Agriculture and land use - part1

from the NASA Goddard Institute for space studies in New York and this session today is going to be on agricultural impacts and the climate information that we need um and we have three speakers today I'm going to get started with with an overview and and bring in some of our approaches from ipcc and agnip and then we're going to hear from Benjamin Sultan uh and Edmund Toten later in the uh in the hour and a half we have or hour and 45 minutes that we have here so I think I'm just going to get going on this uh and and uh I the we'll probably have questions um depending on the time frame we're hoping to have questions at the very end um and then we'll see how we're doing in the in the transitions between if there are quick clarifications um all right so I wanted to start by talking about what I call the quadruple challenge for agriculture and this is that the world is asking the agricultural sector to sustainably increase production to provide healthy food for growing and developing populations it's not just about feeding who we have today it's it's about feeding the growing populations and recognizing that as countries develop they're often asking for different types and quantities of food there's also new kind of efforts for nutrition that are changing the types of food that are recommended even in the richest places in the world um we also are asking the agricultural sector to adapt to climate change and ongoing climate extremes which are already upon us while also mitigating emissions from agricultural lands most importantly from livestock and from Rice uh methane as well as uh you know the the overuse of fertilizer in some places um and then also none of this will be possible if we can't maintain some kind of financial incentive for agriculture uh that that supports so many uh Regional economies so any one of these challenges is substantial and when you put all four together at the same time uh you you understand the challenge that that faces the food sector so what I'm going to talk about today is uh is an inventory of agricultural responses to climatic impact drivers um as well as that term climatic impact drivers cids being a core element of approaches that we take within the agricultural model into comparison and Improvement project or agnip which I'll introduce uh then I'm going to talk about how we build scenarios of future agricultural systems and wrap it up with some key priorities for agricultural risk information development so this is a table that you've seen already several times this week where we have taken the the inventory of climatic impact drivers that we developed within the ipcc working group one uh you can see this in in chapter 12 table 12.2 and uh just as a quick reminder these are the major types of climate conditions that we know Drive responses in the things that we care about and there's a whole table here with many rows but I pulled out the crop system row because this is where I'm going to focus much of my attention today and I'll also note later in the table there is a a row for agricultural lands which is a slightly different different row so this one is really about when there are crops in the field what affects them whereas agricultural lands are questions around you know large scale where do we grow Agriculture and is it is it uh viable so for example you'll notice uh the most obvious example of this is right here where plants that are in the field can get affected by uh coastal flooding but usually if the relative sea level has already risen and taken over your field you're not likely to plant so that is more of a lands issue than a specific crop that might be in the field but the idea behind this table is that where you see darker colors are places where we saw in the literature that people are using this climate information to determine some kind of a response usually it's because they've observed it um and uh and they have some ability to think about the projections or or how we go forward so you'll notice that there are many many different climatic impact drivers that affect agriculture and almost every single one has a unique pathway by which the biophysical processes are affected and unique types of metrics and indices that we have to provide as climate scientists to to enable that so I wanted to go through some of these um and talk about how we do it but before I do that I'm going to give some some context of where I'm coming from so um I am the science coordinator and the climate team leader for the agricultural model intercomparation Improvement project this is agnip uh I'll stop saying the full name and just start saying from now on um but agnip is designed very specifically and intentionally to be a lot like cmap the coupled model engine comparison project that we use for climate models we want to do the same thing for agriculture so we we saw that the agricultural modeling field was decade leads behind the climate modeling field when it came to systematic inner comparison Community engagement and and collaborations across modeling groups so that we could have direct ways of figuring out what we can and can't model and how we can prioritize and Achieve model improvements so this is our we call this our flower diagram that shows major areas in which we have model Edge comparison projects and every single petal of this is a a major uh kind of category and then each row is a specific set of protocol based activities using multiple models and multiple institutions in multiple countries and I could talk all day about what this looks like um but what I I do want to say is since we launched in 2010 this this diagram has gotten more and more complete we're now actually going off the bottom of the screen here that you know Vietnam has is only half on here unfortunately um but you know we're adding more regions we're also working on a bunch of different scales so I'm just going to call out the largest categories here let's see if I can get the uh the pointer to water so we work first of all on the global scale so we try to take everything together and build assessments of global economics and food prices and food trade um but on the opposite side we go all the way down to the experiment model interface where we have entire projects based on the crop water evapotranspiration so the fundamental physical processes in one field trying to get that right on the flex Towers Etc um we work not only on the global scale but we work in the regional areas where we try to put all of our elements together and make the best support for adaptation and risk management planning we also have uh coordinated efforts to to make large-scale gridded bottles of crops uh and while we're doing all of this of course we're building up data and tools looking at interactions with things like water resources and livestock and we have all kinds of cross-cutting themes and I'll talk about them in a little while but maybe what agnip is most famous for is these crop model intercomparisons where we have teams that are fundamentally focused on each species so we have a wheat team that has compared more than 40 different wheat models and you can see the other ones here I don't think anybody stands up to the wheat team but maze has more than 20 models rice has about 18 and you can see the the diversity of what we do all right yeah and then overall agnip is this community we got more than 1200 people around the world and it really is designed to be interdisciplinary so I have to acknowledge first that we are super proud in the agma community that uh one of the aggregate Founders Cynthia Rosen swag was named the 2022 World food Prize winner uh for those who don't know this is like the Nobel Prize for food systems and uh the reason I especially wanted to thank Cynthia for this is that Cynthia has actually donated a portion of this prize to this Workshop to help some of you come here um this is the type of thing Cynthia rather than taking this money and going on a boat trip somewhere she has funneled it back into agnip and is now funding conferences and workshops uh even in small pieces uh to the best that she can all right so the way that we think about agmap is really that we can't understand and prepare for the big food system challenges I mentioned unless we recognize the complexity of the food system so here's one representation of the food system uh you can see there's lots of details but the main thing to recognize is that it is not just what happens in climate um we have to understand the fundamental biology and how it interacts with climate but we also have to recognize that food interacts with a very Dynamic economic system and a political system that can interfere or enable uh and and that's something that we have to recognize so there are all kinds of things that we've done in agnip to kind of build up our understanding of each element and the way they interact but if we kind of Step Up a big Pace backwards we can re we can talk about how the agricultural models fundamentally are trying to understand how the world responds when there are shifts in the genotype of seeds so that's like the genetics and and the seed selection the government itself that's where climate change comes in management of what farmers do like that includes many adaptation types and then value change because it's not just what happens on the field after you harvest and move past the farm gate there is a whole chain of production could be just local markets and it could also be going through processing plants and shipped around the world so we talk a lot about G by E by m that's a little bit Insider speak but this is the way that the genetics and the environment and the management interact and then I've for the first time in this presentation added the value chains uh on top of that so one way we think about uh agricultural modeling is that of course we're fundamentally trying to represent hazards and disasters uh this means that it's not just a climate change it's all time scales um but we're going to build up a figure here and the first thing to note is that we fundamentally have to understand how CL how crops and the agricultural system respond to things like extremes and temperature both on the hot and the cold side rainfall extremes from droughts to floods air pollution pests and diseases Cyclones and extreme storms and then this plus sign in here is doing a lot of work that covers the rest of that CID table we know there's a lot more um but of course if we can fundamentally model these responses we can follow this time scale at the bottom here we can monitor during a season up to present day represented by this this line and we can forecast with our climate models and our our crop models or our seasonal forecast weather models um and then of course we're very fundamentally interested in a lead time because that tells us how we can react and what types of interventions we can be reactive as we see disasters unfolding of course we can take these same models and go backwards in time and this gives us understanding as we we fundamentally look at things like detection and attribution and counter factual management which is another way of saying if we had known that that drought was coming could we have done something differently and we can model that because models allow us to go beyond the observed experience and test out different interventions so between these two things we can you we can develop understanding and you'll notice of course that it also points to certain types of climate information so we might want to look at historical observations retrospective analyzes data assimilation type systems of course if we go far enough on this timeline here we're really not forecasting anymore there's this Wiggly area and then we're really talking about projections this is because of course the future depends not just on the initial conditions of what we see today but on decisions that we make as a as a general society around climate change the way that the markets are shifting policies socioeconomic change and larger questions of environmental sustainability so once we're out here we need scenarios so that we can project and understand and this of course is super valuable because we have to get to this proactive set of interventions that recognize non-stationarity and emerging challenges it's not just the same of as the past we need to get to the Future so uh one of our our more famous set of projections are these Global yield projections that we've made um these were done in in association with the easy MIP project the intersectoral impacts model engine comparison project uh where agnip runs the agricultural sector and what you see here in the bottom left are Maize yield projections and in the upper right are wheat yield projections and you'll see that they're going largely in different directions The Maze yield is getting orange that means lowered yield and the the wheat is in some places getting green so if we want to understand what's Happening Here we need to figure out the fundamental responses and if I can pause it back um uh it looks like I'm not gonna be able to hold on a second what I gotta I have to do is I have to play it again so I'm going to talk through just a couple things that's happening so while you're while you're watching this you'll notice that that the maze yield is particularly uh detrimentally affected in the the tropical region so places that are already near the hot thresholds are being most strongly affected wheat yields you'll see in in some places especially where rainfall is increasing and where it's currently cool a little bit more temperature can actually be helpful and then of course we have the carbon dioxide effect which is generally going up and helping many agricultural regions but notice that there are exceptions parts of Southern Canada Pakistan parts of India Bangladesh these places are also being negatively affected as well as Mexico and Southern U.S so again there's a little bit more danger in the tropical regions but as a cool season cool climate zone crop wheat is faring better than mace so this is also very strong message coming out of the Agricultural sector which is that you can't talk about crops in general you have to talk about systems and regions so this figure um or this study was actually featured in the synthesis report figure SPM 3C which is the first time that we've ever had anything like an impact map in the synthesis report summary for policy maker so this is actually a really big step that we had this map you've already seen some of the other ones on ecosystems and human health uh there's a figure Below on Fisheries which I won't get to but this was a big battle in the synthesis report approval session to get these figures through but for the first time we do have these types of projections um that that we have not just the projections but you'll see the hatching that's the uncertainty across the models both climate and crop um and and there's a lot we can do with this yes the health one no that so there there was a different battle the original figures that we had had both Maize and wheat and we were trying to present just like I did before you know there's some positive some negative and and the system is complicated but in the end there was just too many figures so they said we can only have one and when we put the hatch marks when you put the uncertainty on the wheat map it was much more uncertain which reflects the the wheat team's understanding as well so we decided it would be easier to have one one row here and and this this was approved pretty quickly actually the health was a whole other battle it took much much longer all right so coming back to this uh this table of of cids I wanted to focus on kind of how we understand and respond to these um so the first thing I wanted to say is that we are at risk as a community of What's called the lamp post problem all right and and there's a figure a cartoon here that will help you understand it but the idea behind this is some some drunk person walks out of a bar and they can't find their car keys and they're looking for their car keys and they're looking for them underneath this lamp post and the guy says oh did you lose your keys you know somewhere around here and and he said no no I probably lost them over here but there's light here I can see so I'm looking for my keys over here where there's light and of course you're never going to find your keys over here if you lost them over there but it's just so appealing where there's light and I think we sometimes have the same issue with with some of our impacts modeling which is that we know how to do certain things well so we focus on the things that we know well and there's this whole other dark space over here that we have to at least recognize we have we have to avoid being this this person um so when we look at this table we can start to directly assess if we are doing a good job of representing different pieces of it so here are a couple that I'm calling out the mean air temperature which we might use metrics like Growing Degree Days that's that would be a CID index for those who don't know Growing Degree Days basically counts every degree above some baseline temperature so if your Baseline temperature is 10 degrees Celsius and you have a day that is 13 degrees Celsius you have three Growing Degree Days three points above that above that limit and crops actually grow according to you know a calculation and accumulation of Growing Degree Days it's a very strong metric for crops that's what determines their growth stages and it determines whether we get to that Harvest and have had enough time in the field uh absorbing sunlight and making you know the the carbohydrates that we need for our grains so uh mean air temperature we do a very good job with so I've put a green star that's something crop models do well mean precipitation we also do quite well that's a fundamental part of of all crop models aridity we do a little bit better but a lot of our crop models don't capture the long-term Trend and drying out of certain regions of the world sometimes we assume that crops are planted in a saturated field when that's not always the case so those types of things we can improve and you'll also notice over here that I have atmospheric CO2 at the surface and this was a CID that we really had to fight for in chapter 12 because a lot of people didn't realize carbon dioxide they think of as the cause of climate change but it is itself a direct impact on agriculture and you can't really model the future of Agriculture unless you have CO2 so it's definitely an element of our human influence on the climate system that is affecting uh something that we care about so it deserves to be here and I'll show some examples of how we're understanding this this is a picture over here of a field trial oil in Arizona where we had a whole field of of Wheat and we were able to use heat lamps in one part of the field to synthesize warmer conditions so we had the exact same weather the exact same precipitation the winds you know all of that stuff is the same but we have extra heat in this one part of the field and when we actually did those experiments we got observations here in red and as we increase seasonal mean temperature we get a drop in the grain yield and you can see this pattern and the gray and green here are our crop models that have done a pretty good job overall of representing that drop off and there are things to still figure out there's some some error bars the line is not a perfect one-to-one match but this is the type of thing we're doing in agnep we're trying to find these field experiments and make the models accurate yes that field um I would say that's I mean so there were multiple you can see there's multiple stations there's other ones in the background like over here um but each one is maybe 10 meters wide something like that I don't know uh no there I mean there were um many of these so uh they were running in different fields kind of all around in that area um but yeah there's there's some really fascinating stuff I'm not even going to show the free air carbon enrichment but they have basically a big ring and when the wind blows from this direction they they release carbon dioxide into that Windstream so it blows over the field and then the wind shifts to over here and now they release the carbon dioxide from here and they're trying to maintain a higher CO2 in the field um all right so uh I already mentioned uh the kind of mean air temperature and CO2 as as impacts but of course as the climate is changing both of these things are are changing so this is new not yet published uh results um but it actually calls a little bit of question on the global warming level approach which has taken hold and work in group one and working group three uh it's very convenient from a large-scale policy perspective to say that you know in general a two degree world is the same whether we get there quickly whether we get there late uh and even the climate models you know the ones that are most sensitive to climate and the ones that are least sensitive to climate still basically say the two degree world is about the same and here is one reason that it's not which is that if you actually take different climate models represented as the different colors here and the different shapes being different scenario Pathways when you get to this two degree world you have a very large range of carbon dioxide concentrations that are associated with that two degree world more than 150 PPM difference all right by the time you get out to a four degree World um the range that you see here is uh about 200 and then even there you can be deceived because not all of the climate models are sensitive enough to get to four degrees so the true distribution is even larger the last level that all of the climate models get to is here the Three Degree world and you can see it goes from 550 up to about 750 or 800 so there's very large ranges in CO2 which we know will have an impact on crops um so we actually can run these through our our crop models and unsurprisingly the model the UK esm which is the most sensitive to climate change that is the model that reaches higher global warming levels fastest so when it gets there the CO2 is not as high as other models which need more CO2 concentration to reach those higher levels so that UK esm model gets to the high global warming levels earlier with a higher with a lower CO2 and that is a very bad combination for crops because now it's the same temperature but the CO2 is lower and that means unsurprisingly down here that is the most pessimistic model so the the yield losses this being a maze yield loss for the most uh the models with the highest equilibrium climate sensitivity so that the strongest response to climate change those models are the most pessimistic which means I can draw a direct line from pessimistic crop projections to the equilibrium climate Center sensitivity of the climate models so this is work that that's uh hopefully coming out soon but something that we really need to Grapple with all right so when we're looking at extreme heat the other thing that we we are trying to do is we have to recognize that there are it's not one category there are many thresholds within extreme heat as a category so we had this figure in in chapter 12 in our our FAQ section that is just a simple way of thinking about you know there is a a set of temperatures represent on the x-axis here um where growth is not really limited by temperature it basically is growing happily but as you get across a critical temperature threshold uh you seem you often see this kind of Step change where now you're getting reduced growth so it's a little bit too warm the plant's not quite happy and growth goes down but then you can reach a second threshold a limiting temperature threshold after which the crop can rapidly drop off and fail so we need to spend time in the agricultural Community to identify these thresholds and make sure we understand how close to those limits we are and what types of adaptations we might be able to do for example a genetic adaptation could literally move this threshold to the right give us more space before we start to lose our yields um another thing that I just can't resist but showing is a result that we got back in 2016 um where we compared um I'm gonna have to talk through this this is uh many different models that were run in the in the agnip wheat team and on the bottom axis we have looked at the year by year variation of that crop model's response to climate and we have made a correlation between the yield and the average temperature so as you'd expect most of these models have a negative correlation when it's hot the yield is low when it's a little bit cooler the yield does better right so that negative correlation makes sense but then we asked the question of are the models that are more sensitive to that seasonal variation also more sensitive to climate change so when you increase the mean temperature do the the models that are reactive to climate or to temperature do they drop more and the general pattern is pretty consistent this diagonal line here means uh this was a very warm sensitivity test but very easy to see the models that responded most to seasonal temperature also responded the most to climate um so that General pattern makes sense but you'll also notice that the models do not cross the zero line at this Middle Point and what that means is that even the models that did not respond to the seasonal temperature variation show a climate change response and the reason for that is there is a fundamental difference between a warm season and an overall warmer climate all right so you may have remembered that warm year that happened in your country it may have been a two-week very hot spell that came through and made the temperature for the whole season warm plants respond very differently to a two-week Heat Wave than they do to every single day being a little bit warmer every single day being a little bit faster in your growth stages so this gap between the origin and all of these Crossing Lines for each of these sensitivity experiments the blue one being a cooling experiment that's why the line is different but that Gap really shows that there is a fundamental difference between seasonal and and climate type changes which also calls into question some of these empirical approaches where you fit your climate response to recent seasons and then just assume that that applies to climate change so we have to be careful there and this points us towards mechanistic models I'll also just do a quick look at uh when we look at Frost versus Heat there is a temptation to think that heat might be expanding but at least Frost is moving away that means that we don't have to worry so much about Frost damages now we worry about heat damages maybe that's a good trade-off but in reality what we're seeing at least in North America is all of these red areas are places where extreme heat is going to reach either by 2050 or by 2100 so you can see this dramatic expansion including many agricultural zones I'm looking at Jeff here because there's a lot of Michigan in this chart the heat is expanding up the mountains and up north the Frost is really only going away in purple here in a few smaller places that are not even as agriculturally profound so part of this is that the heat is coming but the frost is still variable enough that you still get frosts even in many of the places with heat which means now we have a middle season limit as well as an end of season limit um we did of course in in uh our ipcc work split hydrological drought and agricultural drought um hydrological drought I think of as the water resources for irrigation and your surface ponds or anything like that uh whereas the Agricultural and ecological drought is really about your availability of soil moisture and I'll just note that there are many many indices here that we could look at uh We've we've looked in particular at spei but there are many that you can examine in general I would say that the models do pretty well with that um we are our crop models have Irrigation in it but oftentimes the models assume that irrigation is available so we need to connect it more with the Water Resource models uh so that we can have a better connect a better set of responses um one other thing that I have to mention is that's drought and CO2 actually interact very strongly in our crop models an example of this is we we have experiments with that elevated CO2 done experimentally and this is maize yields and during a wet year the the y-axis here is the response to climate change during I'm sorry is the response to CO2 so in a wet year there's a very low response whether it's irrigated or rain fed but in a dry year the irrigated response is not very strong whereas the rain fed response is very large all right another way of of understanding this is that when conditions are dry the crops close their stomata they make it so that there is less exchange with the environment because they want to hold on to that moisture that also means that when they close those stomata they are not taking in CO2 in the in the same way um however when there's a high CO2 environment even when they're holding on to that water they can still get enough CO2 so the carbon dioxide is especially beneficial during drought years and we can see that in the models and in the experiments all right uh last couple ones I'm looking at of course we have River flood and pluvial flood pluvial flood is like heavy heavy precipitation events the models do a pretty good job with pluvial flood but with River flood many of our models don't even know what is happening elsewhere in the Basin they are effectively single column models so if there's a flood coming down the Mississippi River that site you have in in Missouri does not know about that flood uh so we we are often missing that and in the United States the worst year for us is almost always 1993. big flood events huge losses of crops and the models often miss it um all right so here's the summary of all of this I've I've skipped a couple of them but I wanted to kind of provide this um and and talk about you know in green here are the are the cids that I think our models do a pretty good job of responding to Blue are the ones where some of the models include a response or maybe there's more work that we think we can do um the darker blue here are things that we think we can add in a soft coupling so for example if you give me maps of coastal areas that are flooding uh we can we can change our you know we can add that that additional factor to our results but you'll notice that there are several columns here where we have pretty much no response at all that's things like fire affecting agricultural zones hail heavy storms some of these things we don't do as well with um and then I've put this little red dot on some places just to call out explicitly things like water logging uh impacts on agricultural laborers Health pests and diseases and sequential extremes these are all areas of active research and I should also note that when we're providing climate information we have to be careful because we don't always provide changes in every one of these cids so if you have used an approach where the only difference between the Baseline climate and the future climate is the average temperature well you're not going to see changes in rainfall you're not going to see changes in uh the extreme characteristics for example all right um we're doing a lot of work in agmap right now to build machine learning models and this is a very complicated figure um but the bottom line of it is that we've taken the top 16 maze producing countries of the world so the number one producers the US number two is China gone across this list and we've taken many many different climate features including uh cool temperatures freezing temperatures hot temperatures at two different levels the average temperature the total rainfall and then some drought characteristics like consecutive dry days or the number of rainy days and then we've taken all of our crop models as an additional uh set of information and we've asked the machine learning model to create the best possible predictor for each of these countries National yields and when you see a big circle here that means that was the top feature and then you get down to the smallest Circle which is elsewhere you know top five features um so one way to think about this is if we look at the USA the top features that were elected were two crop models so that's probably a good sign for the crop modeling Community the the machine learning model said let's start with the crop models and then augment more information but what they choose to augment is the mean precipitation they've added information about the total rainfall which we understand to be a way of representing that 1993 flood that the models are missing they've added additional information maybe that very heavy rainfall season shows up through that characteristic instead and then the final one up here there's a little bit on the number of wet days and uh Frost as another thing that maybe the models are not capturing well enough another example here is South Africa the number one feature is a crop model but then they need more information about extreme heat and cool days so those are those this is directly pointing us to where we need to improve our models I already mentioned uh we have to think about food systems that means not just the crops but the heat tolerance of the Agricultural laborers we heard about this from Robert and goladio yesterday so I'm going to go fast um the other message that I really wanted to say loud and clear here today is that when we form adaptations we are not generically adapting we are targeting some specific climatic impact driver or some specific vulnerability or exposure to a hazard or or something that we care about so here is an example of an adaptation that we've explored in our models to that Growing Degree Days the number of kind of heat units that we get so this one is specifically targeting the mean temperature changes of climate change and what we've done is we've looked all around the world at the different seeds that are grown uh in this case I think it's Maize that we're looking at and we have asked ourselves what is the the longest growing variety that we can find which means the most heat units the most ready for a warmer climate and then we've compared it against the number of heat units that are typical for a growing season in each part of the world and what you'll see here is is in oranges and reds are places where the future growing season is so warm that there are no seeds today that can actually meet that demand there's no genetic material out there that we can use to have the same growing season at the warmer climate and you'll notice that this is not the same map as a mean temperature map because what people don't realize is that the United States here is a very productive region not just because we have a nice temperature but we can grow out in the field for a long time we can spend a long time out in the field collecting that Sunshine making better grain but when it gets warmer that means that the United States agricultural Zone has more days to accumulate that heat and therefore it has a stronger impact than a place with a shorter growing season so this combination of length of the growing season and the growing temperatures combined with genetic information this is really I think very fascinating stuff um the last one I'm going to show here as a scientific plot I believe uh is is thinking about how the global economic models uh view this same problem uh this is a very dry figure but I will talk you through it um we have several different variables on the on the x-axis here and uh what we're plotting on the y-axis is the change in 2050 of a climate scenario compared to a future where there is no climate change and what you'll see is the first thing that comes in is this variable called y EXO for exogenous so this is what comes from the crop model and basically says here is the yield change that the crop models predict and the very first thing that the economic models do is respond to that by saying well if the yield is going down dramatically here they're not going to grow it anymore they're going to move the agriculture somewhere else so that the economic models reshuffle agriculture internally so the actual effect on the agricultural production is less than what the crop model say because they've they've optimized just a little bit so the actual Crop Production changes show up in this one and you can see it's less then what happens is in response to the lowered uh agricultural yields they have to expand the agricultural area that's what this is this is that that reshuffling has caused new area to be uh brought under cultivation which leads to other questions about ecosystems and other things you know that that we know encroachment can cause um in general that allows us to maintain production which is necessary because the consumption demand stays very strong we have to meet that consumption um that can show that can cause big changes in exports and imports and overall the price increases so this is the overall story yields change we have to shift the production regions we have to expand area to to maintain that that same total amount of production to meet higher demands that's going to require new trade and generally higher prices so this is the kind of thing we're building in our models um and yeah so it's not just climate impacts it's also things like land use dietary demand trade policy food waste on the field and Beyond and then the the role of Agro technology um so one of the the major efforts that we do within agnip is we work with local stakeholders to develop scenarios of future agricultural systems so here are uh some of our stakeholder engagements in different parts of Africa and the overall approach that we're taking is that we assess the climate change risks and engage the stakeholders and say you know are you preparing for warmer temperatures different types of drought uh you know higher carbon dioxide environments that usually gets people's attention and then we can have a discussion where we think about what the future agricultural pathways are for a region that's another way of saying how will agriculture develop so that you know here in Italy will they still be growing the same Foods in the same ways in 50 years we could have that conversation and build a scenario of agricultural change even before we bring in the climate itself once we have the the system changes and the climate changes we can design agricultural adaptations especially packages so not just one at a time but multiple adaptations and that allows us to run through models and evaluate the impact and come back to the top and loop through this several different times so that we can iterate and make the best adaptations and then of course in the end we're discussing with with the scenario and policy process all right so last slide I have here is just a summary uh hopefully I've shown that agriculture is responsive to many climatic impact drivers and we're only tracking a subset we need to do better and more and that requires better models but also fundamental agricultural research and experimentation um the modeling approaches allow us to capture specific responses and adaptation options and as I said we need more more data more models um adaptations are targeting specific climatic impact drivers and we need more work to identify the specific indices and thresholds and adaptation technologies that move our tolerance around and that we need to have this co-development process to really make it all work and I think with that I'm on on my schedule here so maybe what I'll do is I'll take just any burning questions in the room before we move on to the next speakers any questions [Applause] thank you very much for a nice presentation I have a question about your crop model ah is it include the other factors that could affect the crop yield like the the quality of seed or soil moisture content availability and pests and diseases and also the land area I mean in few cases we have a larger area for for example a specific yield and then after that after few years the farmer changed their mind yeah and they they harvested another crop so we we tend to um to use maps of crop areas and estimates of crop areas so that is it's not predictive as much as we we track and try to follow there are it's basically a different type of modeling that that would help us understand why the farmer made that change and the behavioral aspect of that when you ask how many bad years would a farmer need before they change is very unknown I think that's something that people really don't spend enough time thinking about and and we need to do more um soil moisture yes we handle that we can do better but we do have soil moisture as a fundamental property of the model pests and diseases we have a whole agnip team working on that but it's very very challenging because there are thousands of pests you know worms bugs you know insects things like that and then pathogens there are thousands of those and then when you combine them with every crop species now you have in combination even more so we're developing generic ways that we can say that we don't have to characterize every single insect but you might characterize the way they attack the plant are they attacking the roots or the leaves or the stem are they eating the the live fruits or are they eating the flowers like that kind of thing we can we can figure out um I think I answered the question but let me know if I missed something Erica question up here yep and I need to figure out how to get back to zoom so I can see oh thank you Alex I just wanted to know like you know there's another Factor uh nutrients availability in the soil so is it possible to like include that as well nutrients availability yeah and yeah sorry I just remembered the other question was the quality so I want to come back to that okay and um there there's another Factor like of when you you were saying like uh the uh changes in um crop type like people were maybe uh cultivating some some other crop and then later they are transferred to another crop so in that case is it possible like um maybe there are some social economic factors because I have seen in my country that people used to like uh cultivate rice more but then the prices of maize when their cell Maze and they get like better price so they shifted to Maize so uh they're like factors like socioeconomic factors so is it possible to include those as well or have is this been considered yeah yeah thanks for the question so um in terms of the nutrient quality of the soils yes we we do track that um the soil databases can be good or bad but we are generally able to find soil information um and then monitoring the long-term soil health is a challenge but we are trying um and uh you know soil ecology is particularly challenging but we have nitrogen phosphorus potassium those types of things and that does affect the the quality of the grains uh the grain quality also depends on carbon dioxide concentration so we know that there are are things like that but in general we do a better job with cereal crops than Plantation crops like you know fruits and and grapes you know things like that where the quality is more important than the the quantity um and then uh your other question about the the economic side you know we do have Farm household models that are looking at the overall economic net returns um and we are trying to track other trends like in your country there's you know the introduction introduction of irrigation means the whole borrow season is now fair play right and if you're growing rice then you can grow other crops in different times so we try to follow those changes and there's a question online yeah all right looks like Vincent has asked a question here um it's an observation I've been to do you want to say this out loud rather than me try to read it here oh sure can you hear me yeah please go ahead yes I have an observation from you earlier statement he said that when it comes to a adaptation we are not really adapting but delegating a specific CID but while working with you from where you've shown that we're working with some communities in Senegal and I think in Mozambique basically it's like you are coming up with a package yes of an adaptation so my concern don't you think that is a contradiction when you you are talking about adaptation and when you are working with the community yeah that's a good good question and yeah so when we do an adaptation package typically there are several elements that that show up in an adaptation package it could be a change of a system where basically there is a determination that a crop is is no no longer as suitable or likely to have a strong economic return as something else so you want to shift you know towards a ground nut system or towards something else um but the other thing is if you are using some kind of improved drought resistant variety or some kind of improved uh Growing Degree day seed selection the the company that's that is making that or the the government subsidy that is enabling that may also couple it with a different uh fertilizer or chemical regime or may couple it with requirements around planting dates or some kind of uh crop insurance or who knows what but the idea is they are are trying to change multiple elements of the economic system but but each element is still oriented around specific types of extremes but I think it's a fair point there which is to what level does the package blur those lines is something we should track more thank you for that point all right I one last question and then I want to make sure we get to our other speakers um I I'm just curious if uh you also consider companion cropping like there are some things like a climate adopted companion cropping yeah so so there's a broad category that I'll call multi-cropping uh that includes uh intercropping where you have multiple crops on the same field and then sequences of crops um and we are looking into some of those but but that is also quite challenging and maybe I'll just say one last word and you can call it a little bit selfish if you if you'd like but the overall level of investment in the crop Community is much much smaller than the level of investment in something like the climate community so as the overall focus of the attention moves from the climate questions towards the climate impact questions and the interventions the adaptation we need a little bit of rebalancing so that we can get the models that that can actually do some of these things that we're asking because I think the attention is calling you know shedding light on on some of these areas that we need better models but they won't just appear if we ask for them we have to make tangible Investments to make that happen all right thank you for that let's uh let's go on our next speaker is uh Benjamin Sultan um and uh he's right there to get started so Benjamin I'm gonna hand it over to you thank you for