Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

A study of students' metacognitive beliefs about foreign language study and their impact on learning

This article reports on an investigation into the language learning beliefs of students of French in England, aged 16 to 18. It focuses on qualitative data from two groups of learners (10 in total). While both groups had broadly similar levels of achievement in French in terns of examination success, they dffered greatly in the self-image they had of themselves as language learners, with one group displaying low levels of self-eficacy beliefs regarding the possibility of future success. The implica tions of such beliefs for students' levels of motivation and persistence are discussed, together with their possible causes. The article concludes by suggesting changes in classroom practice that might help students develop a more positive image of them selves as language learners.

Safety evaluation of oligofructose: 13 Week rat study and in vitro mutagenicity

Oligofructose (OF), comprised of fructose oligomers with a terminal glucose unit, is a family Of oligosaccharides derived from the hydrolysis of inulin. Consumption of OF in animals and humans increases colonic bifidobacteria levels. The present study evaluates the safety of OF in both a 13 week rat feeding Study and Using in Vitro mutagenicity tests. Fecal bifidobacteria levels were also determined by in situ hybridization to assess a biological function of OF. Rats received either a control diet OF diets containing one of four doses of OF. Total, HDL, and LDL-cholesterol levels were significantly lower at several time points during the study in groups receiving OF compared to controls with the largest effects Occurring in the high dose male animals. Weight gain in the male high dose group was significantly lower at early time points compared to controls but]lot Significantly different at the end of study. As expected, cecal weights increased in a dose-related manner and fecal bifidobacteria levels also demonstrated a dose-related increase. There were no consistent differences in gross pathology or histopathology related to dietary OF. OF did not induce a positive response in the Ames test or chromosomal aberration test with CHO cells. These results demonstrate no adverse effects of OF. (C) 2008 Elsevier Ltd. All rights reserved.

Urban tracer dispersion experiment in London (DAPPLE) 2003: field study and comparison with empirical prediction

The DAPPLE (Dispersion of Air Pollutants and their Penetration into the Local Environment) project seeks to characterise near-field urban atmospheric dispersion using a multidisciplinary approach. In this paper we report on the first tracer dispersion experiment carried out in May 2003. Results of concurrent meteorological measurements are presented. Variations of receptor tracer concentration with time are presented. Meteorological observations suggest that in-street channelling and flow-switching at intersections take place. A comparison between roof top and surface measurements suggest that rapid vertical mixing occurs, and a comparison between a simple dispersion model and maximum concentrations observed are presented

The relationship between European Community agricultural Structural Policies and their implementation and agricultural succession and inheritance within Member States

This paper presents an overview of the European Union involvement in agricultural restructuring. By way of introduction it looks at the processes of farm replication operating in Europe and the forces behind them. It then considers the wider application of EU measures in each Member State and how they interrelate with the national property laws and social customs. In conclusion the paper appraises the restructuring policies by examining the balance between legitimising the concentration of property rights amongst a minority and the fragmentation of estates between the majority.

Phosphorus loss from agricultural catchments: pathways and implications for management

The contribution non-point P sources make to the total P loading on water bodies in agricultural catchments has not been fully appreciated. Using data derived from plot scale experimental studies, and modelling approaches developed to simulate system behaviour under differing management scenarios, a fuller understanding of the processes controlling P export and transformations along non-point transport pathways can be achieved. One modelling approach which has been successfully applied to large UK catchments (50-350km2 in area) is applied here to a small, 1.5 km2 experimental catchment. The importance of scaling is discussed in the context of how such approaches can extrapolate the results from plot-scale experimental studies to full catchment scale. However, the scope of such models is limited, since they do not at present directly simulate the processes controlling P transport and transformation dynamics. As such, they can only simulate total P export on an annual basis, and are not capable of prediction over shorter time scales. The need for development of process-based models to help answer these questions, and for more comprehensive UK experimental studies is highlighted as a pre-requisite for the development of suitable and sustainable management strategies to reduce non-point P loading on water bodies in agricultural catchments.