Category Archives: Adaptation to Climate Change

The Blind Spot for Climate Research in Agriculture: Not All Climate Change is Bad

I wrote this article for Huffington Post, and it appeared October 10, 2014. Below, I present two key paragraphs, and a link to the full article. — Tim Thomas

On average (confirmed by an overwhelming majority of the models), by 2050 climate change will have adverse impact on crop yields across the globe, especially in tropical countries. It gets much worse past 2050, because the more that greenhouse gases accumulate, the hotter it will get. The current projections for productivity losses by 2100 are scary.

What is not written about very often — and this is what I consider to be the blind spot — is the fact that we can also see areas that will have higher agricultural productivity as a result of climate change — at least through 2050, which is where my research has focused. That is, many if not most countries have areas within them that are projected to have higher productivity due to climate change. It is reasonably well known that agricultural productivity in some parts of temperate countries would increase because warming could remove some of the limitations on production, particularly in lengthening growing seasons and limiting damaging frosts. But the same general observation is true for many tropical countries, as well.

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A Road Trip Without a Map: Why Research Is Vital for Confronting Climate Change

I wrote this article for Huffington Post, and it appeared September 19, 2014. Below, I present two key paragraphs, and a link to the full article. — Tim Thomas

Much research has been done to predict the effects of climate change. We know much more than ever before about how rising temperatures are likely to affect the planet, and yet much more research needs to be done if we are going to successfully confront the challenges of feeding growing populations living in warmer and less predictable climates.

We need better roadmaps, for instance, to understand how to lower greenhouse gas emissions, particularly from agriculture and land. We need to learn more about how climate change will impact people, particularly farmers. And we need to develop ways for farmers and others adversely affected to adapt to the changes.

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Climate Trends in Rainfall Between 1980 and 2010

This is a companion to my article from 12-days ago, “Dramatic Confirmation of Temperature Change for 1980-2010”, which focused on a detailed geographical analysis of temperature trends in the past 30 years. These seemed to show clear signs of climate change. Rainfall is also a very important indicator of climate change, most importantly for agriculture. In this article, we use the same dataset, but focus on annual rainfall.

AgMerra_change_annual_rainfall31yrs

AgMerra_change_annual_rainfall31yrs_legendThe dataset I use, 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. As before, I run a regression for each pixel on annual rainfall using an intercept and the year as explanatory variables. The year parameter would be a measure of the year-to-year change in rainfall. 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.

In the map we note changes in many parts of the world. Much of the Sahelian region of West Africa appears to be getting more rainfall, as well as typically dry parts of Southern Africa such as Namibia and Botswana. Very large increases seem to be happening in the Amazonian regions of Colombia and Peru, with declines in rainfall near the same area of Brazil where large temperature increases were noted, as well as in neighboring Bolivia. Much of the United States is untouched by changes in rainfall, except for areas of the West near where temperature increases were noted.

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New Support for Temperature Change for 1980-2010

This article is a follow up to my article from 12-days ago, “Dramatic Confirmation of Temperature Change for 1980-2010”, which presented a global map that showed using the AgMERRA dataset temperature trends between 1980 and 2010 that were statistically significant, in some sense showing climate change, or at least the areas that have experienced climate change over the last 30 years.

CRU_TS3pt21_DTMax_global

AgMerraDTMax_global_legendJawoo Koo from the International Food Policy Research Institute (IFPRI) suggested that I examine another global dataset, the CRU Time Series, by Phil Jones and Ian Harris, from the University of East Anglia’s Climate Research Unit. This dataset spans the period 1901 to 2012, much longer than that of AgMERRA, which was only 1980 to 2010. CRU has wider geographic coverage, as well, reaching to the northern- and southern-most parts of the earth. Like the AgMERRA dataset, the CRU dataset has a half-degree resolution. However, unlike the AgMERRA dataset, the CRU only has monthly statistics rather than daily weather.

The map above shows the results. They are very similar to those of AgMERRA from the earlier article. This almost had to be the case, since they were using similar reference data to build the datasets. What is perhaps noteworthy is how AgMERRA’s use of satellite data produced differences. One might note, for example, cooling in southern India that was in AgMERRA but not in CRU.

CRU_TS3pt21_DTMax_table_from_regression_computations_xlsxThe table shows the results of analysis for every 30-year period covered in the CRU dataset. The dataset has 67,420 land-based gridcells with data. We see that the 1980 to 2010 period had the highest number of gridcells that had statistically significant changes in temperature. We also see that of those with at least 10% statistical significance, almost 92% were positive changes (increases in temperature). This was second highest to the 1970 to 2000 period.

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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

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.