A. Cannas

3.2k total citations
72 papers, 2.1k citations indexed

About

A. Cannas is a scholar working on Agronomy and Crop Science, Genetics and Animal Science and Zoology. According to data from OpenAlex, A. Cannas has authored 72 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Agronomy and Crop Science, 30 papers in Genetics and 29 papers in Animal Science and Zoology. Recurrent topics in A. Cannas's work include Ruminant Nutrition and Digestive Physiology (44 papers), Genetic and phenotypic traits in livestock (30 papers) and Effects of Environmental Stressors on Livestock (23 papers). A. Cannas is often cited by papers focused on Ruminant Nutrition and Digestive Physiology (44 papers), Genetic and phenotypic traits in livestock (30 papers) and Effects of Environmental Stressors on Livestock (23 papers). A. Cannas collaborates with scholars based in Italy, United States and Brazil. A. Cannas's co-authors include Anna Nudda, Luís O Tedeschi, Giuseppe Pulina, G. Battacone, D. G. Fox, A.S. Atzori, Alice N. Pell, P.J. Van Soest, Massimiliano Lanza and Alessandro Priolo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Dairy Science and Journal of Theoretical Biology.

In The Last Decade

A. Cannas

67 papers receiving 2.0k citations

Peers

Countries citing papers authored by A. Cannas

Since Specialization

Citations

This map shows the geographic impact of A. Cannas'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 A. Cannas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Cannas more than expected).

Fields of papers citing papers by A. Cannas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Cannas. 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 A. Cannas. The network helps show where A. Cannas may publish in the future.

Co-authorship network of co-authors of A. Cannas

This figure shows the co-authorship network connecting the top 25 collaborators of A. Cannas. A scholar is included among the top collaborators of A. Cannas 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 A. Cannas. A. Cannas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Sustainability of Animal Production Chains: Alternative Protein Sources as an Ecological Driver in Animal Feeding: A Review 2025 ·Animals ·Massimiliano Lanza, (unknown), Luigi Gallo, Francesca Soglia, Fulvia Bovera, Francesco Giunta, Riccardo Primi, L. Biondi, Diana Giannuzzi, Marco Zampiga, Nicola Francesco Addeo, A. Cannas, Pier Paolo Danieli, Bruno Ronchi, G. M. Crovetto	0
2	Fecal NIRS to estimate intake and diet composition in Sarda dairy ewes fed mixed diets or part-time grazing forage crops 2023 ·Small Ruminant Research ·G. Molle, M. Decandia, (unknown), (unknown), Savina Contini, (unknown), Marco Acciaro, A. Cabiddu, (unknown), Mondina Francesca Lunesu, A.S. Atzori, A. Cannas, S. Landau	0
3	A system dynamics approach to model heat stress accumulation in dairy cows during a heatwave event 2023 ·animal ·(unknown), (unknown), Luís O Tedeschi, A. Cannas, A.S. Atzori	2
4	Effect of glucose infusion on glucose and insulin metabolism in early- and mid-lactation ewes and goats fed diets differing in starch and highly digestible fiber concentration 2023 ·Journal of Dairy Science ·Mondina Francesca Lunesu, A.S. Atzori, (unknown), G Bomboi, M. Decandia, (unknown), A. Cannas	1
5	An observational study to verify the influence of different nutritional corn silage-based strategies on efficient use of dietary nutrients, faecal fermentation profile, and profitability in a cohort of intensive dairy farms 2022 ·Italian Journal of Animal Science ·Antonio Gallo, Carla Valsecchi, (unknown), A. Cannas, (unknown), F. Masoero, A.S. Atzori	8
6	Assessment of feed and economic efficiency of dairy farms based on multivariate aggregation of partial indicators measured on field 2021 ·Journal of Dairy Science ·A.S. Atzori, Carla Valsecchi, (unknown), F. Masoero, A. Cannas, Antonio Gallo	10
7	Dietary Starch Concentration Affects Dairy Sheep and Goat Performances Differently during Mid-Lactation 2021 ·Animals ·Mondina Francesca Lunesu, M. Decandia, G. Molle, A.S. Atzori, G Bomboi, A. Cannas	11
8	Metabolic and hormonal control of energy utilization and partitioning from early to mid lactation in Sarda ewes and Saanen goats 2021 ·Journal of Dairy Science ·Mondina Francesca Lunesu, G Bomboi, A. Marzano, A. Comin, Alberto Prandi, (unknown), Paola Nicolussi, M. Decandia, (unknown), A.S. Atzori, G. Molle, A. Cannas	7
9	Review: Managing sheep and goats for sustainable high yield production 2021 ·animal ·João Simões, J.A. Abecia, A. Cannas, (unknown), Delia Lacasta, K. Voigt, Philippe Chemineau	61
10	Prenatal exposure to different diets influences programming of glucose and insulin metabolism in dairy ewes 2020 ·Journal of Dairy Science ·Mondina Francesca Lunesu, (unknown), Fabio Correddu, Francesco Fancello, A. Marzano, F. Mossa, Anna Nudda, A. Cannas, A.S. Atzori	5
11	Sheep and Goats Respond Differently to Feeding Strategies Directed to Improve the Fatty Acid Profile of Milk Fat 2020 ·Animals ·Anna Nudda, A. Cannas, Fabio Correddu, A.S. Atzori, Mondina Francesca Lunesu, G. Battacone, Giuseppe Pulina	55
12	Proceedings of the 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) 2019 ·Advances in Animal Biosciences ·Rafael Muñoz‐Tamayo, A.S. Atzori, A. Cannas, F. Masoero, Sylvie Giger‐Reverdin, Daniel Sauvant, Antonio Gallo	3
13	Milk Urea Concentration in Dairy Sheep: Accounting for Dietary Energy Concentration 2019 ·Animals ·(unknown), (unknown), M. Decandia, G Bomboi, A.S. Atzori, A. Cannas, G. Molle	10
14	Effects of nutrition on main components of sheep milk 2019 ·Small Ruminant Research ·Anna Nudda, A.S. Atzori, Fabio Correddu, G. Battacone, Mondina Francesca Lunesu, A. Cannas, Giuseppe Pulina	29
15	Prediction of voluntary dry matter intake in stall fed growing goats 2018 ·Livestock Science ·A. K. Almeida, Luís O Tedeschi, Kléber Tomás de Resende, (unknown), A. Cannas, Izabelle Auxiliadora Molina de Almeida Teixeira	12
16	Morphology, chemical composition, mechanical properties and structure in antler of Sardinian Red Deer (Cervus elaphus corsicanus). 2017 ·Hystrix ·(unknown), A.S. Atzori, Francisco Ceacero, Tomás Landete‐Castillejos, A. Cannas, Laureano Gallego, (unknown)	5
17	Dairy goat kids fed liquid diets in substitution of goat milk and slaughtered at different ages: an economic viability analysis using Monte Carlo techniques 2015 ·animal ·(unknown), Cristina Mattos Veloso, Marcos Inácio Marcondes, (unknown), Alex Lopes da Silva, (unknown), (unknown), A. Cannas	1
18	The International Sheep and Wool Handbook 2011 ·SHILAP Revista de lepidopterología ·A. Cannas	47
19	Prediction of the relationship between body weight and body condition score in sheep 2011 ·SHILAP Revista de lepidopterología ·A. Cannas, (unknown)	6
20	The energetic cost of maintenance in ruminants: from classical to new concepts and prediction systems 2010 ·Acervo Digital da Universidade Estadual Paulista (Universidade Estadual Paulista) ·A. Cannas, A.S. Atzori, Izabelle Auxiliadora Molina de Almeida Teixeira, R. D. Sainz, J. W. Oltjen	6

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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