scholarly journals Performance of SUBSTOR model on growth and yield of potato varieties under different planting dates in a sub-tropical environment

2021 ◽  
Vol 23 (2) ◽  
pp. 213-220
Author(s):  
YOGESH KUMAR ◽  
RAJ SINGH ◽  
ANIL KUMAR
Keyword(s):  
Potato Crop ◽  
Absolute Error ◽  
Tropical Environment ◽  
Growth And Yield ◽  
Tuber Initiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Planting Dates ◽  
Simulation Performance ◽  
Main Plot

A field experiment was conducted during Rabi season of two consecutive years 2016-17 and 2017-18 at research farm, Department of Agricultural Meteorology, CCSHAU, Hisar under sub-tropical environment of Haryana. DSSAT (v4.7) family of SUBSTOR module was employed to simulate the comparison of observed values with simulated values under field conditions with a view to a view to assess the performance of model. The model was calibrated for (2016-17) and derived their genetic coefficient and further outputs were validated for second year (2017-18) experiments. Calibration and validation were done on crop grown under four planting dates viz. 8th Oct. (D1), 22th Oct. (D2), 5th Nov. (D3) and 23rd Nov (D4) in main plot treatment and sub-plots treatments consisted of three varieties Kufri Bahar (V1), Kufri Pushkar (V2) and Kufri Surya (V3) were tested in split plot design with four replications. The results affirms that model overestimated the phenology (days to tuber initiation and physiological maturity) and growth and yield parameters like accumulation of maximum LAI, tuber and biological yield. The model’s simulation performance was found satisfactory, and the model overestimated with fair agreement (±10). Performance of model tested with help of Mean absolute error (MAE), Mean bias error (MBE), Root mean square error (RMSE), r (correlation) and PE (Percent error). The model had capability for optimum potato crop management, phenology prediction and future yield estimation.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals ELECTRICAL PARAMETERS ESTIMATION OF SINGLE DIODE PV MODULE MODEL USING HYBRID METAHEURISTIC ALGORIHM

2022 ◽  
Vol 26 (1) ◽  
pp. 64-78
Author(s):  
Mawj M. Abbas ◽  
◽  
Dhiaa H. Muhsen ◽  
Keyword(s):  
Differential Evolution ◽  
Execution Time ◽  
Absolute Error ◽  
Standard Test ◽  
Parameters Estimation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
De Algorithm ◽  
Pv Module

In this paper, an improved hybrid algorithm called differential evolution with integrated mutation per iteration (DEIM) is proposed to extract five parameters of single-diode PV module model obtained by combining differential evolution (DE) algorithm and electromagnetic-like (EML) algorithm. The EML algorithm's attraction-repulsion idea is employed in DEIM in order to enhance the mutation process of DE. The proposed algorithm is validated with other methods using experimental I-V data. The results of presented method reveal that simulated I-V characteristics have a high degree of agreement with experimental ones. The proposed model has an average root mean square error of 0.062A, an absolute error of 0.0452A, a mean bias error of 0.006A, a coefficient of determination of 0.992, a standard test deviation around 0.04540, and 15.33sec as execution time. The results demonstrate that the proposed method is better in terms of the accuracy and execution time (convergence) when compared with other methods where provide less errors.

scholarly journals A Generative Adversarial Inpainting Network to Enhance Prediction of Periodontal Clinical Attachment Level

2021 ◽  
Author(s):  
Vasant Kearney ◽  
Alfa-Ibrahim M. Yansane ◽  
Ryan G. Brandon ◽  
Ram Vaderhobli ◽  
Guo-Hao Lin ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Absolute Error ◽  
Generative Adversarial Networks ◽  
Mean Bias Error ◽  
Bias Error ◽  
X Rays ◽  
Clinical Attachment Level ◽  
Adversarial Networks ◽  
Attachment Level ◽  
Pairwise Test

Abstract Deep learning algorithms has recently been used to determine clinical attachment levels (CAL) which aid in the diagnosis of periodontal disease. However, the limited field-of-view of dental bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were conducted using mean bias error (MBE), mean absolute error (MAE) and Dunn’s pairwise test comparing CAL at p=0.05. Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with a MAE of 1.04mm and 1.50mm respectively. The Dunn’s pairwise test indicated a statistically significant improvement in CAL prediction accuracy between both inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn’s pairwise value of -63.89. This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing images.

scholarly journals Quantification of crop weather relationship and the effect of different planting dates on growth and yield of potato cultivars in a sub-tropical environment at Hisar

2019 ◽  
Vol 11 (1) ◽  
pp. 17-22
Author(s):  
Yogesh Kumar ◽  
Raj Singh ◽  
Anil Kumar ◽  
C S Dagar
Keyword(s):  
Field Experiments ◽  
Tuber Yield ◽  
Growth Period ◽  
Potato Cultivars ◽  
Tropical Environment ◽  
Growth And Yield ◽  
Planting Dates ◽  
Meteorological Observatory ◽  
Plot Design ◽  
Agricultural Meteorology

Field experiments were carried out at research farm of Department of Agricultural Meteorology, CCSHAU, Hisar during Rabi seasons of 2016-17 to quantify crop weather relationship and the effect of different planting dates on growth and yield of potato cultivars in a sub-tropical environment at Hisar. The experimental field was adjacent to Agro-meteorological observatory at 290 10' N latitude, 750 46' E longitude and altitude of 215.2 m. The main plots treatments consisted four date of sowing viz. D1- 8th Oct., D2-22th Oct., D3- 5th Nov. and D4- 23rd Nov. The sub-plots treatment consisted of three varieties (V1- Kufri Bahar, V2- Kufri Pushkar and V3- Kufri Surya). The forty eight treatment combinations were tested in split plot design with four replications. The results revealed that various growth and yield observations were recorded higher in second sown crop (22th Oct.) as followed by other planting dates. The maximum tuber yield were produced in D2 (20810.45 kg/ha) and it was least in D4 (14525.46 kg/ha). Among the varieties, Kufri Pushkar recorded highest tuber yield (21478.06 kg/ha) followed by Kufri Bahar (17432.26 kg/ha) and Kufri Surya (15378.11 kg/ha). In crop weather relationship, Tuber yield and plant height were significantly positively correlated with rainfall (0.80 and 0.92) and rainy days (0.50 and 0.53). Evening relative humidity was also positively correlated with LAI (0.59) and tuber yield (0.78) of potato. Vegetables production is considered to be particularly important in satisfying world food demand. Specific research therefore is needed in order to evaluate the effects of environmental factors that crop encounters during its growth period and its production.

scholarly journals Experimental and Theoretical Study on the Internal Convective and Radiative Heat Transfer Coefficients for a Vertical Wall in a Residential Building

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5953
Author(s):  
Piotr Michalak
Keyword(s):  
Heat Transfer ◽  
Vertical Wall ◽  
Experimental Studies ◽  
Residential Building ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Bias Error ◽  
Single Family ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients

Experimental studies on internal convective (CHTC) and radiative (RHTC) heat transfer coefficients are very rarely conducted in real conditions during the normal use of buildings. This study presents the results of measurements of CHTC and RHTC for a vertical wall, taken in a selected room of a single-family building during its everyday use. Measurements were performed using HFP01 heat flux plates, Pt1000 sensors for internal air and wall surface temperatures and a globe thermometer for mean radiant temperature measured in 10 min intervals. Measured average CHTC and RHTC amounted to 1.15 W/m2K and 5.45 W/m2K, compared to the 2.50 W/m2K and 5.42 W/m2K recommended by the EN ISO 6946, respectively. To compare with calculated CHTC, 14 correlations based on the temperature difference were applied. Obtained values were from 1.31 W/m2K (given by Min et al.) to 3.33 W/m2K (Wilkes and Peterson), and in all cases were greater than the 1.15 W/m2K from measurements. The average value from all models amounted to 2.02 W/m2K, and was greater than measurements by 75.6%. The quality of models was also estimated using average absolute error (AAE), average biased error (ABE), mean absolute error (MAE) and mean bias error (MBE). Based on these techniques, the model of Fohanno and Polidori was identified as the best with AAE = 68%, ABE = 52%, MAE = 0.41 W/m2K and MBE = 0.12 W/m2K.

scholarly journals Effect of planting dates and sources of nitrogen on growth and yield of cauliflower at Rampur, Chitwan, Nepal

2020 ◽  
Vol 1 (2) ◽  
pp. 52-58
Author(s):  
Binaya Baral ◽  
◽  
Manisha Shrestha ◽  
Binod Pokhrel ◽  
Puspa Dulal
Keyword(s):  
Sandy Loam ◽  
Poultry Manure ◽  
Nitrogen Sources ◽  
Growth And Yield ◽  
Planting Dates ◽  
Late Season ◽  
Net Returns ◽  
Canopy Area ◽  
Main Plot ◽  
The Difference

Appropriate time of planting and use of suitable sources of nitrogen are highly conducive for better growth and yield of cauliflower. A field experiment was conducted to study the effect of planting dates and sources of nitrogen on growth & yield of cauliflower at horticulture research block of Agriculture and Forestry University, Rampur, Chitwan, Nepal from 1st Nov 2019 to 4th March 2020 using ‘Snow mystic’, a late season variety of cauliflower. The study was laid out in split-plot design with two dates of planting (Dec 1st & Dec 16th) as main plot factors & four sources of nitrogen viz. 100% biochar (BCH), 100% Urea(U), 50% urea+ 50% Poultry manure (U+PM) & 50% Biochar+ 50% poultry manure (BCH+PM) against a control as sub-plot factors and were replicated thrice with 30 experimental units each of 9 m2 size containing 5 rows with 5 plants per row. The recommended dose of fertilizer used for the research was 108:92:60 kg N, P2O5, K2O ha-1 and P and K were supplied through SSP and MOP. The soil of experimental plot was sandy loam with slightly acidic with pH (5.6). The data regarding days to 90% curding, canopy area (cm2), leaf number per plant, above ground dry mater (g m-2) (AGDM), curd size (cm2) and curd weight per plant(g), days to curding to harvesting interval, yield, HI and B:C ratio were recorded and analysed using MS Excel and R studio. Significantly higher number of leaves per plant (16.03), bigger average canopy area (5089.93 cm2), higher AGDM (217.91 g m-2), bigger (1563.03 cm2) and heavier curds (1412.44 g) were recorded in 1st Dec. transplanted cauliflower with significantly higher harvest index (68.20). Regarding the sources of nitrogen, all the above parameters were seen better under BCH+ PM but were statistically at par with other nitrogen sources except control. The 1st Dec. planted crop had 4 more days of curding to harvesting interval than 16th Dec. planted one but the difference was not significant. December 1st planted cauliflower yielded 110% more yield and net returns than 16th Dec. planted crop whereas BCH incurred maximum cost (NRs 322145 ha-1) and U and U+PM were the most profitable in terms of B:C ratio (12.77 and 12.96 respectively).Hence, better crop yield and benefit could be obtained by planting the late season cauliflower (var. Snow mystic) at 1st Dec with the use of 100% urea or U+PM as nitrogen source in plains of Nepal having Chitwan like climate.

scholarly journals Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

2020 ◽  
Vol 24 (1) ◽  
pp. 52-58
Author(s):  
Anderson P. Coelho ◽  
Alexandre B. Dalri ◽  
João A. Fischer Filho ◽  
Rogério T. de Faria ◽  
Laércio S. Silva ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Pearson Correlation ◽  
Irrigation Treatment ◽  
Growth And Yield ◽  
Mean Bias Error ◽  
Bias Error ◽  
Fundamental Factor ◽  
Supplementary Irrigation ◽  
Sugarcane Yield ◽  
Stalk Yield

ABSTRACT Model calibration is a fundamental factor to obtain high accuracy in the estimation of crop growth and yield. This study aimed to parameterize the genetic and ecotype coefficients of the DSSAT/Canegro model for five sugarcane cultivars kept under three water managements, besides evaluating the accuracy of the model in predicting sugarcane stalk yield, sugar yield and height. Experimental field data were obtained from two years (2016 and 2017) of cultivation at FCAV/Universidade Estadual Paulista, Jaboticabal, SP, Brazil. The cultivars were maintained under supplementary irrigation, deficit irrigation and no irrigation. Data of the supplementary irrigation treatment (without stress) were used for the parameterization of each cultivar. Model accuracy was assessed by Pearson correlation (r), root mean squared error (RMSE), mean bias error (MBE), index of agreement (d) and confidence coefficient (c). The DSSAT/Canegro model is highly accurate in predicting stalk and sugar yields of sugarcane grown under water regimes, presenting itself as a viable alternative in sugarcane yield simulation. For better performance of the DSSAT/Canegro model, it is necessary to parameterize the variables related to the ecotype of the cultivars, besides the specific coefficients of the cultivars.

scholarly journals The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5418
Author(s):  
Brighton Mabasa ◽  
Meena D. Lysko ◽  
Henerica Tazvinga ◽  
Sophie T. Mulaudzi ◽  
Nosipho Zwane ◽  
...  
Keyword(s):  
South Africa ◽  
Sunshine Duration ◽  
Absolute Error ◽  
Regression Coefficients ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Observation Data ◽  
Statistical Validation ◽  
Worst Case

The South African Weather Service (SAWS) manages an in situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network, located in all six macroclimate zones of South Africa. A sparsely distributed radiometric network over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström–Prescott (AP) model, which can be used to estimate global horizontal irradiance (GHI) from observed sunshine hours, were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macroclimate zones of South Africa using the observation data that span 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily GHI. The maximum annual relative Mean Bias Error (rMBE) was 0.371%, relative Mean Absolute Error (rMAE) was 0.745%, relative Root Mean Square Error (rRMSE) was 0.910%, and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated GHI values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where daily observation sunshine duration data are available.

scholarly journals Calibration methods for the Hargreaves-Samani equation

2018 ◽  
Vol 42 (1) ◽  
pp. 104-114 ◽  
Author(s):  
Lucas Borges Ferreira ◽  
Fernando França da Cunha ◽  
Anunciene Barbosa Duarte ◽  
Gilberto Chohaku Sediyama ◽  
Paulo Roberto Cecon
Keyword(s):  
Linear Regression ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Bias Error ◽  
Daily Data ◽  
Calibration Methods ◽  
And Performance ◽  
Adjustment Of Parameters

ABSTRACT The estimation of the reference evapotranspiration is an important factor for hydrological studies, design and management of irrigation systems, among others. The Penman Monteith equation presents high precision and accuracy in the estimation of this variable. However, its use becomes limited due to the large number of required meteorological data. In this context, the Hargreaves-Samani equation could be used as alternative, although, for a better performance a local calibration is required. Thus, the aim was to compare the calibration process of the Hargreaves-Samani equation by linear regression, by adjustment of the coefficients (A and B) and exponent (C) of the equation and by combinations of the two previous alternatives. Daily data from 6 weather stations, located in the state of Minas Gerais, from the period 1997 to 2016 were used. The calibration of the Hargreaves-Samani equation was performed in five ways: calibration by linear regression, adjustment of parameter “A”, adjustment of parameters “A” and “C”, adjustment of parameters “A”, “B” and “C” and adjustment of parameters “A”, “B” and “C” followed by calibration by linear regression. The performances of the models were evaluated based on the statistical indicators mean absolute error, mean bias error, Willmott’s index of agreement, correlation coefficient and performance index. All the studied methodologies promoted better estimations of reference evapotranspiration. The simultaneous adjustment of the empirical parameters “A”, “B” and “C” was the best alternative for calibration of the Hargreaves-Samani equation.

scholarly journals Methods for Comparing Simulated and Observed Satellite Infrared Brightness Temperatures and What Do They Tell Us?

2016 ◽  
Vol 32 (1) ◽  
pp. 5-25 ◽  
Author(s):  
Sarah M. Griffin ◽  
Jason A. Otkin ◽  
Christopher M. Rozoff ◽  
Justin M. Sieglaff ◽  
Lee M. Cronce ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Forecast Error ◽  
Absolute Error ◽  
Skill Score ◽  
Mean Bias Error ◽  
Bias Error ◽  
Brightness Temperatures ◽  
Object Based ◽  
Feature Match ◽  
Upper Level

Abstract In this study, the utility of dimensioned, neighborhood-based, and object-based forecast verification metrics for cloud verification is assessed using output from the experimental High Resolution Rapid Refresh (HRRRx) model over a 1-day period containing different modes of convection. This is accomplished by comparing observed and simulated Geostationary Operational Environmental Satellite (GOES) 10.7-μm brightness temperatures (BTs). Traditional dimensioned metrics such as mean absolute error (MAE) and mean bias error (MBE) were used to assess the overall model accuracy. The MBE showed that the HRRRx BTs for forecast hours 0 and 1 are too warm compared with the observations, indicating a lack of cloud cover, but rapidly become too cold in subsequent hours because of the generation of excessive upper-level cloudiness. Neighborhood and object-based statistics were used to investigate the source of the HRRRx cloud cover errors. The neighborhood statistic fractions skill score (FSS) showed that displacement errors between cloud objects identified in the HRRRx and GOES BTs increased with time. Combined with the MBE, the FSS distinguished when changes in MAE were due to differences in the HRRRx BT bias or displacement in cloud features. The Method for Object-Based Diagnostic Evaluation (MODE) analyzed the similarity between HRRRx and GOES cloud features in shape and location. The similarity was summarized using the newly defined MODE composite score (MCS), an area-weighted calculation using the cloud feature match value from MODE. Combined with the FSS, the MCS indicated if HRRRx forecast error is the result of cloud shape, since the MCS is moderately large when forecast and observation objects are similar in size.

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