Translator Disclaimer
1 January 2020 Leveraging Climate Regulation by Ecosystems for Agriculture to Promote Ecosystem Stewardship
Avery Cohn
Author Affiliations +

One in every five patches of tropical forest near agriculture in Brazil appears to contribute more to agricultural production by preventing crop-killing extreme heat exposure than it could produce if it were converted to cropland itself. In this commentary, I refer to this and other forms of climate regulation by ecosystems and beneficial for agriculture as E4A. E4A is a readily employable and largely untapped concept for protecting and restoring tropical ecosystems. The promise of E4A lies in demonstrating sizeable production-protection synergies relevant for critical actors. Using a consultative research process, I gauged the current and future status of E4A science and action in tropical land use decision-making. Stakeholders flagged unmet demand for E4A in support of decisions tied to numerous regulatory, governance, and business processes. Results from a complementary literature review revealed gaps in research, advocacy, and entrepreneurship. I close by discussing opportunities to relieve E4A pain points to catalyze tropical ecosystem stewardship.


Interventions to protect and restore tropical ecosystems must better engage local and agricultural actors (Cohn & Rourke, 2011; Rueda, Garrett, & Lambin, 2017). I propose to increase tropical ecosystem stewardship by these strategic actors by spotlighting an underemphasized environmental service—climate regulation by ecosystems for neighboring agriculture (E4A).

The promise of E4A for ecosystem stewardship lies in demonstrating sizeable, able to be monetized production-protection synergies relevant for critical actors. E4A values ecosystems for many actors who determine ecosystem conversion including investors, local governments, and agribusinesses. E4A also creates a shared agenda for agriculture and conservation by showing that production depends on protection. I led a recent analysis showing that E4A can substantially realign the economics of tropical land use. In Brazil, roughly one in every five locations of tropical forest near agriculture appears to contribute more to agricultural production by preventing crop-killing extreme heat exposure to crops within 25 km than it could produce if it were converted to cropland itself (Cohn & Soares Filho, 2017). We also found the net present value of standing forests for climate regulation exceeded the carbon value of tropical forests in greater than 30% of locations. Combined, carbon value and climate regulation value were worth more than the market price for Brazilian cropland in just under 50% of locations. Carbon and agricultural extreme heat regulation are just two of many values to society from forest ecosystem services. Their sum is a lower bound estimate of the value of ecosystems for society and an even lower bound for the able-to-be-monetized value of ecosystems. Forests supply myriad other sources of value for local to regional natural resource economies (Carrasco, Nghiem, Sunderland, & Koh, 2014; Carpenter et al., 2009; Garibaldi et al., 2016; Maas, Clough, & Tscharntke, 2013; Stickler et al., 2013).

Quantifying E4A Means Linking Research Frontiers in Climate Modeling and Crop Modeling

The value of E4A information stems from avoiding losses to agriculture from the protection and restoration of ecosystems (Figure 1). The scientific foundation of E4A depends on synthesis of the study of the response of climate to ecosystem conversion with the study of the response of crops to climate. Both components must also weigh the changing influence of greenhouse gas-driven climate change on agriculture and ecosystems.

Figure 1.

Information on climate regulation services from tropical ecosystems for agriculture (E4A) can promote ecosystem stewardship. The schematic theorizes how E4A can fuel a set of advocacy and entrepreneurship activities that increase the ambition for tropical ecosystem conservation in an existing set of land use governance and business activities. The provision of E4A information stems from fusion of regional climate modeling and crop modeling.


Regional Climate Modeling

In the tropics, climate change from global greenhouse gas emissions can be rivaled by a second type of climate change from disruptions to energy and water cycling caused by ecosystem conversion (Ellison et al., 2017; Silvério et al., 2015). This latter type of climate change, known as geophysical climate change, accrues at spatial scales from the agricultural plot (Frey et al., 2016) to the planet (Nobre et al., 2016). In regions of high ecosystem conversion intensity, a series of recent findings show that already-occurred amounts of geophysical climate change have in some locations and for some climate metrics exceeded the amount of global climate change projected by end of century under the highest emissions widely modeled scenario (Cohn, Bhattarai, Duncan, & Jeffries, 2017). The two types of climate change can also often combine (Lawrence & Vandecar, 2015) to worsen departures from today’s climate (Bagley, Desai, Harding, Snyder, & Foley, 2014).

Crop Modeling

Extreme heat, extreme precipitation, delayed rainy season onset, and vapor pressure deficit (Lawrence & Vandecar, 2015) are several climate changes caused by ecosystem conversion, worsened by global climate change and threatening tropical frontier agriculture (Cohn, VanWey, Spera, & Mustard, 2016; Pires et al., 2016). Research on risks posed by climate change to tropical crop productivity is rapidly advancing—enabling more precise estimates of the economic costs of climate change for frontier agriculture and the attribution of a share of these costs to ecosystem conversion.

E4A: Transforming the meaning of ecosystems for agriculture

E4A can transform how agribusiness actors engage in climate and ecosystem governance. First, E4A shows tropical ecosystem stewardship to be a source of locally valuable public and private goods. Farmers neighboring a protected area might benefit from climate regulation. Both a farmer and their neighbors might benefit from stewardship of on-farm ecosystem patches. In this way, climate regulation is a type of local incentive for or co-benefit1 from ecosystem stewardship. Generally, environmental governance (including both forest and climate governance) has not widely explored spotlighting or internalize such incentives (Green, 2015). A second transformational dimension of E4A is its potential to shift conceptions of deforestation risk to include not only reputation and regulation risk but also operations risk. This shift can engage and enroll a wider and more influential set of agricultural decision-makers in tropical ecosystem protection. Third, E4A also links forest conservation with climate impact risk; reframing climate impact risk from strictly force majeure (Giannakis & Papadopoulos, 2016) to a type of risk that can be mitigated with local land management. Fourth, perhaps the shift can enlist agritech precision agriculture efforts for the tropical ecosystem protection agenda. Tens of billions of dollars are invested annually in research, development, and information systems for closing yield gaps within farms and even within fields. Finally, ecosystem driven climate change and E4A from ecosystem protection are contemporary observable realities. By contrast, greenhouse gas-driven climate change and especially the benefits of reduced emissions are, respectively, not easily discernible and anticipated to be indiscernible for at least a decade. Getting agricultural actors focused on E4A can be a gateway to deepening climate engagement and ambition in the sector.

Targeting E4A to Decision Processes

Advocacy and entrepreneurship addressing E4A needs scientific evidence that is: (a) tailored to specific leverage or pain points in contemporary decision processes; and (b) built on a foundation of generalizable systems research (and the datasets underlying it) into the climate, land and ecosystem components of E4A (see Figure 2 for a schematic).

Figure 2.

Tailored research questions rest on generalizable E4A evidence and models.


I used an exploratory set of research2 activities with E4A stakeholders to identify priority decision processes for E4A information. Processes identified comprise non-state market-driven governance, land use regulation, and investment and entrepreneurship in tropical agriculture. Informants mentioned numerous decision processes. These included:

The list of decision processes extended beyond forest governance and included a number of themes related to climate mitigation, impacts, and adaptation.

Brazilian Forest Code: How Tailoring E4A Research Makes It Actionable

Within each decision process, respondents stressed how actionable research would need to support discrete decisions. For example, the Brazilian Forest Code entails multiple research questions that can be shaped by E4A-relevant data and evidence including: (a) how much would a given amount of forest reserve increase agricultural productivity? (b) how much does E4A change with patch size? (c) How much more should a producer be willing to pay to maintain on farm forest versus acquire tradeable forest certificates? (d) How should Questions 1 to 3 influence land or agricultural investment decisions? (e) How should Questions 1 to 3 influence lobbying for Forest Code stringency and revisions? (f) How should Questions 1 to 3 affect advocacy strategies seeking to raise Forest Code ambition?

Cross Cutting Demand for Data and Evidence

A set of foundational types of E4A spatial datasets and evidence categories emerged as relevant for many of the decision analyses detailed. The dataset types include most previously identified as germane for terrestrial conservation decision support (Chaplin-Kramer et al., 2015) but also include climate datasets. Notably, limits to in situ data of all germane types at the tropical agriculture-forest frontier means a heavier reliance on remote sensing-derived climate, agricultural production, and land cover data. Evidence categories include all causal relationships depicted by arrows in Figure 1.3 Table 1 contains a discussion of the state of science and information for these datasets and evidence categories, given the information demands detailed in the consultative research. In sum, much data and evidence needed for decision support is readily available in some critical ecosystems of the tropics. However, even in these critical regions, basic research opportunities exist to close data and information gaps of immediate relevance for ecosystem protection.

Table 1.

Relevance of E4A Information and Data.


Concluding Remarks

Agricultural productivity gains from climate regulation by ecosystems can increase support for ecosystem stewardship. A recently completed expert consultation revealed numerous decision processes in which such information could help to justify agricultural decisions and practices that help to steward tropical ecosystems. Engagement should take many forms including actionable research tailored to decision processes, decision support systems, advocacy, entrepreneurial efforts and targeted investment. The science of E4A is rapidly developing and stands ready to support numerous decision processes. Science advocacy and science-industry engagements will also help to grow actionable research.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Support for this article was provided by the Dutch Ministry of Economic Affairs through grant, DGAN I 16099163.



BagleyJ. E.DesaiA. R.HardingK. J.SnyderP. K.FoleyJ. A. (2014) Drought and deforestation: Has land cover change influenced recent precipitation extremes in the Amazon? Journal of Climate 27(1): 345–361. Google Scholar


BarnettB. J.MahulO. (2007) Weather index insurance for agriculture and rural areas in lower-income countries. American Journal of Agricultural Economics 89(5): 1241–1247. Google Scholar

3. GrootR. S.CamposJ. J. (2009) Valuation of tropical forest services and mechanisms to finance their conservation and sustainable use: A case study of Tapantí National Park, Costa Rica. Forest Policy and Economics 11(3): 174–183. Google Scholar


BurkeM.LobellD. B. (2017) Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences 114(9): 2189–2194. Google Scholar


CarpenterS. R.MooneyH. A.AgardJ.CapistranoD.DeFriesR. S.DiazS.PereiraH. M. (2009) Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment. Proceedings of the National Academy of Sciences 106(5): 1305–1312. Google Scholar


CarrascoL.NghiemT.SunderlandT.KohL. (2014) Economic valuation of ecosystem services fails to capture biodiversity value of tropical forests. Biological Conservation 178: 163–170. Google Scholar


CastruccioS.McInerneyD. J.SteinM. L.Liu CrouchF.JacobR. L.MoyerE. J. (2014) Statistical emulation of climate model projections based on precomputed GCM runs. Journal of Climate 27(5): 1829–1844. Google Scholar


Chaplin-KramerR.RamlerI.SharpR.HaddadN. M.GerberJ. S.WestP. C.SimS. (2015) Degradation in carbon stocks near tropical forest edges. Nature Communications 6: 10158. Google Scholar


Chaplin-Kramer, R., Jonell, M., Guerry, A., Lambin, E. F., Morgan, A. J.,…Polasky, S. (2015). Ecosystem service information to benefit sustainability standards for commodity supply chains. Annals of the New York Academy of Sciences, 1355(1): 77–97. Google Scholar


CohnA.MosnierA.HavlikP.ValinH.ObersteinerM.SchmidE.O'HareM. (2014) Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation. Proceedings of the National Academy of Sciences 111: 7236–7241. Google Scholar


CohnA.BowmanM.ZilbermanD.O'NeillK. (2011) The viability of cattle ranching intensification in Brazil as a strategy to spare land and mitigate greenhouse gas emissions (Working Paper Series, p. 43), Copenhagen, Denmark: CCAFS. Google Scholar


CohnA.BhattaraiN.DuncanJ.JeffriesG. (2017) Dispersive heat is a large and unrecognized fraction of warming from tropical forest loss. Nature Climate Change.. Manuscript Submitted for publication. Google Scholar


CohnA. S.Soares FilhoB. S. (2017) Value of forests from reducing extreme heat on neighboring farmland in Brazil. World Development.. Manuscript Submitted for publication. Google Scholar


CohnA. S.RourkeD. O. (2011) Agricultural certification as a conservation tool in Latin America. Journal of Sustainable Forestry 30(1): 158–186. Google Scholar


CohnA. S.GilJ.BergerT.PellegrinaH.ToledoC. (2016) Patterns and processes of pasture to crop conversion in Brazil: Evidence from Mato Grosso State. Land Use Policy 55: 108–120. Google Scholar


CohnA. S.VanWeyL. K.SperaS. A.MustardJ. F. (2016) Cropping frequency and area response to climate variability can exceed yield response. Nature Climate Change 6: 601–604. Google Scholar


de CarvalhoJ. R. P.AssadE. D.EvangelistaS. R. M.da Silveira PintoH. (2013) Estimation of dry spells in three Brazilian regions—Analysis of extremes. Atmospheric Research 132: 12–21. Google Scholar


DuffyP. B.BrandoP.AsnerG.P.FieldC. B. (2015) Projections of future meteorological drought and wet periods in the Amazon. Proceedings of the National Academy of Sciences 112(43): 13172–13177. Google Scholar


ElliottJ.DeryngD.MüllerC.FrielerK.KonzmannM.GertenD.BestN. (2014) Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proceedings of the National Academy of Sciences 111(9): 3239–3244. Google Scholar


EllisonD.MorrisC. E.LocatelliB.SheilD.CohenJ.MurdiyarsoD.PokornyJ. (2017) Trees, forests and water: Cool insights for a hot world. Global Environmental Change 43: 51–61. Google Scholar


EyringV.BonyS.MeehlG. A.SeniorC. A.StevensB.StoufferR. J.TaylorK. E. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9(5): 1937–1958. Google Scholar


FickS. E.HijmansR. J. (2017) WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. Google Scholar


FischerG.NachtergaeleF.PrielerS.Van VelthuizenH.VerelstL.WibergD. (2008) Global agro-ecological zones assessment for agriculture (GAEZ 2008) (p. 10), Laxenburg, Austria: IIASA. Google Scholar


FreyS. J.HadleyA. S.JohnsonS. L.SchulzeM.JonesJ. A.BettsM. G. (2016) Spatial models reveal the microclimatic buffering capacity of old-growth forests. Science Advances 2(4): e1501392. Google Scholar


GaribaldiL. A.CarvalheiroL. G.VaissièreB. E.Gemmill-HerrenB.HipólitoJ.FreitasB. M.ÅströmJ. (2016) Mutually beneficial pollinator diversity and crop yield outcomes in small and large farms. Science 351(6271): 388–391. Google Scholar


GiannakisM.PapadopoulosT. (2016) Supply chain sustainability: A risk management approach. International Journal of Production Economics 171: 455–470. Google Scholar


GibbsH. K.MungerJ.L'RoeJ.BarretoP.PereiraR.ChristieM.WalkerN. F. (2016) Did Ranchers and Slaughterhouses Respond to Zero-Deforestation Agreements in the Brazilian Amazon? Conservation Letters 9(1): 32–42. Google Scholar


GilJ.SieboldM.BergerT. (2015) Adoption and development of integrated crop–livestock–forestry systems in Mato Grosso, Brazil. Agriculture, Ecosystems & Environment 199: 394–406. Google Scholar


GreenF. (2015) Nationally self-interested climate change mitigation: A unified conceptual framework (CCCEP Working Paper, 224), London, England: London School of Economics and Political Science. Google Scholar


Jeffries, G., & Cohn, A. (2017). Combining Satellite and Ground Data to Predict Crop Yields in Brazil. Google Scholar


LambinE. F.MeyfroidtP. (2011) Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences 108(9): 3465–3472. Google Scholar


LawrenceD.VandecarK. (2015) Effects of tropical deforestation on climate and agriculture. Nature Climate Change 5(1): 27–36. Google Scholar


MaasB.CloughY.TscharntkeT. (2013) Bats and birds increase crop yield in tropical agroforestry landscapes. Ecology letters 16(12): 1480–1487. Google Scholar


MirandaM. J.Gonzalez-VegaC. (2011) Systemic risk, index insurance, and optimal management of agricultural loan portfolios in developing countries. American Journal of Agricultural Economics 93(2): 399–406. Google Scholar


NepstadD. C.BoydW.SticklerC. M.BezerraT.AzevedoA. A. (2013) Responding to climate change and the global land crisis: REDD+, market transformation and low-emissions rural development. Philosophical Transactions of the Royal Society B 368(1619): 20120167. Google Scholar


NewtonP.GomezA. E. Araújo MendesT.RasmussenL. V.van der HorstD. (2016) Overcoming barriers to low carbon agriculture and forest restoration in Brazil: The Rural Sustentável project. World Development Perspectives 4: 5–7. Google Scholar


NobreC. A.SampaioG.BormaL. S.Castilla-RubioJ. C.SilvaJ. S.CardosoM. (2016) Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences 113(39): 10759–10768. Google Scholar


PiresG. F.AbrahãoG. M.BrumattiL. M.OliveiraL. J. C.CostaM. H.LiddicoatS.LadleR. J. (2016) Increased climate risk in Brazilian double cropping agriculture systems: Implications for land use in Northern Brazil. Agricultural and Forest Meteorology 228: 286–298. Google Scholar


PolaskyS.NelsonE.CammJ.CsutiB.FacklerP.LonsdorfE.WhiteD. (2008) Where to put things? Spatial land management to sustain biodiversity and economic returns. Biological Conservation 141(6): 1505–1524. Google Scholar


RuedaX.GarrettR. D.LambinE. F. (2017) Corporate investments in supply chain sustainability: Selecting instruments in the agri-food industry. Journal of Cleaner Production 142: 2480–2492. Google Scholar


SchlenkerW.RobertsM. J. (2009) Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proceedings of the National Academy of Sciences 106(37): 15594–15598. Google Scholar


SilvérioD.BrandoP.MacedoM. A.BeckP.S.A.BustamanteM.CoeM. (2015) Agricultural expansion dominates climate changes in southeastern Amazonia: The overlooked non-GHG forcing. Environmental Research Letters 10(10): 104015. Google Scholar


Soares-FilhoB.RajãoR.MerryF.RodriguesH.DavisJ.LimaL.SantiagoL. (2016) Brazil’s market for trading forest certificates. PloS One 11(4): e0152311. Google Scholar


StannyE.ElyK. (2008) Corporate environmental disclosures about the effects of climate change. Corporate Social Responsibility and Environmental Management 15(6): 338–348. Google Scholar


SticklerC. M.CoeM. T.CostaM. H.NepstadD. C.McGrathD. G.DiasL. C.Soares-FilhoB. S. (2013) Dependence of hydropower energy generation on forests in the Amazon Basin at local and regional scales. Proceedings of the National Academy of Sciences 110(23): 9601–9606. Google Scholar


Ürge-VorsatzD.HerreroS. T.DubashN. K.LecocqF. (2014) Measuring the co-benefits of climate change mitigation. Annual Review of Environment and Resources 39: 549–582. Google Scholar


VerdinJ.FunkC.SenayG.ChoulartonR. (2005) Climate science and famine early warning. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1463): 2155–2168. Google Scholar


WohlE.BarrosA.BrunsellN.ChappellN. A.CoeM.GiambellucaT.JuvikJ. (2012) The hydrology of the humid tropics. Nature Climate Change 2(9): 655–662. Google Scholar


[1] Here, I define local co-benefits as local co-benefits net of local co-costs. For more, see Ürge-Vorsatz, Herrero, Dubash, and Lecocq (2014).

[2] Research was performed over the period of September 2014 to March 2017. It included focus groups with farmers in Brazil on constraints to technology adoption, a workshop on supply chain climate risk with agribusiness stakeholders, consultations with Indian and Brazilian agribusiness representatives concerning climate risk, interviews with agritech professionals in Boston and San Francisco on the climate risk-precision agriculture nexus, a consultation with professionals working on agricultural development in multilaterals on the climate risk-sustainable intensification nexus, and a series of conversations with tropical forest conservation advocates on the climate risk-tropical deforestation nexus.

[3] Blue arrows show primary relationship of focus for climate modeling and brick red arrows show primary relationships for crop modeling.

© The Author(s) 2017 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (
Avery Cohn "Leveraging Climate Regulation by Ecosystems for Agriculture to Promote Ecosystem Stewardship," Tropical Conservation Science 10(1), (1 January 2020).
Received: 14 June 2017; Accepted: 15 June 2017; Published: 1 January 2020

Back to Top