Should food security be a priority for the EU?

By David Leclère, IIASA Ecosystems Services and Management Program

August was the warmest ever recorded globally, as was every single month since October 2015. It will not take long for these records to become the norm, and this will tremendously challenge food provision for everyone on the planet. Each additional Celsius degree in global mean temperature will reduce wheat yield by about 5%. While we struggle to take action for limiting global warming by the end of the century to 2°C above preindustrial levels, business as usual scenarios come closer to +5 °C.

However, we lack good and actionable knowledge on this perfect storm in the making. Despite the heat, world wheat production should hit a new record high in 2016, but EU production is expected to be 10% lower than last year. In France, this drop should be around 25-30% and one has to go back to 1983 to find yields equally low. Explanations indeed now point to weather as a large contributor. But underlying mechanisms were  poorly anticipated by forecasts and are poorly addressed in climate change impacts research.

©Paul Townsend via Flickr

©Paul Townsend via Flickr

Second, many blind spots remain. For example, livestock has a tremendous share in the carbon footprint of agriculture, but also a high nutritional and cultural value. Yet, livestock were not even mentioned once in the summary for policymakers of the last IPCC report dedicated to impacts and adaptation. Heat stress reduces animal production, and increases greenhouse gas emissions per unit of product. In addition, a lower share of animal products in our diet could dramatically reduce pollution and food insecurity. However, we don’t understand well consumers’ preferences in that respect, and how they can be translated in actionable policies.

How can we generate adequate knowledge in time while climate is changing? To be able to forecast yields and prevent dramatic price swings like the 2008 food crisis? To avoid bad surprises due to large missing knowledge, like the livestock question?

In short: it will take far more research to answer these questions—and that means a major increase in funding.

I recently presented two studies by our team at a scientific conference in Germany, which was organized by a European network of agricultural research scientists (MACSUR). One was a literature review on how to estimate the consequences of heat stress on livestock at a global scale. The other one presented scenarios on future food security in Europe, generated in a way that delivers useful knowledge for stakeholders. The MACSUR network was funded as a knowledge hub to foster interactions between research institutes of European countries. In many countries, the funding covered travels and workshops, not new research. Of course, nowadays researchers have to compete for funding to do actual research.

So let’s play the game. The MACSUR network is now aiming at a ‘Future and Emerging Technologies Flagship’, the biggest type of EU funding: 1 billion Euros over 10 years for hundreds of researchers. Recent examples include the Human Brain Project, the Graphene Flagship, and the Quantum Technology Flagship. We are trying to get one on modeling food security under climate change.

© Sacha Drouart

© Sacha Drouart

Such a project could leapfrog our ability to deal with climate change, a major societal challenge Europe is confronted with (one of the two requirements for FET Flagship funding).  The other requirement gave us a hard time at first sight: generating technological innovation, growth and jobs in Europe -but one just needs the right lens. First, agriculture already sustains about 44 million jobs in the EU and this will increase if we are serious about reducing the carbon content of our economy. Second, data now flows at an unprecedented speed (aka, big data). Think about the amount of data acquired with Pokemon Go, and imagine we would harness such concept for science through crowdsourcing and citizen-based science. With such data, agricultural forecasts would perform much better. Similarly, light drones and connected devices will likely open a new era for farm management. Third, we need models that translate big data into knowledge, and not only for the agricultural sector. Similarly, models can also be powerful tools to confront views and could trigger large social innovation.

To get this funding, we need support from a lot of people. The Graphene project claimed support from than 3500 actors, from citizens to industrial players in Europe. We have until end of November to reach 3500 votes, at least. If you think EU should give food security under climate change the same importance as improving the understanding of the human brain, or developing quantum computers, we need you. This will simply never happen without you! Please help us out with two simple actions:

  • Go the proposal, and vote for/comment it (see instructions, please highlight the potential for concrete innovations)!
  • Spread the word – share this post with your friends, your family, and your colleagues!

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Pipe dream or savior?

By Daisy Brickhill, IIASA science writer and editor.

Many of the options proposed for achieving a stable climate rely on ‘bioenergy with carbon capture and storage’ — burning plant matter for energy, capturing the carbon, and storing it underground. However, this technology has hardly been deployed on anything approaching the large scale. Is it a realistic tool for climate mitigation?

Limiting global warming to 2°C above pre-industrial times has long been a reference point for policymakers and researchers trying to reduce the chances of dangerous climate change. The 2015 Paris climate summit went even further, with countries agreeing to try and limit temperature rise to 1.5°C.

However, while there is a strong scientific consensus that we need to aggressively cut greenhouse gas emissions immediately, there is also growing evidence that we may not be able to achieve the necessary reductions in the time available. This means that we may need a way of removing CO2 already in the atmosphere — a process known as negative emissions.

Negative emissions can come in many forms: from simply planting more trees, to crushing rocks that naturally absorb CO2. One widely considered option is using plant matter as a fuel to produce energy, then capturing the CO2 that is emitted and storing it underground. This is known as bioenergy with carbon capture and storage (BECCS).

This latter technology is cited by research as being an important part of restricting warming to safe — or at least safer — levels since it contributes to both carbon sequestration and decarbonization of the energy system. In fact, more than half of the future scenarios that give at least a 66% chance of limiting warming to 2°C, which were developed for the Intergovernmental Panel on Climate Change (IPCC), feature BECCS.

However, the technology remains mostly untested on a large scale and there are doubts about its sustainability in terms of land and water use, and other potentially negative impacts on the environment. With so many IPCC scenarios including BECCS, information on whether it is at all a practical solution is desperately needed.

A recent IIASA study addresses deployment of BECCS in Indonesia, examining whether adapting existing coal-fired power stations so that they can burn a mix of coal and plant waste from agriculture (such as seed kernels or stems that are usually discarded), is more effective than building specific biomass-burning power stations.

Rice paddies in Indonesia. Plant waste from agriculture can be used in bioenergy with carbon capture and storage systems.

The team found that although both options saved the same amount of CO2, the combined stations were more efficient, producing more electricity for the amount of biomass burnt. “More efficiency means that burning biomass in adapted coal-fired power stations would be more economically viable,” says IIASA researcher Ping Yowargana, coauthor of the study. “It is also likely to be easier and cheaper to convert existing coal power stations than build new specific biomass-burning stations. With lower investments and existing infrastructure, policymakers and other stakeholders are more likely to embrace the idea.”

There are limitations: the study results indicate that under the current conditions it is not possible to burn any more than 30% biomass in a combined power station, for instance. There are also uncertainties surrounding whether it is possible to collect enough biomass on the scale needed. “We need to do further work on the logistic and financial feasibility of BECCS,” says Yowargana. “But these results are broadly general, and can be applied to other countries and situations, making them a valuable starting point.”

And while a complete conversion to a decarbonized energy system is needed in the long term, this work points the way to how BECCS might be deployed now to help prevent the damaging climate change we have sown for ourselves.

Reference: Hetland J, Yowargana P, Leduc S & Kraxner F (2016). Carbon-negative emissions: Systemic impacts of biomass conversion: A case study on CO2 capture and storage options. International Journal of Greenhouse Gas Control, 49. pp. 330-342.

Further reading:

https://www.carbonbrief.org/beccs-the-story-of-climate-changes-saviour-technology

Moreira, J. R., Romeiro, V., Fuss, S., Kraxner, F. and Pacca, S. A. (2016) BECCS potential in Brazil: Achieving negative emissions in ethanol and electricity production based on sugar cane bagasse and other residues. Applied Energy, 179. pp. 55-63. Item availability may be restricted.

Smith, P., Davis, S.J., Creutzig, F., Fuss, S., Rogelj, J., McCollum, D., Krey, V., Grubler, A., Jonas, M., Kraxner, F., Nakicenovic, N., Obersteiner, M. and Rogner, M. (2016) Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6 (1). pp. 42-50.

Fuss, S., Canadell, J.G., Peters, G.P., Tavoni, M., Andrew, R.M., Ciais, P., Jackson, R.B., Jones, C.D., Kraxner, F., Nakicenovic, N., Le Quere, C., Raupach, M.R., Sharifi, A., Smith, P. and Yamagata, Y. (2014) Betting on negative emissions. Nature Climate Change, 4 (10). pp. 850-853.

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Can we give foresight prescription lenses?

By Daniel Mason-D’Croz, Senior Research Analyst at International Food Policy Research Institute (IFPRI)
(This post was originally published on the IFPRI Research Blog)

There are many challenges confronting decision makers in building robust and effective policies. They must balance pressing short-term needs with long-run challenges. They must confront these varying demands while facing imperfect knowledge of the complex systems (i.e. the economy, the environment, etc.) in which their policies will have impact. Above all, they also face the same uncertainty about the future as the rest of us, making perfect prediction about future outcomes impossible.

Nevertheless, decision makers must make choices in response to future challenges; inaction itself is an implicit choice, as change is inevitable. The challenge is to find a way to improve decision making, and in Multi-factor, multi-state, multi-model scenarios: exploring food and climate futures for Southeast Asia, recently published in Environmental Modelling Software, we believe we have presented a unique methodology to improve the decision-making process, by leveraging a participatory stakeholder-driven scenario development process with a multi-model ensemble to interactively explore future uncertainty with regional stakeholders.

This methodology was first applied in a workshop in Vietnam, where a diverse set of stakeholders from a wide range of sectors in Cambodia, Laos, and Vietnam collaborated to develop four multidimensional scenarios focusing on future agricultural development, food security, and climate change. Through building these multidimensional scenarios, stakeholders were challenged to consider potential interactions between varied parts of complex systems, like society and the environment. By doing this with a diverse set of stakeholders from public and private sectors, participants considered the future in a holistic and multidisciplinary manner. They were asked not only how different the future might look from the present, but also how they might respond to and shape future change. In so doing, regional stakeholders gained a better understanding of future uncertainty, while introspectively reviewing their own assumptions on the drivers of change, while creating four diverse scenarios that presented challenging plausible futures.

Participants at a 2013 workshop in Ha Long Bay, Vietnam – including regional stakeholders from development organizations, governments, the private sector, civil society, and academia – game out policies for the future of agriculture in Southeast Asia under different climate change scenarios, in an innovative approach combining collaboration with predictive modeling. © CGIAR photo

Participants at a 2013 workshop in Ha Long Bay, Vietnam – including regional stakeholders from development organizations, governments, the private sector, civil society, and academia – game out policies for the future of agriculture in Southeast Asia under different climate change scenarios, in an innovative approach combining collaboration with predictive modeling. © CGIAR photo

These scenarios were then quantified and simulated using a series of climate models, crop simulation models, and economic models including IFPRI’s IMPACT model and IIASA’s GLOBIOM model. Quantifying the scenarios in models can assist decision makers by pairing the qualitative aspects of the scenarios with quantitative analysis that systematically considers complex interactions and potential unintended consequences. Doing this quantification across a multi-model ensemble maintains the scenario diversity and richness, which in turn ensures that a broad possibility space is maintained throughout the process. This offers decision makers a larger test bed in which to evaluate potential policies. This multidimensionality and diversity of scenario outputs has been well received in the region, allowing them to be adapted and reused in a variety of policy engagements in Cambodia, Laos, and Vietnam.

  • In Cambodia, scenario results were used to inform their Climate Change Priorities Action Plan (CCPAP) to better target and prioritize the spending of its 164 million U.S. dollar projected budget, a policy engagement that was done over 6 to 8 months as scenario analysis and use were embedded in the CCPAP
  • In Laos, scenario results were presented in a regional workshop led by CCAFS and UNEP WCMC to evaluate regional policies for economic development, agricultural development, and climate change and consider potential environmental tradeoffs
  • In Vietnam, scenario results were shared in a workshop led by CCAFS and FAO to review and revise climate-smart agriculture investments proposals by considering the potential effectiveness of different investments under various climatic and socioeconomic conditions

The regional scenarios were a collaborative effort that involved colleagues from many institutions including IFPRIIIASAFAOUNEP WCMCthe CGIAR research program on Climate Change, Agriculture and Food Security (CCAFS), and the University of Oxford, among others. It would not have been possible without the funding and support from CCAFS, the CGIAR research program on Policies, Institutions, and Markets (PIM),Global Futures and Strategic Foresight, the FAO’s program on Economic and Policy Innovations for Climate-Smart Agriculture (EPIC), and UNEP WCMC through a MacArthur Foundation grant.

Reference
Mason-D’Croz D, et. al. (2016). Multi-factor, multi-state, multi-model scenarios: Exploring food and climate futures for Southeast Asia. Environmental Modelling & Software
Volume 83, September 2016, Pages 255–270. doi:10.1016/j.envsoft.2016.05.008

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Interview: Are we accidentally genetically engineering the world’s fish?

Mikko Heino is a researcher in the IIASA Evolution and Ecology Program who over the past 18 years has worked on the problem of fisheries-induced evolution, showing that selective harvesting of bigger fish can lead to evolutionary changes towards smaller and faster-maturing fish populations. A new review by Heino and colleagues explores the accumulated evidence on the topic, and future directions for new research.

What is fisheries-induced evolution?
In agriculture we are used to thinking about selective breeding: farmers select the best animals or plants to breed, in order to improve their fitness or select for certain traits in the next generation. They are intentionally trying to improve the stock used for breeding. In fisheries, the opposite happens: fishermen usually try to catch the fish that are more valuable—bigger and heavier. Consequently, those valuable kinds of fish are less likely to reproduce and contribute to the next generations.

It’s not intentional, and maybe because we don’t see the fish that are left behind, people are not used to thinking of this as selective breeding—and they may not realize that in the long term it could harm the productivity of the whole fishery.

Atlantic cod are among the best-studied fish populations and most important fisheries worldwide © davidyoung11111 | Dollar Photo Club

Atlantic cod are among the best-studied and most important fisheries worldwide (Credit: © davidyoung11111 | Dollar Photo Club)

What kinds of evolutionary changes do your research show that you would see from fisheries?
Most of our data comes from fisheries institutions. We have a lot of data on maturation—the age at which fish start to reproduce. Studies show that heavily harvested fish populations may mature faster and start reproducing earlier. There’s also some data on growth, showing that fisheries pressures can lead to slower growth rates, with fish staying smaller.

In recent years, experimental studies have shown that fishing is also selective with respect to behavioral traits. Some fish are bolder than others and those bolder fish may be more likely to be captured by gillnets or traps, because bold fish are more explorative, they like to investigate things, and by doing so they may end up in a trap. But at the same time, they may be better in escaping fishing gear like a trawl.

Why is it important that we understand what’s happening with fisheries-induced evolution?
At one level it’s important because changes in these kind of traits will affect the productivity of fish stocks—and based on our current knowledge, these changes are often negative, leading to lower productivity. However, some parts of these changes may be positive in the short term, because they may enable fish stocks to tolerate higher levels of fishing without collapsing. Yet in the long term, we would expect fisheries-induced evolution to lead to reduced productivity, and lower yields. And that’s quite worrying, because fish are an important part of the human diet, especially in many coastal developing countries.

What are the major questions remaining in this research?
One big question arises from the fact that evolution of course implies genetic change. In fisheries, we are observing this evolution at the level of phenotypes—visible, directly measurable characteristics. To be sure that we really observe evolutionary change, we also need to understand the genetic basis for these kinds of changes.

How can you do that?
There are ongoing projects trying to look at it the genetics of fisheries-induced evolution. But it’s a lot more difficult than it sounds because life-history traits and behavioral traits are affected by many genes. It’s not like there’s one gene for early maturation. There are probably tens if not hundreds of genes that have some influence on maturation. And that means that at the level of a single gene we may not see very much change at all. And trying to identify those changes and separate those from random drift, and from changes caused by other factors, is actually quite difficult.

Sequencing genomes is easy nowadays, but finding a signal in the resultant large amounts of data is not simple. If you have more data, you also get more false positives. Basically, either you need to sequence a lot of individuals, so you can separate the different signatures. The other possibility is to try to analyze data from selection experiments, because in an experiment you can try to exaggerate the changes. That’s maybe the most fruitful avenue in the short term.

Evidence for fisheries-induced evolution: research shows fisheries-induced evolution in many fish populations, including marine and freshwater species. (Credit: Heino & Dieckmann, 2015)

Evidence for fisheries-induced evolution: research shows fisheries-induced evolution in many fish populations, including marine and freshwater species. (Credit: Heino & Dieckmann, 2015)

What can fisheries managers do to avoid unintended evolutionary changes?
We’re currently exploring that question. We more or less know that it’s not possible to avoid all types of evolutionary responses. But we can still try to minimize harmful changes, by fishing in a way that does not cause much negative change in productivity.

Of course, fisheries-induced evolution will not be the only thing we care about when managing fish stocks. It has to be seen together with other objectives. Yet the simplest way of reducing unwanted evolutionary changes is to keep fishing pressure at moderate levels. That’s the single easiest and most certain way of reducing unwanted evolution, and that’s in agreement with what scientists recommend from other perspectives too.

What are the dangers of failing to account for fisheries-induced evolution?
The danger is that it’s much easier to cause these changes than to reverse them—on practical time scales, these changes are more or less irreversible. So whatever changes we cause, will be around for many generations to come. That’s a reason to be precautionary. We don’t have absolute certainty that this is happening, but there’s a large body of research showing that it is quite likely to happen, and since if it’s happening it’s more or less irreversible, then we should avoiding it even before we have full scientific certainty.

There is quite a similarity between climate change and fisheries-induced evolution. Both processes happen on long timescales—at the level of a few years, the change is not much. But it is a change that will accumulate, and if you let that happen for longer periods, you end up having very significant changes. So it’s easy and attractive to ignore it in the short term, but that’s a dangerous position in the long term.

Reference
Heino M, Pauli BD, Dieckmann U (2015). Fisheries-induced evolution. Annual Reviews in Ecological Systems. 46:461-480. doi: 10.1146/annurev-ecolsys-112414-054339

Note: This article gives the views of the interviewee, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Interview: Taking Geo-Wiki to the ground

Steffen Fritz has just been awarded an ERC Consolidator Grant to fund a research project on crowdsourcing and ground data collection on land-use and land cover. In this interview he talks about his plans for the new project, CrowdLand. 

Pic by Neil Palmer (CIAT).

Farmers in Kenya are one group which the Crowdland Project aims to involve in their data gathering. Photo credit: Neil Palmer, CIAT

What’s the problem with current land cover data?
There are discrepancies between current land cover products, especially in cropland data. It’s all based on satellite data, and in these data, it is extremely difficult to distinguish between cropland and natural vegetation in certain parts of the world if you do not use so-called very high resolution imagery, similar to a picture you take from space. With this high-resolution data you can see structures like fields and so on, which you can then use to distinguish between natural vegetation and cropland. But this is a task where currently people are still better at than computers–and there is a huge amount of data to look at.

In our Geo-Wiki project and related efforts such as the Cropland Capture game, we have asked volunteers to look at these high-resolution images and classify the ground cover as cropland or not cropland. The efforts have been quite successful, but our new project will take this even further.

How will the new project expand on what you’ve already done in Geo-Wiki?
The big addition is to go on the ground. Most of the exercises we currently do are based on the desktop or the phones, or tablets, asking volunteers to classify imagery that they see on a screen.

What this project aims to do is to improve data you collect on the ground, known as in-situ data.  You can use photography, GPS sensors, but also your knowledge you have about what you see. We will use volunteers to collect basic land cover data such as tree cover, cropland, and wetlands, but also much more detailed land-use information. With this type of data we can document what crops are grown where, whether they are irrigated, if the fields are fertilized, what exact type of crops are growing, and other crop management information which you cannot see in satellite imagery. And there are some things you can’t even see when you’re on the ground, thus you need to ask the farmer or recruit the farmer as a data provider. That’s an additional element this project will bring, that we will work closely with farmers and people on the ground.

For the study, you have chosen Austria and Kenya. Why these two countries?
In Austria we have much better in situ data. For example, the Land Use Change Analysis System (LUCAS) in Europe collects in situ data according to a consistent protocol. But this program is very expensive, and the agency that runs it, Eurostat, is discussing how to reduce costs. Additionally the survey is only repeated every three years so fast changes are not immediately recorded. Some countries are not in favor of LUCAS and they prefer to undertake their own surveys. Then however you lose the overall consistency and there is no Europe-wide harmonized database which allows for comparison between countries.   Our plan is to use gaming, social incentives, and also small financial incentives to conduct a crowdsourced LUCAS survey. Then we will examine what results you get when you pay volunteers or trained volunteers compared to the data collected by experts.

In Kenya, the idea is similar, but in general in the developing world we have very limited information, and the resources are not there for major surveys like in Europe. In order to remedy that the idea is again to use crowdsourcing and use a “bounded crowd” which means people who have a certain level of expertise, and know about land cover and land use, for example people with a surveyor background, university students, or interested citizens who can be trained. But in developing countries in particular it’s important to use financial incentives. Financial incentives, even small ones, could probably help to collect much larger amounts of data. Kenya is a good choice also because it has quite a good internet connection, a 3G network, and a lot of new technologies evolving around mobile phones and smartphone technology.

What will happen with the data you collect during this project?
First, we will analyze the data in terms of quality.  One of our research questions is how good are the data collected by volunteers compared to data collected by experts. Another research question is how can imperfect but large data collected by volunteers be filtered and combined so that it becomes useful and fulfills the scientific accuracy requirements.

Then we will use these data and integrate them into currently existing land use and land cover data, and find ways to make better use of it. For example, in order to make projections about future land-use and to better quantify current yield gaps it is crucial to get accurate current information on land-use, including spatially explicit information on crop types, crop management information and other data.

Once we have done some quality checks we will also make these data available for other researchers or interested groups of people.

Crowdsourcing for land cover is in its infancy. There have been lots of crowdsourcing projects in astronomy, archaeology, and biology, for example, but there hasn’t been much on land use, and there is huge potential there. ”We need to not only better understand the quality of the data we collect, but also expand the network of institutions who are working on this topic.”

Note: This article gives the views of the interviewee, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.