Performance Of Production System Research Articles

Bioeconomic and sustainability performance of dairy-beef steer and heifer production systems differing in stocking rate

There is little published research on dairy-beef heifer systems or comparisons of heifer and steer dairy-beef production. Furthermore, given its impact on the productivity and economics of dairy-beef systems, any comparison of gender must also consider potential interactions with stocking rate (SR). The objective of this study was to evaluate the variability of physical and economic performance, greenhouse gas emissions, feed-food competition and pasture land-use of dairy-beef production steer and heifer systems at differing stocking rates. Performance data from a two (gender: Steers and Heifers) x two (SR: Low and High) x two (breed-types: Early-maturing (EM) and Late-maturing (LM) factorial experiment was used to parameterize a bio-economic farm systems model. Low SR animals were heavier, had higher fat scores and better conformed at slaughter. High stocking rate resulted in greater carcass output per hectare and subsequently were, on average, 22 % more profitable than their Low SR counterparts. Late-maturing animals were found to be more profitable than early-maturing, and steers were more profitable than heifers. GHG emissions of the eight treatments investigated ranged from 10.7 to 17.7 kgs of carbon dioxide equivalents (kg CO2eq) per kilogram of carcass weight produced, with both High SR and heifer systems having lower GHG emissions per kg of product than their Low SR and steer counterparts. Human edible protein efficiency was only favorable for the steer systems. High SR systems had, on average, lower land use per kg of product than their Low SR counterparts. Results from this study indicate that no single treatment was optimal across the range of performance metrics considered.

Opportunities to integrate Ecosystem Services into Life Cycle Assessment (LCA): a case study of milk production in Brazil

World dairy production is growing rapidly having increased by 339 million tons over the last twenty years. However, it remains unclear how anthropic activities in the milk sector can impact the Ecosystem Services (ES) supply to society. The aim of this study was to propose and determine the Net Environmental Performance (NEP) of different milk production systems. For this purpose, a case study on a confined compost barn farm, located in southeastern Brazil was selected as reference scenario and compared with three other systems. The mapping of ES benefits was carried out using the Common International Classification of Ecosystem Services, while environmental impacts were calculated using Life Cycle Assessment (LCA). The LCA results and ecosystem benefits were combined and converted into monetary units to calculate the NEP per 1 kg of milk. The results indicated that semi-confined systems had the worst environmental performance (90 % more impacts) compared to the compost barn milk system. On the other hand, confined systems generate few ES benefits, but their environmental impacts were lower for most LCA impact categories (up to 87 % minimized impacts) compared to semi-confined systems. Finally, we concluded the confined systems in SP and PR showed the best NEP (1.07 and 1.48) aiming for both environmental impacts and ES benefits to fit the win–win situation.

Economic utility of Colombian Romosinuano cattle

The objective of this study was to quantify the economic utility in Romosinuano production systems by developing a bioeconomic model assumed cow-calf, cow-calf plus stocker (CCPS), and complete cycle operations. Each system produced males for sale and females for replacement. Input parameters were established from breed data collected by AGROSAVIA. Revenues were estimated using the official cattle price, and production costs were quantified per activity. In the results, for cow-calf operations, the maximum economic utility was 244.12 USD. CCPS, yielded 231.86 USD, and Complete cycle, 268.94 USD. The genetic progress per generation for W240, W480, W24 and CI was + 3.8 kg, + 5 kg, + 5.9 kg, and -1 d, respectively. The price of livestock was the sensitized variable with the greatest impact on maximum economic utility (± 118.64 USD to ± 155.44 USD), followed by mineral supplementation (16.31 USD to ± 37.34 USD). The sensitized variables with the lowest impact were food (± 1.62 USD to ± 1.8 USD) and health plan supplies (± 6.03 USD to ± 9.13 USD). It is concluded that economic utility defined as a composite trait influenced by the characteristics that shape it favors genetic progress and the identification of animals with optimal performance in different bovine production systems.

Exploration of mobile genetic elements in the ruminal microbiome of Nellore cattle

Metagenomics has made it feasible to elucidate the intricacies of the ruminal microbiome and its role in the differentiation of animal production phenotypes of significance. The search for mobile genetic elements (MGEs) has taken on great importance, as they play a critical role in the transfer of genetic material between organisms. Furthermore, these elements serve a dual purpose by controlling populations through lytic bacteriophages, thereby maintaining ecological equilibrium and driving the evolutionary progress of host microorganisms. In this study, we aimed to identify the association between ruminal bacteria and their MGEs in Nellore cattle using physical chromosomal links through the Hi-C method. Shotgun metagenomic sequencing and the proximity ligation method ProxiMeta were used to analyze DNA, getting 1,713,111,307 bp, which gave rise to 107 metagenome-assembled genomes from rumen samples of four Nellore cows maintained on pasture. Taxonomic analysis revealed that most of the bacterial genomes belonged to the families Lachnospiraceae, Bacteroidaceae, Ruminococcaceae, Saccharofermentanaceae, and Treponemataceae and mostly encoded pathways for central carbon and other carbohydrate metabolisms. A total of 31 associations between host bacteria and MGE were identified, including 17 links to viruses and 14 links to plasmids. Additionally, we found 12 antibiotic resistance genes. To our knowledge, this is the first study in Brazilian cattle that connect MGEs with their microbial hosts. It identifies MGEs present in the rumen of pasture-raised Nellore cattle, offering insights that could advance biotechnology for food digestion and improve ruminant performance in production systems.

OLE! Dairy model

The process of intensification of the dairy sector has been characterized in recent decades by the increase in milk production per hectare, the increase in livestock density, the inclusion of more concentrates in the diet, and the improvement of the genetic merit of dairy cows. The use of models has productive, environmental, and economic advantages. The objectives of the study were to describe a new model, “OLE! Dairy model”, to (a) simulate the biophysical performance of a pasture-based dairy production system; (b) evaluate the predictive capacity of the model with a set of statistical parameters, comparing its results with the biophysical performance of experimental studies of dairy farm systems, and (c) calibrate by adjusting the technical coefficient. The experimental design combines two feeding strategies with a different proportion of pasture in the diet and two animal genotypes. We make a description of the biophysical component and the calculations proposed in the “OLE! Dairy model”. Then a variety of parameters was calculated for model testing, including the Mean Squared Error, the Relative Prediction Error, the square root of the MSE, the Concordance Correlation Coefficient, and the Model Efficiency. The model presented a good predictive capacity for stocking rate and concentrate, pasture, and reserve intake. The predictive capacity of the model for individual production and area production improves after performing a rapid calibration, which allows for avoiding overestimations or underestimations that generate erroneous measurements in the planning and management of milk production systems, and can be adjusted to different conditions of production of the region.

The interrelationship between Lean 4.0 and value co-creation: an empirical study in the dairy sector

Purpose This study aims to examine the impact of Lean 4.0 practices on value co-creation in the dairy ecosystem. Design/methodology/approach Data collection were carried out through a questionary application with 126 professionals linked to the dairy ecosystem, including milk producers, milk cooperatives and milk transporters. The data were analyzed using Cluster Analysis, Mann-Whitney test and Chi-Square test. Findings A strong relation was found between the use of Lean 4.0 tools and the increase in operational performance, in addition to milk quality. Moreover, it can be noted that the use of digital technologies from Industry 4.0 has a strong relation with dairy production optimization, in other words, it is possible to be more efficient in the dairy process via Lean 4.0 adoption. Research limitations/implications The study is limited to analyzing the Brazilian dairy ecosystem. The results presented may not reflect the characteristics of the other countries. Practical implications Once the potential empirical impacts of the relation between Lean 4.0 and value co-creation are elucidated, it is possible to direct strategies for decision-making and guide efforts by researchers and professionals to deal with the waste mitigation present in the dairy sector. Social implications Lean 4.0 proves to be a potential solution to improve the operational performance of the dairy production system. Lean 4.0, linked to value co-creation, allows the integration of the production sector with consumers, through smart technologies, so new services and experiences can be provided to the consumer market. Additionally, the consumer experience can be stimulated based on Lean 4.0, once the quality specification is highlighted based on data science and smart management control. Originality/value To the best of the authors’ knowledge, this is the first study that analyzes the interrelationship between the Lean 4.0 philosophy and the value co-creation in the dairy ecosystem. In this sense, the study reveals the main contributions of this interrelation to the dairy sector via value co-creation, which demonstrates a new perspective on the complementarity of resources, elimination of process losses and new experiences for the user through digital technologies integrated with the Lean Thinking approach.

Planting date and dicamba‐based herbicide programs influence soybean production in the Southern Great Plains

AbstractThe interplay of management decisions involving soybean (Glycine max L.) planting date, herbicide programs, and herbicide application timings is critical to optimize soybean performance and weed control in Southern Great Plains soybean production systems. This research sought to evaluate soybean yield potential and the level of weed control as influenced by early‐, delayed‐, and late‐ planting dates and various combinations of preemergence (PRE), early‐postemergence (EPOST), and mid‐postemergence (MPOST) weed management programs. A field study was established in Bixby, OK in 2017 and 2018 under irrigated conditions and in Perkins, OK in 2017 under dryland conditions, consisting of three planting windows (early, delayed, and late) of XtendFlex soybean, with or without a PRE (chlorimuron + flumioxazin + pyroxasulfone + glyphosate + dicamba) combined with EPOST or EPOST + MPOST (glyphosate + dicamba) versus no in‐season applications. The gap in late‐planted soybean yield potential, compared to early‐planted soybean, was exacerbated in the dryland systems (1346 kg ha−1) versus an irrigated system (2311 kg ha−1). Use of PRE provided 60% weed control until MPOST and increased yields by 657 kg ha−1 and 457 kg ha−1 for delayed and late‐planted soybean, respectively. Late‐planted soybean with EPOST + MPOST provided up to 50% weed control, but lack of biomass production for cultural control reduced weed control by 29% compared to early‐ and delayed‐planted soybean. From an agronomic management standpoint, the time of soybean planting is influential on the success of weed control measures and soybean yields in double‐cropping system in the Southern Great Plains, particularly with late‐planted soybean.

Enhancing Performance of Permanent Magnet Motor Drives through Equivalent Circuit Models Considering Core Loss

Permanent magnet motors (PMMs) have emerged as key components in numerous industrial applications due to their high efficiency, compact size, and robust performance characteristics. However, to attain optimal performance in PMM drives, accurately predicting and mitigating core losses is paramount. This paper aims to provide a comprehensive review of advancements and methodologies for enhancing the performance of PMM drives by integrating equivalent circuit models (ECMs) that account for core losses. Firstly, the significance of core losses in motor drives is underscored, alongside a survey of research endeavors dedicated to core loss reduction. Notably, emphasis is placed on mathematical models offering both swift computation and reasonable accuracy. Subsequently, the paper delves into the development of ECMs, focusing on approaches adept at capturing core loss effects across diverse operating conditions. Moreover, this paper explores the utilization of these improved ECMs in the design and control of PMMs to achieve enhanced performance. By integrating core loss considerations into design and control strategies, PMM drives can optimize efficiency, torque production, and overall system performance. In summary, this paper may consolidate the current state-of-the-art techniques for enhancing PMM performance through the integration of core-loss-aware ECMs. It highlights key research directions and opportunities for further advancements in this critical area, aiming to foster the development of more efficient and reliable PMM-based systems for a wide range of industrial applications.

Optimizing Feed Formulation Strategies for Attaining Optimal Nutritional Balance in High-Performing Dairy Goats in Intensive Farming Production Systems

Abstract Genetic factors play a pivotal role in shaping feed formulation strategies for dairy goats, influencing milk production, composition, and overall productivity through the modulation of genetic parameters and polymorphisms associated with milk protein fractions and yield. Moreover, environmental challenges, including heat stress and feed availability fluctuations, pose significant hurdles in achieving optimal nutrition and performance, necessitating precision livestock farming and the utilization of local by-products to mitigate their impact on herd health and productivity. In addition, nutritional interventions, such as supplement utilization and alternative feed incorporation, offer promising avenues for addressing specific deficiencies and enhancing overall performance, from fatty acid supplementation to herbal feed additives, while balancing rations to meet evolving nutritional needs and ensuring long-term sustainability remains paramount for optimizing herd nutrition, health, and performance in intensive dairy goat production systems. The aim of this study was to explore the multifaceted influences of genetic factors, environmental challenges, and nutritional interventions on feed formulation strategies and overall herd nutrition, health, and performance in intensive dairy goat production systems.

Conversion to organic farming: Does it change the economic and environmental performance of fruit farms?

This paper compares the performance of conventional and organic fruit farms in Spain, using a set of base indicators to assess their economic and environmental performance on a per hectare basis. Composite indicators are also calculated to measure the overall economic and environmental performance of both production systems. Comparisons are made using propensity score matching to minimize the non-randomization biases caused by structural differences between the samples of conventional (n = 552) and organic (n = 127) fruit farms sourced from the Spanish Farm Accountancy Data Network (RECAN). The results based on per hectare metrics point to modest changes in the performance of farms that converted to organic farming. This is mainly because most converted farms were former conventional farms characterized by lower profitability and less intensive input use, consequently minimizing the effects of the conversion process. Nevertheless, the conversion outcomes exhibit a degree of variability depending on the specific type of fruit production. Economic gains are only discernible in the case of nut farms, whereas fruit and tropical fruit farms tend to yield the most favorable results per hectare from an environmental perspective.

Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization

Abstract Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of a water production system by examining four pivotal factors: buried pipe length (TL), air flow rate (AFR), air temperature (AT), and air humidity (AH). A positive correlation between these variables and water vapor production is established, with AT exerting the most pronounced influence. Significantly, the analysis of variance reveals the main and interactive effects of the variables, except for TL × AFR, at a 5% significance level. To enhance understanding of the intricate interplay among these factors, a proficient least squares support vector machines model is devised, employing a radial basis function kernel. This model demonstrates an impressive 98% concurrence between projected and empirical data, with a minimal error of 0.66 mL and 5.99%. An in-depth sensitivity analysis underscores the differential impact of AT, AH, TL, and AFR on water vapor (WV) prediction. Optimal values of 3.98 m, 6.89 m3/h, 46.30 °C, and 86.62% for TL, AFR, AT, and AH, respectively, are obtained through subsequent optimization of independent variables using genetic algorithms, resulting in a notable water production of 23.61 mL.

Tall fescue tiller survival over summer in a subtropical environment: The role of the size and depth of root systems

AbstractPasture persistence is a key determinant of the economic and environmental performance of pastoral animal production systems. Large and deep root systems that help resist summer water stress have been proposed as a relevant trait for vegetative persistence of perennial temperate forage species growing in subtropical climates or under future climatically challenging scenarios. In a previous study [Jauregui et al., 2017. Persistence of tall fescue in a subtropical environment: Tiller survival over summer in response to flowering control and nitrogen supply. Grass and Forage Science 72, 454–466] we have shown that nitrogen fertilization and grazing management aimed at ‘control flowering’ increased the survival of tall fescue tillers during harsh summers in Uruguay (lat. 32°S). Here we assessed: (i) to what extent tiller survival is mediated by root system size in spring and (ii) what consequences tiller survival entails for root mass, depth and morphology the following autumn. In two field experiments, significant increases in tiller survival in response to nitrogen fertilization and grazing management (+60% and +80% in 2011/12 and 2012/13, respectively) were not related to concomitant effects on the size or depth of the root system in spring (p > .10). Even when six‐fold within‐treatment variation in root mass was observed, within‐treatment variation in summer tiller survival was little affected (<15%, p = .08). In turn, differences in tiller survival over summer affected little root system characteristics the following autumn. Therefore, we found scant support for the hypothesis that large and deep root systems contribute to survival of tall fescue tillers in this subtropical humid climate. Except for soils with less than 30 mm of plant available water holding capacity, summer water deficits did not induce severe tiller mortality in tall fescue in this climate.

Influência de suplementos de baixo e médio consumo na fase de crescimento de novilhos mantidos em pastagem tropical e terminados em confinamento

ABSTRACT: The objective was to evaluate the animal production and ingestive behavior of the steers fed different supplements during the growth phase in Aruana grass, and the influence of this supplementation on production performance in feedlot phase. The treatments consisted of three supplements during the growing phase: mineral salt (0.12 g/kg of body weight - BW), low intake energy-protein supplement (1.26 g/kg of BW), and medium intake (3.76 g/kg of BW). Forage canopy characteristics, animal production, and animal behavior on pasture were evaluated in a completely randomized design with three replicates (paddocks). The titanium dioxide marker was used to determine the intake and apparent digestibility during the pasture phase. The production performance in feedlot was evaluated in a completely randomized design with eight replicates (animals). Twenty-four Aberdeen Angus steers (15 ± 1.5 months old; 364.8 ± 21.7 kg BW) were used. At the end of the growth phase the animals were confined and fed a single diet. Dry matter, crude protein, and neutral detergent fiber (NDF) contents of forage and intake were not influenced by the treatments. The average daily gain was higher for the medium intake supplement in pasture phase. The apparent digestibility of NDF (P = and chewing rate were higher for the medium intake supplement. The average daily gain and fat thickness of carcasses were higher for the medium intake supplement in the feedlot phase. The supplements of medium intake increase animal productivity during the growing phase on pastures and cattle’s finishing performance in intensive production systems.

Performance of a concurrent parallel production system through new operating curves of Six Sigma metrics

Performance of a concurrent parallel production system through new operating curves of Six Sigma metrics

Transient analysis of production performance and energy consumption in geometric flexible production systems

To meet the growing need for customized products, flexible production systems are gaining widespread application in modern factories. The inherent complexity of these flexible systems poses new challenges for production management. In this paper, we intend to contribute an operation-level analytical model for such systems, thus providing an effective evaluation tool for productivity and energy consumption analysis. Specifically, a multi-type serial production line with machines obeying the geometric reliability model and intermediate buffers having limited capacity is considered, in which K batches of parts are processed sequentially in each shift. Analytical solutions for key operation metrics are first developed for small systems using a Markov model. For multi-machine lines, a semi-analytical method based on aggregation is proposed. The effectiveness of the proposed analysis method is verified through simulation experiments. Besides, system properties are analyzed to provide insights for continuous improvement and optimization.

A cross-country comparison of pig production systems performance: Evidence from EU countries

This study estimates and analyze the technical efficiency of pork farms from five EU countries. The Generalized True Random-effects (GTRE) model was used to differentiate between persistent and transitory technical efficiency. The results show that elasticities are robust to various specifications. Spanish farms show the highest average efficiency score. In Denmark, Germany, France, and Poland, the most significant opportunities for growth are found in transitional efficiency. The degree of productive specialization in the five countries has a positive impact in the efficiency. In Denmark, Germany, and France, persistent efficiency has a positive association with the fraction of paid labor.

Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

PurposeWith production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.Design/methodology/approachFifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.FindingsThe main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.Originality/valueThis paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes

ABSTRACT Throughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.

Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system

Production systems in industries are undergoing transformative changes, with the rise of Industry 4.0 technologies amplifying the complexity of manual and semi-automated workstations, necessitating advanced training and adaptability from human workers. Human workers, due to their unique blend of cognitive and motor skills, thus flexibility, are indispensable and will continue to play a pivotal role. Because of their unique experiences and attributes, they inherently exhibit variability in their processing times and learning rates, which complicates frequent production ramp-ups. Recognizing the lack of comprehensive models that simultaneously account for stochastic processing times and heterogeneous learning during production ramp-ups, this study aims to bridge this gap. We developed an analytical model of a two-worker production system with an intermediate buffer by focusing on worker learning curves, stochastic processing times, and learning heterogeneity. Through an illustrative case, we derived insights into the performance of such systems, specifically in terms of measures including the mean throughput time of a batch, mean waiting time of a part in the buffer, mean idle time of workers, work-in-progress distribution, and buffer usage during the production run. We found that deterministic learning models can significantly underestimate the throughput times, and even consistent average learning rates can lead to variable throughput times based on the learning patterns. Our findings emphasize the need for production managers to consider these factors for realistic and effective production planning, underscoring the novelty of our approach in addressing these intricate dynamics to improve not only system performance, but also worker well-being.

Study on performance of a full spectrum solar-driven hydrogen production system using Ag@SiO2-Au blended nanofluid filters

Study on performance of a full spectrum solar-driven hydrogen production system using Ag@SiO2-Au blended nanofluid filters