scholarly journals Improving node number simulation in soybean

2009 ◽  
Vol 44 (7) ◽  
pp. 661-668 ◽  
Author(s):  
Nereu Augusto Streck ◽  
Gizelli Moiano de Paula ◽  
Felipe Brendler Oliveira ◽  
Ana Paula Schwantes ◽  
Nilson Lemos de Menezes
Keyword(s):  
Air Temperature ◽  
Temperature Response ◽  
Node Number ◽  
Arithmetic Average ◽  
Soybean Cultivars ◽  
Daily Minimum ◽  
Air Temperatures ◽  
Nonlinear Temperature ◽  
Santa Maria ◽  
Maximum Air Temperature

The objective of this study was to improve the simulation of node number in soybean cultivars with determinate stem habits. A nonlinear model considering two approaches to input daily air temperature data (daily mean temperature and daily minimum/maximum air temperatures) was used. The node number on the main stem data of ten soybean cultivars was collected in a three-year field experiment (from 2004/2005 to 2006/2007) at Santa Maria, RS, Brazil. Node number was simulated using the Soydev model, which has a nonlinear temperature response function [f(T)]. The f(T) was calculated using two methods: using daily mean air temperature calculated as the arithmetic average among daily minimum and maximum air temperatures (Soydev tmean); and calculating an f(T) using minimum air temperature and other using maximum air temperature and then averaging the two f(T)s (Soydev tmm). Root mean square error (RMSE) and deviations (simulated minus observed) were used as statistics to evaluate the performance of the two versions of Soydev. Simulations of node number in soybean were better with the Soydev tmm version, with a 0.5 to 1.4 node RMSE. Node number can be simulated for several soybean cultivars using only one set of model coefficients, with a 0.8 to 2.4 node RMSE.

scholarly journals Comparing two versions of a non-linear model for simulating leaf number and developmental stages in maize based on air temperature

2008 ◽  
Vol 39 (3) ◽  
pp. 642-648 ◽  
Author(s):  
Nereu Augusto Streck ◽  
Luana Fernandes Gabriel ◽  
Flavia Kaufmann Samboranha ◽  
Isabel Lago ◽  
Ana Paula Schwantes ◽  
...  
Keyword(s):  
Air Temperature ◽  
Developmental Stages ◽  
Temperature Response ◽  
Growing Season ◽  
Leaf Number ◽  
Physiological Maturity ◽  
Arithmetic Average ◽  
Daily Minimum ◽  
Maize Variety ◽  
Non Linear

The Wang and Engel (WE) model simulates crop development considering the non-linear response of plant development to temperature. Daily air temperature is the input for the temperature response function [f(T)] in the WE model, and because there are several approaches for computing daily temperatures, there are several ways to calculate the f(T). The objective of this study was to compare two versions of the WE model for simulating leaf number and developmental stages in maize, considering two approaches for imputing daily air temperature (daily mean air temperature and daily minimum/maximum air temperature). A two-year field experiment with the maize variety BRS Missões sown in several sowing dates was conducted in Santa Maria, Rio Grande do Sul State, Brazil, during the 2005-2006 and 2006-2007 growing seasons. The f(T) in the WE model was calculated using daily mean air temperature calculated as the arithmetic average of daily minimum (TN) and maximum (TX) air temperatures (WE Tmean), and calculating an f(T) using TN and an f(T) using TX and then averaging the two f(T)s (WE Tmm). Ligule and tip leaf number, and silking and physiological maturity developmental stages measured in the 2005-2006 growing season were used to estimate model coefficients and the ones measured in the 2006-2007 growing season were used as independent data sets to evaluate models. Predictions of ligule and tip leaf number, silking and physiological maturity of the maize variety BRS Missões were better with the WE Tmm model than with the WE Tmean model.

CORRELATION COMMUNICATION BETWEEN METEOROLOGICAL PARAMETERS AT EXTREME VALUES OF MAXIMUM AIR TEMPERATURES

2020 ◽  
pp. 101-112
Author(s):  
S.V. Savchuk ◽  
V.E. Timofeev ◽  
O.A. Shcheglov ◽  
V.A. Artemenko ◽  
I.L. Kozlenko
Keyword(s):  
Air Temperature ◽  
Meteorological Parameters ◽  
Extreme Values ◽  
Daily Temperature ◽  
Sea Level Pressure ◽  
Maximum Daily Temperature ◽  
The North ◽  
Air Temperatures ◽  
Average Maximum ◽  
Maximum Air Temperature

The object of the study is the maximum daily air temperature during the months of the year over 1991-2016 by the data of 186 meteorological stations of Ukraine. Extreme values of the maximum daily temperature equal to or exceeded their 95th (Tmax95p and above, ºС) percentile were taken as extreme. The article sets the dates (137 cases) of extreme values of maximum air temperature on more than 60 % of the territory. For these dates, 13 meteorological parameters were selected: average, minimum, and maximum air temperatures; average, minimum and maximum relative humidity; station and sea-level pressure; average, maximum (from 8 synoptic hours) wind speed; rainfall; height of snow cover. The purpose of this work is to determine the correlation coefficient (K), in particular, statistically significant (K≤-0.6, K≥0.6), on these dates between selected meteorological parameters at 186 meteorological stations of Ukraine for 1991-2013. The density of the cases of statistically significant dependence between the meteorological parameters in extremely warm days in separate seasons is determined. In extremely warm days, meteorological parameters and areas with statistically significant correlations at K≤-0.6 were detected: T and F (focally in southern and some western regions with significant density) − in winter; T and F (with the highest density ubiquitous or almost ubiquitous), P and V (in a large number of regions, usually west or right-bank, but with less frequency) − in the transition seasons, and in the autumn between − T and F (in the south with smaller density) and P and F (in some areas of the north, northwest, west, lower east). In all seasons, such a correlation between other meteorological parameters had a focal distribution, usually with a smaller density. In these days, a focal distribution with a small frequency of dependencies at K≥0.6 was found between the meteorological parameters detected (F and V in transition seasons, T and F in winter), except for similar ones. However, such dependence is observed between T and V in some regions in winter and autumn and in some areas of south, southeast, east with a smaller density. The study of the maximum daily temperature is relevant, because from the level of natural hydrometeorological phenomena it is accompanied by dangerous phenomena, negatively affecting the weather dependent industries.

scholarly journals Trends of Changes of Maximum Air Temperature in Ukraine as an Impact Factor on Population Health

2020 ◽  
Keyword(s):  
Population Health ◽  
Air Temperature ◽  
Thermal Regime ◽  
Climate Changes ◽  
System Analysis ◽  
Global Climate ◽  
Maximum Temperature ◽  
Air Temperatures ◽  
Average Maximum ◽  
Maximum Air Temperature

Purpose. The aim of this research is detection of trends of changes (according to fact and scenario data) of extreme air temperature as a component of thermal regime in different regions of Ukraine because of global climate change. Methods. System analysis, statistical methods. Results. Time distribution of maximum air temperature regime characteristics based on results of observations on the stations located in different regions of Ukraine during certain available periods: Uzhgorod (1946-2018), Kharkiv (1936-2005), Оdessа (1894-2005), аnd also according to scenarios of low (RCP2.6), medium (RCP4.5) and high (RCP8.5) levels of greenhouse gases emissions. Meanwhile, air temperature ≥ 25°С was considered high (days with maximum temperature within 25,0-29,9°С are hot), ≥ 30°С was considered very high (days with such temperature are abnormaly hot). Trends of changes of extreme air temperatures were identified as a component of thermal regime in different regions of Ukraine within global climate changes. Dynamics of maximum air temperature and its characteristics in ХХ and beginning of ХХІ centuries were researched. Expected time changes of maximum air temperature and number of days with high temperature during 2021-2050 were analyzed by RCP2.6, RCP4.5 and RCP8.5 scenarios. There were identified the highest day air temperatures possible once in a century and also possibility of maximum day temperature more than 30°С by RCP4.5 scenario. Well-timed prediction of climate changes will help evaluate their impact on human and natural systems which will be useful for development and taking preventive measures towards minimization of negative influence of such changes. Conclusions. Processes of climate warming in Ukraine are activating. There was determined a strong trend on increasing of average maximum of air temperature in winter with speed 0.17-0,39 degrees centigrade/10 years. According to climatic norm this index mainly increased mostly (up to 3,3 degrees centigrade) in January in North-East of the country. In future such anomalies will grow. Determination of correlation between climate and health is the base for taking protective measures against perils for population health connected with climate.

scholarly journals Daily Minimum and Maximum Air Temperature in Poland in the Years 1951–2005

2009 ◽  
Vol 2 (1) ◽  
pp. 35-56 ◽  
Author(s):  
Marek Kejna ◽  
Andrzej Araźny ◽  
Rafał Maszewski ◽  
Rajmund Przybylak ◽  
Joanna Uscka-Kowalkowska ◽  
...  
Keyword(s):  
Air Temperature ◽  
North Atlantic ◽  
Daily Maximum ◽  
Grid Data ◽  
The North ◽  
Nao Index ◽  
Air Temperatures ◽  
The North Atlantic ◽  
The North Atlantic Oscillation ◽  
Maximum Air Temperature

Abstract In this study grid data of daily maximum and minimum air temperatures taken from the NCEP/NCAR reanalysis for the territory of Poland for the years 1951-2005 have been used as a basis for an analysis of the spatial distribution of daily maximum and minimum air temperature, the frequency of characteristic days and the variability of these parameters in the period analysed. The results obtained were then compared to the variability in atmospheric circulation in Europe, described by the North Atlantic Oscillation (NAO) index.

scholarly journals GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

2021 ◽  
Author(s):  
Qian He ◽  
Ming Wang ◽  
Kai Liu ◽  
Kaiwen Li ◽  
Ziyu Jiang
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Air Temperature ◽  
Spatial Interpolation ◽  
Gaussian Process Regression ◽  
Temperature Data ◽  
Air Temperatures ◽  
Maximum Air Temperature ◽  
Maximum Minimum

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Physiological response of swine to cycling environmental conditions

1967 ◽  
Vol 9 (4) ◽  
pp. 453-462 ◽  
Author(s):  
T. E. Bond ◽  
C. F. Kelly ◽  
H. Heitman
Keyword(s):  
Constant Temperature ◽  
Air Temperature ◽  
Environmental Conditions ◽  
Physiological Response ◽  
Respiration Rates ◽  
Surface Temperatures ◽  
Air Temperatures ◽  
Group Averaging ◽  
Maximum Air Temperature

Rectal and surface temperatures, and respiration and pulse rates, were obtained for groups of Duroc pigs that were exposed to air temperatures that varied sinusoidally over a 24-hour period. Two groups averaging 37 and 108 kg were exposed to a constant temperature of 21·1°C and then to temperatures that cycled about a mean of 21·1°C (15·6–26·7°C, 10·0–32·2°C, and 4·4–37·8°C). For a third group averaging 53 kg, the minimum was always near 21·1°C, and the maximum air temperature of the cycle was 33·2, 42·5 or 48·8°C.The response of rectal and surface temperatures, and pulse and respiration rates, to the various 24-hour cycling air temperatures are discussed and com-pared with inherent daily fluctuations in these responses that are present even when there is no variation in air temperature.

scholarly journals Comparison Air Temperature under Global Climate Change Issue in Gifu city and Ogaki city, Japan

2016 ◽  
Vol 1 (1) ◽  
pp. 37 ◽  
Author(s):  
Ali Rahmat ◽  
Abdul Mutolib
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Global Climate Change ◽  
Global Climate ◽  
Regional Scale ◽  
Climate Change Issue ◽  
Air Temperatures ◽  
Maximum Air Temperature ◽  
Gifu Prefecture ◽  
Global Data

Increases in air temperature indicate a global climate change. Thus, information in the change of temperature regional scale is important to support global data. The present research was conducted in Gifu city and Ogaki city located in Gifu prefecture, Japan. The results showed that, average air temperatures in both cities are quite similar with a difference value of under 1<sup>o</sup>C. Maximum air temperature in Gifu city is significantly higher than Ogaki city, whereas minimum air temperature in Gifu city is significantly lower than in Ogaki city. Daily range of air temperature in Gifu city significantly higher than in Ogaki city. In both cities, air temperature relatively increased in three decades. This is because of different in land characteristics in both cities.

scholarly journals Winter Nocturnal Air Temperature Distribution for a Mesoscale Plain of a Snow-Covered Region: Field Meteorological Observations and Numerical Simulations

2017 ◽  
Vol 56 (2) ◽  
pp. 519-533 ◽  
Author(s):  
Tomotsugu Yazaki ◽  
Hirokazu Fukushima ◽  
Tomoyoshi Hirota ◽  
Yukiyoshi Iwata ◽  
Atsushi Wajima ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Temperature Distribution ◽  
Wind Speed ◽  
Air Temperature ◽  
Daily Minimum Temperature ◽  
Strong Wind ◽  
Daily Minimum ◽  
Air Temperatures ◽  
Winter Air Temperature ◽  
Minimum Air Temperature

AbstractWinter air temperatures strongly affect crop overwintering and cold resource usage. To clarify how winter air temperature distributions are formed in a mesoscale plain, field observations and simulations were conducted for the Tokachi region in Japan. Results elucidating the winter climate within the plain revealed that the winter mean air temperature at each site was correlated closely with the mean daily minimum air temperature. The daily minimum air temperature was not correlated with altitude, suggesting that local variation of the daily minimum temperature influences the temperature distribution. Observations at different distances from the upwind mountains revealed that nocturnal air temperatures were higher for stronger winds closer to the mountain foot. Low temperatures associated with wind speed suggest that radiative cooling strongly affects the temperature distribution. Wind and temperature conditions in the boundary layer influence the degree of drop in nocturnal air temperature and its distribution. The wind speed and direction, respectively, affect the extent and direction of the high-temperature zone from the northwest mountain foot. Simulations with a spatial resolution of 2 km reproduced the observed temperatures, but the error exceeded 5°C at sites having complex terrain under moderate or strong wind conditions. A higher-resolution model of 0.5 km showed that simulated temperatures approach the observed temperatures in association with a local wind system of down-valley drainage flow. In conclusion, the synoptic background, wind strength and direction over the plain, and microscale valleys affect boundary layer mixing and, thereby, determine the winter air temperature distribution.

scholarly journals GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

2021 ◽  
Author(s):  
Qian He ◽  
Ming Wang ◽  
Kai Liu ◽  
Kaiwen Li ◽  
Ziyu Jiang
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Air Temperature ◽  
Spatial Interpolation ◽  
Gaussian Process Regression ◽  
Temperature Data ◽  
Air Temperatures ◽  
Maximum Air Temperature ◽  
Maximum Minimum

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

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