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Postweaning is one of the most sensitive and energy-demanding phases of swine production. The objective of this research was to assess the energy, production and environmental characteristics of a conventional farm with temperature-based environmental control. The selected energy, environmental and production variables were measured on farm, in a h...
Contexts in source publication
Context 1
... incorporation of the production component to energy performance analysis (Figure 7) revealed that cycles I and VI showed the minimum energy requirements per piglet weight gain, whereas cycles IV and V showed the maximum energy requirements. Table 3 shows the Pearson coefficients (R 2 ) between environmental and energy variables, with outstanding values of 0.91 between T out and C CO2 , and RH and W vs . Table 3 shows the Pearson coefficients (R 2 ) between environmental and energy variables, with outstanding values of 0.91 between Tout and CCO2, and RH and Wvs. ...Context 2
... 3 shows the Pearson coefficients (R 2 ) between environmental and energy variables, with outstanding values of 0.91 between T out and C CO2 , and RH and W vs . Table 3 shows the Pearson coefficients (R 2 ) between environmental and energy variables, with outstanding values of 0.91 between Tout and CCO2, and RH and Wvs. ...Context 3
... average CO 2 concentrations per cycle followed a sinusoidal evolution along the year ( Figure 5) and showed a strong negative linear correlation with outdoor temperature (R 2 = 0.91, Table 3). ...Context 4
... daily average values measured for ventilation energy consumption and heating energy consumption showed a negative linear correlation, with R 2 = 0.66 (Table 3). The restrictions in the ventilation system were justified by the low outdoor temperatures measured during cycle III, but not during cycle V or VI, during which such restrictions caused air saturation and an increase in heating energy costs. ...Context 5
... temperature values per cycle showed a negative correlation with heating energy consumption (R 2 = 0.75), but a strong positive correlation with ventilation energy consumption (R 2 = 0.54, Table 3). The increase in ventilation energy consumption involved a decrease in relative humidity and CO 2 concentrations (R 2 = 0.91 and R 2 = 0.61, respectively, Table 3). ...Context 6
... temperature values per cycle showed a negative correlation with heating energy consumption (R 2 = 0.75), but a strong positive correlation with ventilation energy consumption (R 2 = 0.54, Table 3). The increase in ventilation energy consumption involved a decrease in relative humidity and CO 2 concentrations (R 2 = 0.91 and R 2 = 0.61, respectively, Table 3). Likewise, linear correlations were found between heating energy consumption and relative humidity and CO 2 concentrations (R 2 of 0.66 and 0.63, respectively, Table 3). ...Context 7
... increase in ventilation energy consumption involved a decrease in relative humidity and CO 2 concentrations (R 2 = 0.91 and R 2 = 0.61, respectively, Table 3). Likewise, linear correlations were found between heating energy consumption and relative humidity and CO 2 concentrations (R 2 of 0.66 and 0.63, respectively, Table 3). ...Similar publications
An increase in the frequency of hot periods, which has been observed over the past decades, determines the novel approach to livestock facilities improvement. The effects of heat stress are revealed in disorders in physiological processes, impaired immunity, changes in behaviour and decreases in animal production, thus implementation of cooling tec...
Citations
... Piglets had ad libitum access to feed and water. During the experimental period, the temperature and relative humidity were automatically controlled and followed recommendations for the post-weaning production phase [30]. ...
This study aimed to evaluate the effect of different levels (15% and 20% w.w−1) of bakery meal (BM) inclusion on growth performance, welfare and behavior indicators in post-weaning piglets. Sixty post-weaning castrated male piglets were selected and divided in 3 feeding treatments: standard post-weaning diet with no BM added (CON), standard post-weaning diet with 15% w.w−1 BM added (BM 15) and standard post-weaning diet with 20% w.w−1 BM added (BM 20). Body weight (BW), average daily gain (ADG), feed intake (FI) and feed conversion ratio (FCR) were recorded individually on a weekly basis. Additionally, welfare, quality behavior indicators, wounds and tail-biting incidence were assessed. The supplementation with BM in piglets’ diet had a significant impact on ADG and FCR during certain periods of the trial. BM 15 piglets showed higher ADG and lower FCR in the last week of the experiment compared to CON piglets (1278.57 ± 7.14 g vs. 905.00 ± 47.86 g and 1.69 ± 0.04 g vs. 2.35 ± 0.08 g, respectively). Overall, BM inclusion had no significant effect on performance, quality behavior characteristics and welfare (p > 0.05). The inclusion of BM at either 15% or 20% w.w−1 illustrated no detrimental effects on the overall growth parameters, welfare and behavior indicators for post-weaned piglets.
... Piglet heating energy demand is high in the post-weaning stage because it requires high temperatures (Dolz et al., 2015). Likewise, a poor housing environment could affect piglets' growth performance and intestinal health (Le Dividich and Herpin, 1994;Fernandez et al., 2020;Gilbert et al., 2019;Lu et al., 2018). The result of the current findings confirmed the investigation of various researchers who reported that renewable energy sources as a source of heating and cooling systems in pig houses are sustainable and have no adverse effect on the growth performance of pigs (Choi et al., 2010;Islam et al., 2016;Licharz et al., 2020;Mun et al., 2021). ...
... This suggests that renewable energy sources as a heating source in pig houses maintained the thermal welfare and environmental conditions of pigs, thus maximizing production performance. The animal growth rate was not significantly affected by the relative humidity (RH) than housing temperature (Huynh et al., 2005;Fernandez et al., 2020). In fact, air humidity is not expected to have much influence on the performance of weaned piglets maintained within thermoneutrality (Forcada et al., 2009;Guo et al., 2001). ...
Purpose
Renewable technologies are considered a clean energy source, providing an excellent opportunity to reduce global warming and mitigate greenhouse gas emissions by substituting conventional energy sources based on natural resources. This study compared the effects of different renewable energy sources on the internal pig house environment, harmful gas emissions, energy-saving efficiency, and productivity traits of growing pigs.Methods
Thirty heads of pigs [(Landrace × Yorkshire) × Duroc] were raised for 10 weeks in pig incubators connected with a conventional electric heating system (control), solar power system (SPS), and geothermal heat pump (GHP) energy sources for pig housing heating systems.ResultsThe results revealed no significant difference in the growth performance parameters. Numerically, the highest feed intake and weight gain were recorded in the pigs reared in the GHP-connected incubators. The temperature inside the incubators did not significantly affect the internal environmental housing conditions. Moreover, the humidity (%) was significantly reduced in the GHP-connected pig incubator, but no significant difference was recorded in the temperature-humidity index in all incubators. The concentrations of H2S, NH3, and CO2 emissions decreased in the GHP-connected incubator. The energy-saving efficiency compared to the control was 21.12% for SPS and 57.32% for GHP. The relationship between feed intake and internal housing temperature and humidity of all energy sources for heating systems showed strong positive correlations with R2 = 0.88–0.90 and R2 = 0.84–0.88, respectively.Conclusion
Renewable energy technologies are environmentally friendly and can be used in pig farms without adverse effects on pig production performance.
... Since the assumption of homogeneity of variance according to the Levene's test was Statistically significant data were highlighted in italic ITP not met, Welch statistic (Welch (1947)) statistic was used for the data. The Games-Howell contrast could be used for multiple comparisons when the assumption of homogeneity of variances is not satisfied (Fernandez et al., 2020;Li et al., 2019). SE scores show significant differences according to Internet usage purpose, opinions toward taking courses in an online environment and mostly with whom they contact online (p < 0.05). ...
Purpose
Recently, online learning and online environments have become even more important. Students' engagement, fear of missing out and Internet addiction are seen as interrelated components that affect students' online teaching and learning process. In this context, university students' engagement, fear of missing out and Internet addiction in online environments, the relationship among them and students' demographic characteristics, online environment usage status and Internet usage profiles as their predictors are examined in this study.
Design/methodology/approach
This is a relational study and is carried out with 179 university students. Personal information form, student's engagement, fear of missing out and Internet addiction scales were used as data collection tools. Descriptive statistics, t -test, one-way ANOVA, correlation, hierarchical linear multiple regression analysis are used for the analysis.
Findings
According to the results, variables related to students' demographic characteristics, online environment usage status and Internet usage profiles together significantly predict the students' engagement, fear of missing out and Internet addiction in online environments. When students think positively about taking courses online, their engagement increases accordingly and their fear of missing out levels decrease. Increase in student's academic achievement leads to decline in Internet addiction.
Practical implications
In practice, examining the related variables about students in terms of engagement to the learning environment, fear of missing out and Internet addiction could bring a new perspective to studies on problematic use of the Internet and technology such as nomophobia and digital distraction. The results of this study reveal how and which components to be focused on for increasing the university students' engagement, reducing Internet addiction and fear of missing out in online learning environments.
Originality/value
The findings of this study provide a versatile perspective with the variables of student participation, fear of missing out, Internet addiction and their predictors in online learning environments, which are becoming widespread and increasingly important today and shed light on future researches.
... CO 2 concentrations can be measured affordably, reliably, and with little maintenance using infrared sensors, which can be implemented on farms. Yet, the large number of rooms in transition farms would substantially increase the costs [49]. Moreover, for the estimation of emissions, the flow of the ventilation system must be determined. ...
This paper aims to study the evolution of CO2 concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial was conducted during one transition cycle. Generally, the ventilation flow increased with the reduction in setpoint temperature throughout the cycle, which caused a reduction in CO2 concentration and an increase in emissions. The mean CO2 concentration was 3.12 g m–3. Emissions of CO2 had a mean value of 2.21 mg s−1 per animal, which is equivalent to 0.195 mg s−1 kg−1. A potential function was used to describe the interaction between 10 min values of ventilation flow and CO2 concentrations, whereas a linear function was used to describe the interaction between 10 min values of ventilation flow and CO2 emissions, with r values of 0.82 and 0.85, respectively. Using such equations allowed for simple and direct quantification of emissions. Furthermore, two prediction models for CO2 emissions were developed using two neural networks (for 10 min and 60 min predictions), which reached r values of 0.63 and 0.56. These results are limited mainly by the size of the training period, as well as by the differences between the behavior of the series in the training stage and the testing stage.
... Nevertheless, the results of the simulations highlight that IT-C and DE-C scenarios could be characterized by significative humidity problems since i is often over 70%. These humidity problems may lead to an increase of heating energy consumption, as reported by Fernandez et al. [74]. The presented model, hence, can be used to numerically estimate the needed increase of ventilation air flow rate to decrease i considering the consequent increase of electrical energy consumption due to fan activation and thermal energy consumption due to the increased heat losses. ...
Pig farming is one of the most energy consuming activities of the livestock sector. A considerable amount of energy, in fact, is needed to maintain the adequate indoor climate conditions in mechanically ventilated pig houses. This energy consumption is remarkably increasing due to the boost of pork demand-+45% between 2010 and 2050-enhanced by socio-demographical changes. Consequently, the improvement of the energy performance of this building type is becoming more and more a primary target in the path toward an energy-smart food production. Energy simulation models are essential to achieve such ambitious target by enhancing the analysis of energy consumption patterns and the evaluation of potential energy-efficient measures. A well-established and shared energy framework for this specific aim is not present in literature as well as simulation models for the efficient-energy design of pig houses, outlining an important gap that hinders the transition toward an energy-smart agriculture. In this work, a new dynamic energy simulation model is developed for the estimation of the energy consumption for climate control of mechanically ventilated growing-finishing pig houses. This model contributes to the current body of knowledge by providing stakeholders with an innovative and effective simulation model for energy analyses, especially at the design stage. In addition, this work faces an innovative and challenging energy modelling activity that can contribute to the development of an energy-smart food production by proposing a structured energy simulation framework for livestock houses. Furthermore, this model contributes to bridge the gap between energy and agricultural engineering simulation models by modelling in detail the pig-environment interactions and their effects on the energy consumption of the livestock house. The reliability of the model was performed through an experimental validation against real monitored data in compliance 2 with international protocols. The developed model could represent an effective decision support tool for stakeholders, especially in the design stages of pig houses by favouring the adoption of new technologies in this sector. Through this model, in fact, the impact of new energy-efficient solutions on the energy performance of pig houses can be evaluated in standardized conditions, considering the specific features, such as building layout, geographical location, and farming features.
... It should be cautioned against decreasing the ventilation rate of a building to reduce the seasonal heating cost as air quality and moisture levels will be directly impacted and could negatively impact production within the building. The costs and corresponding energy inputs are higher than reported values from field studies for animal agricultural buildings with similar heat production values per floor area [14][15][16]. These differences are most likely attributed to the method used and the local weather data differences from the reported study's locations. ...
Many climate-controlled agricultural buildings use direct gas-fired circulating heaters (DGFCH) for supplement heat. There is no standardized test to calculate thermal efficiency for these heaters. This study aimed to develop a measurement system and analytical analysis for thermal efficiency, quantify the measurement uncertainty, and assess economics of DGFCH efficiency. The measurement system developed was similar to the ASHRAE 103 standard test stand with adaptations to connect the apparatus to the DGFCH. Two different propane measurement systems were used: input ratings < 30 kW used a mass flow system and input ratings > 30 kW used a volumetric gas meter. Three DGFCHs (21.9, 29.3, 73.3 kW) were tested to evaluate the system. Thermal efficiencies ranged from 92.4% to 100.9%. The resulting uncertainty (coverage factor of 2; ~95% Confidence Interval) ranged from 13.1% to 30.7% for input ratings of 56.3 to 11.4 kW. Key sources of uncertainty were propane and mass flow of air measurement. The economic impact of 1% difference in thermal efficiency ranged from USD 72.0 per heating season. Refinement of the testing system and procedures are needed to reduce the uncertainty. The application of this system will aid building designers in selection of DGFCHs for various applications.
... We also intend to expand the set of events collected from the poultry business, which may allow to support a more complete decision making process. This idea aligns with the capture of climatic events outside a poultry house and to the possibility of involving cost and profit estimations, attempting to stratify the impact of management choices, similarly to what it has already been addressed in the swine industry [76]. Energy saving [77] is another variable to be considered in future extensions. ...
In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCR[t]), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCR[t] for each poultry house. In this article, we claim that the specialist's perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR[t].
The systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCRt) in poultry farming, an acceptable measure for successful production. Appropriate plans provide tailored resources, allowing broilers to grow within the so-called comfort zone, without waste or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on a specialist, which result in variable FCRt. In this article, the specialist’s perception is reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithms, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a network infrastructure to replicate our method over distributed poultry houses, for their smart and interconnected control. A supervision system is provided as interface to users. Experiments using real data suggest an improvement of 5% on the performance of the most productive specialist, staying close to the optimal FCRt.
