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

2015 ◽  
Vol 15 (5) ◽  
pp. 948-957 ◽  
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.

scholarly journals Salinity Duration and Concentration Affect Fruit Yield and Quality, and Growth and Mineral Composition of Melon Plants Grown in Perlite

HortScience ◽  
1999 ◽  
Vol 34 (7) ◽  
pp. 1234-1237 ◽  
Author(s):  
F.M. del Amor ◽  
V. Martinez ◽  
A. Cerdá
Keyword(s):  
Saline Water ◽  
Inhibitory Effect ◽  
Fruit Yield ◽  
Soluble Solids ◽  
Yield Reduction ◽  
Yield And Quality ◽  
Salinity Levels ◽  
Quality Water ◽  
Beginning Of Flowering ◽  
Nutrient Solutions

The shortage of good quality water in semiarid zones necessitates the use of saline water for irrigation. In order to simulate the usage of brackish irrigation water in greenhouse melon (Cucumis melo L. cv. Galia) culture in perlite, plants were supplied with nutrient solutions containing 0 (control), 20, 40, and 60 mm NaCl applied at four different times. Treatments were applied during early vegetative growth [14 days after transplanting (DAT)], beginning of flowering (37 DAT), beginning of fruit set (56 DAT), and beginning of fruit ripening (71 DAT). All vegetative and fruit yield parameters were significantly reduced when salinization was started 14 DAT. This inhibitory effect of salinity was progressively lessened when salinity was imposed at later dates. This suggests that the response of melons to salinity depends on the duration of exposure to saline water. Salinity treatments increased fruit reducing sugars, acidity, and total soluble solids. Fruit yield reduction at each salinization time was correlated with salinity levels, but there was some evidence of a nutrient imbalance, since leaf concentrations of N-NO3, and especially K, were low at higher salinities. These results indicate that brackish waters can be used for growing melon with minimum yield losses if concentration and duration of exposure are carefully monitored.

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 Influence of Different Irrigation Strategies on Yield and Water Use of Dry Bean (Phaseolus vulgaris L.) in Semi-Arid Zone

2021 ◽  
Vol 9 (8) ◽  
pp. 1580-1588
Author(s):  
NURCAN YAVUZ
Keyword(s):  
Water Stress ◽  
Water Policy ◽  
Arid Zone ◽  
Maximum Yield ◽  
Yield Reduction ◽  
Dry Bean ◽  
Irrigation Interval ◽  
Water Stress Condition ◽  
Significant Yield ◽  
Irrigation Levels

Increasing population and challenges among the sectors due to the climate change and incorrect water policy has increased the pressure on water resources. This situation being as a global crisis particularly in respect to the food security has accelerated productive utilization of water supplies. The aim of the current study with 2-year experiments was to identify the effect of different irrigation interval and irrigation regimes on the yield and yield components of dry bean having greater than 50% of total world legumes production. In that experiment, two different irrigation interval, 7 and 14-day, and three different irrigation levels, (I100, I75 and I50, were studied. In results, the maximum yield was obtained from 7-day irrigation interval, and 28% yield reduction was detected at 14-day irrigation interval. In examine the irrigation levels, the highest yield was found at full irrigation (I100), and increasing water stress caused significant yield reductions e.g. 21% and 49% for I75 and I50, respectively. The evapotranspiration and total applied water as an average of 2013-2014 were calculated as 533 mm, and 450 mm, respectively. In assessment of the both the combine year results, the ky value was determined as 1.59, and this finding shows that dry bean crop is sensitive to the water stress condition.

scholarly journals Correction of Eddy Covariance Based Crop ET Considering the Heat Flux Source Area

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 281
Author(s):  
Stuart L. Joy ◽  
José L. Chávez
Keyword(s):  
Heat Flux ◽  
Eddy Covariance ◽  
Agricultural Land ◽  
Flux Measurement ◽  
Source Area ◽  
Mean Bias Error ◽  
Bias Error ◽  
Crop Water ◽  
The Past ◽  
Measurement Results

Eddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces, the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past decades, there has been a proliferation of ‘heat flux source area’ (i.e., footprint) modeling studies, but only a few have explored the accuracy of the models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently well, with an average mean bias error (MBE) of about −0.03 mm h−1 (−4.4%) and root mean square error (RMSE) of 0.09 mm h−1 (10.9%). The same three footprint models were then used to adjust the EC-measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing the adjusted ET estimates with the lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8%, and the RMSE by 1%.

scholarly journals CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

2021 ◽  
Vol 13 (15) ◽  
pp. 2996
Author(s):  
Qinwei Zhang ◽  
Mingqi Li ◽  
Maohua Wang ◽  
Arthur Paul Mizzi ◽  
Yongjian Huang ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Co2 Flux ◽  
Total Carbon ◽  
Mean Bias Error ◽  
Bias Error ◽  
Parallel Experiment ◽  
Concentration Measurements ◽  
Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

scholarly journals Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ground Level ◽  
Reanalysis Data ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Resistance of bacteria isolated from leachate to heavy metals and the removal of Hg by Pseudomonas aeruginosa strain FZ-2 at different salinity levels in a batch biosorption system

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Muhammad Fauzul Imron ◽  
Setyo Budi Kurniawan ◽  
Siti Rozaimah Sheikh Abdullah
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Pseudomonas Aeruginosa ◽  
Fresh Water ◽  
Landfill Leachate ◽  
Saline Water ◽  
Resistant Bacteria ◽  
Sanitary Landfill ◽  
Salinity Levels ◽  
Sanitary Landfill Leachate

AbstractLeachate is produced from sanitary landfills containing various pollutants, including heavy metals. This study aimed to determine the resistance of bacteria isolated from non-active sanitary landfill leachate to various heavy metals and the effect of salinity levels on the removal of Hg by the isolated bacterium. Four dominant bacteria from approximately 33 × 1017 colony-forming units per mL identified as Vibrio damsela, Pseudomonas aeruginosa, Pseudomonas stutzeri, and Pseudomonas fluorescens were isolated from non-active sanitary landfill leachate. Heavy metal resistance test was conducted for Hg, Cd, Pb, Mg, Zn, Fe, Mn, and Cu (0–20 mg L− 1). The removal of the most toxic heavy metals by the most resistant bacteria was also determined at different salinity levels, i.e., fresh water (0‰), marginal water (10‰), brackish water (20‰), and saline water (30‰). Results showed that the growth of these bacteria is promoted by Fe, Mn, and Cu, but inhibited by Hg, Cd, Pb, Mg, and Zn. The minimum inhibitory concentration (MIC) of all the bacteria in Fe, Mn, and Cu was > 20 mg L− 1. The MIC of V. damsela was 5 mg L− 1 for Hg and >  20 mg L− 1 for Cd, Pb, Mg, and Zn. For P. aeruginosa, MIC was > 20 mg L− 1 for Cd, Pb, Mg, and Zn and 10 mg L− 1 for Hg. Meanwhile, the MIC of P. stutzeri was > 20 mg L− 1 for Pb, Mg, and Zn and 5 mg L− 1 for Hg and Cd. The MIC of P. fluorescens for Hg, Pb, Mg, and Zn was 5, 5, 15, and 20 mg L− 1, respectively, and that for Cd was > 20 mg L− 1. From the MIC results, Hg is the most toxic heavy metal. In marginal water (10‰), P. aeruginosa FZ-2 removed up to 99.7% Hg compared with that in fresh water (0‰), where it removed only 54% for 72 h. Hence, P. aeruginosa FZ-2 is the most resistant to heavy metals, and saline condition exerts a positive effect on bacteria in removing Hg.

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

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.

scholarly journals Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao
Keyword(s):  
North Atlantic ◽  
Function Analysis ◽  
Biological Activities ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
The North ◽  
The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

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