Ecosystem services from different livestock management systems

Mpofu, I.D.T.

doi:10.1016/b978-0-12-816436-5.00007-x

Cited by 4 publications

(7 citation statements)

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“…Lee et al [59] Duck house (N) Biosystems Engineering Shin et al [43] Piglet house (M) Biosystems Engineering Costantino et al [31] Fattening pig house (M) Applied Energy Nguyen-Ky and Pentillä [60] Dairy barn (N) Applied Engineering in Agriculture Lee et al [42] Duck house (M) Biosystems Engineering Costantino et al [63] Broiler house (M) Energy and Buildings Hamilton et al [64] Broiler house (M) Advances in Mechanical Engineering Liberati and Zappavigna [41] Generic house (N/M) Transactions of the ASABE Silva et al [65] Broiler house (M) Revista Brasileira de Engenharia Agrícola e Ambiental Wagenberg et al [66] Fattening pig house (M) Biosystems Engineering Schauberger et al [67] Fattening pig house (M) International Journal of Biometeorology Cooper et al [40] Generic house (N/M) Journal of Agricultural Engineering Research Energy assessment Si et al [49] Fattening pig house (M) Science of the Total Environment Qi et al [44] Nursery + fattening pig house (M) Agriculture Nawalany and Sokołowski [45] Broiler house (M) Energies Costantino et al [17] 1 Broiler house (M) Journal of Cleaner Production Kwak et al [20] 2 Piglet house (M) Energy Strategy Reviews Panagakis et al [68] 1 Broiler house (M) CIGR Journal Costantino et al [69] Broiler house (M) Biosystems Engineering Izar-Tenorio et al [46] Broiler house (M) Journal of Cleaner Production Wang et al [57] Laying hen house (M) Computers and Electronics in Agriculture Jackson et al [16] Fattening pig house (M) Biosystems Engineering Jackson et al [70] Fattening pig house (M) Energy and Buildings Axaopoulos et al [47] 1 Fattening pig house (M) Transactions of the ASABE Wang and Xue [48] 1 Piglet house (N) Transactions of the ASABE Zhao et al [58] Laying hen house (M) Biosystems Engineering Menconi et al …”

Section: Model Investigationmentioning

confidence: 99%

“…1 Not recommended to be used alone due to cancellation errors. 2 Mainly recommended for evaluating the estimation of the energy consumption over the entire validation period.…”

Section: Choose Gof Indexes With Defined Thresholdsmentioning

confidence: 99%

“…They account for approximately 25% of the total global protein intake and contribute approximately 18% to global calorie consumption [1]. A substantial share of those food products comes from intensive livestock systems, which contribute to feeding at least 1.3 billion people [2]. In intensive systems, livestock is farmed in livestock houses that are designed and managed to minimize costs and maximize production [3].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…The investigated energy variables are the thermal load (ϕ), energy need (Q), and energy consumption (E) referred to Heating (subscript: H) and Cooling (subscript: C). The term E fan indicates the electrical energy consumption of fans 2. No data is available from the manuscript 3.…”

mentioning

confidence: 99%

“…The Goodness-of-Fit (GoF) indexes are detailed in Appendix B 2. Respectively, for θ air_i , φ air_i , and E H 3.…”

mentioning

confidence: 99%

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Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review

Costantino

2023

Agriculture

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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.

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Section: Model Investigationmentioning

confidence: 99%

“…1 Not recommended to be used alone due to cancellation errors. 2 Mainly recommended for evaluating the estimation of the energy consumption over the entire validation period.…”

Section: Choose Gof Indexes With Defined Thresholdsmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

mentioning

confidence: 99%

“…The Goodness-of-Fit (GoF) indexes are detailed in Appendix B 2. Respectively, for θ air_i , φ air_i , and E H 3.…”

mentioning

confidence: 99%

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Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review

Costantino

2023

Agriculture

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Seroprevalence of trypanosomosis and associated risk factors in cattle from coast and amazonian provinces of Ecuador

Maldonado,

Cáceres,

Burgos

et al. 2024

Vet Res Commun

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Greenhouse Gas (GHG) Emission Estimation for Cattle: Assessing the Potential Role of Real-Time Feed Intake Monitoring

Berdos,

Ncho,

Son

et al. 2023

Sustainability

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This study investigated the impact of feeding systems on the determination of enteric methane (CH4) emissions factor in cattle. Real-time feed intake data, a crucial CH4 conversion rate (Ym value) parameter, were obtained using a roughage intake control (RIC) unit within a smart farm system. Greenhouse gas (GHG) emissions, including CH4 and carbon dioxide (CO2), from Holstein steers were monitored using a GreenFeed (GF) 344 unit. The results revealed satisfactory body weight (383 ± 57.19 kg) and daily weight gain (2.00 ± 0.83 kg), which are crucial factors. CO2 production exhibited positive correlations with the initial body weight (r = 0.72, p = 0.027), feed intake (r = 0.71, p = 0.029), and feed conversion ratio (r = 0.69, p = 0.036). Five different emission factors (EFs), EFA (New Equation 10.21A) and Equation 10.21 (EFB, EFC, EFD, and EFE), were used for GHG calculations following the Intergovernmental Panel on Climate Change (IPCC) Tier 2 approach. The estimated CH4 EFs using these equations were 69.91, 69.91, 91.79, 67.26, and 42.60 kg CH4/head/year. These findings highlight the potential for further exploration and adoption of smart farming technology, which has the potential to enhance prediction accuracy and reduce the uncertainty in Ym values tailored to specific countries or regions.

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Ecosystem services from different livestock management systems

Cited by 4 publications

References 0 publications

Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review

Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review

Seroprevalence of trypanosomosis and associated risk factors in cattle from coast and amazonian provinces of Ecuador

Greenhouse Gas (GHG) Emission Estimation for Cattle: Assessing the Potential Role of Real-Time Feed Intake Monitoring

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