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Agriculture
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Monitoring and Optimization of Livestock and Poultry Housing Environments
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Monitoring and Optimization of Livestock and Poultry Housing Environments

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Farm Animal Production".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 3508

Special Issue Editors

Prof. Dr. Kaiying Wang

E-Mail Website
Guest Editor
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: livestock environment; animal housing; precision livestock farming; climate control in animal facilities; odour management; animal welfare
Special Issues, Collections and Topics in MDPI journals
Dr. Kaixuan Cuan

E-Mail Website
Guest Editor
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Interests: animal welfare; intelligent poultry breeding; animal health detection; deep learning; animal audio analysis; animal behavior recognition
Prof. Dr. Qiuju Xie

E-Mail Website
Guest Editor
College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
Interests: enviormental control; data perception; artificial intelligence; machin learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring and optimization of farm environments is critical to ensure the sustainability and productivity of livestock and poultry. Traditionally, environmental control has relied on standardized ventilation techniques to manage and control the temperature, humidity, and air quality within animal housing. Due to the growing global demand for animal products, intensive production farming has become dominant. As such, challenges to balance productivity with animal welfare, environmental sustainability, and economic viability emerge. It necessitates a fundamental shift from traditional control methods towards intelligent, responsive, and data-based precision management techniques.

The aim of this Special Issue is to explore innovative solutions to improve the environmental conditions in livestock and poultry housing through novel techniques. It encompasses cutting-edge technologies and methods including smart sensor networks, precision welfare monitoring, AI-based behavior recognition, intelligent ventilation, and machine-learning-based environmental prediction models. These innovative methods offer new opportunities to understand and optimize the complex interactions between animals, microclimates, and external environmental factors. Ultimately, this Special Issue seeks contributions that can enhance farm animal production, reduce indoor and outdoor emissions, improve resource efficiency, and strengthen the resilience of animal production systems under evolving climates and increasing production scales.

We invite high-quality contributions including original research, reviews, case studies, and technical developments. In particular, we welcome papers focusing on real-time environmental monitoring, intelligent control systems, animal welfare assessment, greenhouse gas and odor management, data perception, automation, and the integration of artificial intelligence in livestock housing. Interdisciplinary studies that connect animal science, engineering, and digital agriculture are strongly encouraged.

Prof. Dr. Kaiying Wang
Dr. Kaixuan Cuan
Prof. Dr. Qiuju Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precision livestock farming
  • environmental monitoring
  • ventilation control method
  • model predictive control, sustainable animal production
  • animal welfare
  • sustainable agriculture
  • smart sensors
  • machine learning
  • data driven management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 1776 KB  
Article
Regression Meta-Model for Predicting Temperature-Humidity Index in Mechanically Ventilated Broiler Houses Using Building Energy Simulation in South Korea
by Taehwan Ha, Kyeongseok Kwon, Se-Woon Hong and Uk-Hyeon Yeo
Agriculture 2026, 16(8), 824; https://doi.org/10.3390/agriculture16080824 - 8 Apr 2026
Viewed by 446
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring and Optimization of Livestock and Poultry Housing Environments)
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15 pages, 1849 KB  
Article
Numerical Evaluation of a Negative Pressure Ventilation System for Ammonia Emission from a Solid-Covered Manure Storage Tank
by Wenqi Zhang and Xiaoshuai Wang
Agriculture 2026, 16(4), 436; https://doi.org/10.3390/agriculture16040436 - 13 Feb 2026
Viewed by 2077
Abstract
Ammonia (NH3) emissions from temporary manure storage tanks represent a significant environmental concern in livestock production systems. While combining solid covers with negative pressure ventilation is a promising strategy to mitigate these emissions, there is currently a lack of systematic research [...] Read more.
Ammonia (NH3) emissions from temporary manure storage tanks represent a significant environmental concern in livestock production systems. While combining solid covers with negative pressure ventilation is a promising strategy to mitigate these emissions, there is currently a lack of systematic research on its design optimization and performance. This study employs Computational Fluid Dynamics (CFD) to evaluate the effectiveness of a solid-covered manure storage tank combined with negative pressure ventilation for controlling NH3 emissions. A validated CFD model was developed to simulate airflow and ammonia transport under open-field and covered conditions. The influences of tank headspace depth, slot type (top and side), and slot location on outlet ammonia concentration were investigated. Results show that headspace depth is one of the important parameters affecting ammonia transport, with deeper headspaces consistently reducing outlet NH3 concentrations. Compared with no-slot scenarios, top slots could increase ammonia emissions by inducing impinging-jet effects, whereas side slots exhibited depth-dependent impacts, reducing emissions at 1.0 and 1.6 m depths but increasing them at 0.4 m depth. All the differences in ammonia emission across the simulations can be attributed to the difference in the near-wall velocity. The findings provide useful guidance for the design and optimization of ammonia mitigation strategies in manure storage systems. Full article
(This article belongs to the Special Issue Monitoring and Optimization of Livestock and Poultry Housing Environments)
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24 pages, 5284 KB  
Article
Performance Prediction of Condensation Dehumidification System Utilizing Natural Cold Resources in Cold Climate Regions Using Physical-Based Model and Stacking Ensemble Learning Models
by Ping Zheng, Jicheng Zhang, Qiuju Xie, Chaofan Ma and Xuan Li
Agriculture 2026, 16(2), 185; https://doi.org/10.3390/agriculture16020185 - 11 Jan 2026
Viewed by 480
Abstract
Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the [...] Read more.
Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the natural low temperature of cold winters. An integrated energy consumption model, coupling moisture and thermal balances, was developed to evaluate room temperature drop, dehumidification rate (DR), and the internal circulation coefficient of performance (IC-COP). The model was calibrated and validated with experimental data comprising over 150 operational cycles under varied operation conditions, including initial temperature differences (ranging from −20 to −5 °C), air flow rates (0.6–1.5 m/s), refrigerant flow rates (3–7 L/min), and high-humidity conditions (>90% RH). Correlation analysis showed that higher indoor humidity improved both DR and IC-COP. Four machine learning models—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Multilayer Perceptron (MLP)—were developed and compared with a stacking ensemble learning model. Results demonstrated that the stacking model achieved superior prediction accuracy, with the best R2 reaching 0.908, significantly outperforming individual models. This work provides an energy-saving dehumidification solution for enclosed livestock housing and a case study on the application of machine learning for energy performance prediction and optimization in agricultural environmental control. Full article
(This article belongs to the Special Issue Monitoring and Optimization of Livestock and Poultry Housing Environments)
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