Estimation of solar radiation based on air temperature and application with the DSSAT v4.5 peanut and rice simulation models in Thailand

Phakamas, Nittaya; Jintrawet, Attachai; Patanothai, A.; Sringam, P; Hoogenboom, Gerrit

doi:10.1016/j.agrformet.2013.06.015

Cited by 20 publications

(12 citation statements)

References 34 publications

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“…However, Keating et al (1982b) reported that little change in storage root growth rate for cassava genotype M Aus 10 occurred with a mean air temperature range of 13-25 °C in Queensland, Australia (27°37' S and 153°17' E, 45 m a.l.s.). With regard to the effect of solar radiation on crop growth, previous studies have also reported that a large amount of solar radiation during the planting period resulted in an increase in total crop and storage root dry weights in cassava (Fukai et al, 1984a, b) and other crops, such as peanut (Phakamas et al, 2013), soybean (Banterng et al, 2010) and rice (Phakamas et al, 2013;Vilayvong et al, 2015). In the Patia Valley, in southwestern Colombia (2°09' N and 77°04' W, 600 m a.l.s.…”

Section: Discussionmentioning

confidence: 99%

Growth rates and yields of cassava at different planting dates in a tropical savanna climate

Phoncharoen

Banterng

Vorasoot

et al. 2019

Sci. agric. (Piracicaba, Braz.)

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Details on growth and yield for cassava planted on different dates are useful for determining suitable genotypes for particular growing seasons. Our aim was to study growth and yield of cassava planted on different dates. Four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11 and CMR38-125-77) were evaluated using a randomized complete block design (RCBD) with four replications in six growing periods (

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Section: Discussionmentioning

confidence: 99%

Growth rates and yields of cassava at different planting dates in a tropical savanna climate

Phoncharoen

Banterng

Vorasoot

et al. 2019

Sci. agric. (Piracicaba, Braz.)

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“…Making a percentage comparison, the HA model coefficient showed an average variation of 20%, while for the BC model coefficients of variation was 42% on average. These results showed that calibration can improve the accuracy of estimates (Liu et al, 2009b;Silva et al, 2012;Phakamas et al, 2013), especially for models with a greater number of coefficients.…”

Section: Introductionmentioning

confidence: 73%

“…Depending on the model and calibration the errors can vary between 0.1 (De Jong and Stewart, 1993) and 17.8 MJm -2 d -1 (Phakamas et al, 2013). Beyond the evaluation of these models for a wide range of geographical and climate conditions, it is interesting to access how Rs estimated values impact other crop process, as evapotranspiration and crop yield.…”

Section: Introductionmentioning

confidence: 99%

Temperature-based solar radiation models for use in simulated soybean potential yield

Silva¹,

Silva²,

Almorox³

et al. 2016

Aust J Crop Sci

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Solar radiation is the main meteorological element required for crop yield simulation. However, it is not widely measured as air temperature and rainfall. This study evaluated some temperature-based solar radiation models for estimation of daily solar radiation (Rs), and how the estimates may affect soybean yield potential. The evaluated models were Annandale (AN), Hargreaves (HA), Modified Hargreaves (HA-1), Hunt (HU), Bristow and Campbell (BC), Chen (CH), Donatelli and Campbell (DC) and De Jong and Stewart (JS). This research was carried out using historical data from six sites in the Triangulo Mineiro region, where measured values of Rs, minimum and maximum air temperature and rainfall were available. The dataset (2009)(2010)(2011)(2012)(2013)(2014) was separated into two sub-data sets, one for calibration (2009) and the other for evaluation of performance (2010)(2011)(2012)(2013)(2014). The Rs estimated data were used in SoySim software to estimate potential soybean yield. Statistical indexes: (a) root mean square error (RMSE), (b) relative root mean square error (RRMSE), (c) coefficient of determination (R 2 ) and (d) mean error (ME) were used as indicators of the agreement between observed and estimated Rs data. After evaluating the performance, Rs estimated values for each model were used to simulate the soybean potential yield. Although the eight models have presented similar performance for estimating Rs values, when these data were used for simulation of the potential soybean yield, the performances diverged considerably. In this way, only the BC, CH, DC and JS models showed satisfactory performance in yield simulation with R 2 and RRMSE varying from 0.76 to 0.80 and 3 to 4%, respectively. Therefore, the findings suggest that, before choosing the model to estimate Rs, it is important to define the purpose of use of solar radiation data.

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“…The current solar radiation products have fine temporal and coarse spatial resolutions that are not suitable for evaluating land-atmospheric interaction and local weather models (Kim and Liang, 2010). Due to the requirement for expensive equipment and the difficulty in sensor calibration, it is impossible to obtain solar radiation from ground-based measurement at the high resolution spatial scale (Phakamas et al, 2013). Thus, several studies have attempted to estimate solar radiation (both shortwave and longwave) components using remote sensing (RS) data based on radiative transfer models which need many ancillary data (such as CO 2 , O 3 , CH 4 , temperature, cloud optical thickness etc.).…”

Section: Introductionmentioning

confidence: 99%

Estimation of daily average downward shortwave radiation from MODIS data using principal components regression method: Fars province case study

Barzin

Shirvani

Lotfi

2017

International Agrophysics

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A b s t r a c t. Downward shortwave radiation is a key quantity in the land-atmosphere interaction. Since the moderate resolution imaging spectroradiometer data has a coarse temporal resolution, which is not suitable for estimating daily average radiation, many efforts have been undertaken to estimate instantaneous solar radiation using moderate resolution imaging spectroradiometer data. In this study, the principal components analysis technique was applied to capture the information of moderate resolution imaging spectroradiometer bands, extraterrestrial radiation, aerosol optical depth, and atmospheric water vapour. A regression model based on the principal components was used to estimate daily average shortwave radiation for ten synoptic stations in the Fars province, Iran, for the period 2009-2012. The Durbin-Watson statistic and autocorrelation function of the residuals of the fitted principal components regression model indicated that the residuals were serially independent. The results indicated that the fitted principal components regression models accounted for about 86-96% of total variance of the observed shortwave radiation values and the root mean square error was about 0.9-2.04 MJ m -2 d -1 . Also, the results indicated that the model accuracy decreased as the aerosol optical depth increased and extraterrestrial radiation was the most important predictor variable among all.

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Estimation of solar radiation based on air temperature and application with the DSSAT v4.5 peanut and rice simulation models in Thailand

Cited by 20 publications

References 34 publications

Growth rates and yields of cassava at different planting dates in a tropical savanna climate

Growth rates and yields of cassava at different planting dates in a tropical savanna climate

Temperature-based solar radiation models for use in simulated soybean potential yield

Estimation of daily average downward shortwave radiation from MODIS data using principal components regression method: Fars province case study

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