784 research outputs found
Fertimetro, a Principle and Device to Measure Soil Nutrient Availability for Plants by Microbial Degradation Rates on Differently-Spiked Buried Threads
A novel patented method (PCT/IB2012/001157: Squartini, Concheri, Tiozzo, University of Padova) and the corresponding application devices, suitable to measure soil fertility, are presented. The availability or deficiency of specific nutrients for crops is assessed by monitoring the kinetics of progressive weakening of cotton or silk threads due to in situ microbial activity. The method is based on a nutrient-primed incremented substrate degradation principle. Threads are buried as is or pre-impregnated with N or P solutions, and the acceleration of the degradation rate for the N-supplemented or P-supplemented thread, in comparison to the untreated thread, is proportional to the lack of the corresponding nutrient in that soil. Tests were validated on corn crops in plots receiving increasing fertilizer rates in a historical rotation that has been established since 1962. The measurement carried out in May significantly correlated with the subsequent crop yields recorded in October. The analysis allows an early, inexpensive, fast, and reproducible self-assessment at field level to improve fertilization rates. The device is envisaged as a user-friendly tool for agronomy, horticulture, and any environmental applications where organic matter cycling, soil quality, and specific nutrients excess or deficiency are critical considerations
Spatial effects in real networks: measures, null models, and applications
Spatially embedded networks are shaped by a combination of purely topological
(space-independent) and space-dependent formation rules. While it is quite easy
to artificially generate networks where the relative importance of these two
factors can be varied arbitrarily, it is much more difficult to disentangle
these two architectural effects in real networks. Here we propose a solution to
the problem by introducing global and local measures of spatial effects that,
through a comparison with adequate null models, effectively filter out the
spurious contribution of non-spatial constraints. Our filtering allows us to
consistently compare different embedded networks or different historical
snapshots of the same network. As a challenging application we analyse the
World Trade Web, whose topology is expected to depend on geographic distances
but is also strongly determined by non-spatial constraints (degree sequence or
GDP). Remarkably, we are able to detect weak but significant spatial effects
both locally and globally in the network, showing that our method succeeds in
retrieving spatial information even when non-spatial factors dominate. We
finally relate our results to the economic literature on gravity models and
trade globalization
Genome sequence of Rhizobium sullae HCNT1 isolated from Hedysarum coronarium nodules and featuring peculiar denitrification phenotypes
The genome sequence of Rhizobium sullae strain HCNT1, isolated from root nodules of the legume Hedysarum coronarium growing in wild stands in Tuscany, Italy, is described here. Unlike other R. sullae strains, this isolate features a truncated denitrification pathway lacking NO/N2O reductase activity and displaying high sensitivity to nitrite under anaerobic conditions
Null Models of Economic Networks: The Case of the World Trade Web
In all empirical-network studies, the observed properties of economic
networks are informative only if compared with a well-defined null model that
can quantitatively predict the behavior of such properties in constrained
graphs. However, predictions of the available null-model methods can be derived
analytically only under assumptions (e.g., sparseness of the network) that are
unrealistic for most economic networks like the World Trade Web (WTW). In this
paper we study the evolution of the WTW using a recently-proposed family of
null network models. The method allows to analytically obtain the expected
value of any network statistic across the ensemble of networks that preserve on
average some local properties, and are otherwise fully random. We compare
expected and observed properties of the WTW in the period 1950-2000, when
either the expected number of trade partners or total country trade is kept
fixed and equal to observed quantities. We show that, in the binary WTW,
node-degree sequences are sufficient to explain higher-order network properties
such as disassortativity and clustering-degree correlation, especially in the
last part of the sample. Conversely, in the weighted WTW, the observed sequence
of total country imports and exports are not sufficient to predict higher-order
patterns of the WTW. We discuss some important implications of these findings
for international-trade models.Comment: 39 pages, 46 figures, 2 table
Randomizing bipartite networks: The case of the World Trade Web
This is the final version. Available from Nature Research via the DOI in this record. Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization.GROWTHCO
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