Roberto Besteiro

Controlling the microclimate in pig housing is essential for ensuring animal welfare and optimizing production efficiency. However, the dynamic, non-linear interactions of environmental and animal variables make microclimate management a complex undertaking. To better understand these interactions and develop predictive models to improve microclimate conditions in swine housing, in this study we analyzed indoor and outdoor temperature, CO₂ concentration, and animal activity in a naturally ventilated pig-fattening house using wavelet analysis, cosine models and coherence analysis. The wavelet analysis identified dominant periods of 24 and 12 h for temperatures, along with a relevant contribution from animal activity for the 8 h period. CO₂ concentration showed no persistent oscillatory pattern during the production cycle. The three harmonic cosine models provided satisfactory results for outdoor temperature (RMSE = 0.09–0.10; R² = 0.65–0.84), indoor temperature (RMSE = 0.08–0.10; R² = 0.52–0.78) and animal activity (RMSE = 0.06–0.16; R² = 0.43–0.61), but they were not suitable for CO₂ modelling. The wavelet coherence analysis revealed temporal phase differences among variables, notably with outdoor temperature and animal activity leading indoor temperature.

This study analyses the effect of heat stress (HS) on milk production, composition, and somatic cell count in Holstein cows in Galicia. For this purpose, the study uses the temperature and humidity index (THI) obtained from the official network of weather stations and data from the official milk control between 2016 and 2021. For milk production, critical THI thresholds were established at THI = 72, while the somatic cell count increased when THI exceeded 78. For the fat and protein components of milk, the critical thresholds are lower, at 64 and 63, respectively. The analysis shows that for each unit increase in THI above the critical threshold, up to 0.249 kg of milk, 0.008 kg of protein, and 0.006 kg of fat can be lost per cow per day. HS has a greater impact on milk composition than on volume, particularly on protein content. In addition, it was observed that the impact of HS on milk production can last between 7 and 12 days. Similarly, consecutive cumulative days of HS significantly impact both milk production and quality, especially in higher productivity stages multiparous cows. Although Galicia has a less extreme climate than other regions, the presence of moderate HS during the warmer months causes significant economic losses in the dairy sector.

This study characterizes the growth of weaned Large White × Landrace hybrid piglets from 6 to 20 kg live body weight (BW) under real farm conditions. Batches of 50 castrated male pigs and 50 gilts were weighed repeatedly over two 6-week breeding cycles. The data was fitted to various linear (quadratic and exponential) and nonlinear (Gompertz, Richards, logistic, Von-Bertalanffy) mixed models to find the best-performing model. During the postweaning phase, animal growth was modelled, and the effect of sex on growth was determined by incorporating the variable, sex, into the mixed models and using t-tests for paired samples. The average BW at weaning was 6.86 kg, and the average BW by the end of the cycle was 19.46 kg, with an average daily gain (ADG) of 0.324 kg/day. Over the study period, the variable, sex, did not show a significant effect (p < 0.05) on piglet growth. The nonlinear mixed models performed better than the linear mixed models, with the Gompertz (RMSE = 0.296) and Von-Bertalanffy (RMSE = 0.288) curves as the best-performing models. When fitted to the Gompertz curve, the data showed a maximum ADG of 0.508 kg/day on day 27 postweaning. Accordingly, nonlinear mixed models can provide useful information to farmers about the evolution of weaned piglet growth and can be used for the early detection of growth anomalies.

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.

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.

Measuring ammonia inside livestock buildings poses many challenges that hinder the incorporation of this variable into environmental control systems. The aim of this study was to measure various microclimate variables inside a weaned piglet building and analyse their interactions with NH₃ concentrations for setpoint temperatures of 26 and 25 °C, in order to control NH₃ concentrations based on other easily measurable variables. The experimental test was conducted on a conventional farm in Northwest Spain. NH₃ concentrations in the animal zone were best correlated with CO₂ concentrations in the animal zone (R = 0.91 and R = 0.55) and velocity of air extracted through the fan (R = 0.72 and R = 0.65) for setpoint temperatures of 26 and 25 °C, respectively. Similarly, strong correlations were found with relative humidity in the animal zone and temperature of inlet air. Because NH₃ concentration in the animal zone is related to the performance of the ventilation system, strong positive correlations were found between NH₃ concentration and temperature of inlet air whereas negative correlations were found between NH₃ concentration and ventilation rates. Linear regression models based on CO₂ concentrations in the animal zone and temperature of inlet air are recommended, because they provide a good fit for both setpoint temperatures using variables that can be readily measured.

Measuring animal activity is useful for monitoring animal welfare in real time. In this regard, passive infrared detectors have been used in recent years to quantify piglet activity because of their robustness and ease of use. This study was conducted on a commercial farm in Northwest Spain during six complete breeding cycles. The hourly average activity of weaned piglets with a body mass of 6–20 kg was recorded and further analyzed by using a multiplicative decomposition of the series followed by a wavelet analysis. Finally, the real series were compared to the theoretical models of activity. Results showed a high level of movement immediately after weaning and a sustained level of activity throughout the cycles. The daily behavior of the piglets followed a clear circadian pattern with several peaks of activity. No differences in behavior were observed between spring–summer cycles and autumn–winter cycles. Single-peak models achieved the best predictive results. In addition, the installed sensors were found to underestimate mild activity.

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 high livestock density area of NW Spain, for seven production cycles. The quantification of variables was aimed at obtaining the maximum performance with the lowest possible use of resources, focusing on animal welfare and production efficiency. The Brown–Forsythe, Welch and Games-Howell tests revealed significant differences in terms of temperature, relative humidity and CO₂ concentrations among production cycles, and among the critical, postcritical and final periods. Improved humidity management resulted in a 17% reduction of climate control energy, which involved energy savings in the range of 33% to 47% per kg produced at the end of the postweaning cycle. Accordingly, adding humidity as a control variable could result in higher ventilation rates, thereby improving animal welfare, reducing heating energy use and increasing weight gain per unit climate control energy. In addition, the strong correlations found between heating energy and relative humidity (R² = 0.73) and ventilation energy and CO₂ (R² = 0.99) suggest that these variables could be readily estimated without additional sensor costs.