Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Optimization of Livestock Housing Management
share announcement

Optimization of Livestock Housing Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 9360

Special Issue Editors

Dr. Andrea Costantino

E-Mail Website
Guest Editor
Institute of Animal Science and Technology, Universitat Politècnica de València, Camino de Vera s.n., 46022 Valencia, Spain
Interests: agricultural buildings; building energy efficiency; climate control; controlled environment agriculture; energy modeling; energy performance assessment; energy-smart agriculture; HVAC systems; livestock housing; sustainable agriculture
Dr. Guoxing Chen

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering, Aarhus University, Inge Lehmanns Gade 10, DK, 8000 Aarhus, Denmark
Interests: design and control of natural ventilation system; investigation of air movement inside and around buildings; modelling and reducing emissions in livestock buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Livestock housing is a human practice that has existed for millennia, but the advent of intensive agriculture started to submit it to an ongoing engineering process. Nowadays, the increasing social demand for sustainable livestock production and supply chains is pushing for improving animal welfare, farm productivity, resources use, waste management, and food security. All those improvements should be achieved without jeopardizing the environmental sustainability and competitiveness of farms in the market. In this framework, a lot of efforts are being paid to develop new technologies and improved farm practices for optimizing livestock housing management while considering the global challenges represented by climate change and increasing demographic trends.

This Special Issue aims at collecting impactful research focused on the latest scientific and technical advances in optimizing livestock housing management from a multi- and interdisciplinary point of view. Authors are invited to submit papers covering a broad range of topics, including but not limited to:

  • Indoor environmental control;
  • Resource (e.g., feed, water, and energy) utilization;
  • Waste management;
  • Emission control;
  • Heat stress mitigation;
  • Housing cleanliness, and sanitation;
  • Worker safety;
  • Life Cycle Assessment.

All types of articles, such as original research papers and reviews, are welcome.

Dr. Andrea Costantino
Dr. Guoxing Chen
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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

  • animal welfare
  • energy-efficient livestock housing
  • environmental control
  • farm machinery
  • farm management
  • greenhouse gas emissions
  • livestock housing ventilation
  • livestock production
  • odor dispersion and control
  • precision livestock farming

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

22 pages, 6145 KiB  
Article
Development of an Intelligent Service Platform for a Poultry House Facility Environment Based on the Internet of Things
by Mulin Liu, Hongxi Chen, Zhenyu Zhou, Xiaodong Du, Yuxiao Zhao, Hengyi Ji and Guanghui Teng
Agriculture 2024, 14(8), 1277; https://doi.org/10.3390/agriculture14081277 - 2 Aug 2024
Viewed by 850
Abstract
In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service [...] Read more.
In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service platform for a facility environment based on the IoT structure, utilizing the capabilities of Platform as a Service (PaaS). The platform consists of four layers, including an information perception layer, network layer, management service layer, and application layer. By using the cloud service model with a distributed network architecture, asynchronous data transmission, and a distributed file system, the platform can centrally manage multiple farm’s data. The intelligent service platform includes the following functions: displaying environmental data, water and electricity consumption, data analysis, and managing production data. Over a 500-day trial period in a live poultry house, the platform demonstrated high data integrity (>87%) and resilience against network disruptions and power outages. The data validity of each environmental element exceeded 94%, among which the validity of the temperature, humidity, and carbon dioxide concentration exceeded 99%. The overall accuracy of the dataset remained relatively high, providing a robust data foundation for further research. Key features included audio analysis, environmental monitoring, and production data management. The platform’s operational status was efficiently communicated via data statistics and email alerts, facilitating timely system recovery. The demonstrated modules included sound recognition, psychrometric charts for visual alerts, and financial analysis tools, offering versatile solutions for integrating PLF models and advanced analytics. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
►▼ Show Figures

Figure 1

14 pages, 4321 KiB  
Article
Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach
by Carlos Alejandro Perez Garcia, Marco Bovo, Daniele Torreggiani, Patrizia Tassinari and Stefano Benni
Agriculture 2024, 14(2), 316; https://doi.org/10.3390/agriculture14020316 - 17 Feb 2024
Cited by 3 | Viewed by 1292
Abstract
The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms [...] Read more.
The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
►▼ Show Figures

Figure 1

15 pages, 4667 KiB  
Article
Standardized Decision-Making for the Selection of Calf and Heifer Rearing Using a Digital Evaluation System
by Fredrik Regler and Heinz Bernhardt
Agriculture 2024, 14(2), 272; https://doi.org/10.3390/agriculture14020272 - 7 Feb 2024
Viewed by 1359
Abstract
This study addresses the challenge of subjective remounting decisions in calf and heifer rearing, typically driven by the animal caretaker’s feelings and experience, lacking a robust data foundation. Key factors such as developmental delays, diseases, or rearing problems often go unnoticed or are [...] Read more.
This study addresses the challenge of subjective remounting decisions in calf and heifer rearing, typically driven by the animal caretaker’s feelings and experience, lacking a robust data foundation. Key factors such as developmental delays, diseases, or rearing problems often go unnoticed or are forgotten due to the number of animals. To address this gap, an established state-of-the-art sensor network captures behavioral data during rearing, which is supplemented by manually collected data. This facilitates a novel decision network providing well-founded recommendations to the animal owner regarding whether to retain or cull an animal. The approach focuses on four key areas: colostrum supply, milk intake, weight development, and disease history during the rearing time of each individual, offering a transparent decision path for the use of each future cow. Introducing a standardized decision-making approach, the proposed approach enables an efficient, transparent, and targeted management strategy, contributing to the sustainable enhancement of the health and performance of calves and heifers. Additionally, it allows for the comparison of the growth trajectories of different animals over time. Notably, individual and transparent decisions can be made at each growth stage, enhancing the overall decision-making process in calf and heifer rearing. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
►▼ Show Figures

Figure 1

15 pages, 1832 KiB  
Article
Linear and Nonlinear Mixed Models to Determine the Growth Curves of Weaned Piglets and the Effect of Sex on Growth
by Roberto Besteiro, Tamara Arango, Manuel R. Rodríguez and María D. Fernández
Agriculture 2024, 14(1), 79; https://doi.org/10.3390/agriculture14010079 - 30 Dec 2023
Viewed by 1555
Abstract
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
►▼ Show Figures

Figure 1

11 pages, 1181 KiB  
Article
Study on Illumination Intensity and Duration of LED Light Sources for a Weaned Piglet House without Natural Light
by Yaqiong Zeng, Dingbiao Long, Bin Hu, Hao Wang, Shihua Pu, Yue Jian, Zuohua Liu and Shunlai Xu
Agriculture 2023, 13(11), 2121; https://doi.org/10.3390/agriculture13112121 - 9 Nov 2023
Viewed by 1570
Abstract
Lighting is an important environmental parameter in livestock farming, which can affect the physiology and behavior of animals, and it can regulate animal production. To explore the comprehensive effects of light intensity and duration on the performance, behavior, and physiological indicators of piglets, [...] Read more.
Lighting is an important environmental parameter in livestock farming, which can affect the physiology and behavior of animals, and it can regulate animal production. To explore the comprehensive effects of light intensity and duration on the performance, behavior, and physiological indicators of piglets, a 3 × 2 two-factor experiment (three levels of lighting intensity × two lighting durations) was conducted. The three light intensities used were high (100–120 lux), medium (40–50 lux), and low (5–20 lux). The two lighting durations were 8 h and 10 h of light per day. The experiment used a total of six lighting combinations, which corresponded with the six test units. A total of 96 Landrace–Yorkshire hybrid piglets, with an initial body weight of 13.23 ± 0.18 kg, were randomly assigned to six lit units, four pens per unit, and four piglets per pen. The results showed that lighting intensity and duration had no significant effect on the average daily feed intake, average daily gain, feed/gain, or water consumption of pigs (p > 0.05). For IgM, the main effect caused by the light duration was significant. When the light intensity was 5–20 lux and 40–50 lux, the serum IgM levels of piglets in the 10 h/day light group were 45.80% and 39.54% higher than those in the 8 h/day group, respectively (p < 0.05). For SOD and GSH-Px, the interaction between the lighting duration and intensity was significant (p < 0.05). In the 8 h/day light group, the serum SOD levels of piglets at light intensities of 5–20 lux and 40–50 lux were significantly higher than those at 100–120 lux (p < 0.05). When the light intensity was 5–20 lux and 40–50 lux, the SOD level in the 8 h/day group was significantly higher than that of the 10 h/day group (p < 0.05). The main effect of lighting duration on lying down and abnormal behavior was significant (p < 0.05). In the 8 h/day light group, the abnormal behavior of piglets under a light intensity of 5–20 lux was twice that of 40–50 lux (p < 0.05), and the lying percentage of piglets under a light intensity of 40–50 lux was 14.03% higher than that of piglets under a light intensity of 5–20 lux (p < 0.05). Overall, under the conditions used in this study, although extending the duration of light with an intensity of 40–50 lux to 10 h can improve some immune-related indicators, the extent of this effect was limited. The recommended lighting scheme for piglet houses is a light intensity of 40–50 lux and a lighting duration of 8 h. However, the range of lighting conditions set in this study was still very limited, and various environmental factors must be comprehensively considered in an actual production setting. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
►▼ Show Figures

Figure 1

Review

Jump to: Research

28 pages, 1167 KiB  
Review
Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review
by Andrea Costantino
Agriculture 2023, 13(12), 2280; https://doi.org/10.3390/agriculture13122280 - 15 Dec 2023
Cited by 5 | Viewed by 1521
Abstract
The need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents [...] Read more.
The need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents a significant obstacle to their widespread application and scalability in research and industry. The aim of this work is to pave the way for scaling the adoption of BES models for livestock houses by providing a comprehensive analysis of their application, development, and validation. For this aim, a systematic review of 42 papers—selected from over 795 results from the initial database query—is carried out. The findings underscored a growing body of research that involves BES models for different purposes. However, a common approach in both model development and validation is still lacking. This issue could hinder their scalability as a standard practice, especially in industry, also considering the limitations of BES models highlighted in this work. This review could represent a solid background for future research since provides an up-to-date framework on BES models for livestock houses and identifies future research opportunities. Moreover, it contributes to increasing the reliability of BES models for livestock houses by providing some recommendations for their validation. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop