Taehwan Ha

In Korean broiler houses, an evaporative cooling pad (ECP) and a kerosene heater conventionally provide cooling and heating. However, ECP struggles to maintain the optimal rearing temperature under Korea’s hot, humid summer conditions, while winter kerosene heating produces on-site carbon emissions. This study evaluated replacing the conventional cooling and heating system with a rooftop-photovoltaic (PV)-driven air source heat pump (ASHP) that electrifies both cooling and heating. Using a building energy simulation model with a field-validated thermal environment component, 24 scenarios combining climate zone, weather condition, rooftop PV installation, and environmental control strategy were evaluated for thermal environment, energy, and carbon performance. The ASHP reduced setpoint exceedance hours (at least 2 °C) from an ECP average of 387 to 0 h·year⁻¹ and eliminated heat stress hours (temperature–humidity index ≥ 78) but fully electrified the load, raising annual electricity demand to 192–204 MWh. Rooftop PV (323–382 MWh·year⁻¹) exceeded this demand by day, giving 37–38% self-sufficiency and net-negative operational emissions under an export credit approach. Only ASHP + PV removed on-site fossil fuel, making it the substantive decarbonization pathway. PV coupling yielded net revenue with an undiscounted simple payback of 4–13 years. Combining secure thermal control, on-site decarbonization, and self-consumption-oriented operation, ASHP + PV is a promising option for climate-resilient, low-carbon broiler production.

Heat stress poses a critical challenge to animal welfare and productivity in intensive livestock production systems, particularly for broilers and swine with limited thermoregulatory capacity. The temperature-humidity index (THI) is widely applied to assess thermal stress, yet conventional monitoring methods remain constrained by limited spatial coverage and scalability. This study introduces a novel satellite-based framework for estimating indoor heat stress in livestock facilities using machine learning and GEO-KOMPSAT-2A (GK2A) satellite data. The proposed outside-in approach integrates satellite-derived temperature, humidity, and solar irradiance to infer indoor environmental conditions without reliance on on-site sensors or detailed building specifications. Unlike computational fluid dynamics models, which are resource-intensive and difficult to scale, this data-driven method captures nonlinear relationships between outdoor meteorological variables and indoor microclimates. The XGBoost model demonstrated superior accuracy in estimating indoor temperature and humidity across multiple farms. When converted to THI, relative root mean square errors (rRMSE) ranged from 0.623% to 0.693% in swine farms and 0.827% to 1.332% in broiler farms, demonstrating robust performance in heat stress assessment. By leveraging geostationary satellite observations with high temporal and spatial resolution, this framework enables continuous, large-scale monitoring of thermal conditions in livestock facilities. The approach provides a practical and scalable tool to support ventilation management, cooling strategies, and animal welfare decisions under dynamic weather conditions.

Heat stress is a major challenge for broiler production worldwide and is expected to intensify with more frequent heatwaves. This study focuses on mechanically ventilated broiler houses in South Korea, where heatwaves have become increasingly frequent. Three regression meta-models were developed to predict the indoor temperature–humidity index (THI) directly from weather forecast data, using simulated results from a validated building energy simulation (BES) model. A TRNSYS-based BES model was validated against field measurements from four rearing cycles in a commercial broiler house (RMSE 1.31–2.16; MAPE < 2.00%). Using 3072 simulation cases that combined multiple sites, thermal-transmittance levels, cooling conditions, building sizes, and broiler body weights, three regression meta-model approaches were evaluated: a condition-specific regression meta-model for each condition set, a unified regression meta-model with categorical predictors, and a single variable meta-model using only external THI as a predictor. All three showed strong predictive performance, and the unified regression meta-model achieved R² = 0.978, RMSE = 0.817, and MAPE = 0.829, providing the best balance between accuracy and simplicity. This unified model offers a practical tool to link weather forecasts with broiler-house design and environmental-control decisions for heat-stress risk management.

Recent global warming has increased the frequency of extreme weather events, intensifying heat stress in pigs and leading to a growing number of heat-related mortalities. This study aims to investigate the effects of various environmental and operational conditions on the thermal environment inside pig houses and to provide fundamental data for developing strategies to improve energy efficiency and climate resilience. A standard pig house model was developed based on the design guidelines of the Ministry of Agriculture using a Building Energy Simulation (BES) approach. A total of 256 simulations were conducted by combining region (16), weather dataset type (2), insulation level (4), and cooling system operation (2). Hourly indoor temperature and relative humidity were obtained from the simulations and used to calculate the Temperature-Humidity Index (THI). Four comparison groups were then established to evaluate variations in THI under different conditions. The results indicated that region, weather dataset type, and cooling system operation had significant effects on THI, whereas insulation level did not show a statistically significant influence. This study quantitatively demonstrates the relative impacts of regional, meteorological, insulation, and cooling conditions on the thermal environment of pig houses. The findings are expected to provide useful reference data for improving thermal environments in pig housing and for establishing strategies related to energy efficiency and climate adaptation.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.