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  • New IFPRI website features climate change & food policy

    New IFPRI website features climate change & food policy

    Photo: IFPRI/Ian Masias

    The International Food Policy Research Institute (IFPRI) has launched a new website dedicated to climate change and food policy research and impacts. The website covers news, event updates, project profiles, and shares related materials from across their climate change research portfolio, including results from the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and AgMIP.

    In the lead up to the web site, IFPRI has prepared an exciting new series of blog stories to read, share, and comment on (see descriptions and links below.)

    Please check out the new site at: http://climatechange.ifpri.info

     

    Featured Blog Posts:

    New climate change report shows innovative tool to measure value of climate services for farmers

    Climate information can be a powerful tool in helping rural communities adapt to climate risk. But not all information is created equal, nor is access to information equal. To better understand the value of climate information in these communities, researchers started out by asking: does climate information matter to women farmers?

    A recently released report put together by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the International Food Policy Research Institute (IFPRI) and partners, outlines what has been learnt from engaging in projects to deliver climate services to smallholder farmers across Africa and South Asia.

    Learn more and download the report, which includes a well-structured monitoring and evaluation framework from the project at: http://ow.ly/AtwUc

    Forest cover and carbon stocks at risk as road network expands in Democratic Republic of Congo

    Plans to expand road networks in the Democratic Republic of Congo are threatening forest cover in the country with losses in biological carbon stock and biodiversity as immediate consequences. This reveals a recently released article that has developed a land use model to assess the environmental impacts of existing plans for road development in the country.

    Read the blog post here.

    Analysing climate resilience within the climate-smart agriculture concept

    Providing advice on which climate-smart farm practice to choose given a specific context is difficult. Here’s why: http://ow.ly/Atyyb

     

     

  • Agriculture under Threat

    By: Andrea Calderon Irazoque

    The agricultural sector today is facing the daunting challenges of adapting to increasing impacts from climate change while minimizing greenhouse gas emissions and meeting ever-growing food demand. Crop-producing regions will be required to respond to these issues in the coming decades and their ability to do so will potentially affect all of humanity – even those living in highly urbanized areas. To put things into perspective, according to the United Nations, food demand is predicted to double by 2030.

    In response to these concerns Nature Climate Change recently published a web focus collection “A new climate for farming”. The collection includes sixteen recent publications by AgMIP and other authors, including research articles, letters and opinion pieces addressing three essential topics: climate change impacts on agriculture, the influence of agriculture on the climate and our capacity to adapt to these challenges. Highlights of the contributions are succinctly summarized below.

    Climate Change Impacts On Agriculture 

    Crop Models 

    The editorial of the collection, “Yields and more,” is an overview of topics included in the web focus. The editors highlight the importance of crop models as the best tool available to simulate future climate change impacts on yields. The Agricultural Model Intercomparison and Improvement Project (AgMIP) is named as one of the leading efforts to improve these crop models and evaluate their relative performance, addressing some of the challenges and general lessons regarding the potential impacts of climate change on crops and agricultural yields.

    The letter “Uncertainty in simulating wheat yields under climate change” by Asseng and others (2013) presented thelargest standardized model intercomparison for climate change impacts on wheat yields. The authors conclude that projections from individual crop models fail to represent uncertainties known to exist in crop responses to climate change. However, model ensembles have the potential to quantify the significant crop component of uncertainty.

    In the commentary “Crop–climate models need an overhaul” by Rötter and others (2011), AgMIP scientists state the urgency of focusing research on rigorous multi-model ensembles that can lead to more accurate estimates of the amount of food that can be grown in a warmer world. This approach has the potential to provide robust and useful information for farmers and policymakers in the coming decades.

    Food Security 

    In the commentary “Fertilizing hidden hunger,” AgMIP researchers Müller, Elliott and Levermann (2014) analyze the evidence of negative impacts of high CO2 levels on crops. They explain that even if CO2 fertilization could compensate for the impact of climate change on crop yields, it comes at the expense of decreasing the nutritional value of food. A more detailed analysis of this commentary was made in the AgMIP blog post: Are increasing CO2 emissions causing hidden hunger?

    Extreme events 

    The role of how extreme weather will affect crop yields still needs further investigation. The majority of crop impact studies focused on changes in the average state of the climate rather than in adverse weather events. Trnka M. and others (2014) in the article “Adverse weather conditions for European wheat production will become more frequent with climate change” analyze how the rising number of extreme events will increase crop failure. The authors studied fourteen sites across thirteen European countries representing the main wheat-growing areas in the continent. They showed that occurrence of adverse events might substantially increase by 2060 compared to the present (1981-2010). This could lead to global repercussions, since Europe produces around 30% of worldwide wheat supply.

  • AgMIP Responds to President’s Climate Data Call to Action

    Today at the White House the Obama Administration invited leaders of technology and agricultural sectors to announce new public-private partnerships in order to advance the President’s Climate Data Initiative. AgMIP is pleased to announce its collaboration in several of these initiatives.

    AgMIP and CIMSANS form partnership

    In support of the White House Climate Data Initiative AgMIP and the Center for Integrated Modeling of Sustainable Agriculture and Nutrition Security (CIMSANS) have formed a new public-private partnership on open data and open source code modeling to enhance the climate resilience of food systems along with the International Food Policy Research Institute (IFPRI).

    AgMIP and CIMSANS will collaborate on many aspects, such as (1) collecting relevant private- and public-sector datasets that can be made available as open data; (2) harmonizing input and output data formats across multiple modeling systems; (3) improving crop and economic models through novel open source code modeling approaches; and (4) applying these newly assembled data and modeling systems to conduct a robust assessment of sustainable nutrition security.

    In order to conduct this unique assessment, AgMIP and CIMSANS will evaluate seven novel nutrition and sustainability metrics of global food systems, including all of the world’s important staple and non-staple foods, through the year 2050. This methodology will enable thorough evaluation of potential interventions intended to enhance the resilience of food systems to global change impacts.

    CIMSANS-AgMIP Commitment

    CIMSANS and AgMIP Step Up to the Challenge

     

    AgMIP and CIAT Team up with Monsanto

    Also in support of the White House Climate Data Initiative Monsanto will collaborate with AgMIP and the International Center for Tropical Agriculture (CIAT) in the maintenance of open data portals used to improve models investigating climate and water impacts on crop productivity. Monsanto is also working with AgMIP scientists on AgMaize – the Maize Model Improvement Group.

     

     

  • New paper reviews simulations of household models

    New paper reviews simulations of household models

    By Mina Coutsoucos

    Can current farm household models accurately simulate food security driven by climate change?

    There has been considerable effort in the last 44 years by researchers to model climate change impacts on farm households, but relatively little attention have been paid to food security. Food security includes many components and modeling it requires an understanding of how agricultural systems are affected by social and economic factors.

    In order to answer the question of whether current agricultural models are up to the task of assessing agricultural adaptation to climate change and future impacts on food security, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) commissioned a review of farm household modeling. The resulting paper “Farm household models to analyse food security in a changing climate: A review” by M.T. van Wijk and others in the journal Global Food Security has been recently published. The reviewers found that despite the large number of studies published, there is still an ongoing challenge for current farm-household models to effectively simulate the impacts of climate change on food security.

    The term food security was first used at the World Food Summit, 1996, and has four aspects: food availability, economic food access, physical food access, and food utilization. In the review by van Wijk et al., food availability, access and stability were selected as the criteria to estimate the models’ simulation capabilities. Out of 16,000 journal articles, they selected 2,500 that were then narrowed to 480 articles that contained 126 models. These were then evaluated. Each model was characterized and categorized into three main areas: dynamic simulation, mathematical programming, and multi-agent modeling.

    The authors found that there was a substantial increase in the total number of studies concerning farm household models over time. The majority were applications of already existing models using individual or a combination of techniques rather than newly developed ones. By focusing on the three elements of food security, they identified three main weaknesses in the models. First, the models did not have the ability to capture farmers’ real life decision-making processes. Second, the models did not include non-agricultural activities that would have influenced food availability and access, such as jobs in different sectors. Finally, investment costs were not easily quantifiable and had high uncertainty.

    The authors concluded that there is a need to efficiently integrate different key components, such as socio-economic and nutritional information into the models, so as to properly assesses the impacts of climate change on food security. But the review also shows that there are new methodologies and approaches that are dealing with these issues. One example is the Tradeoff Analysis for Multi-Dimensional Impact Assessment (TOA-MD) model (Antle and Valdivia) which is successfully used by AgMIP’s Regional Research Teams in their climate change impact and adaptation assessments. TOA-MD is a model based on advanced statistical methods to simulate economic, environmental and social impacts of agricultural systems of populations of heterogeneous households.

     

  • AgMIP – CCAFS team up to investigate index insurance in Senegal

    A joint CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and Agricultural Model Intercomparison and Improvement Project (AgMIP) meeting was held June 3-4 to kick off a USAID-CIAT (International Center for Tropical Agriculture) funded study “Increasing Productivity and Livelihoods in the Nioro du Rip Basin in Senegal” at the Earth Institute at Columbia University in New York. The meeting brought together researchers from CCAFS, the International Research Institute for Climate and Society (IRI), NASA Goddard Institute for Space Studies, University of Florida, University of Ghana, the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Center for Climate Systems Research. The goal of the study is to investigate how index insurance can be optimized to encourage yield-enhancing farm management choices in Senegal.

    Traditional crop insurance has not been adopted in many developing countries resulting in farmers’ inability to access credit or borrow money for farm investment. Often claims are difficult to verify and necessitate inspection in hard to reach locations, and payouts for yield losses don’t incentivized farmers to improve their crops. Index insurance, an alternative to traditional indemnity insurance, is based on an index, such as the number of rainy days, to trigger payouts. The farmer is paid once the threshold for the index is reached regardless of the outcome of the farm’s harvest and thus alleviates the need for farm visits. However, setting the appropriate index and trigger point for the insurance is critical and will influence whether or not farmers will adopt the insurance. If the index is appropriate, the farmers will feel secure enough to risk investment in fertilizer or other farm improvements that can mitigate the likelihood of food insecurity in the future.

    Both AgMIP and CCAFS have research activities in West Africa. The AgMIP West African Team has focused part of its integrated assessment activities in the Nioro du Rip Basin in Senegal and the data and models from the team’s integrated assessments will form the basis for assessing technology and policy options in the region. CCAFS has studied weather-index insurance for several years as a way of helping West African farmers reduce weather risks associated with increased inputs for achieving higher productivity.

    At the meeting in New York participants developed three questions to focus their research. What is the benefit to farmers that purchase insurance? What is the benefit to farmers that adopt riskier practices? What is the benefit of insurance enabling riskier practices? One of the goals of the research will be to develop an insurance product that can be used to reduce the risk aversion of farmers to invest in a productive opportunity. In addition the possibility that the insurance might give famers access to credit, protect existing assets, and lead to income smoothing will be investigated.

    The researchers plan to use the Tradeoff Analysis – Minimum Data (TOA-MD) economic model in order to test options for the index insurance. Next steps for the group include developing a set of protocols, including any modifications to the model, and reconvening at a workshop in Senegal to help guide the work to be done.

    Jim Jones, AgMIP Co-Principal Investigator and workshop participant, stated about the meeting, “This workshop was very important in planning details of a new joint AgMIP – CCAFS cooperative project funded by USAID. It was perhaps even more important strategically in that it helped expand the integrated assessment approach that we have been developing and using in AgMIP to include benefits and tradeoffs of intensification options combined with weather index insurance in the nearer term.”

    Read the Workshop Report here.

     

     

  • Global Economics Climate Scenario Workshop: FAO, Rome, Italy, June 23-24, 2014

    Five global economic modeling groups met June 23-24, 2014 at FAO headquarters in Rome to discuss preliminary results of a harmonized analysis of climate change impacts on agriculture and food security in 2050. The participating modeling groups, all members of the AgMIP Global Economics team, are from the International Food Policy Research Institute (IFPRI), the Potsdam Institute for Climate Impact Research (PIK), the Food and Agriculture Organization of the United Nations (FAO), LEI Wageningen UR in the Netherlands, and the United States Department of Agriculture’s Economic Research Service (ERS). The Institute for Development Studies at the University of Sussex is also contributing. This work is supported by funds from the US Department of Agriculture through a contract with the University Corporation for Atmospheric Research (UCAR), and by the participating institutions and other donors.<!–more–>

    The participating groups are analyzing harmonized scenarios corresponding to Shared Socioeconomic Pathways 1, 2 and 3 with and without climate change. Climate change is represented in the three SSPs by Representative Concentration Pathways 4.5, 6.0 and 8.5 respectively. Climate change impacts are modeled using three general circulation models (HadGEM2, IPSL and MIROC), one crop model (LPJmL), and five global economic models (ENVISAGE, FARM, IMPACT, MAGNET and MAgPIE). A subset of the modeling groups is also running additional scenarios to assess the impact of a liberalized international trade environment under SSP1 and a more restrictive trade environment under SSP3.

    Following the workshop, the participating modeling groups will refine their analyses. Results will be summarized in a report that is currently being prepared for USDA on global climate change and food security, and will be described more fully in a scientific paper. This analysis will also serve as the basis for additional scenarios and modeling efforts by the participating groups.

  • Are increasing levels of CO2 causing hidden hunger?

    By Andrea Calderon Irazoque

    According to a recent commentary “Food Security: Fertilizing hidden hunger” by Müller C. and others published in Nature Climate Change, CO2 fertilization and climate change will likely exacerbate macro and micro-nutrients deficiency in crops, jeopardizing one of the most important millennium development goals: to eradicate extreme poverty and hunger. This declining nutritional content could lead to “hidden hunger”– defined by the authors as an “insufficient supply of vitamins and minerals in diets with sufficient calorie content.”

    The impacts of climate change on agriculture and food security are normally studied focusing on yields and calories. However, the authors claim that this scope may be misleading when analyzing the effect of CO2 fertilization. In a changing climate, increasing CO2 levels stimulate photosynthesis and plant growth (especially in essential crops like wheat, rice and soy), and reduce water consumption due to a more efficient use of nitrogen, compensating for some of the negative effects of climate change. However, Myers and others presented compelling evidence that higher levels of CO2 will also negatively affect the nutritional value of important food crops, reducing concentrations of essential minerals (like Iron and Zinc) and proteins. This threat to the nutritional value of crops could have important implications for health and nutrition and should definitely be considered in future food security assessments.

    Finally, the authors state two central challenges that need to be faced to improve understanding of risks related to the nutritional quality of food. First, the ambivalent effects of CO2 fertilization on food security need deeper analysis and should be represented in crop models. The authors mentioned that the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) are platforms that could help facilitate interactions between different disciplines (crop modelers, breeders, physiologists and human health researchers) that have to be involved to solve this challenge. Second, models will have to address hidden hunger in upcoming work and expand the scope of the models to include nutritional quality.

     

  • Soils data workshop focuses on assessments in Africa

    Soils data workshop focuses on assessments in Africa

    By Nick Hudson

    Crop models have been extensively tested for yields, but their validation for soil water balance, and carbon and nitrogen cycling in agricultural systems has been limited. In order to improve the use of soil data in assessments of climate change impacts on agriculture, the Agricultural Modeling Intercomparison and Improvement Project (AgMIP) and Columbia University’s Center for Climate Systems Research (CCSR), hosted a joint workshop April 9-11 funded by USAID. The workshop, part of the AgMIP GRIDded crop modeling initiative (AgGRID) brought together leaders from AgMIP soils team and AgGRID along with experts from the Gates-funded African Soil Information Service (AfSIS) project as well as other soil and Africa experts.

    This gathering of crop modelers, soil scientists and data/IT specialists discussed innovative soil modeling techniques concentrating on Sub-Saharan Africa, a region of the world that is increasingly vulnerable to issues of food security posed by climate change. These improved modeling capabilities and analyses will help create more accurate projections for policymakers and stakeholders, and aid in the selection of climate change adaptation strategies.

    There were many noteworthy outcomes from this productive workshop including:

    • The development of a new version of a soil dataset for Africa (S-world Africa)
    • The incorporation of a new soil dataset (AfSIS) into the AgGRID framework
    • The development and improvement of a variety of tools, translators and functions to be used by the modeling community
    • An agreement on the initial soil conditions to best harmonize for future intercomparisons
    • Preliminary intercomparisons between both models and soil datasets in the Sub-Saharan African region
    • Initiation of development on new, parallel version of the SALUS crop model (pSALUS)

    AgMIP extends a special thanks to USAID for providing funding for this workshop. Overall, due to the highly collaborative and rewarding sessions, the three-day workshop was successful in achieving its goals and leading to significant advancement of the AgGRID initiative.

    If you are interested in joining in future modeling efforts by AgGRID, please contact Joshua Elliott. For more information about this workshop, read the full report here.

     

  • Maize Models Compared in New Paper

    By Jean-Louis Durand, Institut National de la Recherche Agronomique, France

    Our climate is changing and crop simulation models can project how climatic factors will affect food production in the coming decades, and what adaptations in farmers’ fields could stabilize global food security. Crop models are computer tools used in combination with present scientific knowledge to project yields under future climate. A recent publication, “How do various maize crop models vary in their responses to climate change factors?” by Bassu and others in the journal Global Change Biology addresses questions regarding our confidence in how well the maize simulation models can predict growth and yields under future climate change.

    According to the latest report of the Intergovernmental Panel on Climate Change (IPCC), crops will face higher temperatures and higher CO2 concentrations, two factors that are known to affect plant growth and production. A study conducted by 37 worldwide scientists within the framework of the Agricultural Model Intercomparison and Improvement Project (AgMIP) just completed an intercomparison of 23 different maize simulation models in order to assess the extent to which the models agree in sensitivity to temperature, CO2, and other climatic factors. They determined the ability of these tools to forecast yields at four sites (US, France, Brazil and Tanzania) that represent major areas of grain production.

    Before detailed site calibration, any single model failed to accurately predict site yield. But most significantly, the ensemble (average) of the 23 models improved the prediction of yields at the four sites, which suggests that multi-model ensembles can be used to increase confidence in the projections of future maize yields. In general, while there was variability among models, they agreed on the effects of rising temperature including shorter growth cycles, less biomass, and lower grain production. There was less water use, but only because the crop cycle was reduced. The models did not agree as well on yield, biomass, and transpiration responses to rising CO2, although the effect of increasing CO2 on maize yield was small on average.

    Overall this study highlights the need to continue model improvement by fine-tuning those responses linked to expected environmental changes, cultivar characteristics, and efficient water management. These and other model improvements will provide farmers and policy makers the tools they need to plan for the changing climate ahead.

     

  • New Innovations for the Use of Site-Specific Data

    By Molly B Schneider

    Recently several studies by AgMIP researchers have been released that highlight the development of improved methods for the use and management of site-specific data for agricultural research. Site-specific data consists of more detailed information about local environmental and economic variables that could impact production. By using site-specific data researchers will be able to create more accurate predictions of future trends in agricultural production. These articles highlight three updated techniques: production modeling, data sharing and yield assessments.

    “New parsimonious simulation methods and tools to assess future food and environmental security of farm populations”, by John Antle and others, published in Philosophical Transactions of The Royal Society on Biological Sciences in 2014, outlines the development of a site-specific modeling process which will enable researchers to predict different agricultural scenarios more accurately.

    Forecasting models and what-if scenarios for agriculture have been limited by the fact that traditionally, they have been developed as representations of farm behavior based on data averages. These models have not taken into account differences in agricultural situations or the tendency for individual farmers to be self-selective. While the models may be able to characterize the average impact of climatic, economic, technological, social and institutional change for a specified region, this average often fails to capture specific characterizations of different farming systems within that region.

    The researchers have created an inexpensive econometric modeling process that takes into account site-specific interactions between biophysical and economic changes. Once the initial outcomes of a sample are collected directly or gathered using other observational, experimental, modeled or expert data, larger population models can be constructed. The use of site-specific data to model for a larger population allows for a more accurate scenario of agricultural responses to internal and external changes.

    The researchers used the model to predict the quantifiable response of poor smallholder farmers in Kenya to changes in the production system such as crop management, fertilizer use and soil health. Researchers were able to indicate the trade-offs farmers faced when choosing whether or not to adopt the use of fertilizer for a range of prices depending on their site-specific environmental and economic conditions. This new modeling system will enable agricultural researchers to develop more accurate predictions of the response to both beneficial interventions and detrimental changes to production capacity.

    “Integrated Description of Agricultural Field Experiments and Production: The ICASA Version 2.0 Data Standards”, by Jeffrey W. White and others, published in Computers and Electronics in Agriculture 2013, outlines the development of a 2.0 version of the International Consortium for Agricultural Applications (ICASA). The new version was developed to create a more inclusive and standardized data sharing system for agricultural experiments.

    Agricultural experiments involve the use of countless different fields of data. There is a large number variables that need to be reported for every experiment, such as: weather conditions, soil quality, cultivation and management techniques, weeds, diseases, pests as well as crop growth. Often, scientists don’t have the means to carry out their own field experiments and must rely on the data that has been collected by others. In order to conduct their own research they require access to an inexpensive and reliable data source.

    In the past, agricultural data reporting sites have lacked a standardization of data reporting, or involved a scope of variables that was too narrow or was expensive to access. In order remedy this problem, ICASA 2.0 has been developed for cataloguing the diverse range of agricultural variables in a uniform way. The new system has a larger scope of variables to choose from and a more organized method of entering, sorting and searching for data, and will be both more efficient and user-friendly. Access to the system is free, in order to encourage collaboration between data collection and use amongst agricultural scientists.

    AgMIP has also developed a platform similar to ICASA Version 2.0 for data use and recording, the AgMIP Crop Experiment (ACE) database. The platform shares ICASA’s goal of providing the global community with reliable information that has been collected from thousands of international field experiments.

    “Climate Adaptation Imperatives: Untapped Global Maize Yield Opportunities”, by David I. Gustafson and others, published in International Journal of Agricultural Sustainability, outlines the up-to-date assessment of the global yield gap for maize production.

    The new analysis differs from ones in the past because it is based on a data-driven empirical approach rather than the simulation models that were traditionally used. This new approach allowed the researchers to get a more precise and accurate measurement of the global yield gap.

    The team used data inputs that had been collected from commercial maize production trial sites and compared them to national yield levels for maize for forty-four different countries. The difference between the national yield and trial site was used to compute the yield gap for that country. Results were categorized into groups of high, medium and low yield gap countries. The research team concluded that if improvements were made to intensify maize production in low and medium yield areas to the levels of high-yield countries, the large global gap could be reduced by forty-five percent.