Emission of CO2 in a livestock barn based on diurnal activities and various growing stages of pigs

Deb, Nibas Chandra; Basak, Jayanta Kumar; Paudel, Bhola; Kim, Na Eun; Moon, Byeong Eun; Kim, Hyeon Tae

doi:10.1007/s11869-023-01352-x

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Thus, cosine models can be a useful tool to study and predict these variables. Whereas other authors presented models with up to four daily peaks [13], in this research, animal behaviour shows a pattern with two daily peaks [8,12,14,15], coinciding with human activity inside the building, with a preference for inactivity when there is less light [2,55] and around midday [12,14]. These two peaks occur after the distribution of feed (08:00 h, 12:00 h and 16:00 h), which may be the cause for such peaks, in agreement with the evolution of the third harmonic.…”

Section: Cosine Modelscontrasting

confidence: 53%

Modelling of Animal Activity, Illuminance, and Noise on a Weaned Piglet Farm

Fernández,

Besteiro,

Arango

et al. 2023

Animals

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Measuring animal activity and its evolution in real time is useful for animal welfare assessment. In addition, illuminance and noise level are two factors that can improve our understanding of animal activity. This study aims to establish relationships between animal activity as measured by passive infrared sensors, and both illuminance and noise level on a conventional weaned piglet farm. First, regression models were applied, and then cosine models with three harmonics were developed using least squares with a Generalized Reduced Gradient Nonlinear method. Finally, all the models were validated. Linear models showed positive correlations, with values between 0.40 and 0.56. Cosine models drew clear patterns of daily animal activity, illuminance and noise level with two peaks, one in the morning and one in the afternoon, coinciding with human activity inside the building, with a preference for inactivity at night-time and around midday. Cosine model fitting revealed strong correlations, both in the measurement and validation periods, for animal activity (R = 0.97 and 0.92), illuminance (R = 0.95 and 0.91) and noise level (R = 0.99 and 0.92). The developed models could be easily implemented in animal welfare monitoring systems and could provide useful information about animal activity through continuous monitoring of illuminance or noise levels.

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Section: Cosine Modelscontrasting

confidence: 53%

Modelling of Animal Activity, Illuminance, and Noise on a Weaned Piglet Farm

Fernández,

Besteiro,

Arango

et al. 2023

Animals

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“…Changes in environmental parameters significantly impact the growth and health status of rabbits. Addressing the monitoring needs of rabbit-house environments, Chandra et al (2023) suggested a livestock environmental management system for measuring CO 2 concentration in barns, enabling remote control of barn environments. Xinrui et al (2023) developed an approach to modulate total atmospheric ammonia emissions concerning space and time by updating high-resolution crop maps and animal housing location databases.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Liu,

Yang,

Zhang

et al. 2024

International Journal of Data Warehousing and Mining

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The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death among rabbits. This paper presents a multi-parameter predictive model for environmental conditions such as temperature, humidity, illumination, CO2 concentration, NH3 concentration, and dust conditions in rabbit houses. The model adeptly distinguishes between day and night forecasts, thereby improving the adaptive adjustment of environmental data trends. Importantly, the model encapsulates multi-parameter environmental forecasting to heighten precision, given the high degree of interrelation among parameters. The model's performance is assessed through RMSE, MAE, and MAPE metrics, yielding values of 0.018, 0.031, and 6.31% respectively in predicting rabbit house environmental factors. Experimentally juxtaposed with Bert, Seq2seq, and conventional transformer models, the method demonstrates superior performance.

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