In this post, I will use a journal article  cited in the discussion above (Elmore, et al.  Glyphosate-Resistant Soybean Cultivar Yields Compared with Sister Lines, Agron. J. 93:408–412, 2001;  Accessed from:  http://digitalcommons.unl.edu/agronomyfacpub/29/). This article examined the effects of genetically engineered herbicide resistance on production characteristics of several soybean varieties.  While multiple varieties and herbicides were considered in the research, those of interest here are the lines genetically altered for glyphosate resistance (GR) and their corresponding “sister” lines which were genetically similar with the exception of having no glyphosate resistance (Non-GR).

Table 5 in the article presents average comparisons for these two varietal groups.  The yield of Non-GR lines is given as 3.68 Mega-grams (Mg) per hectare while that of GR lines is 3.48 Mg per hectare.  Statistical significance between these two groups is not explicitly shown due to an apparent typographic error in the table (no letter designation is given for the GR group).  A standard error (SE) of 0.08 is reported, however, it is not clear whether this represents the error of the means themselves or the error of the mean comparison, 3.68 – 3.48 = 0.20 Mg per hectare. In order to proceed with this discussion I will assume that the standard error is for the contrast itself.   This appears to be consistent with the presentation of other tables in the paper and is the “best case” scenario for the researchers with respect to the variability of the data.  From here, we can see an approximate 95% confidence interval on the difference in means (roughly the difference ± 2*SE or 0.04 to 0.36) would almost, but not quite, cover zero.  This implies some significance, although marginal.   Still, the difference of 0.20 Mg or 200 kg per hectare would not be negligible to a producer, especially when compounded over several hectares.  Perhaps this is a case where statistical tests belie a real practical difference, as recently covered on Biofortified by David Tribe in GMO statistics Part 10: the King of Hearts is NOT equivalent to the King of England .

At this point, it is useful to consider how the experiments were carried out, what was actually measured, and how the data were collected.  In the methods section we find that the soybeans were grown in typical field plots measuring 4 rows wide and 9.1 m (30 feet) long.  The rows were on a standard soybean production spacing of 0.76 m (30 inches).  In order to provide a buffer from adjacent plots, only the center two rows were harvested.  It is not mentioned if a buffer or border zone was used between adjacent plots within a row, however, I will assume a 5 foot border here as this is similar to standard practice in such studies and does not greatly affect the demonstration given here.  This leaves us with an effective plot size of 60 inches x 25 feet or 11.613 m².  Through the magic of metric conversion, it turns out that the yield numbers reported in Table 5 also represent the units of 100 g per square meter.  This, of course, leads us to plot level yields. For the example above, the difference in varietal groups, 20 per square meter, is equivalent to 232.3 g per plot.  Conveniently, the authors have also supplied us with seed size information in Table 5, thus, assuming an average seed size of 0.144 g per seed, the difference observed was approximately 1613 seeds.

A handful of seed.

This still seemed fairly substantial, but was difficult for me to visualize.  To satisfy my curiosity, I took a trip to the local Coop and picked up some soybeans.  From these I determined that 1600 seeds is equivalent to approximately 420 ml (~1.7 cups) in volume or about what you could hold in two hands.  Using similar computations, the 2*SE used in the confidence interval above translates to about 1300 seeds or 340 ml (~1.4 cups).  A cup and a half of seed is translating to a potential difference in metric tons between varietal groups at the production level.  How is this happening?  First consider the process of plot harvesting.  The paper states that a small plot harvester was used for this purpose.  For those not familiar with these machines, they are usually scaled down, car sized versions of full size combines.  They are complete replications of larger machines having a sickle bar cutter and reel to collect plant material which is then passed through the machine where the debris and chaff is separated from the seed.  An operator sits on top controlling the direction, speed, cutter height, etc.  Often, a second person will ride or walk alongside the harvester catching the seed from each plot in a bucket or grocery sack.  Harvesting is a dirty, dusty business subject to human failures.  It is not hard to imagine the loss of a cup or two of seed over 25+ feet of plot during this process.  Seeds can be dropped, missed, shattered to the ground by the cutter/reel, or simply blown out the back with the chaff if the settings on the sieves are incorrect.  Care must also be taken to pause the combine between plots in the border zone (typically mowed down prior to harvest) in order to allow the combine to finish thrashing and processing the plot material.  Matters can be further exacerbated if the seed from each plot is run through a seed cleaner prior to weighing.  Of course, on top of all this, there is variation due to spatial location and arrangement, micro-climates, etc.  These are all sources of variation that skilled researchers strive to minimize.  The problem with scaling small plot results to full scale production levels is that the errors encountered in plot harvesting either do not occur in full scale scenarios or, when they do occur, they do not scale up proportionally.  The proportion of seed missed relative to the total amount taken in, for example, can be much higher for a small machine compared to a full size machine.  Small scale spatial variation is much more influential on small plot measurements compared to those taken across a wide area.  A common way to measure these differences is the CV statistic or coefficient of variation, which is the ratio of the response variability to the response mean.  In full scale production, this ratio is typically much smaller than the corresponding values from small scale research.  In other words, small variations can have a large influence in research data, but similarly scaled errors are not likely to occur at the field level.

To be clear here, I in no way mean to be critical of these particular researchers.  By all accounts they have carried out a set of designed studies to the best of their abilities.  I picked this article because of its relevance, convenience, and reported information.  It should be generally noted, however, that research methods have their limits in resolution and these limitations can translate to apparent large real world differences.  The interpretation of such differences should be considered with caution.

So, given these difficulties, of what use are small scale studies?  How should we interpret their findings?  While I hope I have shown that we should use caution and common sense when extrapolating to field scale levels, small scale studies have much more value than that.  Interpretation of results within a study, whether re-scaled or not, is always important.  Ranking and comparison of treatments, varieties, or other experimental effects are usually unaffected by these problems (see for example http://www.colostate.edu/depts/prc/pubs/ComparisonOfLargePlot_KL.pdf).  Small scale trials also allow researchers to control for outside influences such as environmental conditions, in order to more accurately measure experimental treatment effects.  They are indispensable for “proof of concept” experiments where the objective is to isolate a given process or test a specified hypothesis. As part of the scientific method, small scale studies play an important role helping researchers refine and define their research problems.  Often these results are then expanded to larger scale trials, thereby encompassing a wider array of potential variation and allowing better assessment of their viability in the real world.

Too often, however, the initial small scale results are picked up, extrapolated, and used by interested parties without consideration of potential problems.  Consider the conclusion drawn here by the authors:  “Glyphosate resistant sister lines yielded 5% (200 kg ha^-1) less than the non-GR sisters (GR effect).”   This conclusion has also been cited as definitive evidence of yield drag in the widely distributed report Failure to Yield by Doug Gurian-Sherman (Union of Concerned Scientists).  Yet, we have seen that this difference could have been as low as 40 kg per hectare.  Reporting or interpreting the field level extrapolations without acknowledging the variability is misleading.  A slight increase in the variability (0.02 Mg or ~160 seeds) would have led to a conclusion of non-significance.  Stating that a difference was found is fine, but stating unequivocally that a loss of 200 kg per hectare can be expected is not.  As consumers of research results we must be aware of the limitations regarding small scale trials and correctly assess the interpretations of extrapolations we make from them.

What does it mean for Organic corn yields to be 71% of the national average?  What does it mean that Organic soy yields are 66% of the national average?   One way to put this in perspective is to ask the question, “how many years ago was non-Organic ag getting the kind of yields that Organic saw in 2008?” Through a host of technical and operational advances, the yields of most crops in the developed world have been increasing steadily ever since the mid 20th century.  This is a very good thing because we have thus been able to feed a growing world population without even more land-use-conversion than has happened.  A high research investment crop like corn has yields that have been going up at a pace of 2 bushels/acre/year even for the national average. Even a low research investment crop like oats has seen yields increase by about 0.4 bushels/acre/year. So it becomes interesting to take the 2008 Organic yields and compare them to historical data about yield trends.  The graph below does this for US Soybeans and has a key that will pertain to the following illustrations. The yield data for Organic soy came from a total of 1,331 farms and 98,113 acres, so it is probably not an artifact.  That 2008 Organic yields of a nitrogen fixing crop would be like those of 29 years ago is surprising.  My guess is that it reflects higher weed competition and less moisture retention because of tillage.  Soybeans are not a pesticide-intensive crop, but perhaps some seed treatments and an occasional foliar spray account for some of the difference.

It is interesting that Organic grain corn yields are equivalent to the trend from only 21.5 years ago (2,146 farms, 143,432 acres).  In this case there is also the fertilizer difference, but my guess would be that the Organic growers get the benefit of the massive investment that has been made in Corn genetics. Organic wheat production is equivalent to that from even earlier eras – 57 years for Winter Wheat and 58 years for Spring Wheat on a national basis.  Even on a single state basis, the differential is large.  See the graphs for South Dakota Spring Wheat and New York Winter Wheat below.

(The SD Organic data comes from 92 farms and 20,867 acres)

(The NY Organic data comes from 44 farms and 2,417 acres)

Since wheat is a relatively low input crop, the difference is probably a function of fertilizer efficiency, weed competition, and moisture loss during tillage.

The crops listed above have less than 1% Organic acres and often far less.  However, the same time equivalents are seen for the row crops that have a more significant Organic share.

(Organic Flax is 4.1% of the US total,85 farms, 13,958 acres)

(Organic Oats are 2.94% of the US total, 1,040 farms, 41,016 acres)

(Organic Barley is 1.25% of the US total, 578 farms, 47,227 acres)

As we enter into a new round of rising global food prices, the idea of a production system that effectively eliminates decades of of productivity gain is not attractive.  Organic row cropping is unlikely to ever be employed on a significant acreage, and from a food supply perspective, this is a good thing.

There are additional crops and state-level examples available here (also embedded below).  Graphs by Steve Savage from USDA NASS data.  You are welcome to comment here or to email me at feedback.sdsavage@gmail.com

A Detailed Analysis of US Organic Crops

The director of the Food & Agriculture program at UCS Margaret Mellon, graciously agreed to do an interview with me while at MOSES. This immediately followed her keynote speech, which I referred to in a couple of my questions. (might be required watching if you want to appreciate it fully.) For instance, she excluded mentioning commercialized GE traits that were not Bt and herbicide resistance, trying to say that that is all there was. I also asked her about Golden Rice, knowing full well that she was a critic of it in its early days. What is the position of the UCS on it today? I also asked about Failure to Yield, and how it was that so many people seemed to think that it concluded that there was either no increase in yield due to genetic engineering, or that the opposite was true. (3-4% estimated increase in yield due to Bt) I ended by saying that although there are a few things that I disagree with the UCS on, they are doing a better job of being critics of GE than pretty much anyone else. And then I expressed something about peer review… and something happened! I thought about editing it to put the goodbye’s at the end, but you know what, I decided that the flow of conversation should be preserved how ever it turned out.

Have a listen, and let me know what you think! (Music by the eminent Carl Winter)

Over the last 15 years, agriculture has been changing technologically at an amazing pace. It is something that is truly fun to look back at and realize where we have come. As a producer of corn, soybeans, wheat, seed corn, and popcorn over many of those years it has truly changed what we are able to do and what we will be able to do in the future.

Equipment technology has created a way for us to be able to be better stewards of our ground and resources. Biotechnology has allowed us to push the food, feed, and fuel production to levels that only a few short years ago, many people would not have thought possible. Plus, we are utilizing fertilizer at a better rate. We are reducing our need for irrigation, in irrigated crop production. We are using fewer and fewer pesticides, which not only allows for a healthier product but also for cleaner natural resources like streams and drinking water.

For the farmer, this new wave of biotechnology, has allowed him to plant sooner and get over more acres faster. It also allows for a crop that can remain in the field in good condition longer. It is also allowing for new “green” technologies to come along with the feedstocks from the field being used for future cellulosic ethanol production and for helping coal fired electric plants to create a cleaner energy as well. All this is possible because of the healthy plants that biotechnology is allowing us to have. A plant that can protect itself, will be stronger then the plant that isn’t. Whether that protection is from in field pests or whether that is from the plant being able to be resistant to certain herbicides, it all helps in the final standability and yieldability of the crop that is planted.

Farmers love to plant biotech corn and soybeans. According to the USDA June 2009 Acreage report, US farmers planted 85% of their corn to biotech hybrids which was up from 80% in 2008. They also planted 91% of their soybean acres to biotech which was down 1% from 2008. Farmers have seen the value of these crops and are willing to plant them.

This doesn’t mean there doesn’t need to be more work done. Seed companies are going to have to realize that even though farmers are willing to plant biotech hybrids and varieties, they will start decreasing biotech acres, especially in “multi-stacked traits”, if they do not maintain an acceptable final yield. At the end of the day, farmers want yield. It is the final measuring stick of what the year was like.

As we move forward, we will need to find the way to feed an ever growing world. With population projections of 9 billion by 2030-2050, biotechnology is going to have to be the key to making sure the world has a plentiful, healthy, affordable food supply. And we, as farmers, will continue to plant it.

Brandon Hunnicutt farms in South Central Nebraska with his dad, brother, and cousin. They raise corn, soybeans and popcorn.  All their corn and soybeans contain some aspect of biotechnology in them, except for the popcorn.  Brandon has been involved with defending biotechnology and promoting throughout the years and currently serves as President of the Nebraska Corn Growers Association.