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Review of Polar M400 Running Watch

I have had my Polar M400 Running Watch for a full month now. Prior to purchasing it, when I went running, I was actually wearing 3 devices on my arm: a Timex watch which worked with a chest strap heart rate monitor; another Timex watch which allowed for repeat intervals which I needed in order to use Jeff Galloway’s run-walk-run method; and a FitBit Charge HR. In addition to what I was wearing on my arm, I would sometimes carry my cellphone, on which I would run either the Nike+ app or the MapMyRun app. My Polar M400 replaces all 4 devices — and then does things that none of my devices could do.
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To state succinctly what I am going to say about the M400: I love it! What an awesome device! I bought it from Amazon (http://www.amazon.com/Polar-Sports-Watch-Heart-Monitor/dp/B00NPZ7WNU/) for less than $160 (without tax or shipping), and it came with a chest strap heart rate monitor. It was around $100 less than its competitors, such as a number of Garmin products, such as the Garmin Forerunner 220.

In addition to accurately reading my heart rate with the chest strap monitor — something I couldn’t trust the FitBit Charge HR to do from my wrist — it has a GPS built in, which seems to sample all of the data I am interested in every second (since I can download the data in a csv file and examine it at that level of detail). I had not really paid a lot of attention to either Nike+ or MapMyRun until the end of the run (I found it awkward to be pulling out my big Samsung Note during a run), while wearing a GPS on my wrist has enabled me to be interested in what it can tell me during the run. In addition to various distance measures, it can also tell you your current speed and your average speed for the lap you are on and over the course of the run so far.
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Nathan Trail Mix 4 Water Belt for Running

I am very happy with my new Nathan Trail Mix 4 Water Belt for Running! I needed a belt that would let me carry more than a half liter of water for the longer runs I had started doing this summer. Other systems I tried didn’t work because they bounced too much when the weight of the water got too high.
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Nathan’s solved that problem by designing their water belt with an elastic material that stretches to give a secure fit to the body, and by distributing the water to four, well-designed water bottles and their fitted holsters. The belt is adjustable, and I found that I had to adjust it tighter than I initially, because the elastic waist band stretched more than I thought it would.

A writer of one of the reviews I read suggested that it be worn up by one’s natural waist rather than closer to the hips. I generally think that idea is a good one, though I discovered that when I tried to wear it that high, it slipped downward a little bit from my waist over the course of a run. Nonetheless, it remained comfortable. If it didn’t remain comfortable, I could always tighten the belt a little tighter so it would be less likely to slip.

I tried wearing the belt with the buckle forward, which is what I would consider the normal way to wear such a device. I discovered that the front two water bottles are not spaced very far apart, and it felt uncomfortable the way the water bottles hit my body both above and below the belt. Fortunately, you don’t have to wear it the “normal” way. I tried it with the buckle at the back and the storage compartment in the front, which worked just fine. But in the end, I preferred to wear it side-ways, with the storage compartment on the side (my right side, since I’m right-handed).

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How will climate change affect the world food system?

I wrote this article for the blog of the International Food Policy Research Institute (IFPRI), celebrating its 40th anniversary. The goal was to present a key research topic through the years from the personal perspective of the researcher. This article appeared May 4, 2015. Below, I present a few key paragraphs, and a link to the full article. — Tim Thomas

Long before I joined IFPRI, I served in the U.S. Navy. One of the first lessons I learned in my naval training was how to avoid running into − or being run into by − another ship. You would think it would be pretty simple to avoid, but the trouble is that ships don’t turn very fast nor do they stop instantly. What’s more, on the ocean there are no lane markings to keep ships separated (except for buoys in channels). It turns out that without a simple rule of thumb, keeping two large vessels on different trajectories from running into each other isn’t always straightforward. Fortunately for ships, such a rule exists: if the other ship is on a constant bearing relative to your position and the range is decreasing, you’re going to collide unless somebody changes course or speed − and generally speaking, the sooner the better. Keeping your focus in the right place helps you figure this out.

It’s similar in regard to determining how climate change will affect the world food system. Many things are moving in different directions and speeds at the same time and, if you focus on the wrong thing, you’re going to draw the wrong conclusion. For example, we use crop models together with climate models to determine how much production is going to be hindered by the changing climate, all other things being equal. We generally find that the direct climate impact is negative and sometimes quite large. For example, some of the models suggest that climate change will reduce the productivity of corn in the U.S. by up to 40 percent by 2050. Since the U.S. is the leading corn producer, and since other major producers should be similarly affected, this is a big deal. Focusing on this number, however, as some people do, would lead to a very frightening conclusion. Add on top of that an expanding global population that will be a third larger in 2050 than it is today, and you would think that global food security is in an even more dire condition.

Yet what has been overlooked in all of this is the fact that maize yields have grown globally around 2 percent per year during the past forty years, without a sign of slowing down — and this is all despite the impacts of climate change already being felt. Furthermore, maize production in the last 20 years has grown even faster − at 3 percent per year − because expanding yields also have coincided with expanded harvested area. So while climate change creates a tremendous drag on what could have been − the yields we would experience without climate change − agricultural productivity gains due to technological innovations such as high yield maize varieties and nitrogen-based fertilizers together with expanding production areas have compensated. They will most certainly continue to compensate in the future, though probably with less effectiveness as the intensity of climate change grows.

Continue reading at the IFPRI blog

Climate-Smart Eating: Saving the Planet, One Person at a Time

I wrote this article for Huffington Post, and it appeared February 10, 2015. Below, I present a few key paragraphs, and a link to the full article. — Tim Thomas

I think the Chick-fil-A cow is onto something. You know, the cow that holds up the sign, “Eat mor chikin.” I think that cow is thinking about ways to reduce greenhouse gas (GHG) emissions, and realizes that an easy way to cut emissions from beef consumption by a minimum of 94 percent (and maybe as high as 99 percent) is to switch to eating chicken.

The Chick-fil-A cow could become the first spokes-animal for what could become an extremely important movement that might be dubbed “climate-smart eating,” a sort of mash-up of climate-smart agriculture (launched in 2014 at the UN Climate Summit in New York) and the reduced carbon footprint movement.

Climate-smart eating recognizes that the GHG emissions of what we choose to eat can vary widely, and since GHG emissions associated with the food we eat is estimated at between 44 and 57 percent of total planetary emissions (according to the non-governmental organization, GRAIN, and published by The Wall Street Journal and by the UN), people’s choices concerning the food they eat has the potential to make a huge difference in how much GHGs are emitted. Good choices will serve to slow the rate of climate change, and will ultimately reduce the extent of the damage associated with that change.

While climate-smart agriculture (CSA) tries to get farmers to change how they grow things, climate-smart eating (CSE) seeks to change WHAT they grow, by changing the structure of demand for their products. CSE together with CSA could be the one-two punch on climate mitigation–the process of reducing GHG emissions. The idea of climate-smart eating is starting to catch on, evidenced by its inclusion in the latest report from a United Nations panel, though under a less catchy name, “demand-side options” for mitigation.

Not all of us are farmers, but all of us are eaters. CSA mobilizes farmers; CSE mobilizes the world — and that is critical if goals for GHG reductions are to be met. Climate-smart eating, as a movement, encourages each person to voluntarily help save the planet by reducing the GHG content of the food he or she chooses to eat. Not only could we choose to “eat mor chikin,” but we could reduce some of our meat consumption, since grains, fruits, and vegetables almost always take less GHG emissions to produce. We could consider the emissions from transporting the food to our grocery stores, and buy more locally when possible. And we could do our part to reduce waste by not buying more than needed and saving and eating leftovers. Food discarded is like multiplying the GHG emissions taken to produce food that is actually consumed. And, as mentioned, we could care more about HOW our food was produced — did it use climate-smart agricultural techniques?

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We Still are the World

This past week was the thirtieth anniversary of the recording of “We are the World”, a song which skyrocketed to number one on the pop charts in the U.S., written by Michael Jackson and Lionel Ritchie to raise money for famine relief in Ethiopia. It was performed by over 30 of some of the most popular singers from the eighties. Together with an awareness of the impact of that horrible famine, it changed the direction of my life.

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In the first verse we hear the call: “There are people dying… and it’s time to lend a hand.” Then the chorus affirms our ability to make a difference, reminding us that “We are the world, we are the children. We are the ones who make a brighter day so let’s start giving. There’s a choice we’re making, we’re saving our own lives. It’s true we’ll make a better day, just you and me.”

At the time the song came out, I was an officer in the United States Marine Corps. But that year I decided to leave the Marines as soon as I could and join with a group called Food for the Hungry, and do what I could to help prevent more famines from occurring. When I finally was able to join Food for the Hungry in 1987, they assigned me to work out of their Kenya office, and I spent four years there, mostly doing agriculture and water development. By the time I left Kenya at the end of 1991, I has also gotten involved in refugee work, as those were turbulent years in neighboring countries, and hundreds of refugees came to Kenya across both the Ethiopian and Somali borders.

I am no longer involved in the frontline work of NGOs and others working in the villages of developing countries, but that call of thirty years ago still flows through my veins. After leaving Kenya, I realized my skillset was better suited to help in a different way, so I went off to grad school and studied agricultural economics so that I could use that toolset to better understand how to help governments, international agencies, and donors make better policies to help prevent hunger and to empower the poorest of the poor. Today I work as a research fellow at the International Food Policy Research Institute in Washington, D.C., studying ways to help farmers in developing countries adapt to climate change, as well as investigating ways to reduce the extent of climate change.

Climate change is an issue that brings me back to my original motivation, hating to see people die from hunger, and wanting to do something to help. Climatologists tells us that climate change is likely to increase the frequency and severity of droughts in the years ahead, potentially causing serious and long-lasting droughts worse than the one that triggered the famine in the 1980s in Ethiopia. But we also know that if we are able to reduce greenhouse gas emissions, we decrease the chances of experiencing the most catastrophic effects of climate change.

Perhaps it is time that the people of the world rise up and sing this song all over again, with a conviction about our desire to end poverty, hunger, and malnutrition — but also with an awareness that choices today can have far-reaching consequences. If we as individuals do what we can to reduce emissions — and that involves becoming advocates to those in our spheres of influence, including our governments — we can make a difference. “It’s true we’ll make a better day, just you and me!”

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.

Continue reading at Huffington Post

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.

Continue reading at Huffington Post

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.

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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.

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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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