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.

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.

Inhibited development of Ostertagia ostertagi in relation to production systems for cattle

Parasitology ◽  
1982 ◽  
Vol 85 (1) ◽  
pp. 21-25 ◽  
Author(s):  
M. G. Smeal ◽  
A. D. Donald
Keyword(s):  
Beef Cattle ◽  
Dairy Cattle ◽  
Production System ◽  
Production Systems ◽  
New South ◽  
New South Wales ◽  
Cattle Production ◽  
Ostertagia Ostertagi ◽  
South Wales

SUMMARYOn a coastal farm in New South Wales where beef and dairy cattle production was carried on side-by-side, separate pasture plots were contaminated with eggs of Ostertagia ostertagi by calves from each production system in autumn, winter or spring. Successive groups of parasite-free tracer calves grazed on the plots for 14 days at 4-week intervals and were then killed for worm counts 14 days after removal from pasture. On all plots, the proportion of inhibited early 4th-stage larvae in tracer calves reached a maximum in spring, and was consistently and very significantly higher in calves which grazed plots contaminated with O. ostertagi of beef cattle origin. Factors which may be responsible for this difference between beef and dairy cattle populations of O. ostertagi are discussed.

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

2015 ◽  
Vol 8 (1) ◽  
pp. 183-194 ◽  
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.

scholarly journals Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

2019 ◽  
Vol 11 (9) ◽  
pp. 1052 ◽  
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.

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

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.

scholarly journals Problems of early culling of cows in breeding stocks for milk production

2020 ◽  
Vol 36 (4) ◽  
pp. 381-392
Author(s):  
Predrag Perisic ◽  
Renata Relic ◽  
Cvijan Mekic ◽  
Stefan Stepic
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Production Systems ◽  
Economic Damage ◽  
Cattle Production ◽  
Cattle Breeding ◽  
Breeding Goals ◽  
Specific Technology

A common problem of dairy cattle in all production systems and production directions is their reduced production life and therefore their reduced lifetime span. Among the main reasons of dairy cattle premature culling the problems related to reproduction, udder and legs are prevailing. The order of these reasons according to their significance is not the same in all dairy cattle populations and depends on production direction, level of production and specific technology of cattle breeding. There are also other reasons of culling but these three groups are the main ones. Due to frequency of their incidence, as well as economic damage which they can cause in dairy cattle production, today they must be taken into account when defining breeding goals for certain breeds and populations of cattle.

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

Processes ◽  
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.

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. 

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