scholarly journals Impacts of rainfall and temperature on photoperiod insensitive sorghum cultivar : model evaluation and sensitivity analysis

2021 ◽  
Vol 21 (3) ◽  
pp. 262-269
Author(s):  
F. M. AKINSEYE ◽  
A. H. FOLORUNSHO ◽  
AJEIGBE ◽  
A. HAKEEM ◽  
S. O. AGELE
Keyword(s):  
Grain Yield ◽  
Production Systems ◽  
Significant Risk ◽  
Model Performance ◽  
Farming Systems ◽  
Application Rate ◽  
Weather Data ◽  
Mean Bias Error ◽  
Bias Error ◽  
Total Biomass

A combination of local-scale climate and crop simulation model were used to investigate the impacts of change in temperature and rainfall on photoperiod insensitive sorghum in the Sudanian zone of Mali. In this study, the response of temperature and rainfall to yield patterns of photoperiod insensitive sorghum (Sorghum bicolor L. Moench) using the Agricultural Production Systems Simulator (APSIM) model was evaluated. Following model calibration of the cultivar at varying sowing dates over two growing seasons (2013 and 2014), a long-term simulation was run using historical weather data (1981-2010) to determine the impacts of temperature and rainfall on grain yield, total biomass and water use efficiency at varying nitrogen fertilizer applications. The results showed that model performance was excellent with the lowest mean bias error (MBE) of -2.2 days for flowering and 1.4 days for physiological maturity. Total biomass and grain yield were satisfactorily reproduced, indicating fairly low RMSE values of 21.3% for total biomass and very low RMSE of 11.2 % for grain yield of the observed mean. Simulations at varying Nfertilizer application rate with increased temperature of 2 °C, 4 °C and 6 °C and decreased rainfall by 25 and 50 % (W-25% and W-50%) posed a highly significant risk to low yield compared to increase in rainfall. However, the magnitude of temperature changes showed a decline in grain yield by 10%, while a decrease in rainfall by W-25% and W-50% resulted in yield decline between 5% and 37%, respectively. Thus, climate-smart site-specific utilization of the photoperiod insensitive sorghum cultivar suggests more resilient and productive farming systems for sorghum in semi-arid regions of Mali. 

scholarly journals Simulation of the Optimum Planting Windows for Early and Intermediate-Maturing Maize Varieties in the Nigerian Savannas Using the APSIM Model

2021 ◽  
Vol 5 ◽  
Author(s):  
Aloysius Beah ◽  
Alpha Yaya Kamara ◽  
Jibrin Mohamed Jibrin ◽  
Folorunso Mathew Akinseye ◽  
Abdullahi Ibrahim Tofa ◽  
...  
Keyword(s):  
Grain Yield ◽  
Production Systems ◽  
Model Performance ◽  
Growing Season ◽  
Weather Data ◽  
Ecological Zones ◽  
Maize Varieties ◽  
Attainable Yield ◽  
Agricultural Production Systems

The Agricultural Production Systems Simulator (APSIM) model was calibrated and validated and used to identify the optimum planting windows for two contrasting maize varieties for three agro-ecologies in the Nigeria savannas. The model was run for 11 planting windows starting from June 1 and repeated every 7 days until 16 August using long-term historical weather data from the 7 selected sites representing three agro-ecological zones (AEZs). The evaluation with the experimental data showed that the model performance was reasonable and accurately predict crop phenology, total dry matter (TDM) and grain yield for both maize varieties. The seasonal planting date analysis showed that optimum planting windows for 2009EVDT and IWDC2SynF2 depend on the variety, agro-ecozones and sites. Planting from June 15 to 28 simulated the highest mean grain yield for both varieties in all the agro-ecologies. In the Southern Guinea savanna (SGS) where the length of growing season is 180–210 days, the best planting window was June 8–July 19 for 2009EVDT and June 8–July 26 for IWDC2SynF2 in Abuja. The planting window that gives attainable yield at Yelwa, is June 15–July 5 for 2009EVDT and June 8–28 for IWDC2SynF2. In the Northern Guinea savannah (NGS) where the length of growing season is 150–180 days, the optimum planting window is June 15–July 19 for both varieties at Zaria and June 8–July 19 for 2009EVDT and June 8–August 2 for IWDC2SynF2 at Sabon Gari. In the Sudan savannah (SS) where the growing season is 90–120 days, planting of 2009EVDT can be delayed up to the third week of July. For the medium-maturing variety, IWDC2SynF2, planting should be done by the first week of July. Though Yelwa is in the SGS, lower yields and narrower sowing windows were simulated for both varieties than for those of the other locations. This is probably due to the poor soil fertility in this location.

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 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 Prediction model for day-ahead solar insolation using meteorological data for smart grid

2019 ◽  
Vol 111 ◽  
pp. 06040
Author(s):  
Min Hee Chung
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Electric Power ◽  
Meteorological Data ◽  
Weather Data ◽  
Mean Bias Error ◽  
The Internet ◽  
Bias Error ◽  
Photovoltaic Systems ◽  
Internet Platform

In the overseas market, power generation and energy service companies have been engaged in the business of providing personalized trading services for the production of electric power through the Internet platform. This is, so that the electric power sharing system between individuals is being developed through the Internet platform. The prediction of insolation is essential for the prediction of power generation for photovoltaic systems. In this study, we present a prediction model for insolation from data observed at the Meteorological Administration. We also present basic data for the development of the insolation prediction model through meteorological parameters provided in future weather forecasts. The prediction model presented is for five years of observation of weather data in the Seoul area. The proposed model was trained by using the feed-forward neural networks, taking into account the daily climatic elements. To validate the reliability of the model, the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) were used for estimation. The results of this study can be used to predict the solar power generation system and to provide basic information for trading generated output by photovoltaic systems.

Modelling the comparative growth, water use and productivity of the perennial legumes, tedera (Bituminaria bituminosa var. albomarginata) and lucerne (Medicago sativa) in dryland mixed farming systems

2017 ◽  
Vol 68 (7) ◽  
pp. 643 ◽  
Author(s):  
Chao Chen ◽  
Andrew Smith ◽  
Phil Ward ◽  
Andrew Fletcher ◽  
Roger Lawes ◽  
...  
Keyword(s):  
Field Experiments ◽  
Farming Systems ◽  
Dry Mass ◽  
Weather Data ◽  
Total Biomass ◽  
Management Options ◽  
Diverse Range ◽  
Mixed Farming ◽  
Bituminaria Bituminosa

Tedera (Bituminaria bituminosa var. albomarginata) has been proposed as an alternative perennial forage legume to lucerne in the mixed farming zone of Australia. Simulation of growth and production of tedera would be a useful tool for assessing its integration into Australian farming systems and agronomic and management options. This paper describes the development and testing of a model of the growth and development of tedera in Agricultural Production Systems Simulator (APSIM). The existing APSIM-Lucerne was modified to develop APSIM-Tedera. The key physiological parameters for tedera were obtained from the literature or by measuring and comparing the phenology and growth characteristics of tedera and lucerne in glasshouse experiments and partially from field experiments. The model was tested using data from a diverse range of soil and climatic conditions. Using the modelling approach, the production of tedera and lucerne was also assessed with long-term (1951–2015) weather data at Arthur River, Western Australia. Biomass simulations of tedera (n = 26, observed mean = 510 kg dry mass ha–1) explained 66% of the observed variation in field experiments (root mean square deviation = 212 kg dry mass ha–1). Long-term simulations of a 4-year pasture phase showed that more total annual biomass (5600 kg ha–1) would be obtained from lucerne than tedera if the pasture forage was harvested four times a year. Less biomass (400 kg ha–1) was also simulated for tedera in summer under this management. When the pasture forage was harvested when biomass was more than 2000 kg ha–1, tedera and lucerne produced similar accumulated biomass in the second (8000 kg ha–1), third (12 000 kg ha–1) and fourth (15 000 kg ha–1) years, but much less in the first 2 years for tedera. The model can be used for assessing tedera production, agronomic and management options in the Mediterranean climate of Australia. The present preliminary study indicates that tedera is not as effective as lucerne for total biomass production, but it may provide useful feed in situations where the summer-autumn feed gap is a major constraint to production. Further research is also necessary to determine the potential role of tedera in areas where lucerne is not well adapted.

scholarly journals The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2583
Author(s):  
Brighton Mabasa ◽  
Meena D. Lysko ◽  
Henerica Tazvinga ◽  
Nosipho Zwane ◽  
Sabata J. Moloi
Keyword(s):  
South Africa ◽  
Model Performance ◽  
Detection Algorithm ◽  
Surface Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clear Sky ◽  
Satellite Platform ◽  
Hourly Data

This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE), relative Root Mean Square Error (rRMSE) and the coefficient of determination (R2) are used to categorize model performance. The results show that each of the models performed differently across the 13 stations and in different climatic regions. The Bird model was overall the best in all regions, with an rMBE of 1.87%, rRMSE of 4.11% and R2 of 0.998. The Bird model can therefore be used with quantitative confidence as a basis for solar energy applications when all the required model inputs are available.

scholarly journals Organic Oat Response to Variety, Seeding Rate, and Nutrient Source and Rate

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1418
Author(s):  
Melissa L. Wilson ◽  
Emily E. Evans ◽  
Lee Klossner ◽  
Paulo H. Pagliari
Keyword(s):  
Grain Yield ◽  
Nutrient Management ◽  
Production Systems ◽  
Application Rate ◽  
Organic Production ◽  
Residual Soil ◽  
Limited Information ◽  
Seeding Rate ◽  
Population Number ◽  
Application Rates

Oat (Avena sativa L.) is an important crop for organic production systems in the upper Midwest, but limited information on optimal nutrient management and seeding rates is available. Oat varieties representing three maturity groups were evaluated during 2015 and 2016 in Lamberton, Minnesota on organically certified ground previously planted to alfalfa (Medicago sativa L.). Two oat seeding rates (110 and 145 kg ha−1), two nutrient sources (raw and composted beef manure), and four N application rates (0, 50, 100, and 150 kg ha−1) were studied. Plant population; number of tillers; grain yield; grain nutrient removal (primary and secondary macronutrients); and post-harvest soil nitrate, Bray P-1, and K in the top 0 to 15 cm layer were measured. Grain yield was 4.8, 4.0, and 3.8 kg ha−1 for late maturing Deon, early maturing Tack/Saber, and medium maturing Shelby, respectively. Yield was optimized at a nutrient application rate of 82.3 kg N ha−1 and decreased at higher rates. Grain N content was not related to yield, suggesting that the other nutrients in manure and compost may have been responsible for optimizing yield. High application rates resulted in increased residual soil test P and K levels, which could become problematic if not managed appropriately.

scholarly journals Evaluation of the Modified LIVestock SIMulator for Stall-Fed Dairy Cattle in the Tropics

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 816
Author(s):  
Christian A. Bateki ◽  
Uta Dickhoefer
Keyword(s):  
Dairy Cattle ◽  
Mean Squared Error ◽  
Production Systems ◽  
Simulation Models ◽  
Metabolizable Energy ◽  
Mean Bias Error ◽  
Bias Error ◽  
Biological Processes ◽  
Cattle Production ◽  
Using Data

Ruminant livestock systems in the (Sub-)Tropics differ from those in temperate areas. Yet, simulation models used to study resource use and productive performance in (sub-)tropical cattle production systems were mostly developed using data that quantify and characterize biological processes and their outcomes in cattle kept in temperate regions. Ergo, we selected the LIVestock SIMulator (LIVSIM) model, modified its cattle growth and lactation modules, adjusted the estimation of the animals’ metabolizable energy and protein requirements, and adopted a semi-mechanistic feed intake prediction model developed for (sub-)tropical stall-fed cattle. The original and modified LIVSIM were evaluated using a meta-dataset from stall-fed dairy cattle in Ethiopia, and the mean bias error (MBE), the root mean squared error of prediction (RMSEP), and the relative prediction error (RPE) were used to assess their accuracy. The modified LIVSIM provided more accurate predictions of voluntary dry matter intake, final body weights 140 days postpartum, and daily milk yields than the original LIVSIM, as shown by a lower MBE, RMSEP, and RPE. Therefore, using data that quantify and characterize biological processes from (sub-)tropical cattle production systems in simulation models used in the (Sub-)Tropics can considerably improve their accuracy.

scholarly journals Comparison of evapotranspiration models

MAUSAM ◽  
2022 ◽  
Vol 53 (2) ◽  
pp. 119-126
Author(s):  
R. K. MALL ◽  
B. R. D. GUPTA
Keyword(s):  
Root Mean Square Error ◽  
Model Performance ◽  
Uttar Pradesh ◽  
The Other ◽  
Mean Bias Error ◽  
Wheat Crop ◽  
Bias Error ◽  
Mean Square ◽  
Better Than ◽  
Computing Models

Actual evapotranspiration of wheat crop during different year from 1978-79 to 1992-93 was measured daily in Varanasi, Uttar Pradesh using lysimeter. In this study three evapotranspiration computing models namely Doorenbos and Pruitt, Thornthwaite and Soil Plant Atmosphere Water (SPAW) have been used. Comparisons of these three methods show that the SPAW model is better than the other two methods for evapotraspiration estimation. In the present study the MBE (Mean-Bias-Error), RMSE (Root Mean Square Error) and t-statistic have also been obtained for better evaluations of a model performance.

scholarly journals Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Min Hee Chung
Keyword(s):  
Energy Production ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Absolute Error ◽  
Weather Data ◽  
Mean Bias Error ◽  
Bias Error ◽  
Feed Forward Neural Network ◽  
Pv System ◽  
Solar Insolation

Day-ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed-forward neural network-based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m2/day, −0.09 kWh/m2/day, and 1.15 kWh/m2/day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.

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