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Decision Models in Livestock Production Systems

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A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 10181

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Special Issue Editors

Dr. Lluís Miquel Plà-Aragonés

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Guest Editor

Department of Mathematics, University of Lleida, Lleida, Spain
Interests: pig supply chain management; operations research in agriculture; decision models; stochastic programming

Dr. Esteve Nadal Roig

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Guest Editor

Department of Business Administration, University of Lleida, Lleida, Spain
Interests: livestock systems, decision support systems in agriculture; optimization models; operation research

Special Issue Information

Dear Colleagues,

Livestock production has been evolving rapidly in recent years. The next decade will be critical in food security and sustainable production. The increase of meat demand, regulations, and concerns about consumer safety, animal welfare, and sustainability provoke an increase of the complexity in livestock management. In addition, the Internet of Things (IoT), Agriculture 4.0, and precision livestock farming (PLF) refer to new information technologies capable of collecting, storing, and processing data into information which has never before been available to decision makers. This context requires new or adapted decision models fitted to a wide variety of livestock production systems capable of competing among production systems and satisfying market demand in an environmentally sustainable way. Operational research (OR) and decision support systems (DSS) may illustrate approaches integrating different decision models seeking to help a variety of stakeholders’ profiles of livestock production systems in their decision-making process.

Then, the aim of this Special Issue is to publish original research papers concerning decision models in livestock production systems to create an impact in livestock farming regardless of the type of production and the methodology proposed.

Dr. Lluís Miquel Plà-Aragonés
Dr. Esteve Nadal Roig
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. Animals 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 2400 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

Decision models in livestock farming
Optimization approaches to livestock production systems
Livestock decision support tools
Multi-objective optimization in livestock production systems
Optimization of livestock supply chain management
Sustainable decisions in livestock farming
Decisions assisted by precision livestock farming devices
Adapting livestock decision models to Agriculture 4.0
Decisions on digitalization (or not) of livestock systems
Big data and data analytics for bettering decisions in livestock systems

Published Papers (6 papers)

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Research

16 pages, 1213 KiB

Open AccessArticle

The Economic Evaluation of Mastitis Control Strategies in Holstein-Friesian Dairy Herds

by Melina Richardet, Hernán G. Solari, Victor E. Cabrera, Claudina Vissio, Daniel Agüero, Julián A. Bartolomé, Gabriel A. Bó, Cristina I. Bogni and Alejandro J. Larriestra

Animals 2023, 13(10), 1701; https://doi.org/10.3390/ani13101701 - 20 May 2023

Cited by 1 | Viewed by 1325

Abstract

The economic evaluation of mastitis control is challenging. The objective of this study was to perform the economic evaluation of mastitis control, under different intervention scenarios, quantifying the total cost of mastitis caused by S. aureus in Holstein cows in Argentina. A model was set for a dairy herd of Holstein cows endemically infected with S. aureus. A basic mastitis control plan including proper milking procedures, milking machine test, dry cow therapy, and treatment for clinical mastitis, was compared against other more complex and costly interventions, such as segregation and culling of chronically infected cows. Sensitivity analysis was performed by modifying the intramammary infection transition probabilities, economic parameters, and efficacy of treatment strategies. The basic mastitis control plan showed a median total cost of USD88.6/cow per year, which was close to the infected cows culling scenarios outputs. However, the segregation scenario was the most efficient, in which the total cost was reduced by about 50%. Such cost was more sensitive to probabilities and efficacy than the economic parameters. The model is flexible and can be customized by producers and veterinarians according to different control and herd settings. Full article

(This article belongs to the Special Issue Decision Models in Livestock Production Systems)

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11 pages, 854 KiB

Open AccessArticle

Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation

by Giovanna Ranzato, Ines Adriaens, Isabella Lora, Ben Aernouts, Jonathan Statham, Danila Azzolina, Dyan Meuwissen, Ilaria Prosepe, Ali Zidi and Giulio Cozzi

Animals 2022, 12(24), 3494; https://doi.org/10.3390/ani12243494 - 10 Dec 2022

Cited by 1 | Viewed by 1422

Abstract

Early predictions of cows’ probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows’ first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle. Full article

(This article belongs to the Special Issue Decision Models in Livestock Production Systems)

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18 pages, 757 KiB

Open AccessArticle

Response of Pasture Nitrogen Fertilization on Greenhouse Gas Emission and Net Protein Contribution of Nellore Young Bulls

by Lais Lima, Fernando Ongaratto, Marcia Fernandes, Abmael Cardoso, Josiane Lage, Luis Silva, Ricardo Reis and Euclides Malheiros

Animals 2022, 12(22), 3173; https://doi.org/10.3390/ani12223173 - 16 Nov 2022

Cited by 1 | Viewed by 1162

Abstract

This study aimed to evaluate the greenhouse gas (GHG) emission and net protein contribution (NPC) of Nellore young bulls grazing marandu palisade grass (Urochloa brizantha cv. Marandu) under three levels of pasture nitrogen (N) fertilization during backgrounding and finished on pasture or feedlot, based on concepts of sustainable intensification. The treatments were: System 1: pastures without N fertilizer during backgrounding, and animals finished on pasture supplemented with high concentrate at a rate of (20 g of concentrate per kg of body weight; P0N + PS); System 2: pastures fertilized with 75 kg N ha⁻¹ year⁻¹ during backgrounding and animals finished on feedlot fed a total mixed ration (TMR; P75N + F); and System 3: pastures fertilized with 150 kg N ha⁻¹ year⁻¹ during backgrounding, and animals finished on feedlot fed a TMR (P150N + F). During backgrounding, all pastures were managed under a continuous and put-and-take stock grazing system. All animals were supplemented with only human-inedible feed. Primary data from systems 1, 2 and 3, respectively, in the field experiment were used to model GHG emissions and NPC (a feed-food competitiveness index), considering the backgrounding and finishing phases of the beef cattle production system. Average daily gain (ADG) was 33% greater for the N fertilizer pastures, while carcass production and stocking rate (SR) more than doubled (P75N + F and P150N + F). Otherwise, the lowest GHG emission intensity (kg CO₂e kg carcass⁻¹) was from the P0N + PS system (without N fertilizer) but did not differ from the P75N + F system (p > 0.05; pastures with 75 kg N ha⁻¹). The main source of GHG emission in all production systems was from enteric methane. Moreover, NPC was above 1 for all production systems, indicating that intensified systems contributed positively to supply human protein requirements. Moderate N fertilization of pastures increased the SR twofold without increasing greenhouse gas emissions intensity. Furthermore, tropical beef production systems are net contributors to the human protein supply without competing for food, playing a pivotal role in the food security agenda. Full article

(This article belongs to the Special Issue Decision Models in Livestock Production Systems)

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15 pages, 272 KiB

Open AccessArticle

A Scenario Analysis for Implementing Immunocastration as a Single Solution for Piglet Castration

by Li Lin-Schilstra and Paul T. M. Ingenbleek

Animals 2022, 12(13), 1625; https://doi.org/10.3390/ani12131625 - 24 Jun 2022

Cited by 1 | Viewed by 1364

Abstract

Painful castration of male piglets to avoid boar taint can potentially be replaced by three more ethical alternatives: entire male production in combination with a detection method, immunocastration (an active vaccination against the gonadotrophin-releasing factor, GnRF), and castration with pain relief (anesthesia and/or analgesia). With the aim of abandoning piglet castration and facilitating internal trade, the European Union (EU) was initially in favor of a single alternative. Immunocastration was proposed as a potential solution, but it has not yet been sufficiently assessed regarding its market potential. To address this point, this paper uses scenario analysis to examine whether and under what conditions immunocastration could be the general solution sought by the EU. The study constructs two extreme scenarios: one in which all uncertain elements negatively influence the growth of immunocastration; another in which all uncertain elements have positive influences. These scenarios provide insights into the variance in possible futures for the implementation of immunocastration. The results show that it is unlikely that immunocastration will become a single solution for all producers in the EU, because it is not the optimal solution for all types of EU pork production systems (i.e., cost-efficiency oriented, quality oriented, animal-friendly oriented, import dependent). Rather than debating and looking for evidence about which single method is the best for the entire EU, EU authorities are advised to allow the co-existence of all alternatives and to develop protocols for applying them in the pork industry. Full article

(This article belongs to the Special Issue Decision Models in Livestock Production Systems)

14 pages, 2064 KiB

Open AccessArticle

A Heuristic and Data Mining Model for Predicting Broiler House Environment Suitability

by Angel Antonio Gonzalez Martinez, Irenilza de Alencar Nääs, Thayla Morandi Ridolfi de Carvalho-Curi, Jair Minoro Abe and Nilsa Duarte da Silva Lima

Animals 2021, 11(10), 2780; https://doi.org/10.3390/ani11102780 - 24 Sep 2021

Cited by 2 | Viewed by 1930

Abstract

The proper combination of environment and flock-based variables plays a critical role in broiler production. However, the housing environment control is mainly focused on temperature monitoring during the broiler growth process. The present study developed a novel predictive model to predict the broiler (Gallus gallus domesticus) rearing conditions’ suitability using a data-mining process centered on flock-based and environmental variables. Data were recorded inside four commercial controlled environment broiler houses. The data analysis was conducted in three steps. First, we performed an exploratory and descriptive analysis of the environmental data. In the second step, we labeled the target variable that led to a specific broiler-rearing scenario depending on the age of the birds, the environmental dry-bulb temperature and relative humidity, the ammonia concentration, and the ventilation rate. The output (final rearing condition) was discretized into four categories (‘Excellent’, ‘Good’, ‘Moderate’, and ‘Inappropriate’). In the third step, we used the dataset to develop tree models using the data-mining process. The random-tree model only presented accuracy for predicting the ‘Excellent’ and ‘Moderate’ rearing conditions. The decision-tree model had high accuracy and indicated that broiler age, relative humidity, and ammonia concentration play a critical role in proper rearing conditions. Using a large amount of data allows the data-mining approach to building up ‘if–then’ rules that indicate suitable environmental control decision-making by broiler farmers. Full article

(This article belongs to the Special Issue Decision Models in Livestock Production Systems)

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16 pages, 1824 KiB

Open AccessArticle

Using PRRSV-Resilient Sows Improve Performance in Endemic Infected Farms with Recurrent Outbreaks

by Gloria Abella, Adela Pagès-Bernaus, Joan Estany, Ramona Natacha Pena, Lorenzo Fraile and Lluis Miquel Plà-Aragonés

Animals 2021, 11(3), 740; https://doi.org/10.3390/ani11030740 - 08 Mar 2021

Cited by 2 | Viewed by 1662

Abstract

The selection of porcine reproductive and respiratory syndrome (PRRS) resilient sows has been proposed as a strategy to control this disease. A discrete event-based simulation model was developed to mimic the outcome of farms with resilient or susceptible sows suffering recurrent PRRSV outbreaks. Records of both phenotypes were registered in a PRRSV-positive farm of 1500 sows during three years. The information was split in the whole period of observation to include a PRRSV outbreak that lasted 24 weeks (endemic/epidemic or En/Ep) or only the endemic phase (En). Twenty simulations were modeled for each farm: Resilient/En, Resilient/En_Ep, Susceptible/En, and Susceptible/En_Ep during twelve years and analyzed for the productive performance and economic outcome, using reference values. The reproductive parameters were generally better for resilient than for susceptible sows in the PRRSV En/Ep scenario, and the contrary was observed in the endemic case. The piglet production cost was always lower for resilient than for susceptible sows but showed only significant differences in the PRRSV En/Ep scenario. Finally, the annual gross margin by sow is significantly better for resilient than for susceptible sows for the PRRSV endemic (12%) and endemic/epidemic scenarios (17%). Thus, the selection of PRRSV resilient sows is a profitable approach for producers to improve disease control. Full article

(This article belongs to the Special Issue Decision Models in Livestock Production Systems)

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