N. Fernández

580 total citations
37 papers, 425 citations indexed

About

N. Fernández is a scholar working on Agronomy and Crop Science, Animal Science and Zoology and Genetics. According to data from OpenAlex, N. Fernández has authored 37 papers receiving a total of 425 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Agronomy and Crop Science, 13 papers in Animal Science and Zoology and 11 papers in Genetics. Recurrent topics in N. Fernández's work include Milk Quality and Mastitis in Dairy Cows (21 papers), Genetic and phenotypic traits in livestock (10 papers) and Effects of Environmental Stressors on Livestock (10 papers). N. Fernández is often cited by papers focused on Milk Quality and Mastitis in Dairy Cows (21 papers), Genetic and phenotypic traits in livestock (10 papers) and Effects of Environmental Stressors on Livestock (10 papers). N. Fernández collaborates with scholars based in Spain, Argentina and Mexico. N. Fernández's co-authors include C. Peris, M.P. Molina, R.L. Althaus, Mariano Rodríguez, S. Balasch, J.R. Díaz, A. Molina, Alfredo G. Torres, Marta Roca and Antonio G. Torres and has published in prestigious journals such as Journal of Dairy Science, Journal of Animal Science and Journal of Food Protection.

In The Last Decade

N. Fernández

36 papers receiving 395 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
N. Fernández Spain	14	270	148	118	114	73	37	425
E.N. Escobar United States	6	243 0.9×	57 0.4×	114 1.0×	126 1.1×	39 0.5×	8	303
Valerie E. Ryman United States	13	190 0.7×	59 0.4×	51 0.4×	59 0.5×	36 0.5×	17	404
Maristela Rovai United States	16	335 1.2×	201 1.4×	167 1.4×	162 1.4×	122 1.7×	34	548
Zuzana Farkašová Slovakia	9	184 0.7×	54 0.4×	38 0.3×	109 1.0×	69 0.9×	46	293
Héctor Puente Spain	9	20 0.1×	125 0.8×	33 0.3×	74 0.6×	10 0.1×	18	338
Martin Patrick Ongol Rwanda	13	88 0.3×	32 0.2×	23 0.2×	258 2.3×	9 0.1×	19	377
M.S. Gilbert Netherlands	10	102 0.4×	169 1.1×	45 0.4×	69 0.6×	101 1.4×	26	378
Joseph Wambui Switzerland	11	37 0.1×	49 0.3×	16 0.1×	185 1.6×	10 0.1×	39	309
Ken Steen Pedersen Denmark	13	33 0.1×	121 0.8×	14 0.1×	27 0.2×	160 2.2×	32	319
С. В. Шабунин Russia	8	44 0.2×	48 0.3×	14 0.1×	46 0.4×	24 0.3×	60	267

Countries citing papers authored by N. Fernández

Since Specialization

Citations

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

Fields of papers citing papers by N. Fernández

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of N. Fernández

This figure shows the co-authorship network connecting the top 25 collaborators of N. Fernández. A scholar is included among the top collaborators of N. Fernández 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 N. Fernández. N. Fernández 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

Fraile, Lorenzo, N. Fernández, Ramona N. Pena, et al.. (2020). A probabilistic Poisson-based model to detect PRRSV recirculation using sow production records. Preventive Veterinary Medicine. 177. 104948–104948. 6 indexed citations

Fernández, N., et al.. (2019). Machine milking parameters for Murciano-Granadina breed goats. Journal of Dairy Science. 103(1). 507–513. 7 indexed citations

Fernández, N., et al.. (2019). Effect of stress on somatic cell count and milk yield and composition in goats. Research in Veterinary Science. 125. 61–70. 27 indexed citations

Fernández, N., et al.. (2019). Effect of the rearing system on financial returns from Murciano-Granadina breed goats. animal. 13(8). 1730–1735. 4 indexed citations

Díaz, J.R., et al.. (2017). Teatcups with automatic valves in machine milking of goats. Journal of Dairy Research. 85(1). 64–69. 1 indexed citations

Moya, V.J., et al.. (2016). Interferences on microbial inhibitor tests related to ivermectin treatment in lactating dairy goats. Journal of Dairy Research. 83(3). 341–344. 2 indexed citations

Fernández, N., et al.. (2015). Composition, proteolysis indices and coagulating properties of ewe milk as affected by bulk tank somatic cell count. Journal of Dairy Research. 82(3). 344–349. 6 indexed citations

Requena, Raquel, S. Balasch, C. Peris, Mariano Rodríguez, & N. Fernández. (2010). Dose response of lactating dairy ewes during suckling and milking to bovine somatotropin1. Journal of Animal Science. 88(9). 3136–3144. 4 indexed citations

Roca, Marta, et al.. (2008). Heat Inactivation of β-Lactam Antibiotics in Milk. Journal of Food Protection. 71(6). 1193–1198. 28 indexed citations

10.

Díaz, J.R., C. Peris, Mariano Rodríguez, M.P. Molina, & N. Fernández. (2004). Effect of Milking Pipeline Height on Machine Milking Efficiency and Milk Quality in Sheep. Journal of Dairy Science. 87(6). 1675–1683. 15 indexed citations

11.

Peris, C., et al.. (2003). Influence of Pulsation Rate on Udder Health and Teat Thickness Changes in Dairy Ewes. Journal of Dairy Science. 86(2). 530–537. 15 indexed citations

12.

Peris, C., et al.. (2003). Influence of Vacuum Level and Overmilking on Udder Health and Teat Thickness Changes in Dairy Ewes. Journal of Dairy Science. 86(12). 3891–3898. 23 indexed citations

13.

Molina, M.P., et al.. (2003). Evaluation of Screening Test for Detection of Antimicrobial Residues in Ewe Milk. Journal of Dairy Science. 86(6). 1947–1952. 36 indexed citations

14.

Althaus, R.L., et al.. (2003). Accuracy of BRT and Delvotest Microbial Inhibition Tests as Affected by Composition of Ewe's Milk. Journal of Food Protection. 66(3). 473–478. 18 indexed citations

15.

Althaus, R.L., M.P. Molina, Mariano Rodríguez, & N. Fernández. (2001). Analysis Time and Lactation Stage Influence on Lactoperoxidase System Components in Dairy Ewe Milk. Journal of Dairy Science. 84(8). 1829–1835. 20 indexed citations

16.

Fernández, N., et al.. (2001). Bovine Somatotropin Dose Titration in Lactating Dairy Ewes. 3. Treatment Interval. Journal of Dairy Science. 84(10). 2170–2176. 6 indexed citations

17.

Althaus, R.L., et al.. (2001). Detection Limits of β-Lactam Antibiotics in Ewe Milk by Penzym Enzymatic Test. Journal of Food Protection. 64(11). 1844–1847. 9 indexed citations

18.

Fernández, N., Mariano Rodríguez, C. Peris, M.P. Molina, & Armando Torres. (1997). Bovine Somatotropin Dose Titration in Lactating Dairy Ewes. 2. Dose Determination and Factors Affecting the Response. Journal of Dairy Science. 80(5). 818–829. 3 indexed citations

19.

Fernández, N., et al.. (1995). Bovine Somatotropin Dose Titration in Lactating Dairy Ewes. 1. Milk Yield and Milk Composition. Journal of Dairy Science. 78(5). 1073–1082. 25 indexed citations

20.

Peris, C., et al.. (1991). Variation in Somatic Cell Count, California Mastitis Test, and Electrical Conductivity Among Various Fractions of Ewe's Milk. Journal of Dairy Science. 74(5). 1553–1560. 29 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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