354 Awardee Talk: Midwest Cattle Feeding Over the Past 40 Years

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 197-198
Author(s):  
Daniel D Loy
Keyword(s):  
Health Management ◽  
Growth Models ◽  
Energy System ◽  
Disruptive Technology ◽  
Net Energy ◽  
Feed Conversion ◽  
Disruptive Technologies ◽  
Cattle Feeding ◽  
The Future ◽  
Corn Coproducts

Abstract The success of a cattle feeding region is dependent on many factors including resources and technology. Forty years ago, the once-dominant Midwest region had lost its competitive advantage in both areas. Today this industry has reinvented itself. This presentation reviews this journey and looks forward to new opportunities. There have been at least two disruptive technologies that affected cattle feeding, especially in this region over this time. The first is the convergence of microcomputer technology with the development of growth models based on the California Net Energy System. Decisions based on this new knowledge had a profound impact on feed conversion, growth and management. The second disruptive technology was the development of the ethanol industry and widespread availability of corn coproducts, especially high moisture corn coproducts. This development brought large quantities of a very high-quality feedstuff and inclusions in beef finishing diets became standard. Considerable research and education were needed to develop cost effective feeding programs with this new opportunity. Other changes have occurred across the cattle feeding industry over this time. Cattle size and carcass weights have continued a linear increase. Carcass quality and marbling has increased due to genetic selection, feeding endpoints and other factors. Improved growth technologies have continued to be developed and evaluated. Nutritional requirements of cattle for some nutrients have been fine-tuned but more work is needed. Disruptive technologies in the future may involve the convergence of precision livestock technology, smart sensors, artificial intelligence. Engineering developments and energy costs will affect the future of feed processing and delivery. Regardless of future technologies successful cattle feeding in the future will require access to feed resources, excellent nutrition and health management and strategies to manage risk.

scholarly journals Optimal Planning and Operation of a Residential Energy Community under Shared Electricity Incentives

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2045
Author(s):  
Pierpaolo Garavaso ◽  
Fabio Bignucolo ◽  
Jacopo Vivian ◽  
Giulia Alessio ◽  
Michele De Carli
Keyword(s):  
Renewable Energy ◽  
Power Distribution ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Distribution Networks ◽  
Building Envelope ◽  
Net Energy ◽  
Technical Equipment ◽  
Optimal Planning

Energy communities (ECs) are becoming increasingly common entities in power distribution networks. To promote local consumption of renewable energy sources, governments are supporting members of ECs with strong incentives on shared electricity. This policy encourages investments in the residential sector for building retrofit interventions and technical equipment renovations. In this paper, a general EC is modeled as an energy hub, which is deemed as a multi-energy system where different energy carriers are converted or stored to meet the building energy needs. Following the standardized matrix modeling approach, this paper introduces a novel methodology that aims at jointly identifying both optimal investments (planning) and optimal management strategies (operation) to supply the EC’s energy demand in the most convenient way under the current economic framework and policies. Optimal planning and operating results of five refurbishment cases for a real multi-family building are found and discussed, both in terms of overall cost and environmental impact. Simulation results verify that investing in building thermal efficiency leads to progressive electrification of end uses. It is demonstrated that the combination of improvements on building envelope thermal performances, photovoltaic (PV) generation, and heat pump results to be the most convenient refurbishment investment, allowing a 28% overall cost reduction compared to the benchmark scenario. Furthermore, incentives on shared electricity prove to stimulate higher renewable energy source (RES) penetration, reaching a significant reduction of emissions due to decreased net energy import.

scholarly journals Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Dhruvil Shah ◽  
Devarsh Patel ◽  
Jainish Adesara ◽  
Pruthvi Hingu ◽  
Manan Shah
Keyword(s):  
Machine Learning ◽  
Education Sector ◽  
Student Records ◽  
Job Roles ◽  
Student Data ◽  
Disruptive Technologies ◽  
Valid Data ◽  
The Future ◽  
Student Achievements ◽  
The University

AbstractAlthough the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

scholarly journals The Role of Demand Response in the Future Renewable Northern European Energy System

Energy ◽  
2021 ◽  
pp. 121336
Author(s):  
J.G. Kirkerud ◽  
N.O. Nagel ◽  
T.F. Bolkesjø
Keyword(s):  
Demand Response ◽  
Energy System ◽  
Northern European ◽  
The Future

scholarly journals Relationships of retained energy and retained protein that influence the determination of cattle requirements of energy and protein using the California Net Energy System

2018 ◽  
Vol 3 (3) ◽  
pp. 1029-1039 ◽  
Author(s):  
Luis O Tedeschi
Keyword(s):  
Body Fat ◽  
Energy System ◽  
The Body ◽  
Metabolizable Energy ◽  
Net Energy ◽  
Body Protein ◽  
Protein Requirements ◽  
Growing Cattle ◽  
Lower Correlation

Abstract Interrelationships between retained energy (RE) and retained protein (RP) that are essential in determining the efficiency of use of feeds and the assessment of energy and protein requirements of growing cattle were analyzed. Two concerns were identified. The first concern was the conundrum of a satisfactory correlation between observed and predicted RE (r = 0.93) or between observed and predicted RP when using predicted RE to estimate RP (r = 0.939), but a much lower correlation between observed and predicted RP when using observed RE to estimate RP (r = 0.679). The higher correlation when using predicted vs. observed RE is a concern because it indicates an interdependency between predicted RP and predicted RE that is needed to predict RP with a higher precision. These internal offsetting errors create an apparent overall adequacy of nutrition modeling that is elusive, thus potentially destabilizing the predictability of nutrition models when submodels are changed independently. In part, the unsatisfactory prediction of RP from observed RE might be related to the fact that body fat has a caloric value that is 1.65 times greater than body protein and the body deposition of fat increases exponentially as an animal matures, whereas body deposition of protein tends to plateau. Thus, body fat is more influential than body protein in determining RE, and inaccuracies in measuring body protein will be reflected in the RP comparison but suppressed in the RE calculation. The second concern is related to the disconnection when predicting partial efficiency of use of metabolizable energy for growth (kG) using the proportion of RE deposited as protein—carcass approach—vs. using the concentration of metabolizable energy of the diet—diet approach. The culprit of this disconnection might be related to how energy losses that are associated with supporting energy-expending processes (HiEv) are allocated between these approaches. When computing kG, the diet approach likely assigns the HiEv to the RE pool, whereas the carcass approach ignores the HiEV, assigning it to the overall heat production that is used to support the tissue metabolism. Opportunities exist for improving the California Net Energy System regarding the relationships of RE and RP in computing the requirements for energy and protein by growing cattle, but procedural changes might be needed such as increased accuracy in the determination of body composition and better partitioning of energy.

The use of temporal factors for improved CO2 emissions accounting in buildings

2018 ◽  
Vol 39 (2) ◽  
pp. 196-210 ◽  
Author(s):  
Barny Evans ◽  
Sabbir Sidat
Keyword(s):  
Air Quality ◽  
Energy Demand ◽  
Energy System ◽  
Net Energy ◽  
Electricity Grid ◽  
Electrical Grid ◽  
Significant Difference ◽  
Emissions Factor ◽  
Single Number ◽  
The Impact

This paper is an investigation into the issues around how we calculate CO2 emissions in the built environment. At present, in Building Regulations and GHG Protocol calculations used for buildings and corporate CO2 emissions calculations, it is standard to use a single number for the CO2 emission factor of each source. This paper considers how energy demand, particularly electricity at different times of the day, season and even year can differ in terms of its CO2 emissions. This paper models three different building types (retail, office and home) using standard software to estimate a profile of energy demand. It then considers how CO2 emissions calculations differ between using the single standard emissions factor and using an hourly emissions factor based on real electrical grid generation over a year. The paper also examines the impact of considering lifetime emissions factors rather than one-year factors using UK government projections. The results show that there is a significant difference to the analysis of benefit in terms of CO2 emissions from different measures – both intra- and inter-year – due to the varying CO2 emissions intensity, even when they deliver the same amount of net energy saving. Other factors not considered in this paper, such as impact on peak generation and air quality, are likely to be important when considering whole-system impacts. In line with this, it is recommended that moves are made to incorporate intra- and inter-year emissions factor changes in methodologies for calculating CO2 emissions. (This is particularly important as demand side response and energy storage, although generally accepted as important in the decarbonisation of the energy system at present will show as an increase in CO2 emissions when using a single number.) Further work quantifying the impact on air quality and peak generation capacity should also be considered. Practical application: This paper aims to help practitioners to understand the performance gap between how systems need to be designed in order to meet regulations compared to how buildings perform in reality – both today and in the future. In particular, it considers the use of ‘real-time’ carbon factors in order to attain long-term CO2 reductions. This methodology enables decision makers to understand the impacts of different energy reduction technologies, considering each of their unique characteristics and usage profiles. If implemented, the result is a simple-to-use dataset which can be embedded into the software packages already available onto the market which mirrors the complexity of the electricity grid that is under-represented through the use of a static carbon figure.

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