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

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.

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Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Processes ◽

10.3390/pr9112045 ◽

2021 ◽

Vol 9 (11) ◽

pp. 2045

Author(s):

Nehal Elshaboury ◽

Eslam Mohammed Abdelkader ◽

Ghasan Alfalah ◽

Abobakr Al-Sakkaf

Keyword(s):

Neural Network ◽

Waste Management ◽

Municipal Solid Waste ◽

Solid Waste ◽

Mean Squared Error ◽

Pearson Correlation ◽

Mean Bias Error ◽

Bias Error ◽

P Value ◽

Waste Generation

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

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The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Applied Sciences ◽

10.3390/app10051765 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1765 ◽

Cited By ~ 2

Author(s):

Ghulam Hussain Dars ◽

Courtenay Strong ◽

Adam K. Kochanski ◽

Kamran Ansari ◽

Syed Hammad Ali

Keyword(s):

Mean Squared Error ◽

Pearson Correlation ◽

Wrf Model ◽

Horizontal Resolution ◽

Indus Basin ◽

Spatiotemporal Variability ◽

Mean Bias Error ◽

Bias Error ◽

Upper Indus Basin ◽

Precipitation And Temperature

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

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Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

Indoor and Built Environment ◽

10.1177/1420326x211013076 ◽

2021 ◽

pp. 1420326X2110130

Author(s):

Manta Marcelinus Dakyen ◽

Mustafa Dagbasi ◽

Murat Özdenefe

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Mean Squared Error ◽

Residential Buildings ◽

Mean Bias Error ◽

Bias Error ◽

Sampled Data ◽

Low Carbon ◽

Building Stock ◽

Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

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Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

Atmospheric Measurement Techniques ◽

10.5194/amt-8-183-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 183-194 ◽

Cited By ~ 13

Author(s):

A. Sanchez-Romero ◽

J. A. González ◽

J. Calbó ◽

A. Sanchez-Lorenzo

Keyword(s):

Image Processing ◽

Temporal Resolution ◽

Solar Irradiance ◽

Atmospheric Aerosol ◽

Mean Squared Error ◽

Sunshine Duration ◽

Strong Relationship ◽

Mean Bias Error ◽

Bias Error ◽

High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas), but the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI) and to prove whether this relationship depends on the type of CSSR and burning card. A method of analysis based on image processing of digital scanned images of burned cards is used. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e., visual) determination. The method tends to slightly overestimate SD, but the thresholds that are used in the image processing could be adjusted to obtain an improved estimation. Regarding the burn width, experimental results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error is 24 and 30%, respectively; mean bias error is −0.6 and −30.0 W m−2, respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

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Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Remote Sensing ◽

10.3390/rs11091052 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1052 ◽

Cited By ~ 3

Author(s):

Reto Stöckli ◽

Jędrzej S. Bojanowski ◽

Viju O. John ◽

Anke Duguay-Tetzlaff ◽

Quentin Bourgeois ◽

...

Keyword(s):

Mean Squared Error ◽

Sensor Data ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Cloud Detection ◽

Climate Data ◽

Detection Algorithms ◽

Fractional Cover ◽

Spatio Temporal

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-9537-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 9537-9571

Author(s):

A. Sanchez-Romero ◽

J. A. González ◽

J. Calbó ◽

A. Sanchez-Lorenzo

Keyword(s):

Image Processing ◽

Temporal Resolution ◽

Solar Irradiance ◽

Atmospheric Aerosol ◽

Mean Squared Error ◽

Sunshine Duration ◽

Mean Bias Error ◽

Unbiased Estimation ◽

Bias Error ◽

High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas). Contrarily, the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI), and to prove whether this relationship depends on the type of CSSR and burning card. A semi-automatic method based on image processing of digital scanned images of burnt cards is presented. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e. visual) determination. The method tends to slightly overestimate SD but the thresholds that are used in the image processing could be adjusted to obtain an unbiased estimation. Regarding the burn width, results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error 24 and 30% respectively; mean bias error −0.6 and −30.0 W m−2 respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

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Effect of Supplemental Irrigation on Lentil Yield and Growth in Semi-Arid Environment

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha44110218 ◽

2016 ◽

Vol 44 (1) ◽

pp. 237-244 ◽

Cited By ~ 1

Author(s):

Abdullah KAHRAMAN ◽

Mohd. Kamran KHAN ◽

Anamika PANDEY ◽

Ergun DOGAN

Keyword(s):

Growth Parameters ◽

Irrigation System ◽

Irrigation Treatment ◽

Arid Environment ◽

Growth And Yield ◽

Human Beings ◽

Supplemental Irrigation ◽

Climate Conditions ◽

Supplementary Irrigation ◽

Semi Arid

Lentil is one of the most promising legume crops providing nutritional and food assurance to human beings. Due to extensive production of lentil crop in rain-fed agriculture system, its growth and yield are mainly determined by the levels of precipitation. Consequently, it usually faces drought stress during the generative stage resulting in low yield. In such scenario, controlled supplemental irrigation (SI) can improve and stabilize the productivity. Therefore, the present study was conducted to determine the effect of supplemental irrigation on the growth and yield of lentil crop under semi-arid climate conditions of Turkey. An experiment was performed during two consecutive crop seasons at Sanliurfa, Turkey with annual mean rainfall of 196 and 275 mm in the first and second experimental year, respectively. Six supplementary irrigation treatments were given using drip irrigation system [no supplement irrigation (I0), 25% (I25), 50% (I50), 75% (I75), 100% (I100, full irrigation) and 125% (I125) supplement irrigation depending on the available soil water content]. Results obtained in the study indicated that in both study years, highest biomass, harvest index and grain yield values were obtained from fully irrigated treatments (I100), while non-supplementary irrigated treatments have provided lowest values. It should be clearly noticed that growth parameters including yield were lower under over-irrigation treatment (I125). Hence, it is recommended that farmers need to optimize the supplemental irrigation technique to obtain desired yields. This study will support the successful usage of the supplemental irrigation technology to improve lentil productivity, particularly under semi-arid environment.

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Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches

ASME 2021 Verification and Validation Symposium ◽

10.1115/vvs2021-65272 ◽

2021 ◽

Author(s):

Bingyan Jia ◽

Danlin Hou ◽

Liangzhu (Leon) Wang ◽

Ibrahim Galal Hassan

Keyword(s):

Machine Learning ◽

Power Density ◽

Mean Squared Error ◽

Building Energy ◽

Energy Performance ◽

Mean Bias Error ◽

Support Vector ◽

Bias Error ◽

Model Parameters ◽

Meta Model

Abstract Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as meta-models to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models’ prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed meta-model is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration.

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Simulation of sugarcane yield under salinity and water stress conditions with the OPDM model

Water Science & Technology Water Supply ◽

10.2166/ws.2015.052 ◽

2015 ◽

Vol 15 (5) ◽

pp. 948-957 ◽

Cited By ~ 1

Author(s):

Sahar Palangi ◽

Omid Bahmani

Keyword(s):

Water Stress ◽

Saline Water ◽

Distribution Model ◽

Mean Bias Error ◽

Yield Reduction ◽

Bias Error ◽

Karun River ◽

Sugarcane Yield ◽

Salinity Levels ◽

Irrigation Levels

Limitation of water resources and decline in the quality of soil and water have led to the use of saline water and application of management systems for reducing irrigation water. The subject of this study was to determine the effect of salinity and water stress on sugarcane yield in Iran with an operational and planning distribution model (OPDM) for 7 years (2002–2008). Irrigation scenarios consisted of the full irrigation (I1), 85% (I2) and 70% (I3) of the sugarcane water requirement, and salinity scenarios were the average salinity of the Karun River, S1 (dS m−1), S2 = S1 +2 and S3 = S1 +4. The root mean square error and mean bias error (0.04 and 0.02, respectively) show the low error percentages and the values of EF = 0.65 and d = 0.71 indicated the high accuracy of the yield simulation with OPDM. Significant differences were observed among the different irrigation levels and this difference in I3 was more than in I2. The effect of different salinity levels on yield reduction was not significant. Overall, results showed that there was an individual and combined effect of salinity and water deficit on sugarcane yield; however, the effect of different irrigation levels on the yield was more than the salinity.

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Performance Evaluation of Soil Moisture Sensors in Coarse- and Fine-Textured Michigan Agricultural Soils

Agriculture ◽

10.3390/agriculture10120598 ◽

2020 ◽

Vol 10 (12) ◽

pp. 598

Author(s):

Younsuk Dong ◽

Steve Miller ◽

Lyndon Kelley

Keyword(s):

Soil Moisture ◽

Laboratory Experiment ◽

Mean Squared Error ◽

Water Movement ◽

Field Conditions ◽

Mean Bias Error ◽

Bias Error ◽

Moisture Sensor ◽

Soil Moisture Sensors ◽

Correction Equations

Soil moisture content is a critical parameter in understanding the water movement in soil. A soil moisture sensor is a tool that has been widely used for many years to measure soil moisture levels for their ability to provide nondestructive continuous data from multiple depths. The calibration of the sensor is important in the accuracy of the measurement. The factory-based calibration of the soil moisture sensors is generally developed under limited laboratory conditions, which are not always appropriate for field conditions. Thus, calibration and field validation of the soil moisture sensors for specific soils are needed. The laboratory experiment was conducted to evaluate the performance of factory-based calibrated soil moisture sensors. The performance of the soil moisture sensors was evaluated using Root Mean Squared Error (RMSE), Index of Agreement (IA), and Mean Bias Error (MBE). The result shows that the performance of the factory-based calibrated CS616 and EC5 did not meet all the statistical criteria except the CS616 sensor for sand. The correction equations are developed using the laboratory experiment. The validation of correction equations was evaluated in agricultural farmlands. Overall, the correction equations for CS616 and EC5 improved the accuracy in field conditions.

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