Biogeochemical research priorities for sustainable biofuel and bioenergy feedstock production in the Americas.

Rapid expansion in biomass production for biofuels and bioenergy in the Americas is increasing demand on the ecosystem resources required to sustain soil and site productivity. We review the current state of knowledge and highlight gaps in research on biogeochemical processes and ecosystem sustainability related to biomass production. Biomass production systems incrementally remove greater quantities of organic matter, which in turn affects soil organic matter and associated carbon and nutrient storage (and hence long-term soil productivity) and off-site impacts. While these consequences have been extensively studied for some crops and sites, the ongoing and impending impacts of biomass removal require management strategies for ensuring that soil properties and functions are sustained for all combinations of crops, soils, sites, climates, and management systems, and that impacts of biomass management (including off-site impacts) are environmentally acceptable. In a changing global environment, knowledge of cumulative impacts will also become increasingly important. Long-term experiments are essential for key crops, soils, and management systems because short-term results do not necessarily reflect long-term impacts, although improved modeling capability may help to predict these impacts. Identification and validation of soil sustainability indicators for both site prescriptions and spatial applications would better inform commercial and policy decisions. In an increasingly interrelated but constrained global context, researchers should engage across inter-disciplinary, inter-agency, and international lines to better ensure the long-term soil productivity across a range of scales, from site to landscape.Fil: Gollany, Hero T. USDA. Agricultural Research Service. Columbia Plateau Conservation Research Center; Estados UnidosFil: Titus, Brian D. Pacific Forestry Centre. Canadian Forest Service. Natural Resources Canada; CanadáFil: Scott, Andrew USDA Forest Service. Agricultural Research Center. Southern Research Station; Estados UnicosFil: Asbjornsen, Heidi. University of New Hampshire. Institute for Earth, Oceans and Space. Department of Natural Resources and the Environment and the Earth Systems Research Center; Estados UnidosFil: Resh, Sigrid C. Michigan Technological University. School of Forest Resources and Environmental Science; Estados UnidosFil: Chimner, Rodney Allen. Michigan Technological University. School of Forest Resources and Environmental Science; Estados UnidosFil: Kaczmarek, Donald J. Oregon Department of Forestry; Estados UnidosFil: Leite, Luiz F. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); BrasilFil: Ferreira, Ana C. Climate Change Adaptation Consultant; BrasilFil: Rod, Kenton A. Washington State University. School of the Environment; Estados UnidosFil: Hilbert, Jorge Antonio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Ingeniería Rural; ArgentinaFil: Galdos, Marcelo. Brazilian Center for Research in Energy and Materials (CNPEM). Brazilian Bioethanol Science and Technology Laboratory (CTBE); BrasilFil: Cisz, Michelle E. Michigan Technological University. School of Forest Resources and Environmental Science; Estados Unido

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

There is a data gap in our current knowledge of the geospatial distribution, type and extent of C rich peatlands across the globe. The Pastaza Marañón Foreland Basin (PMFB), within the Peruvian Amazon, is known to store large amounts of peat, but the remoteness of the region makes field data collection and mapping the distribution of peatland ecotypes challenging. Here we review methods for developing high accuracy peatland maps for the PMFB using a combination of multi-temporal synthetic aperture radar (SAR) and optical remote sensing in a machine learning classifier. The new map produced has 95% overall accuracy with low errors of commission (1–6%) and errors of omission (0–15%) for individual peatland classes. We attribute this improvement in map accuracy over previous maps of the region to the inclusion of high and low water season SAR images which provides information about seasonal hydrological dynamics. The new multi-date map showed an increase in area of more than 200% for pole forest peatland (6% error) compared to previous maps, which had high errors for that ecotype (20–36%). Likewise, estimates of C stocks were 35% greater than previously reported (3.238 Pg in Draper et al. (2014) to 4.360 Pg in our study). Most of the increase is attributed to pole forest peatland which contributed 58% (2.551 Pg) of total C, followed by palm swamp (34%, 1.476 Pg). In an assessment of deforestation from 2010 to 2018 in the PMFB, we found 89% of the deforestation was in seasonally flooded forest and 43% of deforestation was occurring within 1 km of a river or road. Peatlands were found the least affected by deforestation and there was not a noticeable trend over time. With development of improved transportation routes and population pressures, future land use change is likely to put South American tropical peatlands at risk, making continued monitoring a necessity. Accurate mapping of peatland ecotypes with high resolution (\u3c30 m) sensors linked with field data are needed to reduce uncertainties in estimates of the distribution of C stocks, and to aid in deforestation monitoring

Mountain fen distribution, types and restoration priorities, San Juan Mountains, Colorado, USA

Mountain fens are vital ecosystems for habitat, biodiversity, water and carbon cycling, but there is little comprehensive information on their distribution, abundance or condition in any region of the western U.S. Our study objectives were to: 1) evaluate fen distribution, abundance and characteristics in the San Juan Mountains of Colorado, 2) quantify disturbances, and 3) prioritize restoration needs of fens. We mapped 624 fens in 37 watersheds and collected field data on 182 of these fens. We estimated that approximately 2,000 fens occur in the San Juan Mountains, primarily in the subalpine zone at an average elevation of 3,288 m. Fens ranged from 0.2 to 20.5 ha in size, peat thickness ranged between 0.40 to \u3e 4.00 m, and surface slope ranged from 0-21%. Groundwater pH ranged from 3.1-7.6 and Ca+2 from 1-341 mg/L, reflecting the diverse geochemistry of watershed parent materials. We identified 188 vascular and 63 bryophyte taxa, and classified the 309 sampled stands into 20 plant communities that formed along complex hydrogeomorphic and geochemical gradients. The majority of fens were in excellent condition; however 10% of our sampled fens had high to very high restoration potential due to impacts from roads, mining, and ditching. © Society of Wetland Scientists 2010