Linear-wavelet models for non-linear identification applied to a pressure plant

A nonlinear regression structure comprising a wavelet network and a linear term is proposed for system identification. The theoretical foundation of the approach is laid by proving that radial wavelets are orthogonal to linear functions. A constructive procedure for building such models is described and the approach is tested with experimental data.

Water reuse for irrigation in Jordan: plant beneficial nutrients, farmers’ awareness and management strategies

The reuse of treated wastewater (reclaimed water) is particularly well suited for irrigated agriculture as it often contains significant quantities of plant essential nutrients. This work has shown that reclaimed water in Jordan can have adequate concentrations of potassium, phosphate, sulphate and magnesium to meet all or part of the crop’s requirements. To fully benefit from these inputs farmers must have an awareness of the water quality and reduce the application of chemical fertilisers accordingly. Interviews with farmers have shown that 75 per cent of farmers indirectly using reclaimed water are aware of the nutrients. Farmers’ decision making as to the application of chemical fertilisers appears to be influenced by a range of factors which include the type of crops being cultivated, the provision of training on nutrient management and the availability of information on the nutrient content of the reclaimed water.

Geographic distribution of plant pathogens in response to climate change

Geographic distributions of pathogens are the outcome of dynamic processes involving host availability, susceptibility and abundance, suitability of climate conditions, and historical contingency including evolutionary change. Distributions have changed fast and are changing fast in response to many factors, including climatic change. The response time of arable agriculture is intrinsically fast, but perennial crops and especially forests are unlikely to adapt easily. Predictions of many of the variables needed to predict changes in pathogen range are still rather uncertain, and their effects will be profoundly modified by changes elsewhere in the agricultural system, including both economic changes affecting growing systems and hosts and evolutionary changes in pathogens and hosts. Tools to predict changes based on environmental correlations depend on good primary data, which is often absent, and need to be checked against the historical record, which remains very poor for almost all pathogens. We argue that at present the uncertainty in predictions of change is so great that the important adaptive response is to monitor changes and to retain the capacity to innovate, both by access to economic capital with reasonably long-term rates of return and by retaining wide scientific expertise, including currently less fashionable specialisms.

Modeling the plant uptake of organic chemicals, including the soil-air-plant pathway

The soil−air−plant pathway is potentially important in the vegetative accumulation of organic pollutants from contaminated soils. While a number of qualitative frameworks exist for the prediction of plant accumulation of organic chemicals by this pathway, there are few quantitative models that incorporate this pathway. The aim of the present study was to produce a model that included this pathway and could quantify its contribution to the total plant contamination for a range of organic pollutants. A new model was developed from three submodels for the processes controlling plant contamination via this pathway: aerial deposition, soil volatilization, and systemic translocation. Using the combined model, the soil−air−plant pathway was predicted to account for a significant proportion of the total shoot contamination for those compounds with log KOA > 9 and log KAW < −3. For those pollutants with log KOA < 9 and log KAW > −3 there was a higher deposition of pollutant via the soil−air−plant pathway than for those chemicals with log KOA > 9 and log KAW < −3, but this was an insignificant proportion of the total shoot contamination because of the higher mobility of these compounds via the soil−root−shoot pathway. The incorporation of the soil−air−plant pathway into the plant uptake model did not significantly improve the prediction of the contamination of vegetation from polluted soils when compared across a range of studies. This was a result of the high variability between the experimental studies where the bioconcentration factors varied by 2 orders of magnitude at an equivalent log KOA. One potential reason for this is the background air concentration of the pollutants under study. It was found background air concentrations would dominate those from soil volatilization in many situations unless there was a soil hot spot of contamination, i.e., >100 mg kg−1.

Pathogenicity and other genomic islands in plant pathogenic bacteria

Pathogenicity islands (PAIs) were first described in uropathogenic E. coli. They are now defined as regions of DNA that contain virulence genes and are present in the genome of pathogenic strains, but absent from or only rarely present in non-pathogenic variants of the same or related strains. Other features include a variable G+C content, distinct boundaries from the rest of the genome and the presence of genes related to mobile elements such as insertion sequences, integrases and transposases. Although PAIs have now been described in a wide range of both plant and animal pathogens it has become evident that the general features of PAIs are displayed by a number of regions of DNA with functions other than pathogenicity, such as symbiosis and antibiotic resistance, and the general term genomic islands has been adopted. This review will describe a range of genomic islands in plant pathogenic bacteria including those that carry effector genes, phytotoxins and the type III protein secretion cluster. The review will also consider some medically important bacteria in order to discuss the range, acquisition and stabilization of genomic islands.

Belowground interactive effects of elevated CO2, plant diversity and earthworms in grassland microcosms

The potential interactive effects of future atmospheric CO2 concentrations and plant diversity loss on the functioning of belowground systems are still poorly understood. Using a microcosm greenhouse approach with assembled grassland plant communities of different diversity (1, 4 and 8 species), we explored the interactive effects between plant species richness and elevated CO2 (ambient and + 200 p.p.m.v. CO2) on earthworms and microbial biomass. We hypothesised that the beneficial effect of increasing plant species richness on earthworm performance and microbial biomass will be modified by elevated CO2 through impacts on belowground organic matter inputs, soil water availability and nitrogen availability. We found higher earthworm biomass in eight species mixtures under elevated CO2, and higher microbial biomass under elevated CO2 in four and eight species mixtures if earthworms were present. The results suggest that plant driven changes in belowground organic matter inputs, soil water availability and nitrogen availability explain the interactive effects of CO2 and plant diversity on the belowground compartment. The interacting mechanisms by which elevated CO2 modified the impact of plant diversity on earthworms and microorganisms are discussed.

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.