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Dáder, B., Gwynn-Jones, D., Moreno, A., Winters, A., & Fereres, A. (2014). Impact of UV-A radiation on the performance of aphids and whiteflies and on the leaf chemistry of their host plants. J. Photochem. Photobiol. B, 138, 307–316.
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.
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Baranowski, P., Krzyszczak, J., Slawinski, C., Hoffmann, H., Kozyra, J., Nieróbca, A., et al. (2015). Multifractal analysis of meteorological time series to assess climate impacts. Clim. Res., 65, 39–52.
Abstract: Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performedwith 11 322 measured time series (31 yr) of daily air temperature, wind velocity, relative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. In log-log plots of the cumulative distributions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifractality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics.
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Semenov, M. A., Stratonovitch, P., Alghabari, F., & Gooding, M. J. (2014). Adapting wheat in Europe for climate change. J. Ceareal Sci., 59(3), 245–256.
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.
Keywords: A, maximum area of flag leaf area; ABA, abscisic acid; CV, coefficient of variation; Crop improvement; Crop modelling; FC, field capacity; GMT, Greenwich mean time; GS, growth stage; Gf, grain filling duration; HI, harvest index; HSP, heat shock protein; Heat and drought tolerance; Impact assessment; LAI, leaf area index; Ph, phylochron; Pp, photoperiod response; Ru, root water uptake; S, duration of leaf senescence; SF, drought stress factor; Sirius; Wheat ideotype
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Murat, M., Malinowska, I., Hoffmann, H., & Baranowski, P. (2016). Statistical modelling of agrometeorological time series by exponential smoothing. International Agrophysics, 30(1), 57–65.
Abstract: Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.
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Murat, M., Malinowska, I., Gos, M., & Krzyszczak, J. (2018). Forecasting daily meteorological time series using ARIMA and regression models. Int. Agrophys., 32(2), 253–264.
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.
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