The effects of nitrogen application and assimilate availability on engorged pollen production and spikelet sterility in rice

Increased rates of nitrogen fertilizer application lead to increased spikelet sterility. A field experiment was conducted to investigate the effects on engorged pollen production and spikelet sterility, of nitrogen and assimilate availability during microspore development, in two rice cultivars (Doongara and Amaroo) grown under two different water depths. Despite the temperature not being low enough during microspore development to cause spikelet sterility, the number of engorged pollen grains was lower in cv. Doongara than in cv. Amaroo. Nitrogen application decreased the number of engorged pollen grains per anther through increased spikelet density. Nitrogen application increased spikelet sterility as a result of increased panicle density showing pronounced indirect effect of N on spikelet sterility. Engorged pollen number was also closely related (r = -0.636*) to the nitrogen content of the leaf blade, indicating a direct negative effect of plant N status on engorged pollen production. The results suggest that the intrinsic pollen producing ability is the key element in the difference in cold tolerance between the two cultivars, particularly under high N rates. Opening the canopy for increased solar radiation interception by the treated plants increased the level of engorged pollen, indicating the importance of immediate assimilate availability for engorged pollen production. Shading reduced crop growth rate, but did not effect engorged pollen production. There was no effect of variation in assimilates production on spikelet sterility.

Where next for PSMs - a grass-roots revolution?

Over the last 30 years, the field of problem structuring methods (PSMs) has been pioneered by a handful of 'gurus'—the most visible of whom have contributed other viewpoints to this special issue. As this generation slowly retires, it is opportune to survey the field and their legacy. We focus on the progress the community has made up to 2000, as work that started afterwards is ongoing and its impact on the field will probably only become apparent in 5–10 years time. We believe that up to 2000, research into PSMs was stagnating. We believe that this was partly due to a lack of new researchers penetrating what we call the 'grass-roots community'—the community which takes an active role in developing the theory and application of problem structuring. Evidence for this stagnation (or lack of development) is that, in 2000, many PSMs still relied heavily on the same basic methods proposed by the originators nearly 30 years earlier—perhaps only supporting those methods with computer software as a sign of development. Furthermore, no new methods had been integrated into the literature which suggests that revolutionary development, at least by academics, has stalled. We are pleased to suggest that from papers in this double special issue on PSMs this trend seems over because new authors report new PSMs and extend existing PSMs in new directions. Despite these recent developments of the methods, it is important to examine why this apparent stagnation took place. In the following sections, we identify and elaborate a number of reasons for it. We also consider the trends, challenges and opportunities that the PSM community will continue to face. Our aim is to evaluate the pre-2000 PSM community to encourage its revolutionary development post-2006 and offer directions for the long term sustainability of the field.

Measurement of key compositional parameters in two species of energy grass by Fourier transform infrared spectroscopy

Two energy grass species, switch grass, a North American tuft grass, and reed canary grass, a European native, are likely to be important sources of biomass in Western Europe for the production of biorenewable energy. Matching chemical composition to conversion efficiency is a primary goal for improvement programmes and for determining the quality of biomass feed-stocks prior to use and there is a need for methods which allow cost effective characterisation of chemical composition at high rates of sample through-put. In this paper we demonstrate that nitrogen content and alkali index, parameters greatly influencing thermal conversion efficiency, can be accurately predicted in dried samples of these species grown under a range of agronomic conditions by partial least square regression of Fourier transform infrared spectra (R2 values for plots of predicted vs. measured values of 0.938 and 0.937, respectively). We also discuss the prediction of carbon and ash content in these samples and the application of infrared based predictive methods for the breeding improvement of energy grasses.

Comprehensive characterization of Napier grass as a feedstock for thermochemical conversion

Study on Napier grass leaf (NGL), stem (NGS) and leaf and stem (NGT) was carried out. Proximate, ultimate and structural analyses were evaluated. Functional groups and crystalline components in the biomass were examined. Pyrolysis study was conducted in a thermogravimetric analyzer under nitrogen atmosphere of 20 mL/min at constant heating rate of 10 K/min. The results reveal that Napier grass biomass has high volatile matter, higher heating value, high carbon content and lower ash, nitrogen and sulfur contents. Structural analysis shows that the biomass has considerable cellulose and lignin contents which are good candidates for good quality bio-oil production. From the pyrolysis study, degradation of extractives, hemicellulose, cellulose and lignin occurred at temperature around 478, 543, 600 and above 600 K, respectively. Kinetics of the process was evaluated using reaction order model. New equations that described the process were developed using the kinetic parameters and data compared with experimental data. The results of the models fit well to the experimental data. The proposed models may be a reliable means for describing thermal decomposition of lignocellulosic biomass under nitrogen atmosphere at constant heating rate.

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.