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So today I'm going to talk about food security from space. When I started working at NASA Goddard fifteen years ago
I saw that there were terabytes of information coming from earth science satellites all over the world and what we found is that we have all this date and what we need to do
is to understand how this can be used for food and food security. So one of the data sets that I am particularly interested in is the vegetation index.
which shows the response of vegetation to changing weather conditions. And you can see in Africa the response to the growing season when the rains happen and in North America
when the change of season happens. And this data can be extraordinarily useful for understanding food and food production. Food security is the ability of all people to have enough food
for an active and healthy life. And I've been studying food security for as long as I've been at Goddard and for
since I was in the peace corps in the early 1990s. This is a family that I lived with in the peace corps. They are an agricultural family like many people in the developing world who
farm and eat the food they grow and are very sensitive to variations in the weather. So these folks
may have problems eating when they do not grow enough food for their families.
So the ingredients of food security are availability. This is the food that is available in the region where a community lives, access, which is the cost of food and how much it costs compared to the income of the people
in the region, and utilization, which is the health of an individual....so there ability to use the food that they eat to have an active life.
So the research that I have been doing is to try and connect availability as seen by satellites and the impact of weather shocks such as droughts, floods, and
cold periods on the satellite data and ultimately on food production. So this is basically availability and the cost of food in the markets where those people live.
And so it's a big jump. And so I've been working a long time to create a model which is basically a computer program that uses observations,
and information, quantitatively, to come up with relationships we can then use to make better decisions. So one of the regions that I look at
when I was developing this model is Niger. Niger is in the middle of the continent, it is a west african country, it is one of the poorest countries in the world, the average income is
around $ 400 a year. And 80 % of people that live in Niger are farmers or work in the agricultural center, moving goods around, or selling food in the market.
So in his country we have this guy, he's a farmer, standing in his millet field...looks sort of like corn. And agriculture in that region is all
done by hand. There are no tractors and not a lot of fancy technology. And this is what the field normally looks like. In
2009 there was a drought, when the rains failed to come on time, and the field looked like that, very dry and empty.
So if you look at the data from satellites, this is the vegetation image from that same place. and you can see that the brown in the field is reflected in the brown in the satellite imagery. And it wasn't just that field.
It was the farmer's neighbors field, the fields in the states next door, in the countries next door, and so there's
not just this one community but all the communities across the continent and so going back to my model what I wanted to do was to use this information about satellites
to take the satellite data and estimate food production and then connect that food production to inter annual changes in prices and monthly food price levels.
to do this we need to understand how markets work. So markets are driven by supply and demand and so when supply goes down because there's a drought
the people who grow that food are also eating the food they grow, the demand will go up at the same time supply goes down. So this is really different than here in the United states where
if a farmer doesn't produce very much food, the demand doesn't change because the farmer is still eating food from the grocery store. In this region that is not the case.
So it's very important to know the variations in production. So in my model what i did was choose to ignore most of the variables which are
important in market functioning. There are several reasons for this but for my model i just have two input variables. I have the satellite remote sensing from satellites
that is a proxy for local food production and the international price of that same commodity. so i just have two parameters to estimate
local food prices. There are a lot of other things that effect prices in markets obviously. There's storage, there's trade, there's transport, but we do not
have locally specific and readily available, comprehensive information on any of these parameters, particularly in the developing regions
We just don't know where food is traded, we dont know whether or not people are releasing food that's stored or not. This is the kind of storage they have there and so I'm
going to neglect those variables and try to make my model work with just those two input variables. One of the reasons I think this might work is because 95 % of all food never croses
an international border. Most food is grown and eaten locally. Most people in the world do not participate in this huge trade and
transport thing that we do in the United States. They grow food and they eat it and therefor local production is absolutely critical and when the local production fails, there's big consequences
on food security. Another thing that's really important is that family income really varies across the world. In Niger,
that farmer spends about 60 % of his income on food, in the U.S. it's 6.6 %, in Europe,it's about 10-15 %, in India
Pakistan, it's 45-50 %. Niger is one of the poorest places in the world so think about trying to eat on $ 400 a year. I mean that's
about my monthly household budget, or 2 weeks, so one of the reasons we spend so little on food is because not only is the united States one of the most productive
regions, we also tend to eat a lot of inexpensive, commercially, industrially-produced food, which is not really good for us but it's very inexpensive, and very efficiently
produced compared to hand cultivation and all the stuff that's happening in places like Niger. Another thing to note is that when you have one of these big droughts that gos across
the entire region, you're going to need to buy food to meet the needs of the country. So Niger, if they produce much less food than normal, are going to need to
import food from the international market and that's okay when the international prices are normal but in 200, 2011, and 2013, we had 250
% of normal which is a real problem if you are in Niger and you're trying to come up with enough foreign currency to buy a huge amount of extra food that you weren't expecting
because of the weather shock. So the variations in the international prices are really important. Another thing you need to have to get food from Kansas to Niger,
is to have roads that work...ones that dont look like this. We need to have bridges that connect one road to another, we need to have trains which are
that work and trucks that are not buses. And we need to have trucks that are not buses and we need to have ports that are efficient and connected to the road network
This port is the port of Lagos, Nigeria, and it has a lot of inefficiencies. It has corruption, it is very slow, they have to move the goods
from the big containers into sacs which takes weeks so it's very hard to move goods around in places like Niger.
So the infrastructure that connects this farm to a market determines the income of the farmer and it determines the food security of that community. So high food prices
can cause problems especially when they change really rapidly over a short period of time and so this is what the map looked like of the places that the very large increases in the international price
in 2008. This is it. It's just places that it's going to be a problem. But there's no quantitative analysis here, there's no connection, maybe there's a lot of extra production there
maybe it's really not a problem. So my work is trying to put numbers on this map, trying to be more quantitative, to allow us to use the satellite data that we have to
better understand food security problems. So going back to my model, here we have the various elements, so we used the satellite data to
look at whether shocks to get at food production, and then we have our two parameters, the international and last year's production from satellite data to understand monthly food price levels.
so now im going to show you what the results of the model were for one particular model run. Oene of the big
messages from this map is that there are a lot of drivers. So in this map, the gray places are countries where i have local food price
information. In this map it's maize, corn prices, and in the red dots, they are places where the local weather shocks as seen by remote sensing
and the international price are both important for understanding and figuring out what the next month's price in these local places are. The yellow dots are places where these two parameters
are not important. And we can see that for example if you're in mozambique for example, you may or may not be affected by these things. It really varies.
This makes a lot of sense. Think about NY and detroit. They are both in the UNited States but they are extraordinarily different economies. One is on the Atlantic Ocean,
thy have totally different wage labor, they do different things, they have completely different road networks, and the same things go for developing regions too.
some places are well connected to the local agricultural system and some places are not. so how can we use this information to improve our understanding of food security problems?
One of the ways I'm interested in using this is to try to group markets into different drivers.
so in this typology we have places where domestic weather shocks influence food prices and places where they have no effect.
And then here we have global prices have no effect and global prices influence the local price. So that map I just showed you is this bottom right quadrant.
where both of these things are important. These are places which are food surplus area in an exporting country where we
have urban, well connected. If you think of a place like Dakaar Senegal, which is on the Atlantic Ocean, it has a lot of other sources of income,
it still is a very agricultural country so when it has a big drought, the broader economy is effected so those places are affected both by
these large international changes in prices and also by local weather shocks. In this next grouping we can see that global prices
dominate. So if you're using the remote sensing information and you see a big drought, it makes no difference really for local food prices
this is like panama city, panama. It's on the canal, huge amounts of international trade, most of the people who live in panama city,
a tiny little country, really big city, so in that place when you have a huge change like we saw in 2008 of the international prices going up 250 % in five months
you need to pay attention to those places that are mostly dominated by international prices. The poor people in those places will be really affected by those changes
even if they aren't actually eating the food that came from the international market, it's just everyone goes, "Ahhh!!" and change the local prices and then you have a food security problem
This next one is our friend in Niger...the middle of the continent of Africa, so far form the international markets that
local weather shocks dominate the local prices. They are isolated, landlocked, high poverty, food surplus areas and so when they have a big drought, not only
will they not have enough food that they produced but it's darn hard, remember those roads, to move goods from Kansas to the
middle of Niger because there are just so many impediments. In this last quadrant is places that my two parameter model doesn't work very well.
The model will work better without those 2 parameters and those are places where all the other things that effect markets are important.
Think of it ...the policies, the economies, the politics, the storage, train, fifty things that are not in my model and so to get at these
places we need better data, we need better diagnoses, information. There's all sorts of things we need that we don't have. So this is where I will spend the next 15 years
looking and also it will moderate the impact of other parameters. Right now
satellite data is used to look at whats going on in Niger and local food price data is used to look at what's going on. what we don't know is what are the interactions
of those two so we're hoping to work with organizations like the famine early warning systems network to better connect these two parameters
and their interactions to improve response during times of need, like our 2009 drought. When all those people are hungry, we need to be able to respond effectively, and not bring
aid when it's not needed but bring effective aid where it is. So before I end I want to say a word about climate change.
Climate change is likely to impact the amount of weather shocks that we see. We are likely to see more droughts, more floods
more wierd weather and that weather will have direct impact on people
s ability to feed themselves and their children. The poeple who are most guilty of making emissions that are causing changes in climate (The red countries here) are the ones that are least vulnerable
to those changes. The most vulnerable, the red countries on the bottom, are the ones
that have the lowest emissions today and so as we go forward, we need to be sensitive and we need to provide help when it's asked for so that everyone in the world can eat and be
food secure. This is written up in my book which is coming up in May so if you want to learn more you can look at it then. Thank you.