Sunday, January 14, 2018

Why Study Plant Breeding and Genetics?

A really nice piece in the Huffington Post by Kevin Folta evangelizing about future careers in plant breeding (link).

I like the way he describes genetic markers and marker assisted selection:

"The trick is to use DNA-based landmarks that physically travel through generations in parallel with the trait of interest. A simple DNA test, much like those used in forensic science, tells a breeder if a seedling has a high likelihood of presenting a coveted trait upon maturation. These tests allow breeders new ways to identify the most relevant parent plants, and eliminate non-candidate offspring at the seedling stage, long before valuable time, labor, acreage, fertilizers, and water are invested."

When I was an undergraduate senior, we had a speaker working on their PhD from UCLA visit WKU to discuss their work in quantitative genetics. That was the first time I realized that there was lots of opportunity and very exciting work that could be done outside the lab, as genetics and crop improvement can be as much a statistical and quantitative science as a lab science. In other words, not all of the cool jobs were being taken by PhDs in molecular biology. As Kevin states:

"While mastery of molecular biology techniques is not required, modern breeders need to be conversant in the technologies, and excited to integrate with collaborators. A modern breeder will have strong skills in statistics, and even computational prowess."

This sounds a lot like a data scientist with domain expertise in plant breeding and genetics. While I did not pursue a career in plant breeding, I have leveraged my quantitative training in agronomy including experimental design and statistical genetics blended with econometrics and programming to carve out a career in data science. From this angle there seems to be tremendous opportunities with the convergence of big data and genomics.

Think about the combination of genomic markers, plant 'wearable' sensors, remote sensing, and other precision ag data sources. Very exciting work for all involved.

Dr. Folta is an excellent evangelist for crop genetic improvement and science communication.  I highly recommend checking out his podcast Talking Biotech where each week he or Dr. Paul Vincelli from the University of Kentucky discuss agricultural and medical biotechnology.

Related: QTL Analysis and Marker Assisted Selection 

Sunday, December 31, 2017

Herbicide Resistance and GE Crops - Thinking like an economist

In a recent issue of Weed Science, Andrew Kniss investigates the relationship between herbicide resistance in weeds and adoption of genetically engineered crops.

There is a popular story with an anti-gmo theme that holds that genetically modified crops tolerant to roundup (glyphosate) herbicide have encouraged excessive levels of use. This has lead to a build up of roundup resistant weeds. In some sense, this paper may provide some evidence in favor of that story. However, I think there is a tendency among critics to extrapolate further that GMOs lead to higher levels of weed resistance (in general). The storyline does not make a distinction between weed resistance in general and specific resistance to roundup.

What this paper does indicate is that there are to some degree externality mitigating aspects of glyphosate tolerant crops. (not exactly but somewhat like the positive externalities we have seen with Bt crops). As stated in the abstract, "Increased glyphosate use in cotton and soybean largely displaced herbicides that are more likely to select for herbicide-resistant weeds, which at least partially mitigated the impact of reduced herbicide diversity....the evolution of new glyphosate-resistant weed species as a function of area sprayed has remained relatively low compared with several other commonly used herbicide SOAs."

This paper definitely provides data and evidence contrary to some of the popular stories condemning biotechnology. Stories that don't look at crop production from a more comprehensive systems based viewpoint miss these nuances. appears that thinking like an economist (systematically considering intended and unintended consequences) has a lot to lend to the nuances of herbicide resistance.

More from the Abstract:

 "adoption of GE corn varieties did not reduce herbicide diversity, and therefore likely did not increase selection pressure for herbicide-resistant weeds in that crop. Adoption of GE herbicide-resistant varieties substantially reduced herbicide diversity in cotton and soybean. Increased glyphosate use in cotton and soybean largely displaced herbicides that are more likely to select for herbicide-resistant weeds, which at least partially mitigated the impact of reduced herbicide diversity. The overall rate of newly confirmed herbicide-resistant weed species to all herbicide sites of action (SOAs) has slowed in the United States since 2005. Although the number of glyphosate-resistant weeds has increased since 1998, the evolution of new glyphosate-resistant weed species as a function of area sprayed has remained relatively low compared with several other commonly used herbicide SOAs."

Link and Citation:

Kniss, A. (2017). Genetically Engineered Herbicide-Resistant Crops and Herbicide-Resistant Weed Evolution in the United States. Weed Science, 1-14. doi:10.1017/wsc.2017.70

See also: 
Game Theoretic Analysis of Bt Resistance
Positive Externalities of Biotech Bt Traits on Non-Biotech Crops and Non Target Insects
Environmental and Health Effects of Bt Cotton
Choices Magazine - Herbicide Resistance

Monday, December 11, 2017

Environmental and Health Effects of Bt Cotton

Kouser, S., Qaim, M., Impact of Bt cotton on pesticide poisoning in smallholder agriculture: A panel data analysis,Ecol. Econ. (2011), doi:10.1016/j.ecolecon.2011.06.008

"While substantial research on the productivity and profit effects of Bt cotton has been carried out recently, the economic evaluation of positive and negative externalities has received much less attention. Here, we focus on farmer health impacts resulting from Bt-related changes in chemical pesticide use. Previous studies have documented that Bt cotton has reduced the problem of pesticide poisoning in developing countries, but they have failed to account for unobserved heterogeneity between technology adopters and non-adopters. We use unique panel survey data from India to estimate unbiased effects and their developments over time. Bt cotton has reduced pesticide applications by 50%, with the largest reductions of 70% occurring in the most toxic types of chemicals. Results of fixed-effects Poisson models confirm that Bt has notably reduced the incidence of acute pesticide poisoning among cotton growers. These effects have become more pronounced with increasing technology adoption rates. Bt cotton now helps to avoid several million cases of pesticide poisoning in India every year, which also entails sizeable health cost savings." 

Tuesday, December 05, 2017

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Recently I was reading an artcile, "The big Washington food fight" in Politico discussing challenges facing bringing diverse interests and perspectives on food issues under one roof through the Grocery Manufacturers association.

There are a couple things influencing my thinking about this.....the idea that voters and consumers may have systemic biases in their knowledge and preferences in general and specifically about food and technology. The other thing is  related to recent research showing a divergence between public perception of science driven by political leaning....a divergence that widens *with* more education and science knowledge (see ).

This research was not directly related to food except for genetically engineered food . Biotech related effects were not significant in this paper, but as the article noted the data is from 2006 and perhaps biotech was not nearly as politicized or polarized as it would be reflected in more recent data.

So in this context, what does it mean to say 'the customer is always right' and how do you give the customer what they want?

For instance, sustainable food seems to be high on the list of priorities. However, there are plenty of cases where the most sustainable technology is completely rejected by some segments. I'm thinking here of rBST, various aspects of biotechnology, even processing mechanics like finely textured beef. These are all examples where scientifically, you can produce more food using fewer resources and have a lower carbon footprint.

There seem to be two dominant approaches or paradigms by food companies for dealing with this.

One approach is going all in with the 'negative' or 'free from' labeling regardless of science. This paradigm feigns or fakes transparency in the sense it acknowledges consumer preferences related to knowing 'what is in their food' but adds lots of confusion about substantial differences related to food safety and sustainability. This group is more likely to engage in negative advertising (think Chipotle) and lobbying for regulations related to food labeling requirements (think Vermont). The other paradigm takes a 'less is more' approach in terms of honest disclosure about these technologies.

Production agriculture is caught in the middle. Whichever paradigm becomes the most dominant (both in the marketplace and the ballot box) I fear will determine the fate of the kinds of crops farmers grow and technologies they have access too, types of products we see on the shelves, and the potential for healthier and more environmentally sustainable solutions to challenging worldwide problems.

See also: Food with Integrity is Catching On

Wednesday, November 29, 2017

Polarization of Controversial Science and Limitations of Science Literacy

I recently reviewed a PNAS article titled "Individuals with greater science literacy and education have more polarized beliefs on controversial science topics". This was interesting along a couple of dimensions.

For one this is similar to research I was working on during my masters degree examining interrelationships between religous, science, and other beliefs and perceptions of genetically engineered foods. However this work had much better data.

The primary finding was that when it comes to certain beliefs about things like climate change, stem cell research, or evolution there are differences influenced largely by politics or religion. This polarization is actually increased as levels of education and science literacy are raised - "Individuals with greater education, science education, and science literacy display more polarized beliefs on these issues."

The authors looked at these effects in relation to genetically modified foods, but results were non-significant. This may be explained by the fact that the data source was ten years old (2006) and perhaps this issue had not become quite as polarized in a political sense like current data may suggest. This begs the question or the hypothesis proposed in the conclusion that "Understanding these mechanisms can guide science communication so that the evidence gets through before it no longer matters" i.e. perhaps before it becomes polarized by political priors.

This is really significant in an age where science literacy is a concern (right that's one reason we just had a march for science). This will ultimately influence the types of products we see on the shelves and the potential for healthier and more environmentally sustainable solutions to challenging worldwide problems.

From an econometric sense,  the polarization was captured in a quantitative sense via interaction terms in logistic regression. Predicted or fitted values were used to plot and visualize the interactions, sidestepping the hassle of attempting to discuss or interpret the estimates from the interaction terms in a statistical sense. Output was provided in the paper and an appendix.

I also would be interested to know more about any related work that may be looking at some of these factors as latent constructs in a structural equation context. 

See also: Voter Irrationality and Systematic Bias

Sunday, October 15, 2017

Endogenous Growth Models and Stagnation in Agricultural Innovation

See also: Ideas and Research Productivity in Agriculture 

From: Big Ideas Are Getting Harder to Find - Stanford Business Insights:

"big ideas are getting harder and harder to find, and innovations have become increasingly massive and costly endeavors, according to new research from economists at the Stanford Institute for Economic Policy Research. As a result, tremendous continual increases in research and development will be needed to sustain even today’s low rate of economic growth." 

One of the key equations in the paper relates the number of researchers working in an area to the number of new ideas: research productivity = (A*/A)/S(t) = # new ideas / # researchers One particular area they looked at was with productivity in agriculture, measuring ideas as crop yields.

 “For instance, to measure productivity in agriculture, the study’s co-authors used crop yields of corn, soybeans, wheat and cotton and compared them against research expenditures directed at improving yields, including cross-breeding, bioengineering, crop protection and maintenance....On average, research productivity in agriculture fell by about 4% to 6% per year, the study found....Research productivity is simply the ratio of average yield growth divided by the number of researchers." 

They were careful to tease out research related to non-yield related traits. However, is 'yield' always the best way to measure productivity? Maybe it is a good proxy, but it looks like their data really starts in the 60's....about the time when corn seed was being transitioned away from double cross to single cross hybrids, which were more uniform and higher yielding. In terms of having a large marginal impact on yield, that seems like it could be a tough act to follow, even 40+ years later (its not like we will rediscover hybrid vigor or heterosis) no matter how many PhD's we throw at increasing yield. Other developments in crop improvement may in fact be inflating the denominator in the equation above. The convergence of big data and genomics has opened the door to numerous potential lines of research related to crop improvement, that although may only marginally impact yield provide other significant technological benefits to growers, consumers, and the environment. This would be important, even if yield were constant.

Advances in big data and genomics may have resulted in many more possible needles to search for in a much greater number of haystacks than before. Each crop has a different genome and a seemingly infinite number of biochemical pathways that could be of interest. Then there is the micro-biome and who knows what is next. We just need way more researchers than before.

 I'm not sure how much this applies in other fields, and in some ways this could be making the authors' point....look at all the lines and directions of research and scientists pursuing them vs. actual improvements in crop yields. But again, even if yield were constant, for every bushel of corn/soy produced today, how much have we improved with regard to CO2 reduction, energy use, erosion/leaching, reduction in toxic chemical exposure (thanks largely to roundup and Bt traits), and biodiversity (in terms of non-pest targets)?

Yield just seems to be one outcome among many of importance to creating sustainable development in the agriculture space (although it is important enough to sustainability that we certainly would not want to revert back to older technologies, genetics, and chemistries as some have argued). It would be interesting to see this modeled with traits or variety patents or total hybrids as a measure of new ideas.


Are Ideas Getting Harder to Find? By Nicholas A. Bloom, Charles I. Jones, John Van Reenen, Michael Webb. September 2017Working Paper No. 3592

Modern Corn and Soybean Production. Rober G. Hoeft, Emerson D. Nafziger, Richard R. Johnson, and Samuel R. Aldrich. MCSP. First Edition. 2000.

Environmental impacts of genetically modified (GM) crop use 1996–2015: Impacts on pesticide use and carbon emissions. Graham Brookes & Peter Barfoot. GM Crops & Food Vol. 8 Iss.2, 2017 Genetically Engineered Crops: Has Adoption Reduced Pesticide Use?

Agricultural Outlook ERS/USDA Aug 2000 Greenhouse gas mitigation by agricultural intensification Jennifer A. Burneya,Steven J. Davisc, and David B. Lobella.PNAS June 29, 2010 vol. 107 no. 26 12052-12057

Friday, September 01, 2017

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

In the Myth of the Rational Voter: Why Democracies Choose Bad Policies, Bryan Kaplan discusses issues related to the median voter theorem and systematic biases by voters.

One interesting concept he discusses is the miracle of aggregation. According to the miracle of aggregation democracies can make decisions as if all were well informed. If we assume that less informed voters make random mistakes,  errors will cancel and the votes that matter will be the informed ones. The well informed median voter determines the outcome.

This all breaks down if the most informed voters make systematic mistakes. In that case the median preference becomes biased away from the optimal policy. But why would well informed voters make systematic mistakes?

Sometimes our values and views are part of who we are. Believing certain things gives people higher levels of utility. They let preferences drive beliefs over evidence. To entertain information or evidence to the contrary would upset preferences and lower utility. To quote Caplan:

"letting emotions or ideology corrupt thinking is an easy way to satisfy such preferences"

He also quotes Lebon:

"the masses have never thirsted after the truth, they turn aside from evidence that is not to their taste...whoever can supply them with illusions is easily their master; whoever attempts to destroy their illusions is always their victim"

This idea of preferences driving beliefs explains a lot. For instance, the election of demagogues. There are clear benefits to be reaped in customizing political platforms and media content that feeds into the preferences of these different segments of the population. The media capitalizes on that at the expense of actually informing the electorate. So do politicians and pundits.

This also may explain the explosion of growth in organic, natural, hormone free and other niche food markets.  Or the popular support for GMO labeling initiatives despite the science behind both safety and environmental benefits of biotechnology.

All of these are cases where acceptance of scientific evidence should potentially change opinions and behavior as it relates to food and agriculture. However to change those opinions and choices would be to drastically upset the preferences of a number of consumers. This makes it hard for those in agriculture and science communication trying to help the public navigate the complex world of modern agriculture. It also makes it hard for companies, wanting to do the right thing, to make a stand for science (i.e. by not going down the non-GMO/hormone/gluten free negative labeling route).

For instance, what if a t-shirt manufacturer wanted to promote the use of Bt cotton in their products on the basis of a reduction in use of toxic pesticides and improved insect biodiversity? Or what if a food company wanted to promote their dairy products for having a lower carbon footprint due to rBST? Taking this position would likely upset the illusions and preferences held dearly by many consumers. Noone wants to become 'their victim' to borrow from Lebon. Just ask Monsanto or BPI, the company behind finely textured beef. (however ABC eventually paid a price for feeding the masses the pink slime 'illusion'). In response, we don't see these kinds of promotions, and to the contrary we actually see companies removing these technologies from their product lines (and advertising the fact!).

Due to systematic bias in relation to food and technology, the median of voters' preference distribution will be biased toward more restrictive regulations than is scientifically appropriate. This will influence the types of products we see on the shelves and the potential for healthier and more environmentally sustainable solutions to challenging worldwide problems.


The Myth of the Rational Voter: Why Democracies Choose Bad Policies, Bryan Kaplan

The Crowd: A Study of the Popular Mind.  Gustave Le Bon.

A Meta-Analysis of Effects of Bt Cotton and Maize on Nontarget Invertebrates.Michelle Marvier, Chanel McCreedy, James Regetz, Peter Kareiva Science 8 June 2007: Vol. 316. no. 5830, pp. 1475 – 1477

Areawide Suppression of European Corn Borer with Bt Maize Reaps Savings to Non-Bt Maize Growers. Science 8 October 2010:Vol. 330. no. 6001, pp. 222 - 225 DOI: 10.1126/science.1190242W. D. Hutchison,1,* E. C. Burkness,1 P. D. Mitchell,2 R. D. Moon,1 T. W. Leslie,3 S. J. Fleischer,4 M. Abrahamson,5 K. L. Hamilton,6 K. L. Steffey,7, M. E. Gray,7 R. L. Hellmich,8 L. V. Kaster,9 T. E. Hunt,10 R. J. Wright,11 K. Pecinovsky,12 T. L. Rabaey,13 B. R. Flood,14 E. S. Raun15

The environmental impact of recombinant bovine somatotropin (rbST) use in dairy production Judith L. Capper,* Euridice Castañeda-Gutiérrez,*† Roger A. Cady,‡ and Dale E. Bauman* Proc Natl Acad Sci U S A. 2008 July 15; 105(28): 9668–9673

The environmental impact of dairy production: 1944 compared with 2007. Journal of Animal Science,Capper, J. L., Cady, R. A., Bauman, D. E. 2009; 87 (6): 2160 DOI: 10.2527/jas.2009-1781

Environmental impacts of genetically modified (GM) crop use 1996–2015: Impacts on pesticide use and carbon emissions
Graham Brookes & Peter Barfoot
GM Crops & Food Vol. 8 , Iss. 2,2017

Batista R, Saibo N, Lourenço T, Oliveira MM. Microarray analyses reveal that
plant mutagenesis may induce more transcriptomic changes than transgene
insertion. Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3640-5. doi:
10.1073/pnas.0707881105. PubMed PMID: 18303117; PubMed Central PMCID: PMC2265136

Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ, Holdsworth MJ, Shewry PR. (2006). Transgenesis has less impact on the transcriptome of wheat grain than conventional breeding. Plant Biotechnol J. 2006 Jul;4(4):369-80