scholarly journals High-temperature Stress and the Fate of Pollen Germination and Yield in Lentil (Lens culinaris Medikus)

2020 ◽  
Author(s):  
Ananya Baidya ◽  
Anjan Kumar Pal ◽  
Mohammed Anwar Ali ◽  
Rajib Nath
Keyword(s):  
High Temperatures ◽  
Air Temperature ◽  
Pollen Germination ◽  
High Temperature Stress ◽  
Cropping Pattern ◽  
Randomized Design ◽  
Pod Development ◽  
Drastic Effect ◽  
Maximum Air Temperature ◽  
Physiological Characters

Background: High temperatures adversely affect the growth and development of plants at vegetative, reproductive and maturity stages of their life cycle. The maximum air temperature of higher than 25oC frequently encountered in lentil at the reproductive stage mainly in West Bengal rice-lentil cropping pattern. Such high temperatures in rabi season impair the crop growth and productivity. Added to this, future climate predictions are indicating 27-28oC during this period. Methods: The field-laboratory experiment was conducted during 2014, with 20 genotypes of lentil. The field experiment was laid in split plot design with 3 different windows of sowing, while the laboratory investigation was carried out in completely randomized design with pollen collected from the field experiment.Result: The present investigation reveals that high air temperature greater than 25oC resulted in the failure of pollen germination, increase in abnormal pollen tube and aborted pollen. A drastic effect in seed filling and pod development in lentil was observed. Out of 20 lentil genotypes studied, ILL-10893 and L-13-113 emerged as tolerant genotypes while, BM-6 and L-4076 as susceptible with special emphasis on physiological characters.

scholarly journals Increase in Air Temperature in The Region of South Sumatra Province as the Indicator of Global Warming and the Effect on Transpiration of Lansium domesticum Corr.

2019 ◽  
Author(s):  
Ari Sugiarto ◽  
Hanifa Marisa ◽  
Sarno
Keyword(s):  
Global Warming ◽  
Air Temperature ◽  
Transpiration Rate ◽  
Temperature Increase ◽  
Secondary Data ◽  
Temperature Data ◽  
Randomized Design ◽  
Maximum Air Temperature ◽  
Lansium Domesticum ◽  
Minimum Air Temperature

Abstract Global warming is one of biggest problems faced in the 21st century. One of the impacts of global warming is that it can affect the transpiration rate of plants that °Ccur. This study purpose to see how much increase in air temperature that occurred in the region of South Sumatra Province and to know the effect of increase in ari temperature in the region of South Sumatra Province on transpiration rate of Lansium domesticum Corr. This study used a complete randomized design with 9 treatments (22.9 °C, 23.6 °C, 24.6 °C, 26.3 °C, 27 °C, 27.8 °C, 31.7 °C, 32.5 °C, and 32.9 °C) and 3 replications. Air temperature data as secondary data obtained from the Meteorology, Climatology and Geophysics Agency (MCGA) Palembang Climatology Station in South Sumatra Province. The measurement of transpiration rate is done by modified potometer method with additional glass box. The data obtained are presented in the form of tables and graphs. Transpiration rate (mm3/g plant/hour) at temperture 22.9 °C = 4.37, 23.6 °C = 7.03, 24.6 °C = 8.03, 26.3 °C = 10.11, 27 °C = 13.13, 27.8 °C = 17.87, 31.7 °C = 23.21, 32.5 °C= 25.45 and 32.9 °C= 27.24. At the minimum air temperature in the region of South Sumatra Province there is increase in air temperature of 1.5 °C, average daily air temperature increase 1.3 °C and maximum air temperature increase 1.2 °C.

scholarly journals Changes in Air Temperature and Snow Cover in Winter in Poland

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 68
Author(s):  
Arkadiusz M. Tomczyk ◽  
Ewa Bednorz ◽  
Katarzyna Szyga-Pluta
Keyword(s):  
Snow Cover ◽  
Air Temperature ◽  
Winter Season ◽  
Primary Objective ◽  
Climatic Conditions ◽  
Cover Depth ◽  
North Eastern ◽  
Northern Regions ◽  
Maximum Air Temperature ◽  
Snow Cover Depth

The primary objective of the paper was to characterize the climatic conditions in the winter season in Poland in the years 1966/67–2019/20. The study was based on daily values of minimum (Tmin) and maximum air temperature (Tmax), and daily values of snow cover depth. The study showed an increase in both Tmin and Tmax in winter. The most intensive changes were recorded in north-eastern and northern regions. The coldest winters were recorded in the first half of the analyzed multiannual period, exceptionally cold being winters 1969/70 and 1984/85. The warmest winters occurred in the second half of the analyzed period and among seasons with the highest mean Tmax, particularly winters 2019/20 and 1989/90 stood out. In the study period, a decrease in snow cover depth statistically significant in the majority of stations in Poland was determined, as well as its variability both within the winter season and multiannual.

Micro-climatological influences on temperature condition in an old people’s home in Helsinki, Finland, caused by extended heat-waves 

2021 ◽  
Author(s):  
Achim Drebs ◽  
Tim Sinsel ◽  
Kirsti Jylhä
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Daily Maximum ◽  
Old People ◽  
Concrete Walls ◽  
Meteorological Forcing ◽  
Clear Sky ◽  
Wave Event ◽  
Construction Type ◽  
Maximum Air Temperature

<p>In our research we describe the micro-climatological influences of two heat-waves around and the air temperature development in a certain old people’s home in Helsinki, Finland. The stand-alone six-storey concrete building was erected in the late 1970’s and represents the prevailing construction type of this area. The building is located on a slightly southwards declining slope.</p><p>The first simulation used real meteorological forcing-data from the heat-wave event in summer 2018, which lasted from July, 13<sup>th</sup> until August, 5<sup>th</sup>. In this period the daily maximum air temperature reached almost every day 25 °C and more, sometimes even more than 30 °C. All air temperature, wind, humidity, and solar radiation (cloudiness) measurements were conducted at a near-by synoptical weather station.</p><p>The second simulation used fourteen-day constructed meteorological forcing-data, based on a clear-sky, slowly increasing air temperature, higher than normal humidity, and low wind conditions assumption starting on July, 13<sup>th</sup> (day 194 of the year).</p><p>We used the holistic ENVI-met simulation soft-ware to simulate the physical environment around the old people’s home and especially the energy fluxes inside the concrete walls to explain the needs for cooling demands.</p><p>The research is part of the HEATCLIM-project financed by the Academy of Finland Science Program CLIHE (2020-2023).</p>

scholarly journals Fuzzy logic simulation of oat yield after using hydrogel and nitrogen biopolymer management

2020 ◽  
pp. 1319-1327
Author(s):  
Osmar Bruneslau Scremin ◽  
José Antonio Gonzalez da Silva ◽  
Ivan Ricardo Carvalho ◽  
Ângela Teresinha Woschinski De Mamann ◽  
Odenis Alessi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Biomass Yield ◽  
New Technologies ◽  
Block Design ◽  
Fuzzy Model ◽  
Logic Simulation ◽  
Efficient Tool ◽  
Randomized Block Design ◽  
Maximum Air Temperature

The fuzzy logic is an efficient tool for simulation and validation of new technologies in agriculture. The objective of the study is to adapt the fuzzy logic model for simulation of biomass and oat grain yield by nitrogen involving the nonlinearity of the maximum air temperature in the conditions of use of the biopolymer hydrogel, considering high succession systems and low release of residual N. The study was conducted in 2014 and 2015, in a randomized block design with four replicates in a 5 x 5 factorial. Five hydrogel doses (0, 30, 60, 90 and 120 kg ha-1) were added in the groove next to the seed; and 5 doses of N-fertilizer (0, 30, 60, 90 and 120 kg ha-1) applied at the fourth expanded leaf stage, respectively. The cultivar was URS Corona. The pertinence functions and the linguistic values established in the input and output variables to simulate the biomass yield and oat grains in the succession systems are adequate observed productivity. The fuzzy model makes it possible to estimate the biomass and oat grains productivity efficiently under the conditions of use of the hydrogel as a function of the nitrogen doses and maximum air temperature, adding to the existing models of simulation.

scholarly journals Phosphoproteomic analysis of lettuce (Lactuca sativa L.) reveals starch and sucrose metabolism functions during bolting induced by high temperature

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244198
Author(s):  
Xiaoxiao Qin ◽  
Panpan Li ◽  
Shaowei Lu ◽  
Yanchuan Sun ◽  
Lifeng Meng ◽  
...  
Keyword(s):  
High Temperature ◽  
High Temperatures ◽  
Lactuca Sativa ◽  
Molecular Mechanisms ◽  
Soluble Sugar ◽  
High Temperature Stress ◽  
Temperature Treatment ◽  
Sucrose Metabolism ◽  
High Temperature Treatment ◽  
Lactuca Sativa L

High temperatures induce early bolting in lettuce (Lactuca sativa L.), which decreases both quality and production. However, knowledge of the molecular mechanism underlying high temperature promotes premature bolting is lacking. In this study, we compared lettuce during the bolting period induced by high temperatures (33/25 °C, day/night) to which raised under controlled temperatures (20/13 °C, day/night) using iTRAQ-based phosphoproteomic analysis. A total of 3,814 phosphorylation sites located on 1,766 phosphopeptides from 987 phosphoproteins were identified after high-temperature treatment,among which 217 phosphoproteins significantly changed their expression abundance (116 upregulated and 101 downregulated). Most phosphoproteins for which the abundance was altered were associated with the metabolic process, with the main molecular functions were catalytic activity and transporter activity. Regarding the functional pathway, starch and sucrose metabolism was the mainly enriched signaling pathways. Hence, high temperature influenced phosphoprotein activity, especially that associated with starch and sucrose metabolism. We suspected that the lettuce shorten its growth cycle and reduce vegetative growth owing to changes in the contents of starch and soluble sugar after high temperature stress, which then led to early bolting/flowering. These findings improve our understanding of the regulatory molecular mechanisms involved in lettuce bolting.

scholarly journals Decorative values of selected cultivars of climbing roses (Rosa L.) with regard to thermal conditions

2012 ◽  
Vol 60 (1) ◽  
pp. 135-142
Author(s):  
Zofia Włodarczyk ◽  
Agnieszka Perzanowska
Keyword(s):  
High Temperatures ◽  
Air Temperature ◽  
Thermal Conditions ◽  
Temperature Pattern ◽  
Flowering Period ◽  
Green Areas

In the years 2004-2006 in Kraków, phenological observations of climbing roses were conducted in order to determine the length and dates of their flowering period. The diameters of their flowers were also compared. Eight flowering repeating cultivars were selected for the experiment: 'Climbing Souvenir de la Malmaison', 'Dortmund', 'Golden Showers', 'Goldstern', 'New Dawn', 'Parade', 'Sympathie' and 'White New Dawn'. During the studies, the shrub roses were not artificially watered in order to create conditions similar to those prevailing in public green areas. It was observed that irrespective of the air temperature pattern in a given year, the studied cultivars did not bloom before 15 June. In 2006 high temperatures (above 20<sup>o</sup>C), which continued throughout the whole flowering period, caused its shortening, and the interval between the first and the next flowering in the season lasted longer than in the previous years. In the years 2004-2006, the cultivar 'New Dawn' bloomed the longest. In 2005 the studied cultivars produced larger flowers than the next year. The cultivars 'Dortmund' and 'White New Dawn' were characterised by the smallest diameter of flowers, whereas 'Climbing Souvenir de la Malmaison', 'Golden Showers' and 'Parade' were marked by the largest diameter.

scholarly journals Impact of High Temperature Stress in Rice

2020 ◽  
Vol 7 (3) ◽  
pp. 108-110
Author(s):  
E. Umarani ◽  
◽  
V. Hemalatha ◽  
A. Saritha ◽  
A. V. Ramanjaneyulu ◽  
...  
Keyword(s):  
High Temperature ◽  
High Temperatures ◽  
Early Morning ◽  
High Temperature Stress ◽  
Oryza Glaberrima ◽  
Rice Grain ◽  
Irreversible Damage ◽  
Peak Period ◽  
Genetic Source ◽  
The Tropics

Global warming has become a big concern in the world and it has been continuously increasing in recent decades due to the greenhouse effect. The increase in temperature has been striking and it can cause irreversible damage to plant growth and development. Though rice originates from the tropics, high temperatures of more than 35 °C during the reproductive stages reduces rice production, especially when the rice plant flowers when the high temperature occurs because it causes low seed setting and low yield. It was found that rice grain yield declined by 10% for each 1 °C increase in growing-season minimum temperature. Early-morning flowering is an important criteria in rice which escapes plant from high temperatures during peak period. It was found that Oryza glaberrima, a wild species of rice, is a useful genetic source since it has a habit of early-morning flowering and high transpiration with sufficient water, both of which are convenient traits for avoiding heat stress.

scholarly journals The effect of air temperature on forest fire risk in the municipality of Negotin

2015 ◽  
Vol 95 (4) ◽  
pp. 67-76
Author(s):  
Stanimir Zivanovic ◽  
Milena Gocic ◽  
Radomir Ivanovic ◽  
Natasa Martic-Bursac
Keyword(s):  
Air Temperature ◽  
Forest Fires ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Fire Risk ◽  
Annual Number ◽  
Absolute Maximum ◽  
Maximum Air Temperature ◽  
Forest Fire Risk

Fires in nature are caused by moisture content in the burning material, which is dependent on the values of the climatic elements. The occurrence of these fires in Serbia is becoming more common, depending on the intensity and duration have a major impact on the state of vegetation. The aim of this study was to determine the association between changes in air temperature and the dynamics of the appearance of forest fires. To study the association of these properties were used Pearson correlation coefficients. The analysis is based on meteorological data obtained from meteorological station in Negotin for the period 1991-2010. Research has found that the annual number of fires, correlating with an average annual air temperature (p = 0.317, ? = 0.21). Also, it was found that the annual number of fires positive, medium intensity, correlate with the absolute maximum air temperature (p = 0.578, ? = 0.26), but not statistically significant (p> 0.05).

scholarly journals Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Air Temperature ◽  
Post Processing ◽  
Learning Methods ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Extended Range ◽  
The Neural Networks ◽  
Maximum Air Temperature

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

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