Fuminori Terada

1.5k total citations
91 papers, 1.1k citations indexed

About

Fuminori Terada is a scholar working on Agronomy and Crop Science, Animal Science and Zoology and Genetics. According to data from OpenAlex, Fuminori Terada has authored 91 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Agronomy and Crop Science, 33 papers in Animal Science and Zoology and 17 papers in Genetics. Recurrent topics in Fuminori Terada's work include Ruminant Nutrition and Digestive Physiology (46 papers), Effects of Environmental Stressors on Livestock (19 papers) and Genetic and phenotypic traits in livestock (17 papers). Fuminori Terada is often cited by papers focused on Ruminant Nutrition and Digestive Physiology (46 papers), Effects of Environmental Stressors on Livestock (19 papers) and Genetic and phenotypic traits in livestock (17 papers). Fuminori Terada collaborates with scholars based in Japan, Egypt and South Korea. Fuminori Terada's co-authors include Masaki Shibata, Yimin Cai, Mitsunori KURIHARA, Takehiro Nishida, Jie Yu Chen, Sumio Kawano, Yonggang Cao, Jiali Yang, Kazuo Iwasaki and Masanori Tohno and has published in prestigious journals such as Scientific Reports, Journal of Dairy Science and Global Biogeochemical Cycles.

In The Last Decade

Fuminori Terada

78 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fuminori Terada Japan 18 647 262 202 185 175 91 1.1k
Elena Albanell Spain 25 570 0.9× 469 1.8× 217 1.1× 274 1.5× 403 2.3× 73 1.7k
K. M. Koenig Canada 24 1.2k 1.9× 351 1.3× 134 0.7× 351 1.9× 59 0.3× 65 1.6k
Franco Tagliapietra Italy 22 790 1.2× 559 2.1× 112 0.6× 344 1.9× 233 1.3× 98 1.3k
Harry Archimède France 23 944 1.5× 463 1.8× 310 1.5× 416 2.2× 119 0.7× 101 1.7k
Arjan Jonker New Zealand 23 901 1.4× 333 1.3× 255 1.3× 217 1.2× 76 0.4× 92 1.3k
Salvatore Claps Italy 23 534 0.8× 552 2.1× 134 0.7× 261 1.4× 451 2.6× 87 1.4k
Sokratis Stergiadis United Kingdom 21 493 0.8× 297 1.1× 295 1.5× 242 1.3× 267 1.5× 78 1.2k
Maurizio Moschini Italy 21 381 0.6× 363 1.4× 97 0.5× 83 0.4× 227 1.3× 56 1.3k
F.A. Martz United States 21 804 1.2× 502 1.9× 120 0.6× 268 1.4× 55 0.3× 81 1.3k
Mitsunori KURIHARA Japan 19 1.0k 1.6× 454 1.7× 291 1.4× 239 1.3× 121 0.7× 73 1.5k

Countries citing papers authored by Fuminori Terada

Since Specialization
Citations

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

Fields of papers citing papers by Fuminori Terada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fuminori Terada

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

All Works

20 of 20 papers shown
1.
Terada, Fuminori, Mitsunori KURIHARA, Tomoyuki Suzuki, et al.. (2025). Methane emission prediction models for lactating cows based on feed intake, body weight, and milk yield and composition: Variable methane conversion factor-based approach. Journal of Dairy Science. 108(7). 7248–7261.
2.
3.
Uemoto, Yoshinobu, Masahiro Masuda, Kohei Suzuki, et al.. (2023). Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows. Animal Bioscience. 37(2). 173–183. 8 indexed citations
4.
Suzuki, Tomoyuki, Yuko KAMIYA, I. Nonaka, et al.. (2021). Prediction of enteric methane emissions from lactating cows using methane to carbon dioxide ratio in the breath. Animal Science Journal. 92(1). e13637–e13637. 14 indexed citations
5.
Munćan, Jelena, et al.. (2020). Near infrared aquaphotomics study on common dietary fatty acids in cow's liquid, thawed milk. Food Control. 122. 107805–107805. 20 indexed citations
6.
Suzuki, Tomoyuki, K. Sommart, Yimin Cai, et al.. (2018). Prediction of enteric methane emission from beef cattle in Southeast Asia. Animal Science Journal. 89(9). 1287–1295. 8 indexed citations
7.
Nonaka, I., Makoto Yamazaki, N. Takusari, et al.. (2010). Prediction of the effect of global warming on heifer growth performance in summer estimated from changes of the mean ambient temperature. Nihon Chikusan Gakkaiho. 81(1). 29–35. 2 indexed citations
8.
Ueda, Kôichiro, et al.. (2000). Relationship Between Voluntary Intake of Rapping Rollbale Silage of Italian ryegrass in Dairy Cattle and Chemical Compositions, Retention Time in the Rumen, Digestibility and Digestion rate. 46(3). 254–260. 1 indexed citations
9.
Ueda, Kôichiro, et al.. (2000). Relationship between voluntary intake of wrapped round-bale silage of Italian ryegrass in dairy cattle and chemical composition, retention time in the rumen, digestibility and digestion rate.. Grassland Science. 46. 254–260. 2 indexed citations
10.
Enishi, O., et al.. (2000). Analysis of in situ Ruminal Disappearance and Nutritive Value of Barley Water Residue. Nihon Chikusan Gakkaiho. 71(8). 252–257. 3 indexed citations
11.
Terada, Fuminori, et al.. (2000). Chemical Composition and Ruminal Disappearance Characteristics of Grains for Cattle Feed.. 46(3). 305–308. 5 indexed citations
12.
Ueda, Kôichiro, et al.. (1999). Effects of Dietary Protein Degradability on Nitrogen and Energy Utilization of Lactating Cows. Nihon Chikusan Gakkaiho. 70(10). 390–396. 1 indexed citations
13.
Kamada, Hachiro, et al.. (1998). Selenium Balance in the Late Pregnancy and Lactation of Dairy Cattle, and Blood Selenium Concentration of Dam and its Calf. Nihon Chikusan Gakkaiho. 69(11). 1044–1049. 2 indexed citations
14.
Nishida, Takehiro, Mitsunori KURIHARA, Fuminori Terada, & Masaki Shibata. (1997). Energy Requirements of Pregnant Holstein Dairy Cows Carrying Single or Twin Japanese Black Fetuses in Late Pregnancy. Nihon Chikusan Gakkaiho. 68(6). 572–578. 5 indexed citations
15.
Terada, Fuminori, et al.. (1997). Prediction of Nitrogen Excretion in Lactating Cows. Nihon Chikusan Gakkaiho. 68(2). 163–168. 4 indexed citations
16.
17.
Terada, Fuminori, et al.. (1988). Prediction of Metabolizable Energy Contents of the Diets for Cattle from Chemical Composition or Digestible Nutrients. Nihon Chikusan Gakkaiho. 59(6). 490–495. 4 indexed citations
18.
Widyastuti, Yantyati, Fuminori Terada, Hiroshi Kajikawa, & Akira Abe. (1987). Digestion of rice straw cell wall constituents in various rumen conditions. Japan Agricultural Research Quarterly JARQ. 21(1). 59–64. 5 indexed citations
19.
Kajikawa, Hiroshi, Fuminori Terada, Kazuo Iwasaki, et al.. (1987). The Effects of Steamed Birch Feeding on the Feed Intake and Body Weight Gain of Holstein Steers. Nihon Chikusan Gakkaiho. 58(2). 101–106. 3 indexed citations
20.
Terada, Fuminori, et al.. (1987). Comparison of Nutritive Values Among Cattle, Sheep and Goats Fed the Same Diets. Nihon Chikusan Gakkaiho. 58(2). 131–137. 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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