scholarly journals 240 Enhancing beef production and quality using big data analytics and computer vision techniques

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 124-125
Author(s):  
Guilherme J Rosa ◽  
Vera C Aiken ◽  
Arthur Fernandes ◽  
Joao R Dorea
Keyword(s):  
Computer Vision ◽  
Beef Cattle ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Live Weight ◽  
Multiple Sources ◽  
Optimal Harvest ◽  
Beef Cattle Production ◽  
Data Driven Decisions

Abstract In this presentation we will discuss our current research on computer vision techniques for optimized management of feed bunks and prediction of live weight in beef cattle. The combination of these two techniques allows not only an enhanced nutritional management in feedlots, but also the determination of economically optimal harvest time for maximized returns. In addition, we will discuss computational and data analytics strategies for integration and analysis of large datasets from multiple sources, including operational farm data, weather and economics, for aiding data-driven decisions to improve beef cattle production.

scholarly journals 31 Determination of Optimal Harvest Weight for Mangalica Pigs Using a Serial Harvest Approach to Measure Growth Performance and Carcass Characteristics

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 34-34
Author(s):  
Maegan A Reeves ◽  
Courtney E Charlton ◽  
Shannon R Wilkerson ◽  
John G Rehm ◽  
Terry D Brandebourg
Keyword(s):  
Live Weight ◽  
Weight Class ◽  
Marbling Score ◽  
Randomized Design ◽  
Carcass Parameters ◽  
Body Weights ◽  
Optimal Harvest ◽  
Completely Randomized Design ◽  
Growth Trial

Abstract Mangalica pigs are a popular niche breed given their reputation for superior quality pork. However, growth and carcass parameters for this breed are poorly documented. Our objective was to better characterize optimal harvest weights for the Mangalica breed. To accomplish this, a growth trial was conducted whereby pigs (n=56) were randomly distributed across stratified harvest weights (50, 57, 68, 82, 93, 102, 127 kg) in a completely randomized design. Pigs were fed standard finisher rations with individual daily feed intakes and weekly body weights recorded for all animals. At 24h postmortem, carcasses were split and ribbed with marbling and loin eye area (LEA) measured at the 10th rib. Primal cuts were fabricated and individually weighed. Fat back was separated from the loin and weighed. As expected, live weight significantly increased across weight class (P < 0.0001). ADG was similar across classes up to 82 kg live weight before steadily declining with increasing weight class (P < 0.0025). Likewise, feed efficiency did not differ between classes until weights heavier than 82 kg (P < 0.03). LEA significantly increased by class up to 82 kg and then plateaued as harvest weight increased further (P < 0.003). Marbling score significantly increased with increasing weight class up to 102 kg where they then plateaued (p < 0.04). Fat back dramatically increased across all weight classes (p < 0.0001) despite negligible increases in LEA or marbling after 102 kg. Primal cut weights for the ham (P < 0.0001), loin (P < 0.0001), Boston butt (P < 0.0001), shoulder (P < 0.0001), and belly (P < 0.0001) all significantly increased with increasing live weight. These data suggest an optimal harvest weight occurs between 82 to 102 kg while offering little objective justification for the current practice of harvesting Mangalica pigs at much heavier live weights.

scholarly journals A Preliminary Systematic Performanceon Critical Success Factors Categories for Big Data Analytics

2019 ◽  
Vol 9 (1) ◽  
pp. 2320-2324 ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Critical Success Factors ◽  
Success Factors ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Successful Implementation ◽  
Critical Success ◽  
Data Driven Decisions

Big Data could be used in any industry to make effective data-driven decisions. The successful implementation of Big Data projects requires a combination of innovative technological, organizational, and processing approaches. Over the last decade, the research on Critical Success Factors (CSFs) within Big Data has developed rapidly but the number of available publications is still at a low level. Developing an understandingof the Critical Success Factors (CSFs) and their categoriesare essential to support management in making effective data-driven decisions which could increase their returns on investments.There islimited research conducted on the Critical Success Factors (CSFs) of Big DataAnalytics (BDA) development and implementation.This paper aims to provide more understanding about the availableCritical Success Factors (CSFs) categoriesfor Big Data Analytics implementation and answer the research question (RQ) “What are the existing categories of Critical Success Factors for Big Data Analytics”.Based on a preliminary Systematic Literature Review (SLR) for the available publications related to Big Data CSFs and their categories in the last twelve years (2007-2019),this paper identifiesfive categoriesfor Big Data AnalyticsCritical Success Factors(CSFs), namelyOrganization, People, Technology, Data Management, and Governance categories.

scholarly journals Analyzation and Comparison of Cloud Computing and Data Mining Techniques: Big Data and Impact of Blockchain

2021 ◽  
Vol 9 (11) ◽  
pp. 712-721
Author(s):  
Adarsh Bhandari
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Scale Up ◽  
Big Data Analytics ◽  
Multiple Sources ◽  
Huge Data ◽  
Amazon Web Services ◽  
Tools And Techniques ◽  
The Impact

Abstract: With the rapid escalation of data driven solutions, companies are integrating huge data from multiple sources in order to gain fruitful results. To handle this tremendous volume of data we need cloud based architecture to store and manage this data. Cloud computing has emerged as a significant infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and scale up the products. Even today heavy applications are deployed on cloud and managed specially at AWS eliminating the need for error prone manual operations. This paper demonstrates about certain cloud computing tools and techniques present to handle big data and processes involved while extracting this data till model deployment and also distinction among their usage. It will also demonstrate, how big data analytics and cloud computing will change methods that will later drive the industry. Additionally, a study is presented later in the paper about management of blockchain generated big data on cloud and making analytical decision. Furthermore, the impact of blockchain in cloud computing and big data analytics has been employed in this paper. Keywords: Cloud Computing, Big Data, Amazon Web Services (AWS), Google Cloud Platform (GCP), SaaS, PaaS, IaaS.

scholarly journals Optimization of Profit for Pasture-Based Beef Cattle and Sheep Farming Using Linear Programming: Young Beef Cattle Production in New Zealand

Agriculture ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 849
Author(s):  
Addisu H. Addis ◽  
Hugh T. Blair ◽  
Paul R. Kenyon ◽  
Stephen T. Morris ◽  
Nicola M. Schreurs
Keyword(s):  
Linear Programming ◽  
New Zealand ◽  
Beef Cattle ◽  
Energy Demand ◽  
Ethical Issues ◽  
Live Weight ◽  
Cattle Production ◽  
Farm Profitability ◽  
Beef Cattle Production ◽  
Cattle Farms

In New Zealand, surplus dairy-origin calves not needed as replacement or for beef cattle farms requirements for finishing are commercially slaughtered within two weeks of age. This system has perceived ethical issues which can potentially negatively affect the dairy industry. Therefore, a young beef cattle production system to maximize the use of excess calves within the land size constraint is considered as an alternative to a traditional 18 to 33-months slaughtering system. The current study examined the effects of young beef cattle production with slaughter ages at 8 to 14 months on pasture utilization, farm profitability and selling policy on class 5, intensive finishing sheep and beef cattle farms in New Zealand. A linear programming model that had previously been developed for this farm class (optimized traditional beef cattle system) was modified to include a young beef cattle slaughter system and identified the carrying capacity for young and traditional beef cattle and the selling policy required to optimize pasture utilization and farm profitability. Systems with young beef cattle slaughtered at 8, 10, 12 or 14-months of age were simulated without (Scenario I) or with (Scenario II) decreasing the number of traditional beef cattle. Daily per head energy demand for maintenance and live weight change was estimated and converted to kg DM/head on a bimonthly basis. Carcasses from young beef cattle were processed as one class under manufacturing beef price (NZ$4.50). The modified young and traditional beef cattle slaughtering system maintained an extra 6% and 35% beef cattle in Scenario I and Scenario II respectively, and finished 90% and 84% of traditional beef cattle before the second winter. Pasture supplied 98% of the feed demand for the beef cattle activities and 79–83% of that was consumed. Mixed young and traditional beef cattle finishing scenarios returned 2% less gross farm revenue per hectare (GFR/ha). However, earnings before tax per hectare (ETB/ha) in Scenario I and Scenario II were 15–25% greater than that of the optimized traditional beef cattle system, respectively. Young beef cattle production increased pasture utilization and farm profitability and increased selling options for finished beef cattle. Therefore, the young beef cattle system is a viable option for farmers and will help to reduce the need to slaughter calves within two weeks of age.

Survey of the Different Type of Data Analytics Algorithms

2017 ◽  
Vol 8 (1) ◽  
pp. 50-64 ◽  
Author(s):  
Michael Kevin Hernandez
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
High Volume ◽  
Data Driven ◽  
Advantages And Disadvantages ◽  
Definition Of ◽  
Data Driven Decisions ◽  
Quantitative Definition

From as early as 1854 to today, society has been gathering, processing, transforming, modeling and visualizing data to help drive data-driven decisions. The qualitative definition of big data can be defined more conclusively as data that has high volume, velocity, and variety. Whereas, the quantitative definition of big data does vary with respect to time due to the dependence of the time's technology and processing capabilities. However, making use of that big data to facilitate data-driven decisions, one should employ either descriptive, predictive, or prescriptive analytics. This article has discussed and summarized the advantages and disadvantages of the algorithms that fell under descriptive and predictive analytics. Given the sheer number of the different types of algorithms and the amount of versatile data mining software available sometimes, the best big data analytics results can come from mixing two to three of the mentioned algorithms.

scholarly journals Editorial "Computer Vision and Big Data Analytics for Remote Sensing"

2018 ◽  
Vol 84 (7) ◽  
pp. 423-423
Author(s):  
Vasit Sagan ◽  
Sidike Paheding ◽  
Dongodng Wang
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Genetic and environmental influences on beef cattle production in Zambia 2. Live weights for age of purebred and reciprocally crossbred progeny

1980 ◽  
Vol 30 (2) ◽  
pp. 235-243 ◽  
Author(s):  
W. Thorpe ◽  
D. K. R. Cruickshank ◽  
R. Thompson
Keyword(s):  
Beef Cattle ◽  
Maternal Effects ◽  
Environmental Influences ◽  
Live Weight ◽  
Dry Season ◽  
The Other ◽  
Cattle Production ◽  
Beef Cattle Production ◽  
Supplementary Feed ◽  
Natural Grassland

ABSTRACTLive weights from birth to 3·5 years are reported for beef cattle reared under ranching conditions in Zambia. The 809 cattle were purebred Africanders, Angonis, Barotses and Borans and the reciprocal crossbreds of the latter three breeds born in 2 years. All animals born in the 1st year and half the males born in the 2nd year grazed natural grassland. The remaining males and all females born in the 2nd year received, in addition, dry season supplementary feed from 1·5 years of age.The interaction of genotype with year-of-birth was important but not the interactions of genotype with management or sex. Purebred progeny of the introduced Africander breed were heavier than the progeny of the indigenous Angoni and Barotse breeds in both year-of-birth groups, but only heavier than progeny of the introduced Boran breed in the first group. On average, the Africander progeny had live-weight advantages of about 16% and 10%, and the Boran progeny advantages of about 12·5% and 5·5% over the purebred Angoni and Barotse progeny respectively. Heterosis estimates tended to increase with age, reaching levels of about 5 to 6% in the Barotse/Boran crosses at and after 1·5 years. Heterosis was not shown by the other crosses. The Barotse and Boran breeds had similar maternal effects which were superior to those of the Angoni breed.

scholarly journals OPTIMIZATION OF THE ACEH BEEF CATTLE PRODUCTION AND PROCESS USING SWOT ANALYSIS AND INDUSTRIAL SUPPLY CHAIN APPROACHES

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Khairol Mizan Us ◽  
M. Aman Yaman ◽  
Edy Fradinata
Keyword(s):  
Supply Chain ◽  
Beef Cattle ◽  
Swot Analysis ◽  
Live Weight ◽  
Cattle Production ◽  
Supply Chain System ◽  
Cattle Industry ◽  
Marketing Process ◽  
Business Efficiency ◽  
Beef Cattle Production

The problem in management process and production of Aceh beef cattle farms in Aceh Besar  has not been explored. This study aimed to determine the basic system of supply chain for the Aceh beef cattle production in Central Aceh Besar developed a model for optimizing the supply chain management and sustainability to increase productivity and business efficiency. This research used SWOT analysis and industrial supply chain approaches. The results showed that the current supply chain system of the Aceh beef cattle industry in Aceh Besar which has been running so far, needs to be strengthened to increase production and population of Aceh beef cattle in the future.  There were 4 issues were identified: time, 29.6% faster than the current supply chain supply time;  method, 60% no longer needed a business intermediary; cost, 21.4% of the live weight price of cattle was cheaper than the live weight price of current supply chain cattle; and stages, 30.8% shorter than the ongoing supply chain stages. The result of the SWOT analysis matrix showed that the SO (strength-opportunities) strategy was the main strategy for business developing of Aceh beef cattle in Central Aceh. In conclusion, it is necessary to optimize the implementation of the supply chain of Aceh Cattle Industry at Central Aceh by utilizing its strengths and suppressing the existing weaknesses from the breeding production to marketing process.

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