Modeling and forecasting daily maximum hourly ozone concentrations using the RegAR model with skewed and heavy-tailed innovations

This paper considers the modeling and forecasting of daily maximum hourly ozone concentrations in Laranjeiras, Serra, Brazil, through dynamic regression models. In order to take into account the natural skewness and heavy-tailness of the data, a linear regression model with autoregressive errors and innovations following a member of the family of scale mixture of skew-normal distributions was considered. Pollutants and meteorological variables were considered as predictors, along with some deterministic factors, namely week-days and seasons. The Oceanic Niño Index was also considered as a predictor. The estimated model was able to explain satisfactorily well the correlation structure of the ozone time series. An out-of-sample forecast study was also performed. The skew-normal and skew-t models displayed quite competitive point forecasts compared to the similar model with gaussian innovations. On the other hand, in terms of forecast intervals, the skewed models presented much better performance with more accurate prediction intervals. These findings were empirically corroborated by a forecast Monte Carlo experiment.     

Decreasing reliance on mineral nitrogen--yet more food

Higher crop production normally demands higher nutrient application rates and consequently increased mineral nitrogen use. With food demand for 2030 estimated around 2800 mill. tonnes (t) yr-1, the corresponding mineral N consumption figure is 96 mill. t (78 mill. t yr-1 in 1995/1997). Global-level mineral N losses to the environment from mineral fertilizer use are currently 36 mill. t yr-1, worth USD 11,700 mill. and with adverse environmental impacts. However, innovative fertilizer-use efficiency (FUE) technologies enable increased production with a less than a proportionate increase in mineral-N use. Moreover, nitrogen-nutrient supplies can be augmented through improvements in agricultural production systems and in the exploitation of alternative sources such as biological nitrogen fixation (BNF). By 2030, with adequate policy, technology transfer, research and investment support, the on-farm adoption of BNF and FUE technologies could generate savings of 10 mill. t yr-1 of mineral N, worth USD 3300 mill.