Dramatic Confirmation of Temperature Change for 1980-2010

I analyzed a new global gridded daily weather dataset to see what it would tell us about what climate change, particularly temperature change, we may have already witnessed during my lifetime. What I discovered surprised me very much. I was expecting that some places would show a 1-degree Celsius temperature increases over a 30-year period, with a few additional pixels showing up to a 2-degree increase. Instead, we see a lot of places with 1- and 2-degree increases, and even others with 3- and 4-degree temperature changes.


AgMerraDTMax_global_legendThe dataset I used, AgMERRA, was recently developed by Alex Ruane of NASA and Richard Goldberg of the University of Chicago. The pixels are a half-degree in size, and the data spans the period of 1980 to 2010. The data includes solar radiation, minimum temperature, maximum temperature, rainfall, and wind speed. My primary interest is in the monthly mean daily maximum temperature for the warmest month of the year (for brevity, “tmax”), since this may be the critical limiting factor for agriculture in the future under climate change.

My idea was simply to run a regression for each pixel on tmax using an intercept and the year as explanatory variables. The year parameter would be a measure of the year-to-year change in tmax. I would be able to tell the statistical significance at each point by looking at the z-statistic for the parameter from the regression. Being somewhat a skeptic, my guess was that only a few pixels — maybe less than 10 percent of them — would have temperature changes that would be significant.

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Identifying Climate Hotspots and Climate Opportunities with Crop Models: Part 4 (Mixed Signals)

In this post, the fourth in the series about the power of crop models to identify climate hotspots and climate opportunities, we consider cases in which we might receive mixed signals in particular areas from the crop models. As we have done previously, for the reader’s convenience, we show below the map that we presented in Part 2, to help us visually identify places discussed in this post.

Figure 1. Productivity Changes in Rainfed Maize in Kenya between 2000 and 2050, based on CNRM GCM and A1B Scenario
ken0_dssat_rainfed_maize_cnr_a1_dssat_yield_change Legend_Excel_yldchg

For the purposes of this post, we will consider two types of mixed signals. The first is the case in which productivity declines for one crop, but increases for another crop. The second comes when the results for a single crop are inconsistent for different climate models (GCMs).

Situation 3: Change in Suitable Crops

An area might be a loser for one crop, and a gainer for another. These might be areas in red or dark orange for maize, that when compared to the same area for other crops, might be blues or greens. For example, areas that become too dry for maize might be great for millet or cowpeas or some other dryland crop. This would suggest a role for extension departments to help farmers become familiar with crops they have not previously cultivated. This would have to be done in conjunction with training on how to cook and serve these crops, since much of the country is subsistence, and households would likely consume at least a portion of what they produce.

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Identifying Climate Hotspots and Climate Opportunities with Crop Models: Part 3 (Focus on Climate Opportunities)

In this post, we pick up where we left off in our case study of rainfed maize for Kenya. In the previous post, we focused on climate hotspots. Here, we focus on climate opportunities. For the reader’s convenience, we show below the map that we presented in Part 2, since the map will also be the focus of the discussion in this post.

Figure 1. Productivity Changes in Rainfed Maize in Kenya between 2000 and 2050, based on CNRM GCM and A1B Scenario
ken0_dssat_rainfed_maize_cnr_a1_dssat_yield_change Legend_Excel_yldchg

Situation 2: Climate Opportunities

Consider the blue areas, any of them. These are areas where maize will grow in the future, but would not in the past. These areas generally come about because of either increased rainfall; or as sometimes happens in higher elevations, the temperatures previously were too cold, but with some warming, the area will become usable for that crop.

Areas that are considered climate opportunities are ones that are potentially exploitable, raising national production and benefiting the farmers who move there. If farmers discover these areas, and land is scarce (i.e., when farm sizes are small and rural population density is high), they will generally have a lot of motivation to move into these areas.

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Bujumbura Launch of East Africa Agriculture and Climate Change Book

RM_2013_Nelson_eaf_cover_frontLast Wednesday (December 11, 2013), the final installation of a 3-volume set studying climate change impacts on agriculture was released during ASARECA’s Second General Assembly held in Bujumbura, Burundi. The book, published by IFPRI, was edited by Michael Waithaka, Timothy S. Thomas, Miriam Kyotalimye, and Gerald Nelson.

The purpose of the book is to help African policymakers, researchers, NGOs, and donors to better plan and prepare for climate change impacts, national-level studies were recently undertaken that provide spatially-refined analysis of impacts of climate on key crops, along with additional analysis that consider global trends and other factors that are changing with the climate, including GDP, population, and agricultural technology development and use. Judging by the enthusiastic reception (200 copies of the book were handed out in 90 minutes), it is one that meets a great need and addresses a serious gap in knowledge.

In the book, the authors used two approaches to study the impact of climate change on agriculture. The first was to apply the DSSAT crop modeling software to climate model data, to see how crop yields of major crops would be impacted by climate change, not accounting for technological change or adaptation. The second was to use a large global partial equilibrium model focusing on food and agriculture, IMPACT, to take into account population and GDP growth on demand, as well as to consider the supply response, as impacted by climate change and also technological change.

Readers interested in downloading one of the chapters or the entire book can find it on the IFPRI website.

Identifying Climate Hotspots and Climate Opportunities with Crop Models: Part 2 (Focus on Climate Hotspots)

In the previous post, we identified studies that we have done that used crop models together with climate models. Here, as promised, we do a brief case study to demonstrate the power of this method to help researchers and policymakers identify specific geographic areas as climate hotpots (areas where productivity declines are projected to be great) and climate opportunities (areas where a crop could not previously grow, but will under climate change; or areas with great yield improvements).

Figure 1. Productivity Changes in Rainfed Maize in Kenya between 2000 and 2050, based on CNRM GCM and A1B Scenario
ken0_dssat_rainfed_maize_cnr_a1_dssat_yield_change Legend_Excel_yldchg

They say a picture is worth a thousand words, and the map in Figure 1 hopefully will prove to be just that. It shows productivity changes for rainfed maize in Kenya as the result of projected climate change. This was generated by computing average yields that would be expected given the soils and climate of the 1950-2000 period, and comparing them to the average yields based on the climate of 2050 that is projected by the CNRM GCM and the A1B scenario. While in our studies of climate change in Africa we use do analysis for 4 different climate models, let us just focus on this one for now.

It’s important to understand what the map in Figure 1 shows. We can see by the legend that

  • Green signifies yield gain as a result of climate change
  • Orange signifies yield decline
  • The darker the green or orange, the bigger the gain or loss.
  • Blue is area gained as a result of climate change (unproductive in 2000, productive in 2050)
  • Red is area lost due to climate (productive in 2000, unproductive in 2050)

Let us now consider ways in which this kind of pixel-level data based on crop models and climate models might help us in prioritizing landscapes for intervention.

Situation 1: Climate Hotspots

Consider the red areas of the map in the western part of Kenya near the Uganda border. These show areas where rainfed maize will no longer be able to be cultivated due to climate change. This part of the country could end up in severe crisis. For most of Kenya, not only is maize the main crop grown, but it is also the main food consumed. If any subsistence farmers live in this area – and surely there are some if not many – they will be devastated by such an occurrence.

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Identifying Climate Hotspots and Climate Opportunities with Crop Models: Part 1

Over the past almost four years, much of my research has been done as part of teams that have been using crop models together with climate models to investigate the direct impact of climate change on crop productivity. Much of the work has been completed and published. Examples include the recently released 3-volume set consisting of West African Agriculture and Climate Change, Southern Africa Agriculture and Climate Change, and East Africa Agriculture and Climate Change. Much of the groundwork for these publications were completed by the end of 2010.

flower_mojave_desert_joshua-tree-national-park-165474_640_PixabayIn some of the more recent work, we expanded the analysis by also using the crop models to explore the effectiveness of a variety of adaptation options. Results from two of these analyses are out as discussion papers: Agriculture and Adaptation in Bangladesh and Cambodian Agriculture: Adaptation to Climate Change Impact. Another, focusing on the Pacific island nations of Fiji, Papua New Guinea, and the Solomon Islands is expected to be published by the Asian Development Bank sometime in 2014.

In using crop models with climate models, we divide up a country or region into a grid (set of squares) that span the study area. Usually these squares are 5 arc-minutes (around 9 or 10 kilometers) on edge, though in the case of West Africa, many of the squares are 15 arc-minutes (around 30 kilometers) on edge. Then, we do analysis on each square, first for the “baseline” climate (that of the 1950 to 2000 period), and then on the climate of the future (most of the work focused on 2050).

The crop model uses mathematical equations to “grow” each crop a day at a time. The DSSAT software, which we used in these studies, allows the user to specify a variety of the crop that is being analyzed (each variety has its own set of parameters to be used in the model, and generally each crop has its own model or set of equations). The model uses soil characteristics together with user-specified crop management methods. In addition, daily weather data is used. Since we usually do not have daily weather, we usually use weather data generated by a simulator that uses the monthly climate parameters to determine what kind of weather would be feasible. Since weather can vary from year to year, we usually simulate between 30 and 90 years worth of weather data, computing yields for each annual set of data.

In the next post (part 2 of this article), we will look at a case study for rainfed maize in Kenya that will illustrate the incredible usefulness of this approach. We will help the reader better understand what the maps are telling us in terms of climate impacts. We also hope to help the reader understand the kind of policies and projects that would be appropriate to use in response to the information they contain.

Next article in series.

Raise the Minimum Wage Only for Part-Time Employees

There has been much discussion lately about raising the minimum wage. This seems to be in response to the growing wage disparity in the United States, along with the recognition that most people relying on the minimum wage for full-time positions live in poverty.

While those receiving minimum wage now may reap the benefits of changes in this law, it would not be without unwanted side effects: some people would lose their jobs, if employers cannot afford the increase; and unemployment would rise, as employers look to automation and customer self-service as alternatives to hiring low-skill labor.

The timing of this proposal is not the best, either. The nation is still recovering from the deepest recession since the Great Depression, and there is a great deal of uncertainty and anxiety over the impact of the Affordable Care Act (ACA) on both individuals and businesses.

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Workshop Held for Reducing GHG Emissions from AFOLU in Bangladesh

The Bangladesh Centre for Advanced Studies (BCAS) and the International Food Policy Research Institute (IFPRI), with the support of the United States Agency for International Development (USAID), held a final workshop on Low Emissions Development Strategies (LEDS) for the agriculture, forestry, and land use change (AFOLU) sectors at the BRAC Inn Centre in Dhaka, Bangladesh, on September 25 and 26. The workshop brought together more than 60 experts in their fields to discuss ways to reduce greenhouse gas (GHG) emissions without seriously hurting agricultural production.

Attendees of LEDS AFOLU workshop in Dhaka.

One of the features of the event was putting together 4 working groups that covered soils and crops; livestock and fisheries; forestry; and biofuels and cookstoves. Each expert chose the working group that best suited their area of expertise. Each of these working groups developed concrete policies in their particular focus topic that could be implemented to deliver “smart mitigation” — reduction of GHG emissions per unit of output without hurting overall production.

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New Book on Climate Change and Agriculture in Southern Africa

Today, FANRPAN, CCAFS, and IFPRI launched a newly published book, Southern African Agriculture and Climate Change: A Comprehensive Analysis, at FANRPAN’s 2013 Annual High-level Food Security Multi-Stakeholder Policy Dialogue held in Maseru, Lesotho. Among those in attendance were deans of almost a dozen agricultural colleges in Southern Africa, a former prime minister (of the Kingdom of Swaziland), donors, NGOs, farmers, students, and researchers. The group was well-represented by women in all these categories.

The book is edited by Sepo Hachigonta, Gerald C. Nelson, Timothy S. Thomas, and Lindiwe Sibanda. The purpose of the book is to help policymakers, researchers, NGOs, donors, and extension agents better understand the likely impacts of climate change on agriculture over the next 40 years, and to give them advice on the types of policies that could be implemented that would best help farmers adapt to the changes.

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New Study Released on Bangladesh, Agriculture, and Adaptation to Climate Change

The International Food Policy Research Institute (IFPRI) today released a discussion paper that did a detailed analysis of adaptation options for agriculture in Bangladesh. The report was written by Timothy S. Thomas, Khandaker Mainuddin, Catherine Chiang, Aminur Rahman, Anwarul Haque, Nazria Islam, Saad Quasem, and Yan Sun. The study’s authors were researchers at either IFPRI or the Bangladesh Centre for Advanced Studies.

The study used several instruments to evaluate the impact of climate change. They included the use of crop models together with climate models to evaluate various adaptation options which included changing crop varieties, changing planting dates, increasing fertilizer use, and looking at the yield impact of irrigation over precipitation only. The study also included a detailed household survey and a release of a special version of IFPRI’s SPAM dataset (You and Wood 2006; You, Wood, and Wood-Sichra 2006, 2009).

The following table briefly summarizes the crop modeling results. The reader will note that the largest negative impact of climate change is on wheat, with more than a 20 percent reduction under high fertilizer use. Yield impacts under high fertilizer use are generally more adverse than under low fertilizer use (in percent changes) — though the yields with high levels of fertilizer are still better than for low levels of fertilizer.

Crop model results

One intriguing result is the improved boro rice yields under climate change and high fertilizer levels with adaptation. This will be the subject of a future post.

The discussion paper can be downloaded from IFPRI’s website.