Journal Paper Digests

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Journal Paper Digests 2017 #25

  • Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence
  • Geochemical proxies of sediment provenance in alluvial plains with interfering fluvial systems: A study case from NE Italy
  • National baselines for the Sustainable Development Goals assessed in the SDG Index and Dashboards
  • Multiresolution analysis of characteristic length scales with high-resolution topographic data
  • A geospatial decision support system for supporting quality viticulture at the landscape scale
  • Farm reorientation assessment model based on multi-criteria decision making

Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence

Authors: Yeoh, YK; Dennis, PG; Paungfoo-Lonhienne, C; Weber, L; Brackin, R; Ragan, MA; Schmidt, S; Hugenholtz, P

Source: NATURE COMMUNICATIONS, 8 215-215; AUG 9 2017

Abstract: Culture-independent molecular surveys of plant root microbiomes indicate that soil type generally has a stronger influence on microbial communities than host phylogeny. However, these studies have mostly focussed on model plants and crops. Here, we examine the root microbiomes of multiple plant phyla including lycopods, ferns, gymnosperms, and angiosperms across a soil chronosequence using 16S rRNA gene amplicon profiling. We confirm that soil type is the primary determinant of root-associated bacterial community composition, but also observe a significant correlation with plant phylogeny. A total of 47 bacterial genera are associated with roots relative to bulk soil microbial communities, including well-recognized plant-associated genera such as Bradyrhizobium, Rhizobium, and Burkholderia, and major uncharacterized lineages such as WPS-2, Ellin329, and FW68. We suggest that these taxa collectively constitute an evolutionarily conserved core root microbiome at this site. This lends support to the inference that a core root microbiome has evolved with terrestrial plants over their 400 million year history.

Geochemical proxies of sediment provenance in alluvial plains with interfering fluvial systems: A study case from NE Italy

Authors: Natali, C; Bianchini, G

Source: CATENA, 157 67-74; OCT 2017

Abstract: This paper demonstrates that geochemistry is useful for the identification of sediment origin and provenance in alluvial plains characterised by a complex hydrographic evolution. The study is focused on the northeastern Padanian plain (Italy), an area primarily characterised by sedimentary contributions from the two largest Italian river systems (Po and Adige), which intimately interacted during the last millennia. X-ray fluorescence analyses of 120 soils and alluvial sediments define three diverse geochemical affinities that have distinctive siderophile/chalcophile elemental ratios. The sample group characterised by high Ni/Zn and Cr/Pb values conforms to modern Po River sediments, whereas a second group showing low Ni/Zn and Cr/Pb values conforms to the geochemical signature of modern Adige River sediments. A third sample group defines a “transitional” affinity that represents a geochemical mixture of Po (70%) and Adige (30%) sedimentary end-members. Based on these geochemical features, it is possible to distinguish alluvial sediments of the Po River basin (Ni/Zn > 1.0 and Cr/Pb > 4.2) from those of the Adige River basin (Ni/Zn < 0.6 and Cr/Pb < 1.9) and to provide evidence of the migration of these rivers during the evolution of the Padanian plain. The interpretation of transitional samples is less constrained and could imply an ancient connection between the two fluvial systems, possibly due to the development of wetlands where both the Po and Adige rivers variably delivered their sedimentary contributions. This study approach, therefore, provides important implications for palaeohydrographic and palaeoenvironmental reconstructions in a complex area that is characterised by significant geomorphological modifications during the last millennia.

National baselines for the Sustainable Development Goals assessed in the SDG Index and Dashboards

Authors: Schmidt-Traub, G; Kroll, C; Teksoz, K; Durand-Delacre, D; Sachs, JD

Source: NATURE GEOSCIENCE, 10 (8):547-+; AUG 2017

Abstract: The Sustainable Development Goals (SDGs) - agreed in 2015 by all 193 member states of the United Nations and complemented by commitments made in the Paris Agreement - map out a broad spectrum of economic, social and environmental objectives to be achieved by 2030. Reaching these goals will require deep transformations in every country, as well as major efforts in monitoring and measuring progress. Here we introduce the SDG Index and Dashboards as analytical tools for assessing countries’ baselines for the SDGs that can be applied by researchers in the cross-disciplinary analyses required for implementation. The Index and Dashboards synthesize available country- level data for all 17 goals, and for each country estimate the size of the gap towards achieving the SDGs. They will be updated annually. All 149 countries for which sufficient data is available face significant challenges in achieving the goals, and many countries’ development strategies are imbalanced across the economic, social and environmental priorities. We illustrate the analytical value of the index by examining its relationship with other widely used development indices and by showing how it accounts for cross-national differences in subjective well-being. Given significant data gaps, scope and coverage of the Index and Dashboards are limited, but we suggest that these analyses represent a starting point for a comprehensive assessment of national SDG baselines and can help policymakers determine priorities for early action and monitor progress. The tools also identify data gaps that must be closed for SDG monitoring.

Multiresolution analysis of characteristic length scales with high-resolution topographic data

Authors: Sangireddy, H; Stark, CP; Passalacqua, P


Abstract: Characteristic length scales (CLS) define landscape structure and delimit geomorphic processes. Here we use multiresolution analysis (MRA) to estimate such scales from high-resolution topographic data. MRA employs progressive terrain defocusing, via convolution of the terrain data with Gaussian kernels of increasing standard deviation, and calculation at each smoothing resolution of (i)the probability distributions of curvature and topographic index (defined as the ratio of slope to area in log scale) and (ii)characteristic spatial patterns of divergent and convergent topography identified by analyzing the curvature of the terrain. The MRA is first explored using synthetic 1-D and 2-D signals whose CLS are known. It is then validated against a set of MARSSIM (a landscape evolution model) steady state landscapes whose CLS were tuned by varying hillslope diffusivity and simulated noise amplitude. The known CLS match the scales at which the distributions of topographic index and curvature show scaling breaks, indicating that the MRA can identify CLS in landscapes based on the scaling behavior of topographic attributes. Finally, the MRA is deployed to measure the CLS of five natural landscapes using meter resolution digital terrain model data. CLS are inferred from the scaling breaks of the topographic index and curvature distributions and equated with (i)small-scale roughness features and (ii)the hillslope length scale.

A geospatial decision support system for supporting quality viticulture at the landscape scale

Authors: Terribile, F; Bonfante, A; D’Antonio, A; De Mascellis, R; De Michele, C; Langella, G; Manna, P; Mileti, FA; Vingiani, S; Basile, A


Abstract: The world of viticulture connected to wine making has become a very important activity in many inland areas permitting both the generation of important income and the sustaining of agriculture systems.Recent progress in both crop modeling and Decision Support Systems (DSS) applied to viticulture promises important changes that combine both high quality production and environmental sustainability. However, most of this progress is only addressed at the farm level and does not challenge the viticulture landscape, which is a key issue when facing DOC, DOCG areas, wine growers’ cooperatives and consortiums and strategic viticulture planning.Thus, this paper aims to demonstrate that a new type of DSS, which is developed on a Geospatial Cyberinfrastructure (GCI) platform, may provide an important web-based operational tool for high quality viticulture as it connects farm and landscape levels better.The GCI platform supports acquisition, management, processing of both static and dynamic data (e.g. pedological, daily climatic, and vineyard distribution), data visualization, and on-the-fly computer applications in order to perform simulation modeling (e.g. grapevine water stress, evaluation of ecosystem services, etc.). These are all potentially accessible via the Web.This is possible thanks to the implementation of a set of modeling clusters that is strongly rooted in soil-plant-atmosphere and physically based simulation modeling.The DSS tool, applied to an area of 20,000 ha in Southern Italy, is designed to address viticulture planning and management by providing operational support for farmers, farmer associations and decision makers involved in the viticulture landscape.Output of the system includes viticulture planning and management scenario analysis, maps and evaluation of potential and current plant water stress.The tool will also be demonstrated through a short selection of practical case studies.

Farm reorientation assessment model based on multi-criteria decision making

Authors: Nikoloski, T; Udovc, A; Pavlovic, M; Rajkovic, U


Abstract: Structural changes in farming present serious challenges at all spatial levels, from individual farms to the state level. The reorientation of a farm (i.e., changing from livestock production to one of horticulture or crops) represents one of these challenges. Here, a model assessing the potential for reorganizing farms to focus on horticulture is presented. The model accounts for various criteria, including: natural resources, demographic, economic, and social factors. The selection, structure, and importance of criteria and their interrelationships in the model are based on statistical data about farms, data gathered through surveys, and expert opinion groups. The model was developed using the Decision Expert method, implemented by the software DEXi, and was validated using a selection of farms. The added value of the approach is a transparent assessment of a farm’s potential, which provides vital support for deciding about its reorientation.

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