About: Empresa Brasileira de Pesquisa Agropecuária is a government organization based out in Brasília, Brazil. It is known for research contribution in the topics: Population & Soil water. The organization has 13406 authors who have published 36742 publications receiving 661021 citations.
Papers published on a yearly basis
TL;DR: The generic framework, which the authors call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited.
Abstract: We review various recent approaches to making digital soil maps based on geographic information systems (GIS) data layers, note some commonalities and propose a generic framework for the future We discuss the various methods that have been, or could be, used for fitting quantitative relationships between soil properties or classes and their ‘environment’ These include generalised linear models, classification and regression trees, neural networks, fuzzy systems and geostatistics We also review the data layers that have been, or could be, used to describe the ‘environment’ Terrain attributes derived from digital elevation models, and spectral reflectance bands from satellite imagery, have been the most commonly used, but there is a large potential for new data layers The generic framework, which we call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited It is based on the seven predictive scorpan factors, a generalisation of Jenny’s five factors, namely: (1) s: soil, other or previously measured attributes of the soil at a point; (2) c: climate, climatic properties of the environment at a point; (3) o: organisms, including land cover and natural vegetation; (4) r: topography, including terrain attributes and classes; (5) p: parent material, including lithology; (6) a: age, the time factor; (7) n: space, spatial or geographic position Interactions (*) between these factors are also considered The scorpan-SSPFe method essentially involves the following steps:
TL;DR: The quantity and the quality of the DNA extracted by this method is high enough to perform hundreds of PCR-based reactions and also to be used in other DNA manipulation techniques such as restriction digestion, Southern blot and cloning.
Abstract: A very simple, fast, universally applicable and reproducible method to extract high quality megabase genomic DNA from different organisms is described. We applied the same method to extract high quality complex genomic DNA from different tissues (wheat, barley, potato, beans, pear and almond leaves as well as fungi, insects and shrimps' fresh tissue) without any modification. The method does not require expensive and environmentally hazardous reagents and equipment. It can be performed even in low technology laboratories. The amount of tissue required by this method is approximately 50-100 mg. The quantity and the quality of the DNA extracted by this method is high enough to perform hundreds of PCR-based reactions and also to be used in other DNA manipulation techniques such as restriction digestion, Southern blot and cloning.
01 Sep 2011
Max Planck Society1, National University of Cordoba2, Centre national de la recherche scientifique3, Macquarie University4, University of Paris-Sud5, University of Minnesota6, University of Western Sydney7, VU University Amsterdam8, University of Arizona9, University of California, Berkeley10, University of Guelph11, Australian National University12, University of Innsbruck13, University of Leeds14, University of Groningen15, Universidade Federal do Rio Grande do Sul16, University of Cape Town17, University of Wollongong18, New Jersey Institute of Technology19, Centro Agronómico Tropical de Investigación y Enseñanza20, Lawrence Berkeley National Laboratory21, University of Alaska Fairbanks22, University of Cambridge23, Kansas State University24, Helmholtz Centre for Environmental Research - UFZ25, Arizona State University26, University of Giessen27, Autonomous University of Barcelona28, University of Maryland, College Park29, Universidad del Tolima30, University of São Paulo31, University of La Réunion32, University of York33, University of Sydney34, Harvard University35, Goethe University Frankfurt36, University of Sheffield37, University of Ulm38, State University of Campinas39, Kenyon College40, Royal Botanic Gardens41, University of Florida42, University of Oldenburg43, University of Nebraska–Lincoln44, Tohoku University45, Northern Arizona University46, University of Wisconsin–Eau Claire47, Naturalis48, James Cook University49, Institut national de la recherche agronomique50, Newcastle University51, University of New South Wales52, Leipzig University53, Columbia University54, Estonian University of Life Sciences55, Polish Academy of Sciences56, Moscow State University57, Kyushu University58, Wageningen University and Research Centre59, Spanish National Research Council60, University of Regensburg61, University of Rennes62, Université du Québec à Trois-Rivières63, Potsdam Institute for Climate Impact Research64, Technical University of Denmark65, University of California, Los Angeles66, Hokkaido University67, Université de Sherbrooke68, Syracuse University69, Empresa Brasileira de Pesquisa Agropecuária70, University of Aberdeen71, Michigan State University72, Oak Ridge National Laboratory73, University of Leicester74, Utah State University75, Smithsonian Institution76, University of Missouri77
TL;DR: TRY as discussed by the authors is a global database of plant traits, including morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs, which can be used for a wide range of research from evolutionary biology, community and functional ecology to biogeography.
Abstract: Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.
TL;DR: In the first experiment, cowpea (Vigna unguiculata (L) Walp) was planted in pots, while in the second experiment lysimeters were used to quantify water and nutrient leaching from soil cropped to rice (Oryza sativa L) as discussed by the authors.
Abstract: Soil fertility and leaching losses of nutrients were compared between a Fimic Anthrosol and a Xanthic Ferralsol from Central Amazonia The Anthrosol was a relict soil from pre-Columbian settlements with high organic C containing large proportions of black carbon It was further tested whether charcoal additions among other organic and inorganic applications could produce similarly fertile soils as these archaeological Anthrosols In the first experiment, cowpea (Vigna unguiculata (L) Walp) was planted in pots, while in the second experiment lysimeters were used to quantify water and nutrient leaching from soil cropped to rice (Oryza sativa L) The Anthrosol showed significantly higher P, Ca, Mn, and Zn availability than the Ferralsol increasing biomass production of both cowpea and rice by 38–45% without fertilization (P<005) The soil N contents were also higher in the Anthrosol but the wide C-to-N ratios due to high soil C contents led to immobilization of N Despite the generally high nutrient availability, nutrient leaching was minimal in the Anthrosol, providing an explanation for their sustainable fertility However, when inorganic nutrients were applied to the Anthrosol, nutrient leaching exceeded the one found in the fertilized Ferralsol Charcoal additions significantly increased plant growth and nutrition While N availability in the Ferralsol decreased similar to the Anthrosol, uptake of P, K, Ca, Zn, and Cu by the plants increased with higher charcoal additions Leaching of applied fertilizer N was significantly reduced by charcoal, and Ca and Mg leaching was delayed In both the Ferralsol with added charcoal and the Anthrosol, nutrient availability was elevated with the exception of N while nutrient leaching was comparatively low
TL;DR: The main functions of rhizosphere microorganisms and how they impact on health and disease are reviewed and several strategies to redirect or reshape the rhizospheric microbiome in favor of microorganisms that are beneficial to plant growth and health are highlighted.
Abstract: Microbial communities play a pivotal role in the functioning of plants by influencing their physiology and development. While many members of the rhizosphere microbiome are beneficial to plant growth, also plant pathogenic microorganisms colonize the rhizosphere striving to break through the protective microbial shield and to overcome the innate plant defense mechanisms in order to cause disease. A third group of microorganisms that can be found in the rhizosphere are the true and opportunistic human pathogenic bacteria, which can be carried on or in plant tissue and may cause disease when introduced into debilitated humans. Although the importance of the rhizosphere microbiome for plant growth has been widely recognized, for the vast majority of rhizosphere microorganisms no knowledge exists. To enhance plant growth and health, it is essential to know which microorganism is present in the rhizosphere microbiome and what they are doing. Here, we review the main functions of rhizosphere microorganisms and how they impact on health and disease. We discuss the mechanisms involved in the multitrophic interactions and chemical dialogues that occur in the rhizosphere. Finally, we highlight several strategies to redirect or reshape the rhizosphere microbiome in favor of microorganisms that are beneficial to plant growth and health.
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|Maarten J. Chrispeels||85||268||22535|
|Philip M. Fearnside||85||377||24776|
|James W. Jones||76||426||26298|
|Carlos Ricardo Soccol||70||544||22933|
|Albert I. Ko||68||337||19056|
|Luiz H. C. Mattoso||66||455||17432|
|Osvaldo N. Oliveira||59||614||16369|
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