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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

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.



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[]).

Figure 1: Adoption of GM maize in United States, expressed as percentage of planted area.
Figure 1: Adoption of GM maize in United States, expressed as percentage of total maize planted area (click to enlarge).

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.

Figure 2: Average maize yield (and standard error) per decade for United States and Western Europe. The 2010s include a single year to replicate the original data set (click to enlarge).
Figure 2: Average maize yield (and standard error) per decade for United States and Western Europe. The 2010s include a single year to replicate the original data set (click to enlarge).

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.


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).
genetically modified photos

The Precautionary Principle/Critique of GMOs

Nassim Nicolas Taleb and colleagues present an (almost?) tautological view of the effect of GMO (PDF): the large areas of establishment, human consumption and some unspecified risk mechanism (which does not seem to affect non-GMO crops, see next paragraph) may cause ruin to humanity, because, hey, they say so. I could come up with a similar scenario in which we should stop working on any new computing development, because there is a non-zero probability in which we may end-up with non-bottom-up tinkering causing the rise of the machines (a Black Swan event) that has ruinous consequences for humanity (with apologies to the Terminator). For some reason that escapes me, I do not find it compelling.

In making their case Taleb and colleagues also display a very naive view of non-GMO agriculture. Selective-breeding accelerated tremendously since the formalization of quantitative (statistical) genetics theory around the 1930s. Moreover, since the green revolution, the speed of creation of new cultivars, their geographical spread and the reduction of the number of genotypes commercially planted has accelerated. This all happened before the introduction of GMO as part of agricultural breeding programs. Using Taleb et al.’s terminology, crop breeding mostly abandoned ‘bottom-up modifications’ more than half a century ago. Therefore, the comparison between GMO and non-GMO crops is not one of ‘between tinkering with the selective breeding of genetic components of organisms that have previously undergone extensive histories of selection and the top-down engineering of taking a gene from a fish and putting it into a tomato’ but between high-speed, fast-deployment breeding programs and the same program with a few extra plant genes (sorry, no fish for you), sometimes even from organisms that could conventionally breed with each other (cisgenesis).

If we accept the statement ‘in addition to intentional cultivation, GMOs have the propensity to spread uncontrollably, and thus their risks cannot be localized’ and put it together with the total area of planted GMO (say 160 million hectares, or 10% of planted area, over the last 20 years) we face potentially billions of interactions between GMO and non-GMO plants. Given that there are no reported events of any large-scale disruption in billions of plant interactions—and relying on their reference to the Russian roulette fallacy ‘one needs a large sample for claims of absence of risk in the presence of a small probability of ruin’— this would support the idea that such probability of ruin is infinitesimally small indeed, as we are way past sample size n = 1. A similar argument would apply to the billions of meals ingested by millions of humans every day: there is no evidence of large (or small) scale effects of consumption of GMO on human health.

From a probabilistic point of view, absence of evidence is evidence (but not proof) of absence (see, e.g., this article). In the same way, we cannot negate the existence of the Chupacabra, but the lack of any reliable sighting puts its existence in the very highly unlikely basket. For any scientist finding real evidence of the deleterious effects of GMOs (or the existence of the Chupacabra) would be a dream come true: guaranteed article in Nature, lecture circuit, book contract, the works. Many are actively looking for negative effects for GMO, but no one has found anything beyond noise. The question is How many billion interactions between plants are enough to put the ruinous effects of GMO at the level of Chupacabra?

I agree with Taleb and colleagues that many of the arguments to advance the use of GMOs are naive or spurious. At the same time, I think ensuring cheap food access to very large populations require the use of many different combinations of agricultural technologies, including GMO and non-GMO crops. There are many problems with modern agriculture both in their industrial and small-scale versions, but I would posit the focus on GMO is more a distraction than one of the core issues.

Transgenic eel, or not (Photo: Luis, click to enlarge).
Transgenic eel, or not (Photo: Luis, click to enlarge).

P.S. 2014-07-27 19:00 The article has a Pascal’s wager feeling: there is a potential for infinite loss (eternity in hell, human ruin), finite—supposedly small—loss (pleasure, agricultural productivity) and quite a bit of window dressing to the basic argument: if we assume a technology has potentially ruinous effect (pretty much infinite loss) with probability greater than 0, then the decision to avoid it is the correct one.

genetically modified policy

A couple of thoughts on biotech and food security

“What has {insert biotech here} done for food security?” This question starts at the wrong end of the problem, because food security is much larger than any biotechnology. I would suggest that governance, property rights and education are the fundamental issues for food security, followed by biotechnological options. For example, the best biotechnology is useless if one is trying to do agriculture in a war-ravaged country.

Once we have a relatively stable government and educated people can rely on property rights, the effects of different biotechnologies will be magnified and it will be possible to better assess them. I would say that matching the most appropriate technologies to the local environmental, economic and cultural conditions is a good sign of sustainable agriculture. I would also say that the broader the portfolio of biotechnology and agronomic practices the more likely a good match will be. That is, I would not a priori exclude any biotechnology from the table based on generic considerations.

Should the success of a biotechnology for food security be measured as yield? It could be one of the desired effects but it is not necessarily the most important one. For example, having less fluctuating production (that is reducing the variance rather than increasing the mean) could be more relevant. Or we could be interested in creating combinations of traits that are difficult to achieve by traditional breeding (e.g. biofortification), where yield is still the same but nutritional content differs. Or we would like to have a reduction of inputs (agrochemicals, for example) while maintaining yield. There are many potential answers and—coming back to matching practices to local requirements—using a simple average of all crops in a country (or a continent) is definitely the wrong scale of assessment. We do not want to work with an average farmer or an average consumer but to target specific needs with the best available practices. Some times this will include {insert biotech, agronomical practices here}, other times this will include {insert another biotech and set of agronomical practices here}.

And that is the way I think of improving food security.

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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:

# Default directory

# Required packages

# Reading FAO corn data
FAOcorn = read.csv('FAOcorn.csv')

# Extracting Area
FAOarea = subset(FAOcorn, Element == 'Area Harvested',
select = c('Country', 'Year', 'Value'))

names(FAOarea)[3] = 'Area'

# and production
FAOprod = subset(FAOcorn, Element == 'Production',
select = c('Country', 'Year', 'Value'))

names(FAOprod)[3] = 'Production'

# to calculate yield in hectograms
FAOarea = merge(FAOarea, FAOprod, by = c('Country', 'Year'))
FAOarea$Yield = with(FAOarea, Production/Area*10000)

# Subsetting only the countries of interest (and years to match paper)
FAOarticle = subset(FAOarea, Country == 'United States of America' | Country == 'Western Europe')

# Plot with regression lines
ggplot(FAOarticle, aes(x = Year, y = Yield, color = Country)) +
geom_point() + stat_smooth(method = lm, fullrange = TRUE, alpha = 0.1) +
scale_y_continuous('Yield [hectograms/ha]', limits = c(0, 100000), labels = comma) +
Figure 1. Corn yield per year for USA and Western Europe (click to enlarge).
Figure 1. Corn yield per year for USA and Western Europe (click to enlarge).

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

# Expressing year as a deviation from 1960, so results
# match paper
FAOarticle$NewYear = with(FAOarticle, Year - 1960)

usa.lm = lm(Yield ~ NewYear, data = FAOarticle,
subset = Country == 'United States of America')

#lm(formula = Yield ~ NewYear, data = FAOarticle, subset = Country ==
#    "United States of America")
#     Min       1Q   Median       3Q      Max
#-18435.4  -1958.3    338.3   3663.8  10311.0
#            Estimate Std. Error t value Pr(>|t|)
#(Intercept) 38677.34    1736.92   22.27|t|)
#(Intercept) 31510.14    1665.90   18.91

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):

cornPenetration = read.csv('GMcornPenetration.csv')

ggplot(cornPenetration, aes(x = Year, y = PerAllGM)) + geom_line() + facet_wrap(~ State) +
scale_y_continuous('Percentage of GM corn') +
theme(axis.text.x  = theme_text(angle=90))
GM corn percentage by state in the USA.
Figure 2. GM corn percentage by state in the USA (click to enlarge).

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:

usa.lm2 = lm(Yield ~ NewYear, data = FAOarticle,
subset = Country == 'United States of America' & Year < 2000)

#lm(formula = Yield ~ NewYear, data = FAOarticle, subset = Country ==
#    "United States of America" & Year < 2000)
#   Min     1Q Median     3Q    Max
#-17441  -2156   1123   3989   9878
#            Estimate Std. Error t value Pr(>|t|)
#(Intercept) 39895.57    2084.81   19.14  < 2e-16 ***
#NewYear      1094.82      90.84   12.05 2.25e-14 ***
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Residual standard error: 6385 on 37 degrees of freedom
#Multiple R-squared:  0.797,	Adjusted R-squared:  0.7915
#F-statistic: 145.2 on 1 and 37 DF,  p-value: 2.245e-14

weu.lm2 = lm(Yield ~ NewYear, data = FAOarticle,
subset = Country == 'Western Europe' & Year < 2000)

#lm(formula = Yield ~ NewYear, data = FAOarticle, subset = Country ==
#    "Western Europe" & Year < 2000)
#   Min     1Q Median     3Q    Max
#-10785  -3348    -34   3504  11117
#            Estimate Std. Error t value Pr(>|t|)
#(Intercept) 29802.17    1813.79   16.43

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:

# Aggregating every decade.
# 2013-07-05 20:10 NZST I fixed the aggregation because it was averaging yields rather
# calculating total production and area for the decade and then calculating average yield
# Discussion points are totally valid
FAOarticle$Decade = cut(FAOarticle$Year,
breaks = seq(1959, 2019, 10),
labels = paste(seq(1960, 2010, 10), 's', sep = ''))

decadeProd = aggregate(Production ~ Country + Decade,
data = FAOarticle,
FUN = sum)

decadeArea = aggregate(Area ~ Country + Decade,
data = FAOarticle,
FUN = sum)

decadeYield = merge(decadeProd, decadeArea, by = c('Country', 'Decade'))
decadeYield$Yield = with(decadeYield, Production/Area*10000)

ggplot(decadeYield, aes(x = Decade, y = Yield, fill = Country)) +
geom_bar(stat = 'identity', position = 'dodge') +
scale_y_continuous('Yield [hectograms/ha]', expand = c(0, 0)) +
Figure 3. Corn yield by decade (click to enlarge).
Figure 3. Corn yield by decade (click to enlarge).

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.

# Reading NASS corn data
NASS = read.csv('NASScorn.csv')
# Conversion to sensical units (see Iowa State Extension article)
NASS$Yield = with(NASS, Value*62.77*10)

# Average by decade
NASS$Decade = cut(NASS$Year,
breaks = seq(1859, 2019, 10),
labels = paste(seq(1860, 2010, 10), 's', sep = ''))

oldYield = aggregate(Yield ~ Decade, data = NASS, FUN = mean)

# Plotting
ggplot(oldYield, aes(x = Decade, y = Yield)) +
geom_bar(stat = 'identity') +
scale_y_continuous('Yield [hectograms]', expand = c(0, 0))
Historic average yield per decade for USA (click to enlarge).
Figure 4. Historic average yield per decade for USA (click to enlarge).

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.

Drought’s Footprint map produced by The New York Times (click on graph to  view larger version in the NYT).
Drought’s Footprint map produced by The New York Times (click on graph to view larger version in the NYT). This can help to understand the Decade patterns in previous figures.

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.

genetically modified stats teaching

GM-fed pigs, chance and how research works

Following my post on GM-fed pigs I received several comments, mostly through Twitter. Some people liked having access to an alternative analysis, while others replied with typical anti-GM slogans, completely ignoring that I was posting about the technical side of the paper. This post is not for the slogan crowd (who clearly are not interested in understanding), but for people that would like to know more about how one would evaluate claims from a scientific article. While I refer to the pig paper, most issues apply to any paper that uses statistics.

In general, researchers want to isolate the effect of the treatments under study (diets in this case) from any other extraneous influence. We want control over the experimental conditions, so we can separate the effects of interest from all other issues that could create differences between our experimental units (pigs in this case). What could create ‘noise’ in our results? Animals could have different genetic backgrounds (for example with different parents), they could be exposed to different environmental conditions, they could be treated differently (more kindly or harshly), etc.

Once we control those conditions as much as possible we would randomly assign animals to each of the treatments (diets). The rationale behind random allocation is easy to follow. Let’s say that we can see that some animals are healthier than others before starting the trial. If I had a pro-GM agenda, I could assign the healthiest animals to the GM-diet and we would not be able to separate the effect of the treatment from the initial difference. To avoid this, we could have many labels in a hat, shake the hat, and for each pig take a label that randomly assigns the pig to a diet so the comparison is fair.

Researchers also like to have a baseline of the conditions before the treatments are applied. This way we can ‘adjust’ the results by any pre-existing differences. For example, there could be measurable differences on health, size, etc. I normally work with trees in experiments and we routinely assess the height of the trees just planted, so we can establish a baseline.

Finally, we often have a ‘default’ treatment which represents the status quo and acts as a comparison point for the new treatments. In the GM pig case, the default is a non-GM diet and the new treatment is the GM-diet.

The paper on GM fed pigs states that they tried to have as much control as possible of the growing conditions and that they used random allocation to the two feeding treatments. I have no problems with the paper up to this point.

When doing research it is good manners to state one’s expectations before the start of the trial. Doing this provides the experimenter with guidance about how to proceed with the evaluation of the experiment:

  1. What are the characteristics under study? Both the response variables (in the past called ‘dependent variables’) and the predictors (old name ‘independent variables’).
  2. What is the magnitude of the differences between groups that we would consider practically significant? Put another way, what would be the size of the difference that one would care about? For example, if we have two groups of pigs and the difference on weight between them is 1 g (0.035 ounces for people with funny units), who cares? If the difference was 5 kg (11 pounds, funny units again) then, perhaps, we are in to something.
  3. What level of statistical significance we consider appropriate? Even if we assume that truly there is no difference between the two diets, we would expect to see small differences between the two groups just by chance. Big differences are more unlikely. It is common in research to state that a difference is statistically significant if the probability of observing the difference is smaller than, say, 0.05 (or 1 in 20). There is nothing sacred about the number but just a convention.

By this stage one would expect a researcher to state one or more hypotheses to be tested. For example, ‘I expect that the GM diet will increase [condition here] by [number here] percent’. One can run an experiment ignoring ‘good manners’, but (and this is a big but) an informed reader will become suspicious if suddenly one starts testing hypotheses like there is no tomorrow. Why? Because if one conducts too many tests one is bound to find statistically significant results even when there are none.

The comic below presents a brief example with jelly beans assuming that we claim significance for an event occurring with probability 0.05 (1 in 20) or less. Notice that the scientists use 20 colors of jelly beans and find that green ones ’cause’ acne. Running so many tests—without adjusting down the probability of the event that we would call significant, so p needs to be much smaller than 0.05 to be significant—results in wrong conclusions.

XKCD explaining significance (click for original, larger version). p refers to the probability of observing that result if jelly beans have no effect on acne.
XKCD explaining significance (click for original, larger version).

In the pig paper there are 8 tests in Table 2, 18 (or 15 with some value) in Table 3, 8 in Table 4 and 17 in Table 5 for a total of 49 (or 46 with some testable values). In fact one would expect to find a couple of significant results (at 0.05 or 1 in 20) by chance even if there are absolutely no differences in reality.

Add to this that many of the tests are unnecessary, because they are performing the wrong type of analysis. For example, there are four separate analyses for stomach inflammation; however, the analysis ignores the type of variable one is testing as I point out in a previous post.

This is why, if I were Monsanto, I would use the paper as evidence supporting the idea that there is no difference between the two diets:

  1. the paper was ‘independent’ (although the authors have a clear anti-GM bias) and
  2. when running proper analyses (accounting for the type of variable and the number of tests that one is running) no difference is statistically significant.

P.S. 2013-06-21. 15:38 NZST A footnote about data availability: it is becoming increasingly common to make both the data and code used to analyze the data available when publishing (part of Open Science). I would expect that a paper that is making bold claims that could have policy implications would provide those, which does not happens in this case. However, the publication does include part of the data in results Tables 3 & 4, in the counts of pigs under different classes of inflammation.

data curious genetically modified sas stats

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:

Testing SAS web editor fitting pigs data
Luis A. Apiolaza - School of Forestry

*Reading data set;
data pigs;
input inflam $ diet $ count;
Nil Non-GMO 4
Mild Non-GMO 31
Moderate Non-GMO 29
Severe Non-GMO 9
Nil GMO 8
Mild GMO 23
Moderate GMO 18
Severe GMO 23

*Showing data set;
proc print data = pigs;

*El quicko analysis;
ods graphics on;
proc logistic data = pigs order = data;
freq count;
class inflam (ref = "Nil") diet (ref = "Non-GMO") / param = ref;
model inflam = diet / link = glogit;
oddsratio diet;

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:

Obs	inflam   diet   count
1	Nil      Non-GMO  4
2	Mild     Non-GMO 31
3	Moderate Non-GMO 29
4	Severe   Non-GMO  9
5	Nil      GMO      8
6	Mild     GMO     23
7	Moderate GMO     18
8	Severe   GMO     23
Odd ratios for the different levels of stomach inflammation.
Odd ratios for the different levels of stomach inflammation.

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.

genetically modified research teaching

On the destruction of a trial of genetically modified pines

The media in New Zealand briefly covered the destruction of a trial with genetically modified pines (Pinus radiata D. Don, vulgar name Radiata pine, Monterey pine) near Rotorua. This is not the first time that Luddites destroy a trial, ignoring that they have been established following regulations from the Environmental Protection Agency. Most people have discussed this pseudo-religious vandalism either from the wasting resources (money, more importantly time, delays on publication for scientists, etc) or from the criminal activity points of view.

I will discuss something slightly different, when would we plant genetically modified trees?

Some background first

In New Zealand, plantations of forests trees are established by the private sector (mostly forest companies and small growers–usually farmers). Most of the stock planted in the country has some degree of (traditional) breeding, and it ranges from seed mixes with a large numbers of parents to the deployment of genetically identical clones. The higher the degree of improvement the most likely is that tree deployment involves a small number of highly selected genotypes. Overall, most tree plantations are based on open-pollinated seed with a modest degree of genetic improvement, which is much more genetically diverse than most agricultural crops. In contrast, agricultural crops tend to deploy named clonal varieties which is what we buy in supermarkets: Gold kiwifruit, Gala apples, Nadine potatoes, etc.

Stating the obvious, tree and agricultural growers will pay more for genetic material if they have the expectation that the seeds, cuttings, tubers, etc are going to provide higher quantity and/or quality of products which will pay for the extra expense. Here we can see a big difference between people growing trees and annual/short rotation crops: there is a large lag between tree establishment and income coming from the trees, which means that when one runs a discounted cash flow analysis to estimate profitability:

  1. Income is in the distant future (say 25-30 years) and are heavily discounted.
  2. Establishment costs, which include buying the genetic material, are not discounted because they happen right now.

Unsurprisingly, growers want to reduce establishment costs as much as they can and remember that the cost of trees is an important component. This means that most people planting trees will go for cheaper, low level of genetic improvement trees (often seedlings), unless they are convinced that they can recover the extra expense with more improved trees (usually clones, which cost at least double than seedlings).

What’s the relationship with genetic modification?

Modification of any organism is an expensive process, which means that:

  1. One would only modify individuals with an outstanding genetic background; i.e. start with a good genotype to end up with a great one.
  2. Successful modifications will be clonally propagated to scale up the modification, driving down unit cost.

Thus, we have a combination of very good genotypes plus clonal propagation plus no discounting, which would make establishment costs very high (although no impossible). There is a second element that, at least for now, would delay adoption. Most large forest growers will have some type of product certification, which establishes that the grower is using good forestry, environmental and social practices. Think of it as a sticker that says the producer of this piece of wood is a good guy, so please feel confident about buying this product; that is, this sticker is part of a marketing strategy. Currently some forest certification organizations do not accept the use of genetically modified organisms (e.g. Forest Certification Council, PDF of GMO policy).

This does not mean that it is not financially possible to plant genetically modified trees. For once, modification costs would reduce with economies of scale (as for most biotechnologies), and one of the reasons we don’t have these economies is the political pressure by almost-religious zealots against GMO, which make people scared about being first to plant GM trees/plants. Another option is to change the GMO policy for some certification agencies or, relying on other certification organizations that do accept GMOs. Each individual forest company would have to evaluate the trade-offs of the certification decision, as they do not work as a block.

A simple scenario

Roughly 80% percent of the forest plantations in New Zealand correspond to radiata pine. Now imagine that we face a very destructive pest or disease that has the potential to severely damage the survival/growth of the trees. I know that it would take us a long time (decades?) to breed trees resistant to this problem. I also know that the GM crowd could insert several disease resistance genes and silence flowering, so we don’t have reproduction of modified trees. Would you support the use of genetic modification to save one of the largest industries of the country? I would.

However, before using the technology I would like to have access to data from trials growing in New Zealand conditions. The destruction of trials makes extremely difficult to make informed decisions and this is the worst crime. This people are not just destroying trees but damaging our ability to properly make decisions as a society, evaluating the pros and cons of our activities.

P.S. These are just my personal musings about the subject and do not represent the views of the forest companies, the university or anyone else. I do not work on genetic modification, but I am a quantitative geneticist & tree breeder.
P.S.2. While I do not work on genetic modification—so I’d struggle to call that crowd ‘colleagues’—I support researchers on that topic in their effort to properly evaluate the performance of genetically modified trees.