## Being data curious: the strange case of lamb consumption in NZ

There is a lot of talk about the skills needed for working in Statistics/Data Science, with the discussion often focusing on theoretical understanding, programming languages, exploratory data analysis, and visualization. There are many good blog posts dealing with how you get data, process it with your favorite language and then creating some good-looking plots. However, in my opinion, one important skill is curiosity; more specifically being data curious.

Often times being data curious doesn’t require statistics or coding, but just searching for and looking at graphs. A quick example comes from Mike Dickinson’s tweet: “This is extraordinary: within a decade, NZers basically stopped eating lamb. 160 years of tradition scrapped almost overnight.”

After reading the news article, many people came up with good potential explanations: Have the relative prices changed? Do we have different demographics with not so much appetite for lamb? etc.

Few comments questioned the data until Peter Ellis voiced exactly what was nagging me:

Do the two data points make sense? In this data-abundant world, it didn’t take long to find the time series from which the points came from in this handy OECD page.

A quick look shows that the series contains both quoted consumption figures, showing the talked-about 10-year decline. Even more surprisingly, one can see that practically most of the decline occurred from 2008 to 2009 (from 17.7 to 4.9 kg/person), which is a bizarre drop for a single year. A single person may have large differences in consumption from one year to the next; however, over a whole country those deviations tend to be averaged out. This highlights another issue with the time series: it wiggles like crazy.

When exploring data is useful to have some sort of benchmark to see if other things are also changing at the same time. I chose our neighbor Australia—with a not so different diet, similar part of the world—as my benchmark. The Australian time series doesn’t show a change like NZ. Besides using the benchmark for the same product, we can also compare what’s going on with other meats. For example, beef and veal, pork and poultry.

All the series are smoother and show similar trends in Australia and New Zealand, which makes the lamb saga increasingly look like a mistake. We can now move from trying to explain social changes that are driving the change between two numbers, to being highly suspicious about the numbers under discussion!

So where could be the problem coming from? Consumption per capita requires i) total domestic consumption of sheep meat and ii) population of the country. We are pretty sure we have good data for population, courtesy of Statistics New Zealand. How would one go about estimating domestic consumption of sheep meat? Probably one would:

• Get the size of the New Zealand sheep flock. We can get sheep numbers from Statistics NZ Agricultural Production Statistics. Livestock numbers are a national indicator, which tend to have high accuracy.
• Get an idea of the proportion of the flock that’s exported, which we know is pretty substantial. I don’t know how good these numbers are, but Beef & Lamb NZ gives us an idea of how many sheep are slaughtered for export. This number, which hovers around 20 million a year seems quite consistent. We have to remember that not the whole population is slaughtered every year, as we have to replace the flock.
• The difference between flock size – (sheep for export + replacement sheep) should be the number of sheep for domestic consumption.
• We need a conversion factor between number of sheep and kg of meat produced, so we can calculate meat consumption/capita.

I would assume that the sheep-meat conversion factor will show little fluctuation from year to year, so perhaps the likely culprit is the penultimate point, estimating the number of sheep for domestic consumption. One thing that grabs my attention is that while the flock is getting smaller, the number of sheep for exports stays around the same, which should mean fewer sheep available for the domestic market, giving credibility to the lower lamb consumption trend.

I don’t know if this the actual explanation for the “lamb consumption crash”. If I had more time I could chase some of the domestic consumption numbers, even call the Beef & Lamb people. But this should be enough to get you started with an example on how to question the news using real data. I’m sure you reader can come up with better ways of looking at this and other stories.

## Comment on Sustainability and innovation in staple crop production in the US Midwest

After writing a blog post about the paper “Sustainability and innovation in staple crop production in the US Midwest” I decided to submit a formal comment to the International Journal of Agricultural Sustainability in July 2013, which was published today. As far as I know, Heinemann et al. provided a rebuttal to my comments, which I have not seen but that should be published soon. This post is an example on how we can use open data (in this case from the USDA and FAO) and free software (R) to participate in scientific discussion (see supplementary material below).

The text below the *** represents my author’s version provided as part of my Green Access rights. The article published in the International Journal of Agricultural Sustainability [copyright Taylor & Francis]; is freely available online at http://dx.doi.org/10.1080/14735903.2014.939842).

While I had many issues with the original article, I decided to focus on three problems—to make the submission brief and fit under the 1,000 words limit enforced by the journal editor. The first point I make is a summary of my previous post on the article, and then move on to two big problems: assuming that only the choice of biotechnology affects yield (making the comparison between USA and Western Europe inadequate) and comparing use of agrochemicals at the wrong scale (national- versus crop-level).

PS 2014-08-05 16:30: Heinemann et al.’s reply to my comment is now available.

• They state that “Apiolaza did not report variability in the annual yield data” and that they had to ‘reconstruct’ my figure. Firstly, the published figure 2 does include error bars and, secondly, the data and R code are available as supplementary information (in contrast to their original paper). Let’s blame the journal for not passing this information to them.
• They also include 2 more years of data (2011 & 2012), although the whole point of my comment is that the conclusions they derived with the original data set were not justified. Heinemann et al. are right in that yields in 2011 & 2012 were higher in Western Europe than in the USA (the latter suffering a huge drought); however, in 2013 it was the opposite: 99,695 Hg/ha for USA vs 83,724 Hg/ha for Western Europe.
• They question that I commented only on one crop, although I did cover another crop (wheat) quickly with respect to the use of pesticides—but not with the detail I wanted, as there was a 1,000 words limit. I would have also mentioned the higher variability for Western European wheat production, as it is weird that they pointed out that high variability is a problems for USA maize production but said nothing for wheat in Europe.
• Furthermore, they also claimed “There is no statistically significant difference in the means over the entire 50-year period” however, a naive paired t-test t.test(FAOarticle$Yield[1:50], FAOarticle$Yield[51:100], paired = TRUE) (see full code below) says otherwise.

***

## Abstract

This comment highlight issues when comparing genetically modified (GM) crops to non-GM ones across countries. Ignoring structural differences between agricultural sectors and assuming common yield trajectories before the time of introduction of GM crops results on misestimating the effect of GM varieties. Further data collection and analyses should guide policy-makers to encourage diverse approaches to agriculture, rather than excluding specific technologies (like GM crops) from the onset.

Keywords: genetic modification; biotechnology; productivity; economics

In a recent article Heinemann et al. (2013) focused “on the US staple crop agrobiodiversity, particularly maize” using the contrast between the yield of Western Europe and United States as a proxy for the comparison between genetically modified (GM) maize versus non-GM maize. They found no yield benefit from using GM maize when comparing the United States to Western Europe.

In addition, Heinemann et al. contrasted wheat yields across United States and Western Europe to highlight the superiority of the European biotechnological package from a sustainability viewpoint.

I am compelled to comment on two aspects that led the authors to draw incorrect conclusions on these issues. My statistical code and data are available as supplementary material.

## 1. Misestimating the effect of GM maize varieties

Heinemann et al. used FAO data[], from 1961 to 2010 inclusive, to fit linear models with yield as the response variable, country and year as predictors. Based on this analysis they concluded, “W. Europe has benefitted from the same, or marginally greater, yield increases without GM”. However, this assumes a common yield trajectory for United States and Western Europe before significant commercial use of GM maize, conflating GM and non-GM yields. GM maize adoption in United States has continually increased from 25% of area of maize planted in 2000 to the current 90% (Figure 1, United States Department of Agriculture 2013[]).

If we fit a linear model from 1961 to 1999 (last year with less than 25% area of GM maize) we obtain the following regression equations $$y = 1094.8 x + 39895.6$$ (United States, R2 = 0.80) and $$y = 1454.5 x + 29802.2$$ (W. Europe, R2 = 0.90). This means that Western Europe started with a considerably lower yield than the USA (29,802.2 vs 39,895.6 hg/ha) in 1961 but increased yields faster than USA (1,454.5 vs 1,094.8 hg/ha per year) before substantial use of GM maize. By 1999 yield in Western Europe was superior to that in United States.

This is even more evident in Figure 2, which shows average yield per decade, removing year-to-year extraneous variation (e.g. due to weather). Western European yields surpassed United States’s during the 1990s (Figure 2). This trend reverses in the 2000s, while United States simultaneously increased the percentage of planted area with GM maize, directly contradicting Heinemann et al.’s claim.

## 2. Ignoring structural differences between agricultural sectors

When discussing non-GM crops using wheat the authors state, “the combination of biotechnologies used by W. Europe is demonstrating greater productivity than the combination used by the United States”. This sentence summarizes one of the central problems of their article: assuming that, if it were not for the choice of biotechnology bundle, the agricultural sectors would have the same intrinsic yield, making them comparable. However, many inputs beside biotechnology affect yield. For example, Neumann et al. (2010) studied the spatial distribution of yield and found that in the Unites States “access can explain most of the variability in wheat efficiency. In the more remote regions land prices are lower and inputs are therefore often substituted by land leading to lower efficiencies”. Lower yields in United States make sense from an economic point of view, as land replaces more expensive inputs like agrochemicals.

Heinemann et al. support their case by comparing pesticide use between United States and France across all crops. However, what is relevant to the discussion is pesticide use for the crops being compared. European cereals, and wheat in particular, are the most widely fungicide-treated group of crops worldwide (Kucket al. 2012). For example, 27% of the wheat planted area in France was already treated with fungicides by 1979 (Jenkins and Lescar 1980). More than 30 years later in the United States this figure has reached only 19% for winter wheat (which accounts for 70% of planted area, NASS 2013). Fungicide applications result on higher yield responses (Oerke 2006).

## Final remarks

Heinemann et al. ignored available data on GM adoption when analysing maize yields. They also mistakenly treated biotechnological bundles as the only/main explanation for non-GMO yield differences between United States and Western Europe. These issues mean that the thrust of their general conclusion is unsupported by the available evidence. Nevertheless, their article also raised issues that deserve more consideration; e.g. the roles of agricultural subsidies and market concentration on food security.

Agricultural sustainability requires carefully matching options in the biotechnology portfolio to site-specific economic, environmental and cultural constraints. Further data collection and analyses should lead policy makers to encourage diverse approaches to agriculture, rather than excluding specific technologies (like GMOs and pesticides) from the onset.

## References

Heinemann, J. A., Massaro, M., Coray, D. S., Agapito-Tenfen, S. Z. and Wen, J. D. 2013. Sustainability and innovation in staple crop production in the US Midwest. International Journal of Agricultural Sustainability (available here).

Jenkins, J. E. E. and Lescar, L. 1980. Use of foliar fungicides on cereals in Western Europe. Plant Disease, 64(11): 987-994 (behind paywall).

Kuck, K. H., Leadbeater, A. and Gisi, U. 2012. FRAC Mode of Action Classification and Resistance Risk of Fungicides. In: Krämer, W., Schirmer, U., Jeschke, P. and Witschel, M., eds., Modern Crop Protection Compounds. Wiley. 539-567.

NASS, 2013. Highlights: 2012 Agricultural Chemical Use Survey. Wheat. United States Department of Agriculture (available here).

Neumann, K., Verburg, P. H., Stehfest, E., and Müller, C. 2010. The yield gap of global grain production: a spatial analysis. Agricultural Systems, 103(5), 316–326 (behind paywall).

Oerke E. 2006. Crop losses to pests. Journal of Agriculture Science, 144: 31-43 (behind paywall, or Free PDF).

United States Department of Agriculture. 2013. Adoption of Genetically Engineered Crops in the U.S. USDA Economic Research Service (available here).

## Supplementary material

You can replicate the analyses and plots produced in this comment using the following files:

• Maize production data for United States and Western Europe (csv, extracted from FAO). []
• GMO maize penetration data (csv, extracted from USDA). []
• R code for analyses (R file, changed extension to .txt so WordPress would not complain).

## My take on the USA versus Western Europe comparison of GM corn

A few days ago I came across Jack Heinemann and collaborators’ article (Sustainability and innovation in staple crop production in the US Midwest, Open Access) comparing the agricultural sectors of USA and Western Europe. While the article is titled around the word sustainability, the main comparison stems from the use of Genetically Modified crops in USA versus the absence of them in Western Europe.

I was curious about part of the results and discussion which, in a nutshell, suggest that “GM cropping systems have not contributed to yield gains, are not necessary for yield gains, and appear to be eroding yields compared to the equally modern agroecosystem of Western Europe”. The authors relied on several crops for the comparison (Maize/corn, rapeseed/canolasee P.S.6, soybean and cotton); however, I am going to focus on a single one (corn) for two reasons: 1. I can’t afford a lot of time for blog posts when I should be preparing lectures and 2. I like eating corn.

When the authors of the paper tackled corn the comparison was between the USA and Western Europe, using the United Nations definition of Western Europe (i.e. Austria, Belgium, France, Germany, Liechtenstein, Luxembourg, Monaco, Netherlands, Switzerland). Some large European corn producers like Italy are not there because of the narrow definition of Western.

I struggled with the comparison used by the authors because, in my opinion, there are potentially so many confounded effects (different industry structures, weather, varieties, etc.) that it can’t provide the proper counterfactual for GM versus non-GM crops. Anyway, I decided to have a look at the same data to see if I would reach the same conclusions. The article provides a good description of where the data came from, as well as how the analyses were performed. Small details to match exactly the results were fairly easy to figure out. I downloaded the FAO corn data (3.7 MB csv file) for all countries (so I can reuse the code and data later for lectures and assignments). I then repeated the plots using the following code:

I could obtain pretty much the same regression model equations as in the article by expressing the years as deviation from 1960 as in:

Heinemann and collaborators then point out the following:

…the slope in yield increase by year is steeper in W. Europe (y?=?1344.2x?+?31512, R²?=?0.92084) than the United States (y?=?1173.8x?+?38677, R²?=?0.89093) from 1961 to 2010 (Figure 1). This shows that in recent years W. Europe has had similar and even slightly higher yields than the United States despite the latter’s use of GM varieties.

However, that interpretation using all data assumes that both ‘countries’ are using GMO all the time. An interesting thing is that USA and Western Europe were in different trends already before the introduction of GM corn. We can state that because we have some idea of when GM crops were introduced in the USA. This information is collected by the US Department of Agriculture in their June survey to growers and made publicly available at the State level (GMcornPenetration.csv):

This graph tells us that by the year 2000 the percentage of planted corn was way below 50% in most corn producing states (in fact, it was 25% at the country level). From that time on we have a steady increase reaching over 80% for most states by 2008. Given this, it probably makes sense to assume that, at the USA level, yield reflects non-GM corn until 1999 and progressively reflects the effect of GM genotypes from 2000 onwards. This division is somewhat arbitrary, but easy to implement.

We can repeat the previous analyzes limiting the data from 1961 until, say, 1999:

These analyses indicate that Western Europe started with a lower yield than the USA (29,802.17 vs 39,895.57 hectograms/ha) and managed to increase yield much more quickly (1,454.48 vs 1,094.82 hectograms/ha per year) before any use of GM corn by the USA. Figure 1 shows a messy picture because there are numerous factors affecting yield each year (e.g. weather has a large influence). We can take averages for each decade and see how the two ‘countries’ are performing:

This last figure requires more attention. We can again see that Western Europe starts with lower yields than the USA; however, it keeps on increasing those yields faster than USA, overtaking it during the 1990s. Again, all this change happened while both USA and Western Europe were not using GM corn. The situation reverses in the 2000s, when the USA overtakes Western Europe, while the USA continuously increased the percentage of GM corn. The last bar in Figure 3 is misleading because it includes a single year (2010) and we know that yields in USA went down in 2011 and 2012, affected by a very large drought (see Figure 4).

At least when looking at corn, I can’t say (with the same data available to Heinemann) that there is no place or need for GM genotypes. I do share some of his concerns with respect to the low level of diversity present in staple crops but, in contrast to his opinion, I envision a future for agriculture that includes large-scale operations (either GM or no-GM), as well as smaller operations (including organic ones). I’d like to finish with some optimism looking further back to yield, because the USDA National Agricultural Statistics Service keeps yield statistics for corn since 1866(!) (csv file), although it uses bizarre non-metric units (bushels/acre). As a metric boy, I converted to kilograms per hectare (multiplying by 62.77 from this page) and then to hectograms (100 g) multiplying by 10.

It is interesting to see that there was little change until the 1940s, with the advent of the Green Revolution (modern breeding techniques, fertilization, pesticides, etc.). The 2010s decade in Figure 4 includes 2010, 2011 and 2012, with the last two years reflecting extensive droughts. Drought tolerance is one of the most important traits in modern breeding programs.

While Prof. Heinemann and myself work for the same university I don’t know him in person.

P.S. Did you know that Norman Borlaug (hero of mine) studied forestry?
P.S.2 Time permitting I’ll have a look at other crops later. I would have liked to test a regression with dummy variables for corn to account for pre-2000 and post-1999, but there are not yet many years to fit a decent model (considering natural variability between years). We’ll have to wait for that one.
P.S.3 I share some of Heinemann’s concerns relating to subsidies and other agricultural practices.
P.S.4 In case anyone is interested, I did write about a GM-fed pigs study not long ago.
P.S.5 2013-07-05 20:10 NZST. I updated Figures 1 and 3 to clearly express that yield was in hectograms/ha and recalculated average decade yield because it was originally averaging yields rather calculating total production and area for the decade and then calculating average yield. The discussion points I raised are still completely valid.
P.S.6 2013-07-07 11:30 NZST. The inclusion of Canadian canola does not make any sense because, as far as I know, Canada is not part of Western Europe or the US Midwest. This opens the inclusion of crops from any origin as far as the results are convenient for one’s argument.
P.S.7 2014-08-06 13:00 NZST My comment was expanded and published in the journal (or here for HTML version with comments on the authors’ reply to my comment.

## Ordinal logistic GM pigs

This week another ‘scary GMO cause disease’ story was doing the rounds in internet: A long-term toxicology study on pigs fed a combined genetically modified (GM) soy and GM maize diet. Andrew Kniss, a non-smokable weeds expert, mentioned in Twitter that the statistical analyses in the study appeared to be kind of dodgy.

Curious, I decided to have a quick look and I was surprised, first, by the points the authors decide to highlight in their results, second, by the pictures and captioning used in the article and, last, by the way of running the analysis. As I’m in the middle of marking assignments and exams I’ll only have a quick go at part of the analysis. As I see it, the problem can be described as ‘there is a bunch of pigs who were fed either non-GM feed or GM feed. After some time (approximately 23 weeks) they were killed and went through a CSI-like autopsy’, where part of the exam involved the following process:

1. Write down the type of feed the pig had during his/her life;
2. Assess the condition of the stomach and put it in one of four boxes labeled ‘Nil’, ‘Mild’, ‘Moderate’ and ‘Severe’.

All this data is summarized in Table 3 of the paper (PDF). How would I go about the analysis? As I see it, we have a categorical response variable—which can take one of four mutually exclusive values—and a categorical predictor (diet). In addition, there is a natural order to the inflammation response variable in that Severe > Moderate > Mild > Nil.

Andrew Kniss wrote a post trying to reproduce the published results. Instead, I present the first approach I would try with the data: ordinal logistic regression. Not only that, but instead of using a hippie statistical software like R, I will use industrial-grade-business-like SAS:

This produces a simple table with the same data as the paper and some very non-exciting results, which are better summarized in a single graph:

The odd ratios would be 1 for no difference between the treatments. The graph shows that the confidence limits for all levels of inflammation include 1, so move on, nothing to see. In fact, GMO-fed pigs tend to have less inflammation for most disease categories.

P.S. There are many ways of running an analysis for this data set, but I’m in favor of approaches that take the whole problem in one go rather than looking at one class at the time. In an ideal situation we would have a continuous assessment for inflammation and the analysis would be a one-way ANOVA. I understand that for practical reasons one may prefer to split the response in four classes.

P.S.2 2013-06-15 I often act as a reviewer for scientific journals. In the case of this article some of my comments would have included: the analysis does not use the structure of the data properly, the photographs of the damaged organs should include both types of diet for each inflammation class (or at least include the most representative diet for the class), and the authors should highlight that there are no significant differences between the two diets for animal health; that is, the trial provides evidence for no difference between feeds. I still feel that the authors should be more forthcoming on terms of disclosing potential conflicts of interest too, but that’s their decision.

P.S.3 2013-07-04 I expand on aspects of the general research process in this post.

Tongue-in-cheek, of course, and with reference to weeds. This blog mostly uses R, but I’m pushing myself to use lots of different software to ‘keep the language’. Now if I could only do this with Spanish.