Abstract: Ultraviolet (UV) radiation directly regulates a multitude of herbivore life processes, in addition to indirectly affecting insect success via changes in plant chemistry and morphogenesis. Here we looked at plant and insect (aphid and whitefly) exposure to supplemental UV-A radiation in the glasshouse environment and investigated effects on insect population growth. Glasshouse grown peppers and eggplants were grown from seed inside cages covered by novel plastic filters, one transparent and the other opaque to UV-A radiation. At a 10-true leaf stage for peppers (53 days) and 4-true leaf stage for eggplants (34 days), plants were harvested for chemical analysis and infested by aphids and whiteflies, respectively. Clip-cages were used to introduce and monitor the insect fitness and populations of the pests studied. Insect pre-reproductive period, fecundity, fertility and intrinsic rate of natural increase were assessed. Crop growth was monitored weekly for 7 and 12 weeks throughout the crop cycle of peppers and eggplants, respectively. At the end of the insect fitness experiment, plants were harvested (68 days and 18-true leaf stage for peppers, and 104 days and 12-true leaf stage for eggplants) and leaves analysed for secondary metabolites, soluble carbohydrates, amino acids, total proteins and photosynthetic pigments. Our results demonstrate for the first time, that UV-A modulates plant chemistry with implications for insect pests. Both plant species responded directly to UV-A by producing shorter stems but this effect was only significant in pepper whilst UV-A did not affect the leaf area of either species. Importantly, in pepper, the UV-A treated plants contained higher contents of secondary metabolites, leaf soluble carbohydrates, free amino acids and total content of protein. Such changes in tissue chemistry may have indirectly promoted aphid performance. For eggplants, chlorophylls a and b, and carotenoid levels decreased with supplemental UV-A over the entire crop cycle but UV-A exposure did not affect leaf secondary metabolites. However, exposure to supplemental UV-A had a detrimental effect on whitefly development, fecundity and fertility presumably not mediated by plant cues as compounds implied in pest nutrition – proteins and sugars – were unaltered.

Abstract: Increasing cereal yield is needed to meet the projected increased demand for world food supply of about 70% by 2050. Sirius, a process-based model for wheat, was used to estimate yield potential for wheat ideotypes optimized for future climatic projections for ten wheat growing areas of Europe. It was predicted that the detrimental effect of drought stress on yield would be decreased due to enhanced tailoring of phenology to future weather patterns, and due to genetic improvements in the response of photosynthesis and green leaf duration to water shortage. Yield advances could be made through extending maturation and thereby improve resource capture and partitioning. However the model predicted an increase in frequency of heat stress at meiosis and anthesis. Controlled environment experiments quantify the effects of heat and drought at booting and flowering on grain numbers and potential grain size. A current adaptation of wheat to areas of Europe with hotter and drier summers is a quicker maturation which helps to escape from excessive stress, but results in lower yields. To increase yield potential and to respond to climate change, increased tolerance to heat and drought stress should remain priorities for the genetic improvement of wheat.

Abstract: The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.