Levent Şaylan

Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.

Solar energy potential for heating and cooling systems in big cities of Turkey

In this study, the solar energy gain on vertical surfaces for heating and cooling systems in big cities, such as Istanbul, Ankara and Izmir of Turkey, has been estimated for different orientations using hourly solar radiation measurements. During summer, the monthly mean solar energy potential on vertical surfaces comes from the east and west directions more than other orientations in all the cities. However, during winter, it comes from the south, and the highest solar energy is received in Izmir, where the average annual global radiation attains its maximum. Additionally, north oriented surfaces produce between 65% and 75% lower total solar energy than other orientations in all the cities.

Nonlinear prediction of near-surface temperature via univariate and multivariate time series embedding

Surface temperature is an important variable, especially for frost prediction and other agricultural applications. For these purposes, accurate surface temperature measurements or predictions are required. The present study uses univariate (UTSE) and multivariate time series embedding (MTSE) for the purpose of nonlinear time series prediction. These two approaches require multidimensional phase space construction. The coordinates of this space are spanned by the time series itself and its shifted versions. Then the prediction is performed by estimating the change in trajectory by a polynomial approximation. In univariate time series embedding, prediction of the soil temperatures measured at 2 cm below the soil surface was performed. In multivariate time series embedding, air temperature and wind speed data were used together with the soil temperature. Predictions obtained from the UTSE and the MTSE were compared with conventional methods such as AR(p) and multivariate AR(p). Errors between the observed and the predicted values are slightly smaller than those of the conventional methods.

Trends of Growing Degree-Days in Turkey

Variations in the surface air temperatures show an associationwith the atmospheric CO2 under the hypothesis that warmertemperatures promote increases in plant growth in mid-northernlatitudes. Historical daily temperature records at seventy-fourweather stations (around 60-yr) in Turkey are used in orderto investigate this hypothesis. Separate calculations and analyses are undertaken for monthly and seasonal growing degree-days (GDD). The sequential version of Mann-Kendall rankstatistic is applied to demonstrate existence of any possible non-linear trends in accumulated GDDs above 5 °C over the months and seasons. Spatially coherent and statisticallysignificant trends of GDD are shown in some parts of Turkey. The statistically significant trends are negative mostly in summer and autumn seasons over the coastal areas of Turkey.These results strongly agree with the previous studies where weak cooling trends are observed in maximum temperatures overTurkey. They suggest the significant decreases in GDD may lead to long-term decreases in vegetation growth mostly overthe coastal regions of Turkey.

IMPACTS OF METEOROLOGICAL VARIABLES ON NET ECOSYSTEM EXCHANGE OF WINTER WHEAT

Understanding the characterization of net ecosystem exchange (NEE) with atmospheric variables is crucial. Measured NEE of CO2 between the atmosphere and different ecosystems helps to understand the CO2 balance and its component (gross primary production (GPP) and ecosystem respiration (Reco). In order to measure long-term CO2 and H2O fluxes over a winter wheat field in the Northwestern part of Turkey, an Eddy Covariance (EC) measurement system was established and used for investigating the impacts of biophysical and atmospheric factors on NEE. It is aimed to determine the relationships between NEE, GPP and Reco and meteorological variables such as soil temperature and global radiation. Results showed that the best relationship occurred between NEE and Photosynthetic Photon Flux Density (PPFD) (r=0.47), which is affected by clear and cloudy sky conditions. GPP and Reco are mostly affected by PPFD and soil temperature, respectively. More attention is to be paid to include also their influences within the process to enhance our insight into the CO2 flux perspectives.

Simulation of Agricultural Meteorological Factors Affecting Evapotranspiration by Fuzzy Inference

Abstract In agricultural meteorological studies, the major difficulty is the determination of evapotranspiration (latent heat flux). There are numerous methods for estimating its value. However, these approaches yield accurate and successful results proportionally with the complexity. The main purpose of this paper is to determine the effects of atmospheric, soil, and field measured plant quantities on the latent heat flux by using the fuzzy inference systems techniques. The necessary measurements are recorded during two different periods in the faba bean plant growth at an agricultural meteorology station by considering a Bowen Ratio Energy Balance System.