 So let me introduce myself first. My name is Josef Eris. I work in the Institute for Applied Economics Research in Brazil. It's a governmental institution, linked to the Ministry of Planning. And Ipeia is the acronym, and we act like a think tank for the support of policymaking. And this work is joint work with a colleague called El Stato Reis and a student in Minas Gerais called Paulo Araújo. So, first of all, what we call ENSO events. In fact, ENSO is just an acronym for El Nino, Sulfur and Oscillation. So it's a climatic event that is about a quasi-periodic distribution of heat across the tropical Pacific. Maybe it's one of the most important coupled ocean atmospheric events that has an impact on the internal variability of climate in the global level. And it can generally, in a very, very informal way to describe it, it can be divided in three phases. So we have what we call an El Nino phase. It's when the sea surface temperature in the Pacific Ocean is above the average. So there is classification that was proposed by the National Geographic and Atmospheric Administration from the States. And every time that's the average temperature of the equatorial Pacific, it's above half degree of the historic mean. We call this El Nino phase. El Nino phase is the contrary, like when we have cooler temperatures than the average in the Pacific. So when this happens, like when it's below less 0.5, so there are cooler temperatures, we save that here in El Nino phase. And when the sea level temperature is between these two thresholds, we see this neutral phase. So this is a kind of life cycle of Ancel. And so we just keep changing between one phase to another. There is some regularity on these phases. And the problem that these Ancel effects, like they have impacts worldwide, and in Brazil they are quite important and we have very different regional effects. So these are the two most affected regions in Brazil with the Ancel events. The Northeast region is the poorest part of Brazil. When we are in an El Nino phase, like warmer waters in the Pacific, we have severe droughts there. So we have lots of socioeconomic problems, migration, because of these severe droughts are in an arid zone. And when there is a La Nina phase, it's exactly the opposite. Like we have increased precipitation, heavy rains, and so El Nino produced a lot of extreme events in this region. On the south region of Brazil is exactly the contrary. Like during El Nino, what we see is increased precipitation, like it's the contrary of what happens in the Northeast, and higher temperatures. And during La Nina, we have severe droughts. So this impact has a lot of reflections in the agriculture production. And so what we try to do is to evaluate it. Just to take a look of the general patterns of what happens in these different phases here, in the first column here you have the average precipitation and temperature in the neutral phases of the Ancel cycle. So we see like when we compare the neutral phase, the first part here is precipitation. During El Nino phases, like here in the 80s and the 90s, we see that we have a decrease in precipitation. And during La Nina, we have the contrary, like an increase in precipitation in the Northeast. And we got in temperatures, like temperatures increase during El Nino and they decrease during La Nina. So just to show up a little bit the general patterns of what happens during El Nino and La Nina. So just to also give an idea of the impact that is in economic terms to agriculture in Brazil, like during one of the most severe El Ninos from the last century in the 82-83, in the south region there was a production loss of more than 5 million tons. And that corresponds to approximately 45% of total regional production. And during the El Nino, in 97, 98, also we have production loss of about 3.5 billion reais, that is about 1.7 to 1.8 billion dollars and more than 15 billion tons of agricultural production losses in the Northeast. So what we tried to do in this paper is to assess the impact of weather-related ensue effects in the agricultural productivity in these two regions that are the most vulnerable to ensue effects. So our methodology was based on a three-stage approach. So we developed an empirical model, an econometric modeling approach. So we divided it like just to give an idea of the modeling. I will not go very deep in the details, but the first stage, we want to isolate the effects of El Nino on the Brazilian climatology. So what we do, we run regressions to assess the relationship between sea-surface temperature in the Pacific and climatology in the Brazilian municipalities. So how the temperature in the Pacific affects temperature and precipitation in the Brazilian municipalities. In the second stage, we propose the reduced form equations to assess how the productivity or the agricultural yields are linked to this temperature and precipitation. And in the last stage, once we have estimated the parameters of the first two stages, we can simulate what are the effects of El Nino and El Aninha on agricultural productivity. So this is very, very broader speaking our methodological approach. So just to give some more details about how we can really isolate and identify the effects of El Nino and El Aninha. So the first stage is about how the effects of the temperature in the Pacific on climatology in Brazil. We adopted what we called a spline function that is a piecewise linear function with respect to sea level temperature. So just to give an idea, like here, the dependent variable is weather. It can be, for example, temperature during summer, temperature during autumn, precipitation during a certain season. And the modern approach here is to consider like the hypothesis is that the sea surface temperature anomaly in each phase has a different marginal impact in weather conditions. So here, for example, is an indicator function to see that if we are in El Aninha phase, the marginal impact of the sea surface temperature on precipitation or temperature will be better one. If we are in a neutral phase, the impacts will be better too. And if we are in an El Nino phase, the marginal impact of changes in the pacific temperature will be better free. So this was a way to try to distinguish between the effects of the free phases of Enso effects. So the idea here is just to use a spline function to account for this heterogeneity of the impacts. We also consider that it depends on the sea surface temperature in the pacific contemporary and also a lagged one. And let's leave it to account for the geographical factors because all the impacts are quite heterogeneous, depending on the region we are. So we put some geographical information with cross terms with the temperature level that there is a cross term for latitude times sea surface temperature and longitude so that we can allow for heterogeneity of impacts depending on the region we are considering. And lastly also, there is a fixed terms effect here to take into account any unobserved characteristic of the municipality that can also affect the weather. For example, topography, depending if you are in a higher municipality, let's say it's very high, or you are at the sea level. Well, the second part, we have the relationship between agricultural yields like it's just production by actor and that depends on temperature observed in the municipality and precipitation. So we introduce linear and quadratic terms to account for possible nonlinearities between weather characteristics and yield and also a time trend to account for unobserved, for example, technological progress since we estimate this for the period 1907-2002. And also we introduce seasonal temperature characteristics as you'll see in the next slide. Well, and once we have these, we can simulate and evaluate the effects of the expected changing yields due to our NINU and due to La Nina. So what we do? We just simulate the expected temperature in our NINU, the first phase, no less the expected temperature during the neutral phase. And so we have, for example, the temperature of the precipitation variation attributed to El Nino range. And since we say that the yield responds according to gamma, so we just use these gammas to see how yields will respond to the variations attributed to El Nino and to La Nina. Okay, so that is the simulation strategy that we use to identify the El Nino and La Nina effects. Okay, this is just to, of course, this is more in detail in the paper that is in the website, but here, so we have here, for example, like the first stage, we have the dependence, for example, precipitation summer in autumn, winter, spring, and how this is affected by each ensue phase. So we have here, for example, anomalies during La Nina phase, anomalies during neutral phases, and El Nino phase. Then we have our second stage that we have, we know how different temperatures and precipitations affect the yields for each of the important crops in Brazil, so corn, sugarcane, beans, and manioc. Okay, so we get all the parameters in the first and the second stage, and we finally simulate. So I'll go straight to the simulation. So this is the results for the Northeast and also after for the south of Brazil. So what we see is the average productivity observed in Brazil, and this is the reduction in productivity, like during El Nino phase, and we see that in Northeast, like the most important reductions, it's in corn and bean. That no, this can be like a 50% reduction in the production of these two crops, and the sugarcane and manioc are less vulnerable, but anyway it's a 5% reduction that is quite significant also. So this is most attributed to reduction in precipitation that we find in the El Nino phase. On the other hand, in La Nina, we don't have so much impact for sugarcane and manioc, but we have significant impacts for bean production, okay? And these numbers are particularly important in terms of socioeconomic impacts because in this region office, most of the household farmers in Brazil are located and they are specialized in corn production and bean production. So like they are the most affected ones. So this causes really lots of socioeconomic problems and migration and all the other problems. So we have some maps to show, like most of the impacts, I will not have the time to pass through it, but normally it's in the south of the region, it's in Bahia, Salvador de Bahia and Bahia region. And so we have losses, like most in the south, and this is the most productive area also. So like the most productive area in agriculture in the Northeast is the most affected one. Okay, we have the same thing for El Nino, this left side is El Nino, like the blue side, the blue, it's not as bad, sometimes it can be even a small gain, but when it's red, it's really a more important losses, okay? When we go to the south, we see on the contrary that La Nina is much more harmful than El Nino, like for most of the crops, like so you see here that in the years of La Nina, we have reductions varying between 10% of the average productivity to 85%. Like you almost collapse all your wheat production in the south of Brazil, okay? And El Nino, no, the main problem is soybean, also that's very important for Brazilian experts, and we have very important impacts in terms of soybean production and the south region. And so the most affected region is Rio Grande do Sul, Porto Alegre, and it's the most vulnerable state and one of the most dynamic in terms of agricultural productivity also. So we have also the same kind of maps that you can consult in the paper. The red parts are the most affected ones, the blue ones are the least affected, okay? So here in the extreme south of Brazil, it's affected by droughts during La Nina, okay? And so like what we conclude from the papers that as we expect, there is very important regional impacts in agriculture production, especially when we are talking about water-intensive and rain-fed crops, because the most problems of El Nino and La Nina is associated with precipitation, okay? So we identify a need to invest in irrigation methods and also like to increase the modeling capacity to project what happens in El Nino phase, okay? And also for further investigation in order to account for the benefits in adapting by irrigation, we also want to make the same model but to see the impact between irrigators and non-irrigators, how they... So how irrigation can be used as adaptation strategy. Thank you.