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Ali Rahimikhoob – One of the best experts on this subject based on the ideXlab platform.

  • Estimating sunshine duration from other climatic data by artificial neural network for ET_0 estimation in an Arid Environment
    Theoretical and Applied Climatology, 2014
    Co-Authors: Ali Rahimikhoob

    Abstract:

    Sunshine duration data are desirable for calculating daily solar radiation ( R _s) and subsequent reference evapotranspiration (ET_0) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under Arid Environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET_0 was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient ( R ^2), the root mean square error (RMSE) and the mean bias error (MBE). Average R ^2, RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET_0 by using Ångström and FAO-PM equations, respectively.

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  • Estimating sunshine duration from other climatic data by artificial neural network for ET_0 estimation in an Arid Environment
    Theoretical and Applied Climatology, 2014
    Co-Authors: Ali Rahimikhoob

    Abstract:

    Sunshine duration data are desirable for calculating daily solar radiation ( R _s) and subsequent reference evapotranspiration (ET_0) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under Arid Environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET_0 was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient ( R ^2), the root mean square error (RMSE) and the mean bias error (MBE). Average R ^2, RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET_0 by using Ångström and FAO-PM equations, respectively.

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  • estimating global solar radiation using artificial neural network and air temperature data in a semi Arid Environment
    Renewable Energy, 2010
    Co-Authors: Ali Rahimikhoob

    Abstract:

    Global solar radiation (GSR) data are desirable for many areas of research and applications in various engineering fields. However, GSR is not as readily available as air temperature data. Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to test an artificial neural network (ANN) for estimating the global solar radiation (GSR) as a function of air temperature data in a semi-Arid Environment. The ANNs (multilayer perceptron type) were trained to estimate GSR as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1994–2001) of daily climatic data collected in weather station of Ahwaz located in Khuzestan plain in the southwest of Iran. The empirical Hargreaves and Samani equation (HS) is also considered for the comparison. The HS equation calibrated by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated HS method. The study demonstrated that modelling of daily GSR through the use of the ANN technique gave better estimates than the HS equation. RMSE and R2 for the comparison between observed and estimated GSR for the tested data using the proposed ANN model are 2.534 MJ m−2 day−1 and 0.889 respectively.

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K. R. Thorp – One of the best experts on this subject based on the ideXlab platform.

  • Camelina water use and seed yield response to irrigation scheduling in an Arid Environment
    Irrigation Science, 2013
    Co-Authors: D. J. Hunsaker, A. N. French, K. R. Thorp

    Abstract:

    Camelina sativa (L.) Crantz is a promising, biodiesel-producing oilseed that could potentially be implemented as a low-input alternative crop for production in the Arid southwestern USA. However, little is known about camelina’s water use, irrigation management, and agronomic characteristics in this Arid Environment. Camelina experiments were conducted for 2 years (January to May in 2008 and 2010) in Maricopa, Arizona, to evaluate the effectiveness of previously developed heat unit and remote sensing basal crop coefficient ( K _ cb ) methods for predicting camelina crop evapotranspiration (ET) and irrigation scheduling. Besides K _ cb methods, additional treatment factors included two different irrigation scheduling soil water depletion (SWD) levels (45 and 65 %) and two levels of seasonal N applications within a randomized complete block design with 4 blocks. Soil water content measurements taken in all treatment plots and applied in soil water balance calculations were used to evaluate the predicted ET. The heat-unit K _ cb method was updated and validated during the second experiment to predict ET to within 12–13 % of the ET calculated by the soil water balance. The remote sensing K _ cb method predicted ET within 7–10 % of the soil water balance. Seasonal ET from the soil water balance was significantly greater for the remote sensing than heat-unit K _ cb method and significantly greater for the 45 than 65 % SWD level. However, final seed yield means, which varied from 1,500 to 1,640 kg ha^−1 for treatments, were not significantly different between treatments or years. Seed oil contents averaged 45 % in both years. Seed yield was found to be linearly related to seasonal ET with maximum yield occurring at about 470–490 mm of seasonal ET. Differences in camelina seed yields due to seasonal N applications (69–144 kg N ha^−1 over the 2 years) were not significant. Further investigations are needed to characterize camelina yield response over a wider range of irrigation and N inputs.

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P K Sharma – One of the best experts on this subject based on the ideXlab platform.

  • effect of weather on seed yield and radiation and water use efficiency of mustard cultivars in a semi Arid Environment
    Agricultural Water Management, 2014
    Co-Authors: Sanatan Pradhan, Ravender Singh, Vinay Kumar Sehgal, Alka Jain, K K Bandyopadhyay, P K Sharma

    Abstract:

    Abstract The date of sowing of Indian mustard ( Brassica juncea ) varies from year to year depending upon the harvesting of previous wet season crop in the northern and north-western part of India, which exposes the mustard crop to variable weather conditions. So an experiment was conducted in Indian Agricultural Research Institute research farm to study the interactive effect of variable weather and cultivars on yield, radiation and water use efficiency of mustard. In this experiment, split plot design was adopted with date of sowing (early, normal and late) as main plot treatment and mustard cultivars (Pusa Gold, Pusa Jai Kisan and Pusa Bold) as subplot treatments. Pooled over the years, mustard seed yield, radiation and water use efficiency was significantly ( p  = 0.05) lower in late sowing compared to early sowing (by 46, 32 and 40%, respectively) and normal sowing (by 44, 26 and 41%, respectively). Early sowing and normal sowing were statistically at par with respect to mustard seed yield and water use efficiency. Among the cultivars, Pusa Jai Kisan and Pusa Bold were statistically at par with respect to seed yield, radiation and water use efficiency whereas Pusa Gold registered significantly lower seed yield, radiation and water use efficiency compared to the cultivars Pusa Jai Kisan (by 55, 23 and 52%, respectively) and Pusa Bold (by 56, 20 and 54%, respectively). There was significant interaction between date of sowing and cultivars with respect to seed yield, radiation and water use efficiency of mustard. From the above study it was concluded that normal or early sowing of the Pusa Jai Kisan or Pusa Bold cultivar may be practiced for achieving higher seed yield, radiation and water use efficiency in the semi Arid Environment of north and north-western part of India

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  • synthetic and organic mulching and nitrogen effect on winter wheat triticum aestivum l in a semi Arid Environment
    Agricultural Water Management, 2010
    Co-Authors: Debashis Chakraborty, Ritu Garg, R K Tomar, Ravender Singh, Sunita Sharma, Reeti Singh, S M Trivedi, R Mittal, P K Sharma, Kalpana Kamble

    Abstract:

    Field experiments were conducted in 2002-2003 and 2003-2004 to evaluate the relative performance of synthetic (black polyethylene) and organic (paddy husk and straw) mulches on soil and plant water status vis-a-vis N uptake in wheat in a semi-Arid Environment of India. Scope of better utilization of soil moisture was documented through all the mulches, especially during initial crop growth stages, when the moisture content was 1-3% higher in mulches. Soil temperature was more moderate under organic mulches. Paddy husk recorded significantly higher plant biomass, while the effect of mulching in enhancing root growth was clearly documented. Organic mulches produced more roots (25 and 40% higher root weight and root length densities compared to no-mulch) in sub-surface (>0.15m) layers, probably due to greater retention of soil moisture in deeper layers and relatively narrow range of soil temperature changes under these systems. Incremental N dose significantly improved all the plant parameters in both mulch and no-mulch treatments. Grain yield was 13-21% higher under mulch and so with increasing N levels. Nitrogen uptake was higher in organic mulches and also with higher N doses, while polyethylene mulch showed mixed trend. Mulches were effective in reducing 3-11% crop water use and improved its efficiency by 25%. Grain yield and biomass were well-correlated with leaf area index (r=0.87 and 0.91, respectively) and water use was better correlated with root length than its weight. Results indicated substantial improvement in water and N use efficiency and crop growth in wheat under surface mulching, and the organic mulches, especially rice husk performed better than synthetic mulches.

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