Wednesday, January 3, 2018

Take 2: what I wish I'd put in my recent book chapter

I asked my wife Sarah to take a look at my recent chapter in the book "Effective Conservation Science: Data Not Dogma" and she made an excellent point: what should the take-away be? Here are the things I was hoping to convey but wasn't sufficiently clear about.

The key fact I wanted to convey is that the global agriculture situation is complex: the global land used for agriculture hit its peak in 1998 (so it's not true agricultural land is rapidly expanding around the world), BUT in some places there is a lot of agricultural expansion and/or reliance on unsustainable levels of water and nutrients. So there is good news and bad news.

Some other important facts I didn't go into in much detail:
  • In the last two decades we have been able to meet increasing demand for food through intensification (producing more food on existing lands). But going forward projected demand rises faster than what we're likely to be able to produce through intensification. So between 2030-2050 we can expect conversion to agriculture to speed up and lead to a net expansion of land used for agriculture.
  • Agriculture in many regions currently relies on unsustainable irrigation. As groundwater is depleted and crops get thirsty we can expect yields to eventually fall substantially on agricultural lands in water-scarce areas. The California drought gives us a taste of what that could look like. Note that changes in irrigation technology are likely insufficient to solve this on their own (see the summary of Richter 2017).
  • Other agricultural inputs may start to run out or at least limit improved crop yields. That could include rock phosphate for fertilizer or even nitrogen fertilizer if actions to limit climate change make it more expensive.
The key message of the piece for other scientists is: don't assume that things that seem obvious (like ag land rapidly expanding) are true, and don't assume that global data sets are reliable enough to inform policy or other action. Dig deeper! Ask questions, and look for more local data to corroborate your suspicions.

Let me know if you have other questions or suggestions!

Monday, January 1, 2018

January 2018 science journal article summary

Happy new year! Here are a handful of articles focused on global agriculture analyses, plus one with bad news on climate change, and a 2018 #MyScienceResolution. 

Cauliflower, romesco, and broccoli at the farmer's market

 I also want to pass on a cool resource Eddie Game alerted me know. It's a tool to help you figure out which journal to submit a paper to:  You enter the title and abstract of your paper and it gives you a list of appropriate journals. You may also want these tips on how to write an abstract to get found easily in Google and Google Scholar:

On to the articles!

Somehow I'd missed West et al 2014, which is a great (and very short) summary of opportunities to improve agriculture around the world. Just looking at the two figures is highly educational: Fig 1 shows the potential to increase yields on poorly performing croplands to even 50% of their potential yields (which would provide enough food for 850 million more people, while still leaving plenty of room to improve), and Fig 2 shows how much we can reduce environmental impacts of ag in key regions without reducing yields. The spatial patterns aren't surprising, but the specific numbers are highly motivating. For example, China alone produces 28% of global N2O emissions (a potent greenhouse gas). Reducing excessive nutrients and improving water efficiency of crops around the world would have a big impact, as would reducing the amount of animal products we eat and the amount of food that is wasted. Be sure to check out the supplement for more great maps.

Phalan et al 2016 tackles a tricky problem at the heart of TNC's work with ag: how can we ensure that intensification reduces conversion rather than incentivizing it through higher profits? It's under two pages, so I'd recommend just reading it. But the mechanisms they propose to boost yield and promote nature are: 1) land use zoning (specify land for ag and land for conservation, as Costa Rica did), 2) use payments, subsidies, or land taxes (e.g. a program in India where herders set aside habitat in exchange for insurance and technical assistance), 3) "spatially strategic" deployment of tech / infrastructure / ag knowledge (e.g. focusing on staple crops which have more stable demand), and 4) standards (including voluntary ones) and certification. I found this to be good food for thought about how TNC could tighten our theory of change.

Hanspach et al 2017 looks at trade-offs between food security and biodiversity for ag in the "global south" (developing countries). They surveyed 110 self-reported experts and looked for patterns. Surprisingly, while in many landscapes there were clear trade-offs between food security and biodiversity, several respondents reported other cases where the two goals were linked (either in "win-win" or "lose-lose" cases). Figure 2a shows where each landscape fell. Infrastructure, market access, and financial resources were all associated with poor biodiversity but good food security, meaning investment in intensification on its own will likely not lead to conservation outcomes. Social equity and land access were found to be necessary but not sufficient for both food and biodiversity goals. Relying on expert assessments isn't a replacement for good empirical data, but this still has useful elements for TNC to incorporate in our ag work.

Gerber et al 2016 is a global analysis of N2O emissions from croplands. The key point is that areas with very low N use and production can use much more fertilizer with relatively small increases in N2O emissions, while areas with high N excess can use a little less to get big reductions in N2O. For example, cutting N application by 5% in Shandong province (China) would reduce N2O by 9%. Be sure to check out Table 2 (N application totals and rates by country) and Fig 3 (N2O emissions per unit of N applied at a sub-national level), and fig 4 if you're interested in specific crops. Note that they used a new approach which in general predicts significantly lower emissions than other models (they go over several caveats in detail).

Zomer et al 2017 estimates that globally cropland soils could sequester 0.90-1.85 Pg C / yr (1 Pg = 1 billion metric tons) for at least 20 years. This estimate derives from how much soil C has been lost relative to historic levels, along with estimates from an earlier paper of how much sequestration can be achieved through a range of conservation practices (e.g. cover cropping, conservation tillage, rotational grazing, etc.). Table 2 and Figure 2 show where the authors see the most room for improvement (the Midwest US, India, and Europe in particular). TNC's Deborah Bossio is second author so she should be able to answer any questions you may have.

A new paper from NatureNet fellow Kyle Davis (2017) investigates the impact of changing and moving crops on existing croplands around the world to improve yield and reduce water consumption. The hypothetical optimal crop patterns consumed 14% less rainwater and 12% less "blue" water (irrigation from surface and ground), while also producing 10% more calories, 19% more protein, and other benefits. A big caveat is that this involves not only shifting what is grown where (already a big task) but also shifting how much we produce of each crop. For example, they cut production of wheat, rice, corn, and sugar in favor of more soy and tubers (like potato and sweet potato). The interesting part to me is thinking about how this approach could be used in a national land use planning exercise with more realistic constraints.

Brown & Caldeira 2017 has some bad news about climate change. They looked at several climate models and scenarios and evaluated how well they predicted the recent past (looking at 9 variables, not just temperature). They predict warming ~15% higher than currently predicted and have a narrower confident interval for predictions. For climate wonks, they note that emissions in line with the RCP 4.5 scenario are likely to produce warming previously associated with RCP 6.0. There are several important caveats in the discussion, but this nonetheless raises the urgency to take aggressive action on climate to minimize the projected impacts. Take a look at Figuyre 2 which shows the new narrower predictions in red for different emissions scenarios. You can read an overview of the paper here or a longer blog from the authors here:


I haven’t read the Pelger 2017 study it’s based on, but this blog post gave me an idea for a 2018 science resolution! Essentially students writing for a non-scientific audience found that it helped their science writing as well. So if you work as a scientist, commit to writing a blog, or talking to your friends and family about your work without making their eyes glaze over! I'm going to shoot for my next peer-reviewed article to be readable by an ordinary human being. If you need more motivation, check out this inspirational talk by Dan Rather with a vision for a revolution in science communications as a foundation for changing how we think about truth and “fake news” in society: (thanks to Laurel Saito for the link).

Brown, P. T., & Caldeira, K. (2017). Greater future global warming inferred from Earth’s recent energy budget. Nature, 552(7683), 45–50.

Davis, K. F., Rulli, M. C., Seveso, A., & D’Odorico, P. (2017). Increased food production and reduced water use through optimized crop distribution. Nature Geoscience, 10(12), 919–924.

Gerber, J. S., Carlson, K. M., Makowski, D., Mueller, N. D., Garcia de Cortazar-Atauri, I., Havlík, P., … West, P. C. (2016). Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management. Global Change Biology, 22(10), 3383–3394.

Hanspach, J., Abson, D. J., French Collier, N., Dorresteijn, I., Schultner, J., & Fischer, J. (2017). From trade-offs to synergies in food security and biodiversity conservation. Frontiers in Ecology and the Environment, 15(9), 489–494.

Phalan, B., Green, R. E., Dicks, L. V., Dotta, G., Feniuk, C., Lamb, A., … Balmford, A. (2016). How can higher-yield farming help to spare nature? Science, 351(6272), 450–451.

West, P. C., Gerber, J. S., Engstrom, P. M., Mueller, N. D., Brauman, K. A., Carlson, K. M., … Siebert, S. (2014). Leverage points for improving global food security and the environment. Science, 345(6194), 325–328.

Zomer, R. J., Bossio, D. A., Sommer, R., & Verchot, L. V. (2017). Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports, 7(1), 15554.

Friday, December 1, 2017

December science journal article summary

2008-01-26 (Editing a paper) - 31
Photo from Nic McPhee under Creative Commons license


As we approach end of year deadlines, I've been doing less science reading. I'm guessing others are in the same boat, so this is a small review. The focus this month is how we can do science in a way that leads to greater impact. Two articles are about writing science articles more clearly, two are about collaboration, and one is a call for academics to do more to promote action in sustainability. Enjoy!

Many scientists bristle at the notion that we should write more clearly. But we also hate reading articles and finding they don't deliver on what the title and abstract promises. Mensh & Kording 2017 offers 10 rules for writing more clearly. Different disciplines have different norms around structure, language, etc., but I think this is a great place to begin. Short on time? Read Table 1 (on p8) which summarizes the rules and how to know if you get it wrong. I'm excited to try this for my next paper!

Ever wonder how scientists pick authorship order, sort out who did what, and decide who's on a paper at all? If so, you may want to skim Sauermann & Haeussler 2017. It's on the long and dense side, but has some interesting insights (especially if you're a scientist making those decisions). In general, contribution statements offer more info than author order (although they can be hard to read), see Figure 4. Figure 1 shows that after the first author, the last author is most likely to have played a broad role in the paper. Finally, I was struck by the "inclusion as an author" section on page 4 which lists an international standard for being an author which is significant higher than what I usually see. Basically, this is food for thought for folks who read and/or write a lot of science papers.

As we increasingly focus on big, tough problems, collaboration across sectors is more important than ever. It's also really hard. In cross-sector projects I've worked on, differences in terminology, priorities, expertise, etc. have slowed down progress. The Bridge Collaborative is an initiative led by TNC aiming to help collaboration across environmental, development, and health sectors. Their new guidance report has insights for those of you doing this kind of cross-sector work (or aspiring to).

Bodin 2017 asks a more basic question: when and how does collaboration really make sense (with a specific focus on collaborative governance for environmental problems)? Their brief answer: "The capacity of collaborative governance to deliver sustainable solutions for any given environmental problem ranges from highly effective to essentially worthless." That may seem flippant, but he provides useful parameters to answer the question for a given context. The 1-page summary is better written than the longer version (which is broad enough to feel a bit unfocused), but the long version has details about knowledge diffusion and building functional social networks that people trying to work in this way will likely appreciate.

Keeler et al 2017 is a paper led by some folks at the Natural Capital Project (with current and former TNC co-authors) calling for academic institutions and do more to help "serve society and the planet." It's a quick and well written read, but they have five main ideas: provide training to help students become environmental leaders (not just professors), recognize the value of applied / relevant work (which is sometimes seen as inferior to basic research), move faster (accepting uncertainty and the need for iteration), make people front and center in environmental science, and shift academic structure to encourage innovation (e.g. NatCap itself is one example of academics partnering with NGOs to do rapid applied science). I often meet academics frustrated that their work isn't being put to ues more, and this gives them some ideas of changes to promote at their institutions.

Bodin, Ö. (2017). Collaborative environmental governance: Achieving collective action in social-ecological systems. Science, 357(6352), eaan1114.

Keeler, B. L., Chaplin-Kramer, R., Guerry, A. D., Addison, P. F. E., Bettigole, C., Burke, I. C., … Vira, B. (2017). Society Is Ready for a New Kind of Science—Is Academia? BioScience, 67(7).

Mensh, B., & Kording, K. P. (2017). Ten simple rules for structuring papers. PLOS Computational Biology, 13(9).

Sauermann, H., & Haeussler, C. (2017). Authorship and contribution disclosures. Science Advances, 3(11), e1700404.

Tallis H, Kreis K, Olander L, Ringler C et al. 2017. Bridge Collaborative Practitioner’s Guide: Principles and Guidance for Cross-sector Action Planning and Evidence Evaluation. Washington DC: The Nature Conservancy

Wednesday, November 1, 2017

November science journal article summary

Nihao November!

Fall sumac

I've got a good one for you this month! It's less focused than usual, but there are three key topics, plus a mix of a few others:
First, if you're about to delete this unread, please take this survey (which takes <1 minute) to let me know if you have input on how these summaries could be more useful: . Thanks to all who responded; results are summarized at the end of this email.

Second, the long-awaited "Natural Climate Solutions" paper from TNC is out. Read it: it's only 5 pages and will be highly relevant to virtually everyone working in conservation. It makes a solid case for how immediately investing in nature to reduce GHGs can buy us much-needed time to bring down emissions and invent new technology.

Third, a new book came out Oct 12: Effective Conservation Science: Data Not Dogma. It includes chapters from myself and several TNC authors, and is full of fascinating stories of how we react to science that counters conventional wisdom. I also share related articles below on how we can work through our biases.

Griscom et al 2017 (the natural climate solutions paper) packs a lot of good content in, but two things in particular excite me. First is making the case for massive rapid investment in nature: while we develop new tech and bring down emissions, we can use proven solutions like trees to buy time and make progress (see figure 2: nature could get us 37% of mitigation needs by 2030 at <$100/t CO2e / yr). We need the tech too, but nature is something that works today to bring down GHGs. Second is breaking down their top 20 options for nature-based climate mitigation into the theoretical maximum impact (about 1/2 of which would cost <$100 / t CO2e / yr), what we would need to hit Paris targets of <2 degrees C, and the subset of mitigation which is cheap (<$10/t CO2e / yr). See Figure 1 for this breakdown, which highlights that forests are absolutely critical (2/3 of cost-effective mitigation), and that the biggest opportunities for cheap mitigation are preventing forest loss (and improving forest management), improving fertilizer use on farms, and keeping peatlands intact. The forest goals rely heavily on a small reduction in grazing lands (4%). I'm leaving out lots of important details to keep this short: just read the paper. It's worth it. Read all about it (or watch videos) at

The book Effective Conservation Science: Data Not Dogma tells stories of scientists whose unconventional and inconvenient results challenge us all to broaden our thinking and consider how we respond to new information that undermines what we think we know. My chapter is around how my analysis and blog post showing that globally agriculture has been taking up a smaller footprint since 1998. You can buy the book here: and read a review of one chapter here: and read an ugly (unformatted) version of my chapter here:  

Here are three more papers on the topic of scientific bias:
In 1992 E.O. Wilson asserted that invasive species were the second greatest driver of species extinction (second only to habitat destruction). He did so without providing evidence or details behind his calculations, but this claim was rapidly repeated and taken as gospel by environmental scientists. In fact, TNC played a major role in elevating Wilson's claim by not only citing it (in a BioScience paper and related book), but adding that "scientists generally agree" with Wilson's claim (again without evidence). Chew 2015 tells the captivating story about how this happened, using clear writing, thought-provoking questions, and numerous examples of bias in language that should be neutral and scientific. He also tells us how the idea eventually became subject to critique. I have seen this phenomenon firsthand; I follow a trail of citation breadcrumbs from authors to discover a primary source with an assertion that cannot be supported by what's in the paper (e.g. a book chapter on soil by Rattan Lal). When scientists don't closely read the papers we cite (or read them at all), our biases blossom and spread. If you're interested in invasive species or how spurious claims spread, this is a great read (albeit long).

Warren et al 2017 asks how common it is for scientists to be biased with regard to invasive species: using value-laden language and favoring interpretation that emphasizes the impacts of invasive species even when the data are not clear (as exemplified by the Chew 2015 article). They found bias to be common, but also that it has been declining since a series of papers in 2004-2005 that argued against language vilifying invasive species. This paper is fairly simplistic but gets at a key nuance: even a bias which is generally true is counter-productive in science. This paper shows hope that with awareness of bias, we can make efforts to at least reduce the expression of that bias in our work.

Holman et al 2015 provides more evidence of scientific bias, and argues for the use of "blinding" when conducting research to limit the potential for bias to affect study results. This means scientists collecting data don't know whether the subjects or area they're observing is a treatment or a control. This makes it harder for preconceptions to affect measurements (whether subjective, or even "rounding" seemingly objective metrics to fit bias), and they present evidence that nonblind studies often inflate the effect of the actions being studied. If "working blind" sounds extreme to you, read my blog post about "Clever Hans" - a horse who was believed to be able to do math (but in fact was only skilled at reading when his audience believed he had the right answer):

As a final thought on bias, check out the Minasny & McBratney article in the Soil section below, which challenges a key assertion for TNC's agriculture work (that boosting soil organic matter improves water holding capacity). Read the summary below, and observe your feelings and reaction if it challenges what you believe.

Minasny & McBratney 2017 use a meta-analysis to argue against something generally believed to be true by people working on sustainable agriculture: they provide evidence that increasing soil organic matter has a relatively small effect on water holding capacity (particularly for plant-available water content). If they're right, it reduces TNC's argument that improving soil health via boosting organic matter on farms will substantially improve crop resilience to drought. The authors note that soils that benefit most from increases in organic matter are sandy and very low in organic matter to begin (both of which make sense). They have a good discussion of limitations of their analysis, in particular the fact that they focused only on soil and not what's above it. Cover crops and crop residue / stubble are likely to add to the small benefits shown via soil. There is also a lot of nuance and potential to reframe their analysis in a way that could show larger benefits. At the same time, recognizing that most of us have a bias on this topic, this is a useful reminder to check our assumptions about both the efficacy of practices and the key mode of action and metrics that we should focus on. The authors led a key paper on the "4 per mille" initiative on boosting soil carbon, so are not hostile to the notion of boosting soil carbon. You can read a news article about this one here:

Remember as a kid how many bugs would get splattered on the windshield of your car? Ever notice there are less now? A recent study (Hallman et al 2017) indicates this is a real phenomenon, with dramatic declines in flying insects. The authors tracked the total biomass of insects at 63 locations within nature preserves in Germany; from 1989 to 2016 biomass plummeted by 76%. They sampled several habitat types and found consistent declines. It's alarming to see this within protected areas, although the authors note virtually all are surrounded by agriculture. That could both pull insects away from natural areas, and provide more pesticide drift into the natural areas. Other studies have shown major insect declines, but none this severe, and I don't know of others within protected areas.

I've been pondering what we think we know and how to communicate thorny issues (as per data not dogma). I'd recommend a book I'm reading: "Do I make myself clear?" by Harold Evans, which is helping me. While not for scientists, I saw my writing sins laid bare in this book. I'm looking to simplify my writing in science papers, and to better talk about science in general. I have a long way to go! I'm working on summarizing key lessons amidst all of the stories in the book. One useful tool is the Hemingway app, which helps you identify problematic text and how to improve it:

As noted in my August 2017 review, neonicotinoids (neonics for short) are a class of insecticide currently under close scrutiny for impacts on bees. Mitchell et al 2017 found neonics in 75% of the 198 honey samples they tested, although mostly at very low levels. All neonics were at safe levels for humans, and most were at levels considered safe for bees. This is useful to show both that these pesticides are very common, that they are being consumed by bees, and that they often occur in concert with other neonics (all of which is concerning). But the reporting (and fundraising) around this has glossed over the very low levels. While 48% of samples had total neonic levels over a very conservative threshold for potential harm to bees (0.1 ng / g, a more reasonable (still likely conservative, albeit arbitrary) threshold of 2 ng / g was only detected in 8% of samples. The honey was collected via "citizen science"; the researchers asked colleagues, friends, and family to bring them honey produced in a known location. That also raises the question of whether or not these honey samples are typical.

I'm guessing the folks who didn't respond would have had more critical feedback, but overall here's what I learned from the ~40 respondents:
  • 90% of you usually at least skim these for relevant content
  • 90% of you found the level of detail about right (including some who said they could use less detail but were content to tolerate the current length), the rest found them too long.
  • Several folks especially liked both grouping articles by topic, and focusing each month primarily on one topic. I'll endeavor to keep that up, despite failing to do so this month.
Some opportunities to improve I'll be mulling over:
  • Set up a monthly journal club to talk about the papers (this one is already in the works, stay tuned for more info and let me know if you would like to provide input)
  • Make a lead theme more clear up front and include a short summary of the entire email
  • Tie each article to TNC's shared conservation agenda
  • Each quarter send a list of bullets of main issues under debate in conservation to encourage us to follow up
Chew, M. K. (2015). Ecologists, Environmentalists, Experts, and the Invasion of the “Second Greatest Threat.” International Review of Environmental History, 1, 7–41. Retrieved from 

Evans, H. (2017). Do I make myself clear? Why writing well matters. Little, Brown, and Company: New York, NY. 416p.

Fisher, J. R. B. (2017). Global agricultural expansion – the sky isn’t falling (yet). In Kareiva, P., Silliman, B, and Marvier, M. (Eds), Effective Conservation Science: Data not Dogma. Oxford University Press, Oxford, UK, pages 73-79.

Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., … Fargione, J. (2017). Natural Climate Solutions. Proceedings of the National Academy of Sciences, (6), 11–12.

Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., … de Kroon, H. (2017). More than 75 percent decline over 27 years in total flying insect biomass in protected areas. Plos One, 12(10), e0185809.

Holman, L., Head, M. L., Lanfear, R., & Jennions, M. D. (2015). Evidence of experimental bias in the life sciences: Why we need blind data recording. PLoS Biology, 13(7), 1–12.

Minasny, B., & Mcbratney, A. B. (2017). Limited effect of organic matter on soil available water capacity. European Journal of Soil Science, (2000), 1–9.

Mitchell, E. A. D., Mulhauser, B., Mulot, M., & Aebi, A. (2017). A worldwide survey of neonicotinoids in honey. Science, 111(October), 109–111.

Warren, R. J., King, J. R., Tarsa, C., Haas, B., & Henderson, J. (2017). A systematic review of context bias in invasion biology. PLoS ONE, 12(8), 1–12.

Wednesday, October 18, 2017

New book: "Effective Conservation Science: Data not Dogma"

I have a chapter in a new book that was just published:
Effective Conservation Science: Data not Dogma (click the link to read more and buy it if you like).

The book has a really cool theme: what happens when we find evidence that contradicts what "everyone knows"? How do people react, and how do we resolve the disconnect?

In my case, while doing research for another book, I discovered that global land used for agriculture had actually been declining since 1998, despite the narrative that ag was rapidly expanding around the world.

I got a lot of pushback when I blogged about it a few years ago, and this chapter tells the story of what I found, what the reaction was, and what it all means going forward.

I really think the book is a great read based on the several chapters I've read so far, so if you're interested I encourage you to buy it. If you're not sure, you can read a review of a different chapter, or read the ugly (unformatted) version of my chapter here:
Global agricultural expansion: the sky isn't falling (yet)

Here's a map showing where around the world agriculture IS expanding, and where it's contracting:

Sunday, October 1, 2017

October science journal article summary


Here's some science to make your October outstanding! I have a 3-question survey about these summaries that should take a minute or less to answer; please consider taking it (or emailing me if you prefer). I'm trying to get a sense of how often people read them, whether the level of detail is right or not, and get any other feedback people have:

The focus of this review is on reducing the impacts of animal agriculture (especially cattle). For anyone who missed my June 2016 review, I'll re-recommend Herrero et al 2016 as a fantastic overview of the potential for improving GHG emissions in the livestock sector. Their top picks were improved feed digestibility (including more cereals, distiller's grains, etc. to supplement or replace grass and hay), feed additives, avoiding land use change through intensification, and carbon sequestration from better grazing.

There a lot of discussion on how to shrink the high carbon footprint of cattle (beef and dairy), and one focal area is on enteric methane (mainly cow burps). Hristov et al 2015 is a study showing that a feed additive (3NOP) was able to reduce dairy methane production by ~30% (with oddly similar impact regardless of the dose) without substantially affecting milk yield (although it increased weight gain by 80% over the 12 week period). This is a relatively small study (48 cows) and it would be see what the impact is throughout the life of dairy cattle (as often gut flora adapts to these kinds of additives over time), but this combined with a couple of similar studies they cite are exciting enough to be worth recommending more trails and pilots.

Kinley et al 2016 is a similar paper looking at a different feed additive (this one based on seaweed). This is only an in vitro study (messing with petri dishes rather than actual cows) but they found adding doses of 2% or greater to a grass diet virtually eliminated methane production. Note that a very similar paper (Machado et al 2015) had similar results, but with two key differences: they saw a strong benefit at 1% dose (where Kinley had a weaker response at that dose), and they also saw some side effects that could impact cattle health at 2% and above. While this was only in vitro and only tested for 3 days, it's still worth investigating and comparing to 3NOP for efficacy and potential positive and negative side effects.

Swain et al 2018 (it came out online early) is a paper from the Breakthrough Institute arguing for a shift to more intensive livestock systems, especially switching from grass-finished to grain-finished beef. They make a number of good points; it's not really debatable that feedlot cattle require less land and time, and most scientists agree the GHG emissions are lower per unit of meat in feedlot systems as well. They briefly discuss some of the potential tradeoffs including animal welfare and antibiotic use and how they might be addressed, and the issue of how to ensure that higher productivity actually leads to land sparing as opposed to driving more habitat conversion. While a good read, there are a few things they don't cover that should also be part of the conversation. One is that in some cases we may actually prefer a high land use footprint, if the grazing lands are high-quality natural grasslands that would otherwise be converted to other uses. But it's still a worthwhile read with good food for thought.

Odadi et al. 2017 (authored by a NatureNet fellow, along with TNC's Joe Fargione) looks at the impact of planned grazing (focus on intensive rotational grazing, but including several other factors) on a variety of outcomes in Kenya. They found substantial improvements in vegetation (cover, species richness and diversity, etc.), presence and richness of wildlife, cattle weight gain during dry periods when they were in poor condition, and the amount of cattle supported per unit of land area. The cool thing to highlight here is that they were able to improve cattle condition as well as wildlife habitat. One critical ingredient to success was more active involvement from pastoralists; this does mean more effort for them but it appears that the benefits make it worth promoting.


Remember the synthesis of evidence for how several agricultural practices impact a suite of outcomes that Rodd Kelsey led (I sent it out last month)? This month I'm sharing one more 2-page document, which has a great chart summarizing the evidence for each of the practices evaluated on each of the outcomes. If you want to explore more deeply, you can do so at[]=22

Herrero, M., Conant, R., Havlik, P., Hristov, A. N., Smith, P., Gerber, P., … Thornton, P. K. (2016). Greenhouse gas mitigation potentials in the livestock sector. Nature Climate Change, 6(May), 452–461.

Hristov, A. N., Oh, J., Giallongo, F., Frederick, T. W., Harper, M. T., Weeks, H. L., … Duval, S. (2015). An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production. Proceedings of the National Academy of Sciences of the United States of America, 112(34), 10663–10668.

Kinley, R. D., De Nys, R., Vucko, M. J., MacHado, L., & Tomkins, N. W. (2016). The red macroalgae Asparagopsis taxiformis is a potent natural antimethanogenic that reduces methane production during in vitro fermentation with rumen fluid. Animal Production Science, 56(3), 282–289.

Machado, L., Magnusson, M., Paul, N. A., Kinley, R., de Nys, R., & Tomkins, N. (2016). Dose-response effects of Asparagopsis taxiformis and Oedogonium sp. on in vitro fermentation and methane production. Journal of Applied Phycology, 28(2), 1443–1452.

Odadi, W. O., Fargione, J., & Rubenstein, D. I. (2017). Vegetation, Wildlife, and Livestock Responses to Planned Grazing Management in an African Pastoral Landscape. Land Degradation and Development, (March).

Swain, M., Blomqvist, L., McNamara, J., & Ripple, W. J. (2018). Reducing the environmental impact of global diets. Science of the Total Environment, 610–611, 1207–1209.

Friday, September 1, 2017

September science journal article summary

Kein Bett im Maisfeld Photo by Torsten Flammiger under Creative Commons

This month I've got a number of good papers, but I want to highlight two in particular. First, you know how hard it is to keep track of science, and Rodd Kelsey has just put out a book that summarizes the impacts of 20 different agricultural management practices (focused on Mediterranean climates). This will be a great reference for anyone working on ag. The other is a paper of mine that just came out. It's an analysis of the Camboriú water fund in Brazil with broadly useful suggestions on how to pick the right data source in a given context ("how much data is enough").

My new paper (Fisher et al 2017) is essentially an analysis for the Camboriú water fund of how the choice of input data impacts the decision you'd make as a result. We compared a relatively quick analysis on free 30m resolution data to a more complex analysis using 1m data. I'd recommend most people skip most of the paper (which is quite technical) and just start with the two blogs I wrote about it (an overview at, and a more technical one for people working with spatial data at In short, we found that the simpler analysis would have led us to the same decision in Brazil, but that for other water funds the choice of data could be critical (as the ROI was over 1 with 1m data, but below 1 with 30m data). Table 5 and the discussion have several guidelines to consider in how to select whether relatively low or high resolution data is most appropriate for a given context. I'm pretty excited about that part of the paper, and I'd really welcome feedback on it from anyone so inclined.

Rodd Kelsey and his team just released a synopsis of the evidence synthesis they did for 20 different ag management practices and their effect on several ecosystem services (Shackelford et al 2017). It's a reference for finding information on a practice of interest, and a peer-reviewed version is forthcoming which will include expert assessment and scoring of the evidence as well. Everything in this book is also available and searchable online at under the Mediterranean Farmlands set of practices. I think this is a big step forward for TNC, especially for agriculture, but also as an example of what stepping up our evidence as part of CbD 2.0 can look like. Contact Rodd with any questions or comments you have.

Snyder 2017 is a useful reference with a lot of data on nutrient losses in the Mississippi River Basin and hypoxia in the Gulf of Mexico (as well as some global info, see Fig. 16). They show that overall the amount of nitrogen exported to the Gulf has trended down over the last 35 years (although with tons of annual variation, largely due to changes in precipitation) but phosphorous has trended up (Figs 10-13). Hypoxia in the Gulf is expected to lag way behind stream nutrient levels, and again is highly variable based on several climate variables each year, but Figure 14 shows that the average hypoxic zone from 2010-2015 is almost triple the size of the recently revised target for 2035 (<5,000 km2). So while it's not a surprise, this is more evidence that we really need to step up our game, especially given the projected impacts of climate change (see Sinha et al 2017 below) which will make our task significantly harder.

You've probably heard about "payment for ecosystem services" (PES) where a land owner / manager is paid to do something (e.g. change how they farm) or to NOT do something (e.g. not cutting down trees they would otherwise clear). Until now there hadn't been a robust, fully randomized experiment to test how well they work. Jayachandran et al 2017 is a study looking at 121 villages in Uganda, half of which were paid for two years to not cut trees (with payments tied to area of intact forest as measured via remote sensing). The good news is that overall it worked well: participating villages deforested half as much as control (4.2% forest loss vs 9.1% loss), and there didn't seem to be leakage (cutting down other neighboring forests). It also appeared to be cost-effective (based on assumptions about how villagers would respond after the 2-year program ended). Remaining questions: what would happen under a long-term version of this program (or if it was actually abandoned after 2 years), could the program be adjusted to reduce deforestation even more from participants, how can program overhead costs (1/2 of total) be cut, and could there be side-effects on biodiversity or humans? The bigger question is whether or not this would scale, the authors note that only 1/3 of people they approached agreed to participate, that if scaled up nationally it could impact timber prices which could cause some rebound, and that weaker enforcement or monitoring in a large-scale effort could impact efficacy. The calculations on costs and benefits in particular are a bit tricky, let me know if you'd like to discuss further. I'd recommend only people involved in PES schemes actually read the paper (and a longer version I can share), for others check out and/or for a good overview of the paper.

Several of you sent me articles about Harwatt et al 2017, which calculated how much impact replacing all beef consumed in the US with beans would have on climate change. Note that this paper doesn't model a real world scenario, rather it performs a very simple calculation by first calculating the GHG impact of switching from beef to beans (they ran it two ways, keeping total calories the same, and keeping protein intake the same), and then comparing that to the US 2020 GHG reduction targets under the Paris agreement. They found that this switch could meet between 46-75% of the US obligations (which is a lot), based almost entirely on Nijdam et al 2012 which provided the data on emissions. I have a few concerns about the methods of this paper; I don't see the US-specific data in the Nijdam paper they cite for it, and this paper's assertion that emissions in the US per kg of beef are almost double a global average appears contrary to the underlying paper's findings that intensive systems have much lower emissions. I'm guessing this may be due to inappropriately weighting culled dairy in Europe but I can't tell b/c they don't provide the detail. So while the general idea (we should eat more beans and less beef to fight climate change) is sound, I wouldn't trust these specific numbers.

Kim et al 2017 argues that especially warm weather in the Arctic has led to reduced vegetation growth (from forests to crops) in Canada and some of the U.S., primarily via colder temperatures (as well as less rain in South-central U.S.). In the U.S. crop yields were 1-4% lower on average as a result, up to 20% lower for corn yields in Texas (but with the majority of states unaffected, and only a few showing a very strong relationship). As with much of climatology, this is more about concerning patterns than ironclad proof of trouble ahead. But it makes a good point about some of the complex and unexpected impacts of climate change for us to watch out for.
There's a news article about the paper here: and you can read the full paper here.

Zhao et al 2017 also looks at how climate change may reduce crop yields, although through the lens of how global temperature increases will affect wheat, rice, maize, and soy yields. They draw on and summarize four independent analytical methods (historic data, field trial data, and both global and local crop models), which is a cool trick to increase confidence in the findings. On average, they predict each degree C increase will drop wheat yields roughly 6%, rice by 3%, maize by 7%, and soy by 3%. As you'd expect, results are quite spatially heterogeneous (including a few isolated positive effects), see Fig 3 for details. There are a lot of somewhat simplistic assumptions necessary to make these estimates work but they make a good case for temperature increases causing yields to drop on existing farms. Note that they did not account for shifting cultivation (e.g. moving plantings north to reflect new conditions) or other forms of adaptation.

One concern about climate change is the shift to more intense rain (causing more runoff, erosion, and flooding than steadier weaker rain), as well as increased rain in some areas (including the US). Sinha et al 2017 does some modeling based on climate projections to predict global changes in nitrogen loads in rivers (which leads to eutrophication in coastal waters, e.g. the dead zone in the Gulf), finding that they will increase substantially in 2070-2100 (with some increase 2031-2060). There are a lot of scenarios in the paper, but under "business as usual" for climate change they predict an overall increase in N loading of 19% for 20170-2100 (driven primarily by the Northeast, Upper Mississippi, and Great Lakes regions (see Fig 1 for details, Fig 2 is less useful since it groups areas with opposing trends). They note that simply to offset that increase, we would need to reduce nitrogen inputs to farms by 33%; to actually make progress on reducing eutrophication we would have to do substantially more. They also show other countries at risk of increasing N loading, especially India, parts of China, and SE Asia. It's worth noting there are a lot of assumptions in this paper, but the overall trend that moving to flashier rain is likely to make the problem with nutrient runoff from agriculture worse is something we need to be thinking about, especially if we are unsuccessful in limiting climate change. There's a news article about the paper at

Fisher, J. R. B., Acosta, E., Dennedy-Frank, P. J., Boucher, T., Kroeger, T., & Giberti, S. (2017). The impact of satellite imagery’s spatial resolution on land use classification and modeled water quality. Remote Sensing in Ecology and Conservation, 1–13.

Harwatt, H., Sabaté, J., Eshel, G., Soret, S., & Ripple, W. (2017). Substituting beans for beef as a contribution towards US climate change targets. Climatic Change, 143 (1-2)(July), 261–270.

Jayachandran, S., de Laat, J., Lambin, E. F., Stanton, C. Y., Audy, R., & Thomas, N. E. (2017). Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation. Science, 357(6348), 267–273.

Kim, J.-S., Kug, J.-S., Jeong, S.-J., Huntzinger, D. N., Michalak, A. M., Schwalm, C. R., … Schaefer, K. (2017). Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nature Geoscience, 10(8), 572–576.

Shackelford, G. E., Kelsey, R., Robertson, R. J., Williams, D. R., & Dicks, L. V. (n.d.). Sustainable agriculture in California and other Mediterranean ecosystems. Synopses of Conservation Evidence Series. University of Cambridge, Cambridge, UK.

Sinha, E., Michalak, A. M., & Balaji, V. (2017). Eutrophication will increase during the 21st century as a result of precipitation changes. Science, 357(6349), 405–408.

Snyder, C. S. (2017). Progress in Reducing Nutrient Loss in the Mississippi River Basin – But Effects on Gulf Hypoxia Still Lag. IPNI: Peachtree Corners, Georgia.

Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., … Asseng, S. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 201701762.