate.
- Okay.

989
00:50:25,920 --> 00:50:28,860
- Because it's just damn hard
to remember all this stuff.

990
00:50:28,860 --> 00:50:33,580
So they asked the mother about the child

991
00:50:34,540 --> 00:50:36,300
over the past 24 hours.

992
00:50:36,300 --> 00:50:41,300
And by not asking two weeks
or 48 or 36 hours ago,

993
00:50:42,840 --> 00:50:44,490
you don't have to
remember it all that much.

994
00:50:44,490 --> 00:50:46,373
It's just yesterday, right?

995
00:50:47,630 --> 00:50:50,760
And then, they also do not ask quantities

996
00:50:50,760 --> 00:50:52,520
for the same reason.

997
00:50:52,520 --> 00:50:55,080
They find the data is much more comparable

998
00:50:55,080 --> 00:50:59,130
and replicatable if you ask only

999
00:50:59,130 --> 00:51:02,550
like types of food

1000
00:51:02,550 --> 00:51:06,254
and essentially, because
none of us eat a lot of like,

1001
00:51:06,254 --> 00:51:10,370
I can remember whether
or not I had a banana,

1002
00:51:10,370 --> 00:51:12,280
but I can't remember how many bananas

1003
00:51:12,280 --> 00:51:14,650
or if I also had an orange.

1004
00:51:14,650 --> 00:51:17,170
And I can tell you whether
or not I typically keep

1005
00:51:17,170 --> 00:51:19,540
those things in the house as a mother

1006
00:51:19,540 --> 00:51:21,920
and then whether or not
my kid would be eating it,

1007
00:51:21,920 --> 00:51:23,490
basically we don't have chips,

1008
00:51:23,490 --> 00:51:24,940
so we don't eat chips.

1009
00:51:24,940 --> 00:51:27,100
So it's that kind of thing, right?

1010
00:51:27,100 --> 00:51:28,890
You remember it.

1011
00:51:28,890 --> 00:51:31,420
So it's all about the consumption.

1012
00:51:31,420 --> 00:51:34,780
Humans are terrible at data
and they're very bad at recall,

1013
00:51:34,780 --> 00:51:37,680
but if you just ask what type of food,

1014
00:51:37,680 --> 00:51:39,520
then you can easily answer

1015
00:51:39,520 --> 00:51:42,770
according to what you
typically have in the pantry.

1016
00:51:42,770 --> 00:51:44,200
- Okay, thank you.
- So that, yeah.

1017
00:51:44,200 --> 00:51:46,410
So it's all about data accuracy.

1018
00:51:46,410 --> 00:51:47,243
- Right.

1019
00:51:49,400 --> 00:51:52,890
- Another the question from
the chat, the question answer,

1020
00:51:52,890 --> 00:51:55,350
is it possible to reverse
wasting, stunting,

1021
00:51:55,350 --> 00:51:57,859
and other food
insecurity-induced problems?

1022
00:51:57,859 --> 00:51:59,790
What is the window of time to respond

1023
00:51:59,790 --> 00:52:02,180
with a reasonable
likelihood of mitigation?

1024
00:52:02,180 --> 00:52:03,390
- Very excellent question.

1025
00:52:03,390 --> 00:52:08,390
So this is why we are
really into anticipatory.

1026
00:52:09,160 --> 00:52:13,290
I have a paper about out
anticipating all of these things,

1027
00:52:13,290 --> 00:52:15,860
which came out last year,

1028
00:52:15,860 --> 00:52:19,540
but essentially, once you are stunted,

1029
00:52:19,540 --> 00:52:21,160
you cannot reverse.

1030
00:52:21,160 --> 00:52:22,900
You cannot catch up,

1031
00:52:22,900 --> 00:52:25,330
however, wasting you can catch up,

1032
00:52:25,330 --> 00:52:27,070
which is why our-

1033
00:52:27,070 --> 00:52:29,260
we really have been focused on wasting.

1034
00:52:29,260 --> 00:52:32,640
And that 2020 review paper
was all about wasting

1035
00:52:32,640 --> 00:52:34,283
and not about stunting really.

1036
00:52:35,270 --> 00:52:38,080
And basically stunting is multiple times

1037
00:52:38,080 --> 00:52:39,070
of when you lose weight,

1038
00:52:39,070 --> 00:52:40,120
but you can't catch up

1039
00:52:40,120 --> 00:52:41,670
'cause there's not enough food available,

1040
00:52:41,670 --> 00:52:43,627
right?
- Uh-huh.

1041
00:52:43,627 --> 00:52:44,810
- And the other thing is,

1042
00:52:44,810 --> 00:52:46,230
if you're interested in this question,

1043
00:52:46,230 --> 00:52:47,777
you can Google a thousand days,

1044
00:52:47,777 --> 00:52:49,750
"First Thousand Days"

1045
00:52:49,750 --> 00:52:51,490
that is a new thing in The Lancet.

1046
00:52:51,490 --> 00:52:54,870
There's a great paper about the
first thousand days of life.

1047
00:52:54,870 --> 00:52:58,460
If you are undernourished
in that first thousand days,

1048
00:52:58,460 --> 00:53:01,610
you have cognitive and psychological

1049
00:53:01,610 --> 00:53:06,610
and physical delays
and you cannot catch up

1050
00:53:06,970 --> 00:53:09,200
and then it gets much worse.

1051
00:53:09,200 --> 00:53:10,600
Actually, it's sad.

1052
00:53:10,600 --> 00:53:12,910
But the reality is if you are stunted,

1053
00:53:12,910 --> 00:53:17,910
you have a much lower
potential for making money

1054
00:53:19,000 --> 00:53:20,860
when you're 45

1055
00:53:20,860 --> 00:53:22,470
and your society,

1056
00:53:22,470 --> 00:53:26,500
the community that you're in
has a much lower probability

1057
00:53:26,500 --> 00:53:28,281
of growing faster,

1058
00:53:28,281 --> 00:53:31,880
and your children are more
likely to be stunted and wasted

1059
00:53:31,880 --> 00:53:34,140
if you have been stunted and wasted

1060
00:53:34,140 --> 00:53:36,280
or you may be obese.

1061
00:53:36,280 --> 00:53:39,830
So normality is very hard to recapture

1062
00:53:39,830 --> 00:53:42,370
once you lose it, (hand thuds) basically.

1063
00:53:42,370 --> 00:53:44,253
And so it's really very,

1064
00:53:45,748 --> 00:53:49,520
what we want to do is
move towards anticipation

1065
00:53:49,520 --> 00:53:52,490
and prevent prevention.

1066
00:53:52,490 --> 00:53:55,280
Just like once you contaminate a lake,

1067
00:53:55,280 --> 00:53:58,640
it will cost a thousand times
more to clean up that lake

1068
00:53:58,640 --> 00:54:01,233
than to keep it clean in the first place.

1069
00:54:01,233 --> 00:54:02,950
(Nelson laughs)
Same with people.

1070
00:54:02,950 --> 00:54:04,230
Don't screw it up,

1071
00:54:04,230 --> 00:54:05,880
try to keep it,

1072
00:54:05,880 --> 00:54:09,010
provide healthcare,
provide feeding centers,

1073
00:54:09,010 --> 00:54:10,160
do whatever you can,

1074
00:54:10,160 --> 00:54:13,860
because it will be much more
expensive for the household,

1075
00:54:13,860 --> 00:54:16,120
the community and the country

1076
00:54:16,120 --> 00:54:19,630
if you don't prevent that malnutrition

1077
00:54:19,630 --> 00:54:23,460
than to try to mitigate it
after the fact. (hand thuds)

1078
00:54:23,460 --> 00:54:25,780
Yeah, so there's tons of work on that,

1079
00:54:25,780 --> 00:54:27,240
tons of literature,

1080
00:54:27,240 --> 00:54:28,720
easy to find.

1081
00:54:28,720 --> 00:54:29,553
- [Nelson] Cool.

1082
00:54:31,963 --> 00:54:34,652
- I think we have four minutes folks.

1083
00:54:34,652 --> 00:54:36,639
(John laughs)

1084
00:54:36,639 --> 00:54:37,510
- There's lots-
- I'm gonna-

1085
00:54:37,510 --> 00:54:38,820
- [Nelson] Go ahead.

1086
00:54:38,820 --> 00:54:43,290
- I'm gonna ask one question
from one of our colleagues.

1087
00:54:43,290 --> 00:54:47,740
We rely heavily on very
small number of staple crops

1088
00:54:47,740 --> 00:54:50,120
and the genetic diversity
of these crops tends

1089
00:54:50,120 --> 00:54:51,230
to be quite low.

1090
00:54:51,230 --> 00:54:53,830
- Right.
- What concerns might you have

1091
00:54:53,830 --> 00:54:55,860
this lack of diversity of crops

1092
00:54:55,860 --> 00:54:58,003
with regard to food security?

1093
00:54:58,920 --> 00:55:02,080
- Yeah, that's a really
very great question.

1094
00:55:02,080 --> 00:55:04,023
Thank you so much for that question.

1095
00:55:05,100 --> 00:55:09,030
This is where the,

1096
00:55:09,030 --> 00:55:14,030
you might have noticed in
that 2020 paper, review paper,

1097
00:55:14,070 --> 00:55:17,950
crop diversity, and
also in the Niles et al,

1098
00:55:17,950 --> 00:55:19,150
we had crop diversity.

1099
00:55:19,150 --> 00:55:21,870
It is not very well
connected to diet diversity,

1100
00:55:21,870 --> 00:55:23,070
it turns out,

1101
00:55:23,070 --> 00:55:26,360
but the question that you
have is extremely important

1102
00:55:26,360 --> 00:55:30,750
and continues to be very,

1103
00:55:30,750 --> 00:55:33,420
a big worry.

1104
00:55:33,420 --> 00:55:37,480
I would also say that in general,

1105
00:55:37,480 --> 00:55:39,290
there's this huge push towards

1106
00:55:40,470 --> 00:55:43,010
natural-based climate solutions,

1107
00:55:43,010 --> 00:55:46,500
where we need to move away from mono crop

1108
00:55:46,500 --> 00:55:48,470
into much more diverse pattern

1109
00:55:48,470 --> 00:55:50,800
so that we're less vulnerable.

1110
00:55:50,800 --> 00:55:53,080
And the long and the short of it is that

1111
00:55:53,080 --> 00:55:58,080
if we have multiple regions
experiencing climate shocks,

1112
00:55:58,390 --> 00:56:00,460
either too wet or too dry,

1113
00:56:00,460 --> 00:56:04,600
we could easily have a
huge spike of prices,

1114
00:56:04,600 --> 00:56:06,820
which then would have
very negative effects

1115
00:56:06,820 --> 00:56:09,540
on food security of these communities.

1116
00:56:09,540 --> 00:56:12,620
So although, we have a global food system,

1117
00:56:12,620 --> 00:56:14,270
it's connected by the market

1118
00:56:14,270 --> 00:56:17,780
and the market will be
very sensitive to having,

1119
00:56:17,780 --> 00:56:22,780
think about it, a huge
wheat production decline

1120
00:56:23,070 --> 00:56:26,160
in both Europe and
North America and Canada

1121
00:56:26,160 --> 00:56:27,610
at the same time,

1122
00:56:27,610 --> 00:56:30,100
then you have a huge
spike of wheat prices,

1123
00:56:30,100 --> 00:56:32,860
which then cascades into corn prices.

1124
00:56:32,860 --> 00:56:35,920
So that would be bad for food security.

1125
00:56:35,920 --> 00:56:37,320
But essentially when you're thinking

1126
00:56:37,320 --> 00:56:39,590
about that crop diversity question,

1127
00:56:39,590 --> 00:56:44,440
you need to remember the
complexity of the food system

1128
00:56:44,440 --> 00:56:47,160
and how all these elements are connected

1129
00:56:47,160 --> 00:56:50,560
and how the economy is
the mediating factors,

1130
00:56:50,560 --> 00:56:52,250
not just one-to-one.

1131
00:56:52,250 --> 00:56:55,440
In other words, food security
is not food production,

1132
00:56:55,440 --> 00:56:59,210
it's production plus income and livelihood

1133
00:56:59,210 --> 00:57:03,510
and trade and roads and
all sorts of other things.

1134
00:57:03,510 --> 00:57:08,210
And then, your own personal
food security outcome

1135
00:57:08,210 --> 00:57:11,260
if you see what I'm saying, so that-

1136
00:57:11,260 --> 00:57:12,760
There is a great paper.

1137
00:57:12,760 --> 00:57:15,320
I did a study from the USDA

1138
00:57:15,320 --> 00:57:18,840
in 2015 called "Climate Change,

1139
00:57:18,840 --> 00:57:21,220
Food Security and the U.S. Food System,"

1140
00:57:21,220 --> 00:57:22,590
have a look at that paper.

1141
00:57:22,590 --> 00:57:24,900
We addressed this crop,

1142
00:57:24,900 --> 00:57:29,900
this diversity in the major
crops extensively in that paper,

1143
00:57:29,920 --> 00:57:31,310
in that report.

1144
00:57:31,310 --> 00:57:32,590
2015 USDA.

1145
00:57:34,168 --> 00:57:35,309
- Thank you.

1146
00:57:35,309 --> 00:57:36,142
- You're welcome.
- Another question

1147
00:57:36,142 --> 00:57:41,142
from a student is do you perceive

1148
00:57:41,530 --> 00:57:43,060
population growth of humans,

1149
00:57:43,060 --> 00:57:44,240
which is rapidly increasing

1150
00:57:44,240 --> 00:57:46,120
with every passing year on our planet

1151
00:57:46,120 --> 00:57:48,490
and then a demand for food
resources becoming an issue

1152
00:57:48,490 --> 00:57:49,653
in your research?

1153
00:57:51,900 --> 00:57:54,840
If so, any suggestions on what
can be done to resolve this?

1154
00:57:54,840 --> 00:57:57,020
So basically asking
about population growth.

1155
00:57:57,020 --> 00:57:58,130
- Yeah. (exhales sharply)

1156
00:57:58,130 --> 00:58:02,620
I think that the answer is no,

1157
00:58:02,620 --> 00:58:03,690
I don't really think

1158
00:58:03,690 --> 00:58:07,660
population growth is a
huge issue basically.

1159
00:58:07,660 --> 00:58:10,150
It's a very complex issue,

1160
00:58:10,150 --> 00:58:13,600
but I do not think that solving
the problem by telling women

1161
00:58:13,600 --> 00:58:15,990
that you shouldn't have
babies is a good idea

1162
00:58:15,990 --> 00:58:19,970
because people, it's really a
poverty problem, essentially.

1163
00:58:19,970 --> 00:58:20,803
- That's right.
- So think about it.

1164
00:58:20,803 --> 00:58:23,000
If you are a woman who
lives in these year,

1165
00:58:23,000 --> 00:58:24,900
you're gonna want 10 kids

1166
00:58:24,900 --> 00:58:27,690
because eight of them
might die of starvation

1167
00:58:27,690 --> 00:58:30,880
or you know, or an infection

1168
00:58:30,880 --> 00:58:32,620
'cause you have no access to healthcare.

1169
00:58:32,620 --> 00:58:34,980
And somebody has to take
care of you when you're old.

1170
00:58:34,980 --> 00:58:36,130
(hand thuds) If you live in Singapore,

1171
00:58:36,130 --> 00:58:37,950
you might have one or zero kids

1172
00:58:37,950 --> 00:58:39,840
because you don't need children.

1173
00:58:39,840 --> 00:58:41,310
What the heck your children for?

1174
00:58:41,310 --> 00:58:42,593
They're a pain, right?

1175
00:58:43,867 --> 00:58:44,700
(John laughs)
So essentially,

1176
00:58:44,700 --> 00:58:46,560
what we need is better development,

1177
00:58:46,560 --> 00:58:48,380
we need better economic,

1178
00:58:48,380 --> 00:58:51,040
we need better migration,

1179
00:58:51,040 --> 00:58:52,773
more open societies.

1180
00:58:53,868 --> 00:58:55,560
(hand thuds) It's not
a population problem.

1181
00:58:55,560 --> 00:58:56,950
It's a development problem.

1182
00:58:56,950 --> 00:58:59,080
It's an economy problem.

1183
00:58:59,080 --> 00:59:01,510
It's a structural problem,

1184
00:59:01,510 --> 00:59:04,000
not a population problem,

1185
00:59:04,000 --> 00:59:06,424
but call me up and we'll
argue about it more. (laughs)

1186
00:59:06,424 --> 00:59:08,550
(Ethel and John laughing)

1187
00:59:08,550 --> 00:59:11,290
- I wish we could continue
this for another half an hour.

1188
00:59:11,290 --> 00:59:15,423
We have many more questions
than we have time to ask,

1189
00:59:16,300 --> 00:59:21,300
but I think we need to
respect the time limits

1190
00:59:21,320 --> 00:59:22,560
that everybody's expecting.

1191
00:59:22,560 --> 00:59:26,040
And so thank you so much for your time

1192
00:59:26,040 --> 00:59:27,430
and wonderful presentation.

1193
00:59:27,430 --> 00:59:28,263
We really,

1194
00:59:28,263 --> 00:59:29,473
really appreciate it.
- Okay.