Efficiency Of Production Systems Research Articles

Efficiency analysis of watermelon under plastic film mulching systems

Iran is the main watermelon producer in the world after China and Turkey. However, there is no practical information on which region, tillage and cultivation systems can achieve the most efficient watermelon production system in Iran. This study was conducted to assess the most efficient ways of watermelon production in three agroecosystems (cold, moderate, warm) of Iran using Data Envelopment Analysis. The data were collected from two tillage systems (reduced and conventional) and two groups of watermelon cultivations (conventional system and plastic film mulching system). Accordingly, twelve watermelon production systems were under the study including all inputs and outputs. Finally, an Analytical Hierarchy Process model was used to determine the best region and the best combination of cultivation and tillage systems for watermelon production. The technical and pure technical efficiencies of watermelon production systems were respectively 89%–100%. The Analytical Hierarchy Process model determined that the most efficient watermelon production systems were from combination of plastic film mulching system and reduced tillage in cold and moderate agroecosystem which reduced water and diesel fuel consumption by 500–2500 m3/ha followed by fertilizers by 100–150 kg/ha while efficiencies are enhanced by 20%–32%.

Nitrogen fertilizer and tillage intensity affected winter wheat macronutrient uptake and utilization efficiencies

AbstractApplication of N fertilizer and no‐tillage (NT) can increase winter wheat (Triticum aestivum L) production through improvements in plant available soil water and nutrient availability. However, long‐term tillage and N management interaction effects on winter wheat nutrient uptake are not well known. The objective of this study was to quantify winter wheat grain yield, macronutrient removal, and utilization responses to N fertilizer application and tillage intensity. The study was conducted in 2019 and 2020 at Hays, Kansas after 45 years of tillage and N fertility experiment. Treatments were a combination of two tillage practices (CT, conventional tillage; NT) and four rates of N (0, 45, 90, and 134 kg ha−1). Results showed a significant tillage and N fertilizer interaction effect on wheat grain yield, nutrient removal (NR) (grain‐N, ‐P, ‐K, ‐Mg; stover‐K, and total P), and nutrient concentration (stover‐K and ‐S). Two different quadratic models fit the N rate‐to‐yield relation for CT and NT with yields of 4.3 and 5.2 Mg ha−1 at agronomic optimal N rates of 119 and 199 kg N ha−1, respectively. This suggests that the N rates were not high enough to predict optimum N rate for NT in this environment. With an average yield of 3.30 Mg ha−1, wheat removed about 113 kg N ha−1, 17 kg P ha−1, 56 kg K ha−1, 7 kg Ca ha−1, 8 kg Mg ha−1, and 8 kg S ha−1, irrespective of tillage practice. Grain yields and NR were greater for CT at smaller N rates (<90 kg N ha−1) and for NT at greater N rates (>90 kg ha−1). Findings of the study suggest that adequate N fertility (>90 kg ha−1) should be maintained to improve grain yield, nutrient uptake, and utilization efficiency in dryland NT wheat production systems.

The interaction between dietary fiber and gut microbiota, and its effect on pig intestinal health.

Intestinal health is closely associated with overall animal health and performance and, consequently, influences the production efficiency and profit in feed and animal production systems. The gastrointestinal tract (GIT) is the main site of the nutrient digestive process and the largest immune organ in the host, and the gut microbiota colonizing the GIT plays a key role in maintaining intestinal health. Dietary fiber (DF) is a key factor in maintaining normal intestinal function. The biological functioning of DF is mainly achieved by microbial fermentation, which occurs mainly in the distal small and large intestine. Short-chain fatty acids (SCFAs), the main class of microbial fermentation metabolites, are the main energy supply for intestinal cells. SCFAs help to maintain normal intestinal function, induce immunomodulatory effects to prevent inflammation and microbial infection, and are vital for the maintenance of homeostasis. Moreover, because of its distinct characteristics (e.g. solubility), DF is able to alter the composition of the gut microbiota. Therefore, understanding the role that DF plays in modulating gut microbiota, and how it influences intestinal health, is essential. This review gives an overview of DF and its microbial fermentation process, and investigates the effect of DF on the alteration of gut microbiota composition in pigs. The effects of interaction between DF and the gut microbiota, particularly as they relate to SCFA production, on intestinal health are also illustrated.

Exploring the hybrid route of bio-ethanol production via biomass co-gasification and syngas fermentation from wheat straw and sugarcane bagasse: Model development and multi-objective optimization

Biomass co-gasification and syngas fermentation is one of the least trotted paths in the Indian biofuel production scenario. The aim of the research here is to develop an equilibrium model of biomass co-gasification and syngas fermentation in Aspen plus. Two Indian lignocellulosic biomass namely wheat straw and sugarcane bagasse have been selected as feedstock for this co-gasification process followed by the syngas fermentation. The process shows promising results in terms of ethanol production and energy usage. The effects of gasification process parameters on overall process performance have been analysed. Empirical regression equations were constructed to account for the effects of biomass ratio, which is the ratio of the masses of wheat straw and sugarcane bagasse, gasification temperature and equivalence ratio over ethanol yield, lower heating value of syngas, cold gas efficiency, overall system efficiency, and CO2 emission using response surface methodology. Finally, a multi-objective optimization has been conducted to maximize the ethanol production and overall system efficiency, and to minimize the CO2 emission of the system. Results suggest that the wheat straw and sugarcane bagasse ratio of 4:1, 919.5oC temperature and 0.15 equivalence ratio is the optimum point with an ethanol production rate of 20.917 tonnes/hr, overall efficiency of 52.184%, and a minimum CO2emission of 32669.862 kg/h.

SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals

Sensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand, while maximizing the efficiency of production systems on the other. Current digital approaches do not meet these challenges due to a lack of efficient and lack of real-time non-invasive precision measurement technologies that can detect and monitor animal diseases and identify resilience in animals. In this opinion review paper, I offer a critical view of the potential of wearable sensor technologies as a unique and necessary contribution to the global market for farm animal health monitoring. To stimulate the sustainable, digital and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and developing new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI)-based data acquisition and processing platform, the current needs, which have not yet been met, can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to its significant and innovative potential.

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers.

Process Models in the Management of the Decision Support System Efficiency in Production Systems

Process Models in the Management of the Decision Support System Efficiency in Production Systems

Hospital Management of the Eastern Mexico Regional Administrative Operating Body in the Sars-Cov-2 Pandemic: Technical Efficiency

Introduction: In the face of the new health emergency due to SARS-COV-2, the Mexican Social Security Institute (IMSS) Spanish acronym, implements the Institutional Strategic Plan for Contingency Care due to COVID-19, which defines the modes of action in preparation and response in the presence and sustained SARS-COV-2 disease transmission cases Data envelopment analysis makes it possible to estimate the technical efficiency of service production systems. Objective: to estimate the technical efficiency of the hospital units of the Eastern Mexico State Regional Decentralized Administrative Operation Body of the IMSS (Spanish acronym) in the response to the SARS-COV-2 health contingency from March 2020 to March 2021. Methods: The COOPER Methodological Ordering was applied to define the input-oriented BCC model and estimate the hospitals’ technical efficiency in the treatment response of COVID-19 during 1st and 2nd periods of highest incidence. Inputs: Hospitalized and Covid-19 beds. Outputs: improvement/death ratio. Results: in the study period, 30,656 were hospitalized and 1,324 Covid-19 beds were managed in 10 hospitals. Improvement 15,067; death 15,589 (improvement/death ratio: 0.97). Average technical efficiency: 82%. Conclusions: 8 of 10 hospitals in technical efficiency. Data envelopment analysis enables health managers to estimate the production capacity of health services and decision-making in the event of a public health emergency.

Genetic parameters for mouth size and their influence on growth traits in pasture-raised Nelore cattle.

Evaluating traits that allow breeders to increase production efficiency in beef production systems is important. The mouth size score is a trait easily measured and implemented by breeders. Bite size is related to mouth size in beef cattle and is a determinant of daily feed intake of pasture-raised animals, influencing their growth. The aim of this study was to estimate genetic parameters for mouth size (MS), weaning weight (WW) and postweaning weight gain (PWG) of Nelore cattle and to evaluate the influence of the interaction between MS and WW on PWG. Phenotypic records of 134,282 Nelore animals born between 1995 and 2019 were used. Variance components were estimated using multi-trait animal model with the Bayesian method. The model included the contemporary group as fixed effect, age at measurement of the trait as linear covariate, and direct additive genetic and residual effects as random effects. For WW, random maternal and maternal permanent environmental effects were added to the model. A Bayesian approach was used to analyze the interaction between WW clusters and MS. The heritability estimates were 0.24, 0.15 and 0.23 for MS, WW and PWG, respectively. The genetic correlation between variables studied ranged from 0.24 to 0.46. The results suggest that animals with a larger mouth tend to have greater PWG, demonstrating the positive influence of MS score on the postweaning performance of cattle. The direct heritability estimates confirm the possibility of selecting animals for the traits studied.

Process models in the management of the decision support system efficiency in production systems

Process models in the management of the decision support system efficiency in production systems

Creating the value of services as a factor of efficiency of production activities of transport enterprises in the digital economy

At the present stage of economic and logistics development, information technologies, artificial intelligence, as well as other achievements of scientific and technological progress are singled out as one of the promising areas, the introduction of which exposes the conditions for creating value of services to forced transformation. Many factors have become the catalyst for such rapid changes. However, among the key ones, it is worth noting the pandemic, thanks to which contradictions between the development of elements of economic systems, both at the state level and in individual production and marketing links were revealed. The pandemic has shown that it is technology, digitalization and innovation that determine the most significant factors for increasing the efficiency of production and distribution systems, including transport and logistics. In this regard, the subject of the study is the process of creating the value of transport services in the new conditions of maritime business. The purpose of the study is to develop a conceptual approach to creating the value of transport services, which will increase the efficiency of production activities of enterprises by maximizing the satisfaction of new business conditions. The methods of studying and generalizing domestic and foreign experience, the analytical method were used in the work. A review of scientific sources was carried out, analytical materials were investigated, and an oral survey of representatives of the maritime business was conducted. It is determined that in the new conditions the value of the transport service is formed due to the coordinated interaction of all participants in the cargo delivery chain. A model of value creation of transport services based on a globally synergetic approach and considering information innovations implemented today in maritime transport is presented.

Industrial Social Robots (SOBOTS) to Enhance Smart Production Systems

This paper explores the potential use of social robots in smart manufacturing industries, presenting several use cases where such robots could bring benefits. The design considerations for developing social robots that can effectively operate in industrial production environments are also discussed, using the TRIZ methodology. Furthermore, the paper highlights the advantages of using social robots together with traditional robots in some use cases. The aim is to provide insights on how social robots can be designed and utilized to improve efficiency, safety, and worker satisfaction in smart industrial production systems.

Relationship between carcase traits of Bos taurus genotypes and ultrasound measurements across six stages of growth from weaning through to long-feedlot finishing

Context Identification earlier in life of beef cattle with capacity for increased marbling, improved carcase yield and/or reduced subcutaneous fatness can facilitate more rapid genetic improvement and identification of cattle more appropriate for feedlot or pasture finishing. Earlier in life, ultrasound predictions have potential for significant improvements in compliance with market specifications and overall efficiency of beef production systems. Aims To assess the relationships just prior to slaughter and earlier in life between measurements of intramuscular fat, subcutaneous fat and eye-muscle (Longissimus thoracis et lumborum) cross-sectional area made using ultrasound, and carcase fatness, muscling and yield measurements following slaughter of Bos taurus steers that differed genetically in potential for subcutaneous fatness and marbling. Methods Ultrasound measurements of intramuscular fat (IMF), rib fat (RFT), P8 rump fat (P8FAT), eye-muscle area (EMA) and retail beef yield (RBY) of cattle heterogeneous for marbling and fat distribution were made at six stages of growth from weaning to long-feedlotting. Carcase measurements of the same traits were made at five stages of growth from weaning through to long-feedlotting. Key results IMF ultrasound scans 168 days post-weaning were more strongly correlated (r ≤ 0.79) with marbling in the carcase following long-feedlotting than were IMF ultrasound scans at the end of backgrounding (r ≤ 0.71, 326 days post-weaning), short-feedlotting (r ≤ 0.69, 431 days) and during (r ≤ 0.59, 521 days) and at the end of long-feedlotting (r ≤ 0.53, 585 days). P8FAT was the trait with the highest and most consistent accuracy across all stages of life (r = 0.80) between ultrasound and carcase measurements of P8FAT. Conclusions The results of the present study showed that ultrasound-scanned measures of carcase IMF, subcutaneous fat and EMA can be used to predict carcase characteristics with good accuracy earlier in life. Implications The finding that prediction of marbling in the carcase of long-feedlot-fed cattle was stronger by using ultrasound measurement of IMF in younger post-weaned cattle than in older post-weaned cattle warrants further investigation for genetic improvement and management applications, including using newer scanning technologies.

Economic Analysis of Dryland Integrated Production System in Western Agroclimatic Zone of Tamil Nadu, India: A DEA Approach

Background: Efficiency of production system is calculated by the input spent to amount of output produced. The Agronomic efficiency has been found to be below 48% than the world efficiency standard of 40 kg ha-1. The average fertilizer usage of India is 133 kg ha-1, 232% higher than the global standard fertilizer of 40 kg ha-1. The certified seeds and agriculture machinery usage has been increased by 15% and 13% in India but the efficiency of farm stays behind. Methods: With this back ground the present study applied cost approach variable return to scale Data Envelopment Analysis (DEA) model to evaluate the Indian farms resource using 200 rural farms of Tamil Nadu state. The data envelopment analysis models have the advantage of simultaneous measure of technical, allocative and cost efficiency of the individual farms. Result: The cost efficiency among the respondents ranged from 0 to 1 with a mean efficiency of 0.66. The study found that Decision-Making Units (DMU) have cost inefficiency, suggesting that most of the DMU operated very far away from the efficiency frontier. Allocative inefficiency (0.55) is worse than the technical inefficiency (0.80), implying that the lower economic efficiency is the result of high allocative inefficiency.

Macadamia Nuts (Macadamia integrifolia) Value Chain and Technical Efficiency among the Small-scale Farmers in Zimbabwe

Agriculture value chains are an indispensable tool for stakeholder collaboration, improving market access, foreign currency generation and increase in macadamia nut production in most developing countries. In Zimbabwe macadamia nut is a relatively new crop that has the potential to alleviate poverty and enhance livelihoods. Zimbabwe’s macadamia nut industry is still in its infancy and remains an under-researched area. Despite this, the technical efficiency of macadamia nuts production systems as proxied by production levels among small-scale farmers in the country is least understood. This study analyses the existing value chains by scrutinizing every step required in the production and marketing of macadamia nuts focusing on the technical efficiency of value chain nodes. The macadamia nut value chain in Zimbabwe is predominantly an emergency value chain system that does not embrace the modular value chain model and associated networks to enhance technical efficiency and overall welfare. The value chain system is characterised by imperfect market conditions with limited knowledge of exact specifications, attributes and prices of macadamia nuts. The existing value chain models for macadamia nut need to be re-aligned to accommodate all stakeholders, hence widening resource and knowledge-sharing platforms. There is a need for the Zimbabwe macadamia nut value chain to shift towards captive networks which embrace and scale out practices such as out-grower contractual arrangements. There is potential to design macadamia nut value chain models in Zimbabwe to increase the technical efficiency of the industry.

Precise delivery of nitrogen at tillering stage enhances grain yield and nitrogen use efficiency in double rice cropping systems of South China

Nitrogen fertilizer management substantially affects the growth, productivity, and nitrogen use efficiency (NUE) of rice production systems. To assess the effects of precise application of nitrogen fertilizer at tillering stage as ‘tillering-fertilizer’ on grain yield and nitrogen use efficiency, field trials were conducted in the late season of 2019 and 2020, in addition to a radioisotope 15 N tracer experiment. The experiment was comprised of four N fertilizer treatments i.e., no fertilizer application as a control (T1), traditional surface broadcast fertilization (90 kg N/ha as basal fertilizer and 60 kg N/ha as tillering fertilizer, T2), deep placement of tillering fertilizer (surface broadcast at 90 kg N/ha as basal fertilizer and 45 kg N/ha as tillering fertilizer at 10 cm depth, T3), deep placement of tillering fertilizer (surface broadcast at 90 kg N/ha as basal fertilizer and 30 kg N/ha as tillering fertilizer at 10 cm depth, T4) and two rice cultivars i.e., Wufengyou 615 and Yuxiangyouzhan . Results showed that grain yield remained 18.8% and 12.4% higher owing to increase in productive panicles and grain filling, 42.2% and 38.9% higher nitrogen agronomic efficiency, 4.6% and 10.4% higher nitrogen recovery efficiency, and 10.2% and 7.8% higher nitrogen physiological efficiency in T3 than T2 for Wufengyou 615 and Yuxiangyouzhan , respectively. Moreover, nitrogen derived from fertilizer in T3 was increased by 8.0% and 4.4% compared to T2 for Wufengyou 615 and Yuxiangyouzhan , respectively whereas the residual rate of T3 was increased by 31.5%. In addition, the T3 treatment substantially enhanced the leaf area index and leaf chlorophyll content that led to improved photosynthesis and the activities of enzymes related to nitrogen metabolism. Overall, 45 kg N/ha as tillering fertilizer at 10 cm depth and surface broadcast at 90 kg N/ha as basal fertilizer can improve grain yield as well as nitrogen use efficiency in late season rice production system of South China. • Precise delivery of nitrogen (PDN) at tillering stage (TS) can enhance grain yield in rice. • PDN at TS can improve nitrogen use efficiency in rice. • PDN at TS can increase nitrogen residual rate in the soil and decrease nitrogen unaccounted rate.

Measuring the compliance of management system in manufacturing SMEs: An integrated model

Research on the efficiency and sustainability of production systems has drawn attention to the importance of monitoring the social and environmental impacts of production processes and management systems. In particular, integrated models and tools need to be developed to assess the application of sustainable operations – especially in those sectors characterized by a high presence of SMEs.This paper proposes an integrated assessment model – the Small and Medium Enterprise Compliance Assessment (SECA) – for SMEs involved in manufacturing and production. SECA provides an integrated tool to detect a firm's compliance with mandatory requirements on environmental and social issues in accordance with relevant legislation applicable in the country. This assessment model employs a new Index of Small and Medium Enterprise Compliance to synthesize both the levels of compliance and the complementary risks to which the enterprise is exposed to, given the applicable legislative domains.To test the model, a pilot application was conducted based on the analysis of 99 Chinese-owned SMEs operating in the textile and fashion manufacturing district in Prato. These enterprises voluntarily underwent the assessment procedure to measure their compliance with mandatory processes and regulations.The results that emerge from this pilot application demonstrate the relevance and capacity of SECA to assess and understand the degree of SMEs' compliance with relevant legislation on sustainability matters. The application also demonstrates the models' potential to be adapted to other local contexts and SMEs as required.

Assessment of Environmental, Social, Governance and Technogenic Components of Investment Risks

Introduction. To assess the investment attractiveness (IA) and development opportunities for investment objects (IO), non-financial factors characterizing the environmental, social, governance and technogenic (ESGT) features of objects of possible financing have been increasingly used recently. The purpose of this data analysis is to establish how the ESGT-parameters of IO may reflect their financial health and performance prospects in a rapidly changing world. Having built an ESGT risk profile with the help of mathematical models, the IA of the object of study and the strategy of practical measures to increase it are determined. When modeling these processes, one should consider the growing uncertainty of the modern world due to the emergence of new risks; a large number of systemic links between the structures of the modern technosphere; the power-law nature of the distribution of the probability density of catastrophe damage, which decreases more slowly than the Gaussian dependence. In addition, the efficiency of complex production systems is largely determined by the balance of their individual links. Therefore, to assess investment risks, new methods are required to formalize the dependence of IA on ESGT-factors for the integrated management of the level of credit, market, insurance and operational risks under conditions of uncertainty. The purpose of the article is to develop mathematical methods for quantifying IA and determining real costs to improve the management, social and technological structure of IO, and minimize environmental pollution. Results. A mathematical model has been developed for assessing the environmental, social, managerial and technogenic leaving risks of investment, which makes it possible to determine the optimal strategies for increasing the IA of a possible IO. For a comprehensive risk assessment, methods of the theory of singularities of smooth reflections (TOGO) and the method of analysis of hierarchies (MAH) are used. The following algorithm for estimating IA is proposed: 1) determining the indices of the ESGT-components of risk; 2) calculation of bifurcation index values; 3) determination of the weakest links, which are associated with a decrease in IA; 4) identification of priority measures to prevent the reduction of IA or restore it to a predetermined level and minimize the negative impacts of extreme events and ensure sustainable development. Conclusions. The obtained results show that mathematical models based on the use of TOGO and MAH methods are an important tool for estimating IA under conditions of uncertainty. They allow: 1) to calculate the degree of approximation of the parameters characterizing the functioning of the object to their critical values ??when the IA changes; 2) to determine effective controls to minimize the risk of losing IA or minimize the time and losses for returning IA; 3) to consider the uncertainty factor associated with the features of the decision-making process. The development of this work is aimed at creating an information system for assessing IA for the integrated management of the level of credit, market, insurance and operational risks in the face of uncertainty and determining effective scenarios for minimizing investment risks. Keywords: mathematical modeling, system analysis, investment risks.

Decision-Making Model of Mechanical Components in a Lean–Green Manufacturing System Based on Carbon Benefit and Its Application

The key to achieving low-carbon manufacturing is to effectively reduce the carbon emissions of production systems and improve carbon benefits. The use of lean and green tools aids in measuring the added value of products, and increases the efficiency and sustainability of production systems. To address this problem and verify that the synergetic relationship between lean and green innovation increases the efficiency and sustainability in production systems, a new low-carbon manufacturing evaluation indicator—carbon benefit—in lean manufacturing systems was discussed. A low-carbon decision-making model of multiple processes aiming at carbon benefit maximization, as well as the dynamic characteristics of carbon benefit and sustainable process improvements in a lean production system, was established. A case study of a certain satellite dish parts manufacturing line was introduced to analyze and verify the feasibility of the proposed model. After improvement, the processing time of unit parts was reduced from 63 s to 54 s. The workstations were optimized again according to the lean–green manufacturing concept, and the number was reduced by 37.5%. The process was recombined and reduced from 8 to 5 to achieve continuous-flow processing. This reduced the distance by 77 m, and at the same time, the number of operating personnel was reduced, and the after-improvement carbon efficiency increased from 12.98 s/kg CO2e to 36.33 s/kg CO2e in comparison with that before the improvement. The carbon benefit after improvement was 193.92% higher than that before the improvement.

Testicular hypoplasia in Nellore Cattle: Genetic analysis and functional analysis of genome-wide association study results.

Characterized by the incomplete development of the germinal epithelium of the seminiferous tubules, Testicular hypoplasia (TH) leads to decreased sperm concentration, increased morphological changes in sperm and azoospermia. Economic losses resulting from the disposal of affected bulls reduce the efficiency of meat production systems. A genome-wide association study and functional analysis were performed to identify genomic windows and the underlying positional candidate genes associated with TH in Nellore cattle. Phenotypic and pedigree data from 207,195 animals and genotypes (461,057 single nucleotide polymorphism, SNP) from 17,326 sires were used in this study. TH was evaluated as a binary trait measured at 18 months of age. A possible correlated response on TH resulting from the selection for scrotal circumference was evaluated by using a two-trait analysis. Thus, estimated breeding values were calculated by fitting a linear-threshold animal model in a Bayesian approach. The SNP effects were estimated using the weighted single-step genomic BLUP method. Twelve non-overlapping windows of 20 adjacent SNP that explained more than 1% of the additive genetic variance were selected for candidate gene annotation. Functional and gene prioritization analysis of the candidate genes identified genes (KHDRBS3, GPX5, STAR, ERLIN2), which might play an important role in the expression of TH due to their known roles in the spermatogenesis process, synthesis of steroids and lipid metabolism.