Philip Shine

559 total citations
18 papers, 385 citations indexed

About

Philip Shine is a scholar working on Ecology, Animal Science and Zoology and Genetics. According to data from OpenAlex, Philip Shine has authored 18 papers receiving a total of 385 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Ecology, 8 papers in Animal Science and Zoology and 3 papers in Genetics. Recurrent topics in Philip Shine's work include Agriculture Sustainability and Environmental Impact (9 papers), Effects of Environmental Stressors on Livestock (8 papers) and Genetic and phenotypic traits in livestock (3 papers). Philip Shine is often cited by papers focused on Agriculture Sustainability and Environmental Impact (9 papers), Effects of Environmental Stressors on Livestock (8 papers) and Genetic and phenotypic traits in livestock (3 papers). Philip Shine collaborates with scholars based in Ireland, Czechia and Poland. Philip Shine's co-authors include Michael D. Murphy, John Upton, Ted Scully, Paria Sefeedpari, Seyyed Hassan Pishgar-Komleh, L. Shalloo, Shahin Rafiee, Mohammad Sharifi, Th.V. Vellinga and Ferenc Istvan Bánkuti and has published in prestigious journals such as Journal of Cleaner Production, Applied Energy and Sensors.

In The Last Decade

Philip Shine

17 papers receiving 377 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Philip Shine	100	80	71	69	57	18	385
Giuseppe Todde	90 0.9×	50 0.6×	75 1.1×	26 0.4×	70 1.2×	29	482
Leonardo Conti	121 1.2×	93 1.2×	101 1.4×	22 0.3×	46 0.8×	55	554
P. Panagakis	48 0.5×	145 1.8×	57 0.8×	20 0.3×	68 1.2×	35	467
Lelia Murgia	87 0.9×	98 1.2×	218 3.1×	95 1.4×	69 1.2×	38	725
S. Failla	55 0.6×	52 0.7×	53 0.7×	30 0.4×	31 0.5×	63	495
Luís P. Andrade	102 1.0×	42 0.5×	76 1.1×	21 0.3×	112 2.0×	40	383
Luís Roberto Almeida Gabriel Filho	46 0.5×	142 1.8×	40 0.6×	72 1.0×	22 0.4×	140	944
Liv Lundberg	36 0.4×	89 1.1×	20 0.3×	44 0.6×	47 0.8×	20	488
Othman Alqaisi	100 1.0×	117 1.5×	42 0.6×	7 0.1×	112 2.0×	27	530
Qiuju Xie	44 0.4×	105 1.3×	52 0.7×	12 0.2×	73 1.3×	30	464

Countries citing papers authored by Philip Shine

Since Specialization

Citations

This map shows the geographic impact of Philip Shine's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Philip Shine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Shine more than expected).

Fields of papers citing papers by Philip Shine

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip Shine. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Philip Shine. The network helps show where Philip Shine may publish in the future.

Co-authorship network of co-authors of Philip Shine

This figure shows the co-authorship network connecting the top 25 collaborators of Philip Shine. A scholar is included among the top collaborators of Philip Shine based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Philip Shine. Philip Shine is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Shine, Philip, et al.. (2024). The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid. Sustainability. 16(18). 8120–8120. 1 indexed citations

Shine, Philip, et al.. (2022). The development of a national-level energy assessment tool for the dairy industry. 2 indexed citations

Bhat, Rajeev, et al.. (2022). Global Dairy Sector: Trends, Prospects, and Challenges. Sustainability. 14(7). 4193–4193. 21 indexed citations

Shine, Philip & Michael D. Murphy. (2021). Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study. Sensors. 22(1). 52–52. 31 indexed citations

Shine, Philip, et al.. (2021). Utilising grassland management and climate data for more accurate prediction of herbage mass using the rising plate meter. Precision Agriculture. 22(4). 1189–1216. 11 indexed citations

Murphy, Michael D., et al.. (2021). DSSED: Decision Support System for Energy use in Dairy Production. Arrow@dit (Dublin Institute of Technology).

Shine, Philip, John Upton, Paria Sefeedpari, & Michael D. Murphy. (2020). Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies. 13(5). 1288–1288. 58 indexed citations

Shine, Philip, Michael D. Murphy, & John Upton. (2020). A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming. Sustainability. 12(17). 7201–7201. 10 indexed citations

Sefeedpari, Paria, Th.V. Vellinga, Shahin Rafiee, et al.. (2019). Technical, environmental and cost-benefit assessment of manure management chain: A case study of large scale dairy farming. Journal of Cleaner Production. 233. 857–868. 39 indexed citations

10.

Pishgar-Komleh, Seyyed Hassan, Asadollah Akram, Alireza Keyhani, et al.. (2019). Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran. The International Journal of Life Cycle Assessment. 25(3). 620–632. 24 indexed citations

11.

Shine, Philip, et al.. (2019). <i>A decision support and optimization platform for energy technology investments on dairy farms</i>. 2019 Boston, Massachusetts July 7- July 10, 2019. 1 indexed citations

12.

Shine, Philip, Ted Scully, John Upton, & Michael D. Murphy. (2019). Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Applied Energy. 250. 1110–1119. 42 indexed citations

13.

Upton, John, et al.. (2019). Effect of introducing weather parameters on the accuracy of milk production forecast models. Information Processing in Agriculture. 7(1). 120–138. 13 indexed citations

14.

Shine, Philip, Michael D. Murphy, John Upton, & Ted Scully. (2018). Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture. 150. 74–87. 61 indexed citations

15.

Shine, Philip, Ted Scully, John Upton, & Michael D. Murphy. (2018). Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture. 148. 337–346. 42 indexed citations

16.

Shine, Philip, John Upton, Ted Scully, L. Shalloo, & Michael D. Murphy. (2018). Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms. 2018 Detroit, Michigan July 29 - August 1, 2018. 1 indexed citations

17.

Shine, Philip, Ted Scully, John Upton, L. Shalloo, & Michael D. Murphy. (2017). Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis. Applied Energy. 210. 529–537. 27 indexed citations

18.

Shine, Philip, et al.. (1993). RADIATIVE CHARACTERISTICS OF AEROSOLS AT GOLMUD ON THE TIBETAN PLATEAU. 气象学报：英文版. 199–211. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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