Abstract: The numerical crop growth model Italian Vineyard Integrated Numerical model for Estimating physiological values (IVINE) was developed in order to evaluate environmental forcing effects on vine growth. The IVINE model simulates vine growth processes with parameterizations, allowing the understanding of plant conditions at a vineyard scale. It requires a set of meteorology data and soil water status as boundary conditions. The primary model outputs are main phenological stages, leaf development, yield, and sugar concentration. The model requires setting some variety information depending on the cultivar: At present, IVINE is optimized for Vitis vinifera L. Nebbiolo, a variety grown mostly in the Piedmont region (northwestern Italy). In order to evaluate the model accuracy, IVINE was validated using experimental observations gathered in Piedmontese vineyards, showing performances similar or slightly better than those of other widely used crop models. The results of a sensitivity analysis performed to highlight the effects of the variations of air temperature and soil water potential input variables on IVINE outputs showed that most phenological stages anticipated with increasing temperatures, while berry sugar content saturated at about 25.5 °Bx. Long-term (60 years, in the period 1950–2009) simulations performed over a Piedmontese subregion showed statistically significant variations of most IVINE output variables, with larger time trend slopes referring to the most recent 30-year period (1980–2009), thus confirming that ongoing climate change started influencing Piedmontese vineyards in 1980.

Abstract: The Northeast Farming Region (NFR) is the most important and the largest rain-fed maize production region in China, accounting for 30% of China’s maize. We investigated the effects of climatic factors, drought risk and irrigation requirement on maize yield in different maize growth phases during 1961 to 2010 using a statistical analysis of observed yield from 44 stations in NFR. We divided the maize growing season into four growth phases, comprising seeding, vegetative, flowering and maturity. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration (ETa) and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961 to 2010, mean temperature increased significantly in all growth phases in NFR, while radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases leading to less water deficit, whereas decreased effective rainfall in the flowering and maturity phases enhanced water deficit. More days with drought stress were concentrated in western NFR where larger volumes of irrigation were needed. Our results indicate that the increase of mean temperature in the seeding and maturity phases was beneficial for maize yield, higher ETa in each growth phase would lead to yield increase, but too high rainfall would damage maize yield. The results also show that water deficit and drought stress days had significant negative effects on maize yield, and the absence of irrigation would manifest such effects on maize production in NFR. Therefore, the development of irrigation and drainage systems is highly needed for ensuring the stability of maize production in NFR. In addition, other adaptation measures like introducing new cultivars and optimizing soil and crop management to better conserve soil water would be beneficial for future maize production. No Label

Abstract: The most usual records of observable agro-meteorological quantities are in the form of time series and the knowledge about their scaling properties is fundamental for  transferring locally measured fluctuations to larger scales and vice-versa. However, the scaling analysis of these quantities is complicated because of  the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the  chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). The MDFA analysis was performed  for time series of the air temperature, wind velocity and relative air humidity (at the height of 2 m above the  active surface) as well as the soil temperature (at 10 cm depth in the soil). The studied data were hourly interval, 12 years’ time series from the agro-meteorological station in Felin, near Lublin, Poland. The empirical singularity spectra  indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating their considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response  indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality, that underlies the q-dependence of the generalized Hurst exponent, by analyzing the corresponding shuffled and surrogate time series. For majority of studied quantities, the multifractality was due to different long-range correlation for small and large fluctuations.