Interval of days required for determining efficient relations between climatic factors and cotton flower and boll production

2002 ◽  
Vol 82 (3) ◽  
pp. 499-506 ◽  
Author(s):  
Zakaria M Sawan ◽  
Louis I Hanna ◽  
Willis L McCuistion
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Cultural Practices ◽  
Agricultural Research ◽  
Climatic Factors ◽  
Sunshine Duration ◽  
Field Trials ◽  
Cotton Production ◽  
Surface Soil Temperature

The cotton plant (Gossypium spp.) is sensitive to numerous environmental factors. This study was aimed at predicting effects of climatic factors grouped into convenient intervals (in days) on cotton flower and boll production compared with daily observations. Two uniformity field trials using the cotton (G. barbadense L.) cv. Giza 75 were conducted in 1992 and 1993 at the Agricultural Research Center, Giza, Egypt. Randomly chosen plants were used to record daily numbers of flowers and bolls during the reproductive stage (60 days). During this period, daily air temperature, temperature magnitude, evaporation, surface soil temperature, sunshine duration, humidity, and wind speed were recorded. Data, grouped into intervals of 2, 3, 4, 5, 6, and 10 d, were correlated with cotton production variables using regression analysis. Evaporation was found to be the most important climatic variable affecting flower and boll production, followed by humidity and sunshine duration. The least important variables were surface soil temperature at 0600 and minimum air temperature. The 5-d interval was found to provide the best correlation with yield parameters. Applying appropriate cultural practices that minimize the deleterious effects of evaporation and humidity could lead to an important improvement in cotton yield in Egypt. Key words: Cotton, flower production, boll production, boll retention

scholarly journals Site specific parameterizations of longwave radiation

2016 ◽  
Author(s):  
Giuseppe Formetta ◽  
Marialaura Bancheri ◽  
Olaf David ◽  
Riccardo Rigon
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Longwave Radiation ◽  
Site Specific ◽  
Regression Parameters ◽  
Specific Parameter ◽  
Different Temperatures ◽  
Surface Soil Temperature ◽  
Leave One Out

Abstract. In this work ten algorithms for estimating downwelling longwave atmospheric radiation (L↓) and one for upwelling longwave radiation (L↑) are integrated into the hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for twenty-four sites in North America to assess their reliability. These new JGrass-NewAge model components are used i) to evaluate the performances of simplified models (SMs) of L↓ , as presented in literature formulations, and ii) to determine by automatic calibration the site-specific parameter sets for SMs of L↓. For locations where calibration is not possible because of a lack of measured data, we perform a multiple regression using on-site variables, such as mean annual air temperature, relative humidity, precipitation, and altitude. The regressions are verified through a leave-one-out cross validation, which also gathers information about the possible errors of estimation. Most of the SMs, when executed with parameters derived from the multiple regressions, give enhanced performances compared to the corresponding literature formulation. A sensitivity analysis is carried out for each SM to understand how small variations of a given parameter influence SM performance. Regarding the L↓ simulations, the Brunt (1932) and Idso (1981) SMs, in their literature formulations, provide the best performances in many of the sites. The site-specific parameter calibration improves SM performances compared to their literature formulations. Specifically, the root mean square error (RMSE) is almost halved and the Kling Gupta efficiency is improved at all sites. The L↑ SM is tested by using three different temperatures (surface soil temperature, air temperature at 2 m elevation, and soil temperature at 4 cm depth) and model performances are then assessed. Results show that the best performances are achieved using the surface soil temperature and the air temperature. Models and regression parameters are available for any use, as specified in the paper.

scholarly journals Asymmetric Soil Warming under Global Climate Change

2019 ◽  
Vol 16 (9) ◽  
pp. 1504 ◽  
Author(s):  
Hui Zhang ◽  
Binhui Liu ◽  
Daowei Zhou ◽  
Zhengfang Wu ◽  
Ting Wang
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Global Climate ◽  
Average Rate ◽  
Daily Maximum ◽  
Agricultural Practice ◽  
Soil Warming ◽  
Surface Soil Temperature ◽  
The Difference

Daily surface soil temperature data from 360 weather stations in China during 1962–2011 were retrieved and analyzed. The data revealed two aspects of asymmetric soil warming. Firstly, there was asymmetry between day and night in terms of increases in soil temperature. The daily maximum surface soil temperature ( S T max ) and daily minimum surface soil temperature ( S T min ) increased at rates of 0.031 and 0.055 °C/year over the 50-year interval, respectively. As a consequence of the more rapid increases in S T min , the soil diurnal temperature range (SDTR) decreased at most stations (average rate of –0.025 °C/year), with the most profound decrease in winter (–0.08 °C/year). The solar duration (SD) was positively related to SDTR and is regarded as the key underlying cause of the decreasing SDTR. Secondly, there was asymmetry between the soil and air in the temperature increase. The differences between soil and air temperature ( T D ) were highest in summer (2.76 °C) and smallest in winter (1.55 °C), which decreased by 0.3 °C over the study interval, this meant agricultural practice plans based on air temperature alone may be severely limited. The difference between soil temperature and air temperature reduces at night. This would facilitate the wintering of perennials in areas near the zero-contour line.

scholarly journals Performance of site-specific parameterizations of longwave radiation

2016 ◽  
Vol 20 (11) ◽  
pp. 4641-4654 ◽  
Author(s):  
Giuseppe Formetta ◽  
Marialaura Bancheri ◽  
Olaf David ◽  
Riccardo Rigon
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Longwave Radiation ◽  
Site Specific ◽  
Specific Parameter ◽  
Different Temperatures ◽  
Surface Soil Temperature ◽  
Model Components ◽  
Leave One Out

Abstract. In this work 10 algorithms for estimating downwelling longwave atmospheric radiation (L↓) and 1 for upwelling longwave radiation (L↑) are integrated into the JGrass-NewAge modelling system. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. These new JGrass-NewAge model components are used (i) to evaluate the performances of simplified models (SMs) of L↓, as presented in literature formulations, and (ii) to determine by automatic calibration the site-specific parameter sets for L↓ in SMs. For locations where calibration is not possible because of a lack of measured data, we perform a multiple regression using on-site variables, i.e. mean annual air temperature, relative humidity, precipitation, and altitude. The regressions are verified through a leave-one-out cross validation, which also gathers information about the possible errors of estimation. Most of the SMs, when executed with parameters derived from the multiple regressions, give enhanced performances compared to the corresponding literature formulation. A sensitivity analysis is carried out for each SM to understand how small variations of a given parameter influence SM performance. Regarding the L↓ simulations, the Brunt (1932) and Idso (1981) SMs, in their literature formulations, provide the best performances in many of the sites. The site-specific parameter calibration improves SM performances compared to their literature formulations. Specifically, the root mean square error (RMSE) is almost halved and the Kling–Gupta efficiency is improved at all sites. Also in this case, Brunt (1932) and Idso (1981) SMs provided the best performances. The L↑ SM is tested by using three different temperatures (surface soil temperature, air temperature at 2 m elevation, and soil temperature at 4 cm depth) and model performances are then assessed. Results show that the best performances are achieved using the surface soil temperature and the air temperature.

X.—The Cooling of the Soil at Night, with Special Reference to late Spring Frosts

1920 ◽  
Vol 39 ◽  
pp. 120-136
Author(s):  
T. Bedford Franklin
Keyword(s):  
Soil Temperature ◽  
Special Reference ◽  
Air Temperature ◽  
Minimum Temperature ◽  
Surface Soil ◽  
Late Spring ◽  
Rapid Radiation ◽  
Surface Soil Temperature ◽  
Spring Frosts

In this paper I am mainly concerned with the surface soil temperature, but as we are accustomed by long usage to think of the grass minimum temperature as determining the occurrence or non-occurrence of frost, a few words of explanation on this point is perhaps necessary at the outset.The grass minimum on nights of rapid radiation certainly does fall considerably below the surface soil minimum, but this is due to the very fact that it is the grass minimum, i.e. the air temperature just on the grass.

scholarly journals Dew Yield and Its Influencing Factors at the Western Edge of Gurbantunggut Desert, China

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 733 ◽  
Author(s):  
Zhifeng Jia ◽  
Zhiqiang Zhao ◽  
Qianyi Zhang ◽  
Weichen Wu
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Annual Rainfall ◽  
Gurbantunggut Desert ◽  
Dewpoint Temperature ◽  
Dew Formation ◽  
Surface Soil Temperature ◽  
Gurbantünggüt Desert ◽  
The Difference

Dew is a significant water resource in arid desert areas. However, information regarding dew is scarce because it is difficult to measure due to the harsh environment of locations such as Gurbantunggut Desert, China. In this study, a non-destructive field experiment was conducted from 2015 to 2018 at a desert test station located in the western edge of the Gurbantunggut Desert, using a calibrated leaf wetness sensor (LWS) to measure dew yield. The results are as follows: (1) Dew formed after sunset with the atmospheric temperature gradually dropping and evaporated after sunrise with the temperature increasing in the second morning. (2) Dew was featured as ‘high frequency and low yield’. The average daily dew yield during dew days was 0.10 mm with a daily maximum of 0.62 mm, while dew days accounted for 44% of the total monitoring days, with a monthly maximum of 25 days. Compared with rainfall, dew days were two times as frequent as rainy days, while the average annual dewfall (12.21 mm) was about 1/11th of the average annual rainfall (134.6 mm), which indicates the dew contribution to regional water balance is about 9%. (3) March–April and October–November are the main periods of dew occurrence in this region because accumulated snow begins to melt slowly in March–April, providing sufficient vapor for dew formation, and the air temperature difference between day and night in October–November is the highest in the year, meaning that the temperature drops rapidly at night, making it easier to reach the dewpoint for vapor condensation. (4) Daily dew yield (D) was positively correlated to relative humidity (RH) and the difference between soil temperature at 10 cm below the ground and surface soil temperature (Tss), and negatively correlated to wind speed (V), air temperature (Ta), surface soil temperature (Ts), cloud cover (N), dewpoint temperature (Td) and the difference between air temperature and dewpoint temperature (Tad). It should be noted that the measured values of all factors above were the average value of the overnight period. The multivariate regression equation, D = −0.705 + 0.011 ×RH− 0.006 ×N− 0.01 × V, can estimate the daily dew yield with the thresholds of the parameters, i.e., RH > 70%, N < 7 (oktas) and V < 6 m/s.

Relation between soil temperature and biophysical parameters in Indian mustard seeds

2013 ◽  
Vol 27 (4) ◽  
pp. 359-367 ◽  
Author(s):  
T. Adak ◽  
N.V.K. Chakravarty
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Soil Temperature ◽  
Surface Soil ◽  
Linear Regression Analysis ◽  
Indian Mustard ◽  
Biophysical Parameters ◽  
Oil Accumulation ◽  
Area Index ◽  
Surface Soil Temperature

Abstract Temporal changes in surface soil temperature were studied in winter crop. Significant changes in bare and cropped soil temperature were revealed. Air temperature showed a statistically positive and strong relationship (R2 = 0.79** to 0.92**) with the soil temperature both at morning and afternoon hours. Linear regression analysis indicated that each unit increase in ambient temperature would lead to increase in minimum and maximum soil temperatures by 1.04 and 1.02 degree, respectively. Statistically positive correlation was revealed among biophysical variables with the cumulative surface soil temperature. Linear and non-linear regression analysis indicated 62-69, 72-86 and 72-80% variation in Leaf area index, dry matter production and heat use efficiency in Indian mustard crop as a function of soil degree days. Below 60% variation in yield in Indian mustard was revealed as a function of soil temperature. In contrast, non-significant relationship between oil content and soil temperature was found, which suggests that oil accumulation in oilseed crops was not affected significantly by the soil temperature as an independent variable.

Cover crop effects on soil temperature in a clay loam soil in southwestern Ontario

2021 ◽  
pp. 1-10
Author(s):  
X.M. Yang ◽  
W.D. Reynolds ◽  
C.F. Drury ◽  
M.D. Reeb
Keyword(s):  
Soil Temperature ◽  
Cover Crops ◽  
Environmental Performance ◽  
Cover Crop ◽  
Crop Production ◽  
Surface Soil ◽  
Clay Loam ◽  
Near Surface ◽  
Soil Temperatures ◽  
Surface Soil Temperature

Although it is well established that soil temperature has substantial effects on the agri-environmental performance of crop production, little is known of soil temperatures under living cover crops. Consequently, soil temperatures under a crimson clover and white clover mix, hairy vetch, and red clover were measured for a cool, humid Brookston clay loam under a corn–soybean–winter wheat/cover crop rotation. Measurements were collected from August (after cover crop seeding) to the following May (before cover crop termination) at 15, 30, 45, and 60 cm depths during 2018–2019 and 2019–2020. Average soil temperatures (August–May) were not affected by cover crop species at any depth, or by air temperature at 60 cm depth. During winter, soil temperatures at 15, 30, and 45 cm depths were greater under cover crops than under a no cover crop control (CK), with maximum increase occurring at 15 cm on 31 January 2019 (2.5–5.7 °C) and on 23 January 2020 (0.8–1.9 °C). In spring, soil temperatures under standing cover crops were cooler than the CK by 0.1–3.0 °C at 15 cm depth, by 0–2.4 °C at the 30 and 45 cm depths, and by 0–1.8 °C at 60 cm depth. In addition, springtime soil temperature at 15 cm depth decreased by about 0.24 °C for every 1 Mg·ha−1 increase in live cover crop biomass. Relative to bare soil, cover crops increased near-surface soil temperature during winter but decreased near-surface soil temperature during spring. These temperature changes may have both positive and negative effects on the agri-environmental performance of crop production.

scholarly journals Seasonal dynamics of methane emissions from a subarctic fen in the Hudson Bay Lowlands

2013 ◽  
Vol 10 (7) ◽  
pp. 4465-4479 ◽  
Author(s):  
K. L. Hanis ◽  
M. Tenuta ◽  
B. D. Amiro ◽  
T. N. Papakyriakou
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Growing Season ◽  
Near Surface ◽  
Growing Seasons ◽  
Air Temperatures ◽  
Soil Temperatures ◽  
Surface Soil Temperature ◽  
Filling Method ◽  
Highly Correlated

Abstract. Ecosystem-scale methane (CH4) flux (FCH4) over a subarctic fen at Churchill, Manitoba, Canada was measured to understand the magnitude of emissions during spring and fall shoulder seasons, and the growing season in relation to physical and biological conditions. FCH4 was measured using eddy covariance with a closed-path analyser in four years (2008–2011). Cumulative measured annual FCH4 (shoulder plus growing seasons) ranged from 3.0 to 9.6 g CH4 m−2 yr−1 among the four study years, with a mean of 6.5 to 7.1 g CH4 m−2 yr−1 depending upon gap-filling method. Soil temperatures to depths of 50 cm and air temperature were highly correlated with FCH4, with near-surface soil temperature at 5 cm most correlated across spring, fall, and the shoulder and growing seasons. The response of FCH4 to soil temperature at the 5 cm depth and air temperature was more than double in spring to that of fall. Emission episodes were generally not observed during spring thaw. Growing season emissions also depended upon soil and air temperatures but the water table also exerted influence, with FCH4 highest when water was 2–13 cm below and lowest when it was at or above the mean peat surface.

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Diandong Ren
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Initial Guess ◽  
Surface Model ◽  
Soil Moisture Condition ◽  
Wide Range ◽  
Surface Soil Temperature

AbstractBased on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.

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