Showing posts with label water fund. Show all posts
Showing posts with label water fund. Show all posts

Monday, February 3, 2020

February 2020 science journal article summary

Ice crystals on windshield

Greetings,

This month is a mix of topics; some papers came out recently that were too cool for me to wait to review with other similar ones, and I couldn't resist plugging the latest paper I worked on (Hamel et al. 2020). If you know someone who wants to sign up to receive these summaries, they can do so at http://bit.ly/sciencejon

Also, a 12-minute video came out recently about the 2017 March for Science, and I show up in it a few times. It's called "SciComm: Raising Our Voice for Science and Public Policy," it's directed & produced by Larry Kirkman (larry@american.edu) and Shannon Shikles at the Center for Environmental Filmmaking at American University, and you can watch it here: https://vimeo.com/383209723


CLIMATE CHANGE:
Sippel et al. 2020 bucks the pattern where climatologists emphasize how distinct climate and weather are. Past research has shown the impact of climate change on certain weather events, but this paper actually detects the impact of climate change on any given day since late March 2012! By summarizing weather data all over the world, they found that we’ve been clearly outside of natural variability for the past 8 years on a daily basis. On a monthly basis, climate change has been detectable from global weather data since 2001. You can read a newspaper article about the paper at https://www.washingtonpost.com/weather/2020/01/02/signal-human-caused-climate-change-has-emerged-every-day-weather-study-finds/  I love the description of this paper from meteorologist Maria LaRosa: “[it’s] like looking closely at an impressionist painting –  you can't say what the picture is until you step back and look at the whole”


ECOSYSTEM SERVICES:
Chaplin-Kramer et al. 2019 produced global maps summarizing ecosystem services (sort of) for coastal protection, water quality regulation, and crop pollination, now and in 2050 (under three different scenarios). One twist is that they go beyond the usual definition of ecosystem services (benefits provided by nature and received by people who need them) to also look at the 'benefit gap' where there are people with needs nature is not currently meeting (see Fig 2, bottom row, pink / lavender color). There's a lot to explore here, but one finding is that both SE Asia and Africa are expected to have increasing gaps for all three services. There's plenty of uncertainty, but this is a great set of data to think about trade-offs under different future paths. You can explore their results in a web map at http://viz.naturalcapitalproject.org/ipbes/

Johnson et al. 2019 analyzed where it makes economic sense to protect undeveloped land within 100-year floodplains across the U.S. They compared  expected flood damages (over the next 30-50 years) to land acquisition cost (to prevent development and avoid damages). They found benefits exceeded acquisition cost for about 1/3 of unprotected natural areas, and that the strongest benefits were within the 20-year floodplain but outside of the 5-year floodplain. Compared to the 5-year floodplain, these areas are more likely to get developed even though they flood less often, leading to more potential damages. Figure 3 has a map of the counties with the highest benefit:cost ratio, focused in Appalalachia, Arizona, and a mix of other places. Note that buying undeveloped lands avoids the controversy associated with asking or forcing people already living within floodplains to move.

Brancalion et al. 2019 looks at opportunities to restore lowland tropical rainforests around the world. They evaluate both the benefits (including biodiversity, climate mitigation & adaptation, and water security) and feasibility (land opportunity cost, ecological uncertainty, and chance of forest persistence). Figure 2 shows where there's the most opportunity, both by area (Brazil) and by combined benefit and feasibility (Madagascar, Tropical Andes). Where to focus depends on your goal - the places with the most total benefits are generally less feasible (higher land costs and competition, e.g. areas where habitat loss is recent and ongoing). But Figure 3 shows several examples of countries with restoration commitments who appear to have large areas with relatively high benefits and feasibility.


LANDSCAPE ECOLOGY:
Fahrig 2017 & Fahrig et al. 2019 are challenging but important reviews on the ecological impact of habitat fragmentation at the landscape scale (big areas). Their key findings are that with the amount of habitat loss being equal, fragmentation per se typically (70% of the time) didn't significantly impact biodiversity or ecological function at all. Even stranger, when it did have a significant impact, 3/4 of the time it was positive (even for threatened and rare species)! Section 5 in the 2017 paper summarizes the different explanations that authors of the primary studies provided, from fragmentation boosting functional connectivity (by reducing distance between patches) to edge effects and others. She concludes that in most cases we confound habitat loss with fragmentation, and that most of the time our intuition (that fragmentation per se is bad) is incorrect. She also notes that authors sometimes bury or caveat their findings of positive effects of fragmentation, which is one reason her findings continue to seem so wrong. My key take-aways are: 1) it's very hard (but very important) to examine our biases and deeply held beliefs when reading contrary science, 2) there is a good case made in the 2019 paper that small patches are under-protected, given their importance in many landscapes.

Jones et al. 2019 used GPS collars to track both migratory and resident pronghorn, and to model what features they avoided and which they ignored. They found that pronghorn were very reluctant to cross fences (consistent with under studies - they tend to crawl under rather than jump over), and avoided roads, but mostly ignored oil and gas well pads. There's a lot of other findings in their model, but the one I found most interesting was the concern that if ranches add more fencing to allow rotational grazing, it could have serious negative impacts on pronghorn and  mule deer unless wildlife-friendly fences are used.


RESEARCH IMPACT:
Hamel et al. looks at how scientific information was perceived and used in decision making for a water fund in Brazil. Through interviews, we determined that the hydrological modeling and monitoring data was NOT used in designing and implementing the water fund. But counter-intuitively, having done the analysis using complex models and high-resolution data was seen as important for the water fund to be seen as scientifically credible. So ironically, even though the credible models were not actually used, their existence helped build support for the overall water fund. Despite this, as long as monitoring data was used to calibrate and validate the model, a simpler model (InVEST, as opposed to SWAT) and coarser data resolution (30m, as opposed to 1m) would have met the information needs of the users. We should have had more frank discussions up front with the ultimate users of the information to produce a model seen as credible and actually used, while avoiding over-investment in model complexity that wasn't needed.

Samanta et al. 2019 is a paper about a program in Michigan to improve water quality issues from agriculture via a really well thought out collaboration (w/ scientists, practitioners, universities, farmers, and industry). Lack of public funding has pushed many farmers to rely on private crop advisors, who don't always share conservation opportunities (like cover crops, reduced tillage, and nutrient management) with farmers (especially as tied to programs involving lots of paperwork). They found it was critical to improve active communication & trust at all levels (especially about funding available).  Conservation was constrained by funding, and potentially by the shift of crop advisors to often be less comprehensive and represent a single company. The authors emphasize the need to integrate social science like this from the very beginning of projects.


REFERENCES:
Brancalion, P. H. S., Niamir, A., Broadbent, E., Crouzeilles, R., Barros, F. S. M., Almeyda Zambrano, A. M., … Chazdon, R. L. (2019). Global restoration opportunities in tropical rainforest landscapes. Science Advances, 5(7), eaav3223. https://doi.org/10.1126/sciadv.aav3223

Chaplin-Kramer, R., Sharp, R. P., Weil, C., Bennett, E. M., Pascual, U., Arkema, K. K., … Daily, G. C. (2019). Global modeling of nature’s contributions to people. Science, 366(6462), 255–258. https://doi.org/10.1126/SCIENCE.AAW3372

Fahrig, L. (2017). Ecological Responses to Habitat Fragmentation Per Se. Annual Review of Ecology, Evolution, and Systematics, 48(1), annurev-ecolsys-110316-022612. https://doi.org/10.1146/annurev-ecolsys-110316-022612

Fahrig, L., Arroyo-Rodríguez, V., Bennett, J. R., Boucher-Lalonde, V., Cazetta, E., Currie, D. J., … Watling, J. I. (2019). Is habitat fragmentation bad for biodiversity? Biological Conservation, 230(October 2018), 179–186. https://doi.org/10.1016/j.biocon.2018.12.026

Hamel, P., Bremer, L. L., Ponette-González, A. G., Acosta, E., Fisher, J. R. B., Steele, B., … Brauman, K. A. (2020). The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil. Science of The Total Environment, 135871. https://doi.org/10.1016/j.scitotenv.2019.135871

Johnson, K. A., Wing, O. E. J., Bates, P. D., Fargione, J., Kroeger, T., Larson, W. D., … Smith, A. M. (2019). A benefit–cost analysis of floodplain land acquisition for US flood damage reduction. Nature Sustainability. https://doi.org/10.1038/s41893-019-0437-5

Jones, P. F., Jakes, A. F., Telander, A. C., Sawyer, H., Martin, B. H., & Hebblewhite, M. (2019). Fences reduce habitat for a partially migratory ungulate in the Northern Sagebrush Steppe. Ecosphere, 10(7). https://doi.org/10.1002/ecs2.2782

Samanta, A., Eanes, F. R., Wickerham, B., Fales, M., Bulla, B. R., & Prokopy, L. S. (2019). Communication, Partnerships, and the Role of Social Science: Conservation Delivery in a Brave New World. Society & Natural Resources, 0(0), 1–13. https://doi.org/10.1080/08941920.2019.1695990

Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E., & Knutti, R. (2020). Climate change now detectable from any single day of weather at global scale. Nature Climate Change, 10(1), 35–41. https://doi.org/10.1038/s41558-019-0666-7

Sincerely,

Jon

p.s. If you'd like to keep track of what I write as well as what I read, I always link to both my informal blog posts and my formal publications (plus these summaries) at http://sciencejon.blogspot.com/

Monday, January 6, 2020

Can science boost credibility even if it isn't used?

Hamel et al. 2020 ("The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil") looked at how scientific information was perceived and used in decision making for a water fund in Brazil (where a water treatment company pays for upstream conservation to reduce the costs of treating the water).

Through interviews, we determined that the hydrological modeling and monitoring data that we provided was NOT used in designing and implementing the water fund. But counter-intuitively, having done the analysis using complex models and high-resolution data was seen as important for the water fund to be seen as scientifically credible.

So ironically, even though the credible models were not actually used, their existence helped build support for the overall water fund. Despite this, as long as monitoring data was used to calibrate and validate the model, a simpler model (InVEST, as opposed to SWAT) and coarser data resolution (30m, as opposed to 1m) would have met the information needs of the users. We should have had more frank discussions up front with the ultimate users of the information to produce a model seen as credible and actually used, while avoiding over-investment in model complexity that wasn't needed.

You can read the full article here: https://www.sciencedirect.com/science/article/pii/S0048969719358668

Reference:
Hamel, P., Bremer, L. L., Ponette-González, A. G., Acosta, E., Fisher, J. R. B., Steele, B., … Brauman, K. A. (2020). The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil. Science of The Total Environment, 135871. https://doi.org/10.1016/j.scitotenv.2019.135871

Wednesday, January 2, 2019

January 2019 science journal article summary: best of 2018

Christmas cookie decorating party

Happy new year!

Resolved to try harder to keep with science? Why not start with some of the best papers from last year that you may have missed? This month I picked my favorite 15 articles that I reviewed in 2018, plus a few other resources. A few were published earlier, but I read them all last year. I picked some because of importance, others because they were interesting, and two plug my own work.

There is one new article I couldn't resist mentioning, which is about the Camboriú water fund that I worked on. Kroeger et al. 2019 talks about how the water fund was designed, including estimating the impact it would have on land use change and water quality. We were able to show that it provided a positive financial return on investment after 44 years (if you include some modest societal co-benefits like flood control and biodiversity). PDF available here until ~Feb 10 after which an unformatted PDF is available here.

I also wanted to once again plug a cool resource  to help you figure out which journal to submit a paper to: http://jane.biosemantics.org/  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: https://authorservices.wiley.com/author-resources/Journal-Authors/Prepare/writing-for-seo.html and my blog on how to ensure all of your own research is viewable by others: http://sciencejon.blogspot.com/2018/03/tips-for-helping-people-to-find-your.html

Finally, my wife's comment on my book chapter on global agriculture land use trends (that there was no clear key take-away point) has stuck with me as a reminder of how important it is to get input from non-scientists on science writing. Here's a short blog where I tried to supplement the chapter: http://sciencejon.blogspot.com/2018/01/take-2-what-i-wish-id-put-in-my-recent.html

ARTICLES:
Carvin et al 2018 is a study I've been eagerly awaiting for years. It is a rigorous paired watershed study looking at the impact of a carefully targeted set of agricultural interventions, and is one of the first papers in the US to show we CAN improve water quality at a watershed scale (50 km2) through shifting ag. Initial work had found 9% of the area was contributing 40% of the phosphorous load, so the authors really targeted those heavy contributors. They found a 55% reduction in phosphorus runoff loads and suspended sediment event loads decreased by 52% for events during unfrozen soil conditions  into the Pecatonica River tributary during storm events. This is big news as these outcomes have been elusive. However, this watershed was picked as one of the most likely to respond well, and those seeking to replicate these results should also carefully select their watersheds. Contact Steve Richter at TNC for more info.

Cui et al 2018 reports on the results of an ambitious study that worked with 21 million farmers (!) of maize, rice, and wheat over 10 years. China currently has some of the least efficient farms in the world, presenting a huge need to improve. This study used a soil & crop management framework that resulted in ~11% improved yield while reducing N application by ~16% (and reactive N losses by ~25%), and GHGs by 14-22% depending on crop. The scale is impressive: altogether they influenced 37.7 million ha. Interestingly, extension staff impacted over 10 times the area per staff person (471 ha / person) compared to agribusiness partners (see Fig 2). Regardless, this is good news in showing that it's possible to achieve "win-win" outcomes at scale even with smallholders. On the other hand, nitrogen efficiency is so poor in China, that much larger changes are needed to bring them in line with world averages, let alone truly sustainable targets (highlighting that policy changes are likely needed as well). Fig 1 has a great breakdown of impacts by crop and region.

Almost everyone who works for or closely with The Nature Conservancy heard about the 2017 "Natural Climate Solutions" paper (Griscom et al. 2017, I reviewed it in November 2017). If you've been waiting for the sequel - good news! Fargione et al. 2018 just provided a similar analysis specifically for the United States. It's short, excellent, and worth reading, but if you're impatient skip to Figure 1. That summarizes the potential of each pathway and splits out how much is achievable at different carbon prices. They found a maximum potential of 1.2 Pg (aka 1200 million metric tons) CO2e / yr (21% of current US emissions and ~27% of 2005 emissions), and ~300 Tg (million metric tons) achievable at $10 / t CO2e (~5% of US emissions). The biggest low cost opportunities are in planting cover crops followed by forest management, avoided habitat conversion, and improved farm nutrient management. You can read more about it on TNC's web site at https://www.nature.org/en-us/explore/newsroom/natural-climate-solutions-study/ or at https://eurekalert.org/pub_releases/2018-11/cu-nsr111418.php

Fisher et al. 2018 ("Knowledge diffusion within a large conservation organization and beyond") looks at how people find information about innovations and share them, specifically the spread of Conservation by Design 2.0 (CbD 2.0). We review how earlier versions of CbD spread from TNC (looking at published science articles and expert interviews), then use tons of varied data to look at CbD 2.0. I wrote a blog about the paper here: http://sciencejon.blogspot.com/2018/03/share-good-news-paper-on-improving.html
and the full paper is at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193716
but here's a summary of what we learned:
  1.  Sending repeated broadly-targeted communications (e.g. all-staff email / newsletters / etc.) that make it easy for recipients to find out more worked better than more narrowly focused communications (e.g. plenary talks, emails from executives).
  2. Expert interviews revealed several factors to promote diffusion: bringing in partners early to develop and test methods, committing up front to sustain support for the planning methods, having in-person workshops, using peer-review and shared learning, providing financial support, explaining how the methods address existing needs planners already have, and the existence of a support and learning network like the conservation coaches network (CCNET). 
  3. Organizations may wish to use internal data to identify staff likely to play a key role in diffusing so that they can encourage that process (the paper has details on how, with more forthcoming in an upcoming paper)
  4. Working with academics on publications represents a potential way to get the word out with relatively low effort for organizations (academics I have worked with in other contexts are often very interested in data no one else has access to, and have published cool papers from those data). 
  5. For scientists interested in this topic, we learned a lot about how to study knowledge diffusion, and share tips for researchers (e.g. thinking about image-blocking, legal and privacy constraints, distinguishing internal and external website visits, etc.).

Fisher & Kareiva 2019 (still in press) is a book chapter about sustainable agriculture that I write a few years ago. The first half is OK but is out of date and was written when I knew far less about agriculture. I'd skip to the 2nd half (start with the "Can Corporate Sustainability reporting be a force for improved agricultural practices?" section). There's some interesting content I haven't seen anywhere else on corporate sustainabiltiy and food labels. The chapter is available from: http://fish.freeshell.org/publications/FisherKareiva_CUP_2019_preformatted.pdf

Garnett et al. 2017 ("Grazed and Confused") is a very thoughtful review of the climate change / GHG impact of ruminants (largely cattle). Their first key findings is that even with good grazing ruminants still have high net GHG emissions. They also note sequestering soil carbon often has trade-offs with methane and nitrous oxide. Finally, as demand for animal protein rises sharply there is likely to be both land conversion and increasing GHGs as a result. These have all been reported widely in other studies, but it's a nice summary. On the one hand, it's hard to pull out quantitative results from this paper. On the other, it does a great job of covering the various arguments and counterpoints around cattle and carbon, and presenting the data in a value-neutral tone. Anyone interested in this topic should at least skim the 8-page summary.

Given how much research there is on trying to get crops to fix their own nitrogen, the finding by Griesmann et al. 2018 that many plants have lost the ability to fix N blew my mind. By comparing genomes of N-fixing plants to those that don't, they were able to find that ~3/4 of the species in their sample that didn't fix N had an ancestor that could! They suggest that the fact this ability has been lost multiple times reflects that plants spend a lot of energy to support N fixation, and that when N levels are adequate in the soil they eventually can lose the ability to fix it. In other words, as we try to engineer plants to fix their own N, it's worth reflecting on the costs that may have led plants in the past to reject this evolutionary path.
There's a blog on this one at http://www.sciencemag.org/news/2018/05/many-plants-need-bacterial-roommates-survive-so-why-do-some-kick-them-out

Hansen et al 2018 is a cool paper using empirical data to test how effective wetlands in the Minnesota River basin are at reducing nitrates in an ag landscape compared to cover crops and land retirement. They compared river water quality at ~200 sites under different flow conditions to high-resolution data on wetlands and land use to map correlations (they didn't get at true causation). They found wetlands were 5 times more effective per unit area at removing nitrates compared to cover crops and land retirement (although it's much harder to make a business case to a farmer around wetland creation). They also found wetlands strategically placed to intercept as much flow as possible were much more effective (see Fig 4 - the concept is obvious but the numbers are interesting). All these findings align well with prior work emphasizing the critical role of well-placed wetlands to improve water quality. If you read this paper watch out for the term "crop cover" (% of a given site area used to grow crops) as opposed to "cover crops" (presence of an additional crop on farmland that would otherwise be fallow for part of the year), as they're not super clear how they use the two terms.

Klein et al. 2007 is a fantastic reference examining dependence on animal pollination across 115 major crop species (ignoring crops like corn which are entirely wind-pollinated). I mainly use Appendix 2, which for each crop lists how much it benefits from animal pollination (from entirely dependent on animal pollinators like cocoa or squash, to receiving almost no benefit) as well as listing the type of pollinator, pointing to references, etc. While the appendix is my favorite part, they also note in the main paper that a) non-insect pollinators (e.g. birds and bats) are less well studied and b) as agriculture intensifies wild pollinators are likely to decline. This means thinking about pollinator habitat in and around farms can be important for some crops, and the appendix can identify which ones are most likely to see more benefit.

Nevle & Bird 2008 is grim but fascinating. They find a connection between seemingly unrelated factors: global CO2 levels and pandemics among indigenous people in the Americas brought on by European contact. They link the population crash to a reduction in burning of forests for swidden agriculture, subsequent forest regrowth storing ~5-10 Gt carbon, and argue this is a likely contributor to a small measured reduction in global atmospheric CO2 at the same time. It's more of an interesting hypothesis with data which is consistent than real 'proof' but it's still a fascinating (if depressing) read.

Rasmussen et al. 2018 is a global review of whether or not agricultural intensification is good for both people and the environment. While they find income and food production generally go up, ecosystem services go down in most cases. The figures have great summaries of results by geography, by metric of ecosystem services or human well being, and by separating 'win-win' cases from 'lose-lose' and mixed results in different contexts. The specific case studies are very interesting and thought provoking. Surprisingly, increased inputs were more likely to lead to win-win outcomes, with crop changes as reduced fallow more likely to lead to lose-lose. This is a relatively understudied area (this paper summarizes 53 studies) given the importance of intensification strategies; the lack of evidence for consistent positive outcomes doesn't mean intensification CAN'T work, but shows more work (design and monitoring) is needed to ensure we succeed in our goals. See https://www.scidev.net/global/agriculture/news/intensified-farming-rarely-aids-wellbeing-environment.html for a blog on the subject.

Springmann et al. 2018 asks what it would take to sharply reduce the impact of global food production by 2050 (and stay within resource constraints) without simply offsetting impacts like GHGs through reforestation or other mitigation. They look at 3 options (diet change, tech and management, and  reducing food waste) across 5 aspects: GHGs, fresh water use, land use, nitrogen, and phosphorous. They key finding is that no one category of solution is enough, and that for GHGs in particular major diet change (towards mostly plant based foods) would have to be part of the solution. Figure 3 summarizes this set of scenarios nicely. With their medium ambition scenario, they find halving food loss and waste improves impact 6-16% (relative to 2050), improving tech and management reduces impact 3-30%, and modest diet change improves 5-29% (see Figure 2), or they could all be combined for a 25-45% reduction. Note that their findings are global averages, and some places will deviate considerably (e.g. they find nuts and seeds don't account for much overall water use, but in places like California they have a big water footprint). Check the methods for country-level data. You can read two articles about this study here: https://www.washingtonpost.com/health/2018/10/10/how-will-or-billion-people-eat-without-destroying-environment/ and here: https://www.theguardian.com/environment/2018/oct/10/huge-reduction-in-meat-eating-essential-to-avoid-climate-breakdown

For over a year now, TNC staff have been hearing about a science analysis asking whether it's possible for both people and nature to thrive (in a shared conservation vision). Tallis et al 2018 is the newly available science paper behind that analysis. It compares two 2050 global scenarios: business as usual (BAU), and one designed to improve human and environmental outcomes (Sustainability). The latter would result in 577 million ha more habitat than BAU, while limiting climate change, improving air quality, and more. It doesn't assume we can drastically change diets, and sticks with biophysical constraints, but it does recognize that there are major social, economic, and political barriers to making the sustainability scenario a reality. The discussion has several thoughtful limits and caveats, but it's still exciting to see what is at least possible, if not easy to achieve. You will have to read the supplemental material to get a good sense of the work, but the main paper is conveniently short. One final note is that they assume climate change won't impact ag much in either scenario, which is optimistic. You can read all about the paper and its findings here: https://www.nature.org/en-us/what-we-do/our-insights/perspectives/the-science-of-sustainability/?vu=r.v_twopaths

VanZanten et al. 2018 is a really thoughtful paper that takes a refreshing approach to looking at the environmental impact of animal foods in our diet. They note that while using arable land to feed livestock (rather than directly feeding humans) is inherently inefficient, there are some grasslands, food waste, and food by-products like distillers grains that humans can't eat. So to minimize land used to feed the world, ~10% of calories (& ~1/3 of protein needed) could come from animal foods. Fig 4 shows how animal consumption in different regions compares to the protein goal, and Fig 5 shows a similar breakdown for calories and other nutrients. They cover how different animals fit in (e.g. ruminants for grasslands, pigs for food waste, etc.), noted that GHGs are still higher in their scenario than an all-vegan diet, and cover several interesting caveats and twists. One thing they didn't mention - some of the underlying studies have a large role for milk, which people have trouble digesting in many places around the world. But is is a really well done paper and I highly recommend it.

Woodard & Verteramo-Chiu look at how much better the Federal Crop Insurance Program (FCIP) could perform if it used soil data to establish rates and coverage. In other words, how could FCIP incentivize soil health practices that would reduce risks and costs of the program, while avoiding perverse incentives (e.g. in the past crop insurance was not available to farmers using cover crops). It's a fairly wonky economics paper, but they make a good case for much errors and bias exist in the current program. The key finding is that farms with high-quality soils are generally overpaying, and low-quality farms are underpaying. See Fig 3 for an example of how strong the pricing erors are (up to a factor of 6). By accounting for soils data (and perhaps current practices), this program could be an important driver to get farmers to start rebuilding healthier soils to keep premiums low. They focus on top corn producing states where soil quality is relatively homogeneous; benefits of accounting for soil should be higher in regions with more varied soil. With predicted volatility from climate change, improving crop insurance will be increasingly important.

REFERENCES:
Carvin, R., Good, L. W., Fitzpatrick, F., Diehl, C., Songer, K., Meyer, K. J., … Richter, S. (2018). Testing a two-scale focused conservation strategy for reducing phosphorus and sediment loads from agricultural watersheds. Journal of Soil and Water Conservation, 73(3), 298–309. https://doi.org/10.2489/jswc.73.3.298

Cui, Z., Zhang, H., Chen, X., Zhang, C., Ma, W., Huang, C., … Dou, Z. (2018). Pursuing sustainable productivity with millions of smallholder farmers. Nature, 555, 363–366. https://doi.org/10.1038/nature25785

Fargione, J. E., Bassett, S., Boucher, T., Bridgham, S. D., Conant, R. T., Cook-patton, S. C., … Griscom, B. W. (2018). Natural climate solutions for the United States. Science Advances, 4(November).

Fisher, J. R. B. and Kareiva, P. (In Press, 2019). Using environmental metrics to promote sustainability and resilience in agriculture. In Gardner et al. (Eds), Agricultural Resilience: Perspectives from Ecology and Economics. Cambridge University Press. Manuscript accepted for publication.

Fisher, J. R. B., Montambault, J., Burford, K. P., Gopalakrishna, T., Masuda, Y. J., Reddy, S. M. W., … Salcedo, A. I. (2018). Knowledge diffusion within a large conservation organization and beyond. PLoS ONE, 13(3), 1–24. https://doi.org/10.1371/journal.pone.0193716

Garnett T., Godde C., Muller A., Röös E., Smith P., de Boer I.J.M., Ermgassen E., Herrero M., van Middelaar C., Schader C. and van Zanten H. (2017). Grazed and confused? Ruminating on cattle, grazing systems, methane, nitrous oxide, the soil carbon sequestration question. Food Climate Research Network, University of Oxford http://www.fcrn.org.uk

Griesmann, M., Chang, Y., Liu, X., Song, Y., Haberer, G., Crook, M. B., … Cheng, S. (2018). Phylogenomics reveals multiple losses of nitrogen-fixing root nodule symbiosis. Science, 361(6398). https://doi.org/10.1126/science.aat1743

Hansen, A. T., Dolph, C. L., Foufoula-Georgiou, E., & Finlay, J. C. (2018). Contribution of wetlands to nitrate removal at the watershed scale. Nature Geoscience. https://doi.org/10.1038/s41561-017-0056-6

Klein, A.-M., Vaissière, B. E., Cane, J. H., Steffan-Dewenter, I., Cunningham, S. a, Kremen, C., & Tscharntke, T. (2007). Importance of pollinators in changing landscapes for world crops. Proceedings. Biological Sciences / The Royal Society, 274(1608), 303–313. https://doi.org/10.1098/rspb.2006.3721

Kroeger, T., Klemz, C., Boucher, T., Fisher, J. R. B., Acosta, E., Cavassani, A. T., … Dacol, K. (2019). Returns on investment in watershed conservation: Application of a best practices analytical framework to the Rio Camboriú Water Producer program, Santa Catarina, Brazil. Science of The Total Environment, 657, 1368–1381. https://doi.org/10.1016/j.scitotenv.2018.12.116

Nevle, R. J., & Bird, D. K. (2008). Effects of syn-pandemic fire reduction and reforestation in the tropical Americas on atmospheric CO2 during European conquest. Palaeogeography, Palaeoclimatology, Palaeoecology, 264(1–2), 25–38. https://doi.org/10.1016/j.palaeo.2008.03.008

Rasmussen, L. V., Coolsaet, B., Martin, A., Mertz, O., Pascual, U., Corbera, E., … Ryan, C. M. (2018). Social-ecological outcomes of agricultural intensification. Nature Sustainability, 1(6), 275–282. https://doi.org/10.1038/s41893-018-0070-8

Springmann, M., Clark, M., Mason-D’Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., … Willett, W. (2018). Options for keeping the food system within environmental limits. Nature. https://doi.org/10.1038/s41586-018-0594-0

Tallis, H. M., Hawthorne, P. L., Polasky, S., Reid, J., Beck, M. W., Brauman, K., … McPeek, B. (2018). An attainable global vision for conservation and human well-being. Frontiers in Ecology and the Environment, 1–8. https://doi.org/10.1002/fee.1965

Van Zanten, H. H. E., Herrero, M., Hal, O. Van, Röös, E., Muller, A., Garnett, T., … De Boer, I. J. M. (2018). Defining a land boundary for sustainable livestock consumption. Global Change Biology, (April). https://doi.org/10.1111/gcb.14321

Woodard, J. D., & Verteramo-Chiu, L. J. (2017). Efficiency impacts of utilizing soil data in the pricing of the federal crop insurance program. American Journal of Agricultural Economics, 99(3), 757–772. https://doi.org/10.1093/ajae/aaw099

Friday, September 1, 2017

September science journal article summary

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").

VALUE OF INFORMATION (VOI):
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 https://blog.nature.org/science/2017/08/24/camboriu-data-for-water-funds/, and a more technical one for people working with spatial data at https://rsecjournalblog.wordpress.com/2017/08/25/how-much-data-is-enough-investigating-how-spatial-data-resolution-impacts-conservation-decision-making/). 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.
UPDATE: in December 2018 another science paper came out about the overall water fund project: https://www.sciencedirect.com/science/article/pii/S0048969718349611?via%3Dihub


AGRICULTURE:
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 http://www.conservationevidence.com 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.


CLIMATE CHANGE:
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 https://www.theatlantic.com/science/archive/2017/07/paying-people-to-preserve-their-trees/534351/?utm_source=atltw and/or http://www.nber.org/digest/aug16/w22378.html 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.


CLIMATE CHANGE & AGRICULTURE:
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: https://www.washingtonpost.com/news/energy-environment/wp/2017/07/10/the-stubbornly-persistent-idea-about-climate-change-that-just-wont-go-away/?utm_term=.9a374e5ce552 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 https://www.nytimes.com/2017/07/27/climate/nitrogen-fertilizers-climate-change-pollution-waterways-global-warming.html


REFERENCES:
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. https://doi.org/10.1002/rse2.61

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. https://doi.org/10.1007/s10584-017-1969-1

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. https://doi.org/10.1126/science.aan0568

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. https://doi.org/10.1038/ngeo2986

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. https://doi.org/10.1126/science.aan2409

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. https://doi.org/10.1073/pnas.1701762114

Friday, August 25, 2017

New paper and two blogs asking "how much data is enough?"


My new paper (Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality) 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 30 m resolution data to a more complex analysis using 1 m data. I'd recommend most people skip most of the paper (which is quite technical) and skip to the discussion, or even the two blogs I wrote about it.

The first blog explains the overall project and the paper at a high level here:
Camboriú Conservation Field Test: How Much Data is Enough?

I also wrote a second blog aimed specifically at people who actually do spatial analysis to guide them in picking the right source of remotely sensed imagery:
How much data is enough? Investigating how spatial data resolution impacts conservation decision making

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. The return on investment was over 1 with 1m data, but below 1 with 30m data, meaning if financial return was the dominant factor this distinction would be critical.

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.