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Article
Publication date: 6 June 2016

Ellen Goddard, Albert Boaitey, Getu Hailu and Kenneth Poon

The purpose of this paper is to evaluate cow-calf producer incentive to adopt innovations in traits with important environmental and economic implications for the beef…

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Abstract

Purpose

The purpose of this paper is to evaluate cow-calf producer incentive to adopt innovations in traits with important environmental and economic implications for the beef supply chain.

Design/methodology/approach

A whole farm multi-year farm optimization model that tracks changes in discounted net returns and methane emissions from the use of newer DNA-related technologies to breed for feed efficient cattle is developed. The analysis is situated within the context of whole beef cattle supply chain. This allows for the derivation of the entire value and environmental impact of the innovation, and the decomposition of value by different participants. The impact of different policies that can stimulate producer uptake and the diffusion of the innovation is also addressed.

Findings

The results of the study showed that whilst the use of the breeding technology yielded positive economic and environmental benefits to all producers in the supply chain, primary adopters were unlikely to adopt. This paper finds evidence of the misalignment in incentives within the supply chain with a significant proportion of the additional value going to producers who do not incur any additional cost from the adoption of the innovation. The study also highlighted the role of both public and market-based mechanisms in the innovation diffusion process.

Originality/value

This paper is unique as it is the first study that addresses producer incentive to adopt genomic selection for feed efficiency across the entire beef cattle supply chain, and incorporates both economic and environmental outcomes.

Details

British Food Journal, vol. 118 no. 6
Type: Research Article
ISSN: 0007-070X

Keywords

Book part
Publication date: 25 July 2011

Robert W. Herdt and Rebecca Nelson

The products of transgenic technology have captured the attention of enthusiasts and detractors, but transgenics are just one tool of agricultural biotechnology. Other…

Abstract

The products of transgenic technology have captured the attention of enthusiasts and detractors, but transgenics are just one tool of agricultural biotechnology. Other applications enable scientists to understand biodiversity, to track genes through generations in breeding programs, and to move genes among closely related as well as unrelated organisms. These applications all have the potential to lead to substantial productivity gains.

In this chapter we provide an introduction to basic plant genetic concepts, defining molecular markers, transgenic and cisgenic techniques. We briefly summarize the status of commercialized biotechnology applications to agriculture. We consider the likely future commercialization of products like drought tolerant crops, crops designed to improve human nutrition, pharmaceuticals from transgenic plants, biofuels, and crops for environmental remediation. We identify genomic selection as a potentially powerful new technique and conclude with our reflections on the state of agricultural biotechnology.

Research at universities and other public-sector institutions, largely focused on advancing knowledge, has aroused enormous optimism about the promise of these DNA-based technologies. This in turn has led to large private-sector investments on maize, soybean, canola, and cotton, with wide adoption of the research products in about eight countries. Much has been made of the potential of biotechnology to address food needs in the low-income countries, and China, India, and Brazil have large public DNA-based crop variety development efforts. But other lower income developing countries have little capability to use these tools, even the most straightforward marker applications. Ensuring that these and other applications of biotechnology lead to products that are well adapted to local agriculture requires adaptive research capacity that is lacking in the lowest income, most food-insecure nations. We are less optimistic than many others that private research will fund these needs.

Article
Publication date: 7 April 2022

Amr S. Allam, Hesham Bassioni, Mohammed Ayoub and Wael Kamel

This study aims to compare the performance of two nature-inspired metaheuristics inside Grasshopper in optimizing daylighting and energy performance against brute force in…

Abstract

Purpose

This study aims to compare the performance of two nature-inspired metaheuristics inside Grasshopper in optimizing daylighting and energy performance against brute force in terms of the resemblance to ideal solution and calculation time.

Design/methodology/approach

The simulation-based optimization process was controlled using two population-based metaheuristic algorithms, namely, the genetic algorithm (GA) and particle swarm optimization (PSO). The objectives of the optimization routine were optimizing daylighting and energy consumption of a standard reference office while varying the urban context configuration in Alexandria, Egypt.

Findings

The results from the GA and PSO were compared to those from brute force. The GA and PSO demonstrated much faster performance to converge to design solution after conducting only 25 and 43% of the required simulation runs, respectively. Also, the average proportion of the resulted weighted sum optimization (WSO) per case using the GA and PSO to that from brute force algorithm was 85 and 95%, respectively.

Originality/value

The work of this paper goes beyond the current practices for showing that the performance of the optimization algorithm can differ by changing the urban context configuration while solving the same problem under the same design variables and objectives.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Content available
Book part
Publication date: 25 July 2011

Abstract

Details

Genetically Modified Food and Global Welfare
Type: Book
ISBN: 978-0-85724-758-2

Content available
Article
Publication date: 6 June 2016

Rosa Caiazza, Tiziana Volpe and John L Stanton

801

Abstract

Details

British Food Journal, vol. 118 no. 6
Type: Research Article
ISSN: 0007-070X

Article
Publication date: 18 April 2017

Mahmoud Al-Ayyoub, Ahmed Alwajeeh and Ismail Hmeidi

The authorship authentication (AA) problem is concerned with correctly attributing a text document to its corresponding author. Historically, this problem has been the…

Abstract

Purpose

The authorship authentication (AA) problem is concerned with correctly attributing a text document to its corresponding author. Historically, this problem has been the focus of various studies focusing on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Another approach to this problem, known as the bag-of-words (BOW) approach, uses keywords occurrences/frequencies in each document to identify its author. Unlike the first one, this approach is more language-independent. This paper aims to study and compare both approaches focusing on the Arabic language which is still largely understudied despite its importance.

Design/methodology/approach

Being a supervised learning problem, the authors start by collecting a very large data set of Arabic documents to be used for training and testing purposes. For the SF approach, they compute hundreds of SF, whereas, for the BOW approach, the popular term frequency-inverse document frequency technique is used. Both approaches are compared under various settings.

Findings

The results show that the SF approach, which is much cheaper to train, can generate more accurate results under most settings.

Practical implications

Numerous advantages of efficiently solving the AA problem are obtained in different fields of academia as well as the industry including literature, security, forensics, electronic markets and trading, etc. Another practical implication of this work is the public release of its sources. Specifically, some of the SF can be very useful for other problems such as sentiment analysis.

Originality/value

This is the first study of its kind to compare the SF and BOW approaches for authorship analysis of Arabic articles. Moreover, many of the computed SF are novel, while other features are inspired by the literature. As SF are language-dependent and most existing papers focus on English, extra effort must be invested to adapt such features to Arabic text.

Details

International Journal of Web Information Systems, vol. 13 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 25 July 2019

Xia Li, Ruibin Bai, Peer-Olaf Siebers and Christian Wagner

Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of…

Abstract

Purpose

Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions.

Design/methodology/approach

The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case.

Findings

The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper.

Research limitations/implications

The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances.

Practical implications

The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions.

Originality/value

This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

Details

VINE Journal of Information and Knowledge Management Systems, vol. 49 no. 3
Type: Research Article
ISSN: 2059-5891

Keywords

Article
Publication date: 18 April 2022

Lilia Inés Stubrin, Anabel Marin, Lara Yeyati Preiss and Rocío Palacín Roitbarg

The purpose of this paper is to expand the understanding of the type of strategies that can be successful for firms located in the South to get integrated and compete in…

Abstract

Purpose

The purpose of this paper is to expand the understanding of the type of strategies that can be successful for firms located in the South to get integrated and compete in modern export fruit markets.

Design/methodology/approach

To achieve the research purpose of the paper the authors carry out an in-depth case study. They analyze the export strategy of Patagonian Fruits Trade, an Argentinean leading exporter of apple, pear and kiwi.

Findings

Results revealed that Patagonian Fruits Trade developed a strategy focused on supplying decommoditization to compete in modern fruit export markets. A key aspect of the firms' business model relies on its capability to meet the demand of high-income markets by providing conventional, organic and biodynamic club varieties. However, the sustainability of the strategy heavily relies on the firm's capability to fund club varieties' licenses and on the firm's ability to negotiate with clients and suppliers.

Research limitations/implications

Adopting a case study method limits the generalization of results. However, it provides new insights into the type of export strategies that can be successful in modern fruit markets as well as its main limitations.

Originality/value

Results of the study, based on original empirical evidence, shed light on key factors for the integration of Southern fruit producers into modern fruit markets.

Details

Journal of Agribusiness in Developing and Emerging Economies, vol. 12 no. 4
Type: Research Article
ISSN: 2044-0839

Keywords

Article
Publication date: 28 June 2021

Meseret Getnet Meharie, Wubshet Jekale Mengesha, Zachary Abiero Gariy and Raphael N.N. Mutuku

The purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.

Abstract

Purpose

The purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.

Design/methodology/approach

The proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.

Findings

The findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.

Research limitations/implications

The study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.

Originality/value

The study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

Details

Engineering, Construction and Architectural Management, vol. 29 no. 7
Type: Research Article
ISSN: 0969-9988

Keywords

Book part
Publication date: 16 July 2015

Martine Lappé and Hannah Landecker

This study analyzes the rise of genome instability in the life sciences and traces the problematic of instability as it relates to the sociology of health. Genome…

Abstract

Purpose

This study analyzes the rise of genome instability in the life sciences and traces the problematic of instability as it relates to the sociology of health. Genome instability is the study of how genomes change and become variable between generations and within organisms over the life span. Genome instability reflects a significant departure from the Platonic genome imagined during the Human Genome Project. The aim of this chapter is to explain and analyze research on copy number variation and somatic mosaicism to consider the implications of these sciences for sociologists interested in genomics.

Methodology/approach

This chapter draws on two multi-sited ethnographies of contemporary biomedical science and literature in the sociology of health, science, and biomedicine to document a shift in thinking about the genome from fixed and universal to highly variable and influenced by time and context.

Findings

Genomic instability has become a framework for addressing how genomes change and become variable between generations and within organisms over the life span. Instability is a useful framework for analyzing changes in the life sciences in the post-genomic era.

Research implications

Genome instability requires life scientists to address how differences both within and between individuals articulate with shifting disease categories and classifications. For sociologists, these findings have implications for studies of identity, sociality, and clinical experience.

Originality/value

This is the first sociological analysis of genomic instability. It identifies practical and conceptual implications of genomic instability for life scientists and helps sociologists delineate new approaches to the study of genomics in the post-genomic era.

Details

Genetics, Health and Society
Type: Book
ISBN: 978-1-78350-581-4

Keywords

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