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The benefits of biochar application are well described in tropical soils, however there is a dearth of information on its effects in agricultural temperate soils. An interesting and little explored interaction may occur in an intensive agriculture setting; biochar addition may modify the effect of commonplace N-fertilization. We conducted a field experiment to study the effects of biochar application at the rate of 0, 10 and 20 t ha-1 (B0, B10 and B20) in combination with 0, 40 and 80 kg N ha-1 of N-fertilizer (N0, N40, N80). We followed nitrous oxide (N2O) emissions, analysed a series of soil physicochemical properties and measured barley yield in a Haplic Luvisol in Central Europe. Seasonal cumulative N2O emissions from B10N0 and B20N0 treatments decreased by 27 and 25% respectively, when compared to B0N0. Cumulative N2O emissions from N40 and N80 combined with B10 and B20 were also lower by 21, 19 and 25, 32%, respectively compared to controls B0N40 and B0N80. Average pH was significantly increased by biochar addition. Increased soil pH and reduces NO-3 content seen in biochar treatments could be the two possible mechanisms responsible for reduced N2O emissions. There was a statistically significant increase of soil water content in B20N0 treatment compared to B0N0 control, possibly as a result of larger surface area and the presence of microspores having altered pore size distribution and water-holding capacity of the soil. Application of biochar at the rate of 10 t ha-1 had a positive effect on spring barley grain yield. [ABSTRACT FROM AUTHOR]

Many scientific disciplines are currently experiencing a “reproducibility crisis” because numerous scientific findings cannot be repeated consistently. A novel but controversial hypothesis postulates that stringent levels of environmental and biotic standardization in experimental studies reduces reproducibility by amplifying impacts of lab-specific environmental factors not accounted for in study designs. A corollary to this hypothesis is that a deliberate introduction of controlled systematic variability (CSV) in experimental designs may lead to increased reproducibility. To test this hypothesis, we had 14 European laboratories run a simple microcosm experiment using grass (Brachypodium distachyon L.) monocultures and grass and legume (Medicago truncatula Gaertn.) mixtures. Each laboratory introduced environmental and genotypic CSV within and among replicated microcosms established in either growth chambers (with stringent control of environmental conditions) or glasshouses (with more variable environmental conditions). The introduction of genotypic CSV led to 18% lower among-laboratory variability in growth chambers, indicating increased reproducibility, but had no significant effect in glasshouses where reproducibility was generally lower. Environmental CSV had little effect on reproducibility. Although there are multiple causes for the “reproducibility crisis”, deliberately including genetic variation may be a simple solution for increasing the reproducibility of ecological studies performed under stringently controlled environmental conditions., Organismic and Evolutionary Biology

Natural image processing and understanding encompasses hundreds of different algorithms. Each algorithm generates best results for a particular set of input features and configurations of the objects/regions in the input image (environment). To obtain the best possible result of processing in a reliable manner, we propose an algorithm selection approach that selects the best algorithm for a each input image. The proposed algorithm selection starts by first selecting an algorithm using low level features such as color intensity, histograms, spectral coefficients or so and a user given context if available. The resulting high-level image description is analyzed for logical inconsistencies (contradictions) and image regions that must be processed using a different algorithm are selected. The high-level description and the optional user-given context are used by a Bayesian Network to estimate the cause of the error in the processing. The same Bayesian Network also generates new candidate algorithm for each region containing the contradiction in an iterative manner. This iterative selection stops when the high-level inconsistencies are all resolved or no more different algorithms can be selected. We also show that when inconsistencies can be detected, our framework is able to improve high-level description when compared with single algorithms. In order for such complex and iterative processing being computationally tractable we also introduce a hardware platform based on reconfigurable VLSI that is well suited as the platform of the proposed approach. We show that the algorithm selected approach is ideally suited for either a hybrid type VLSI processor or for a Logic-In-Memory processing platform. [ABSTRACT FROM AUTHOR]