I. M. Ray

1.0k total citations
45 papers, 711 citations indexed

About

I. M. Ray is a scholar working on Plant Science, Agronomy and Crop Science and Environmental Chemistry. According to data from OpenAlex, I. M. Ray has authored 45 papers receiving a total of 711 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Plant Science, 20 papers in Agronomy and Crop Science and 15 papers in Environmental Chemistry. Recurrent topics in I. M. Ray's work include Wheat and Barley Genetics and Pathology (27 papers), Turfgrass Adaptation and Management (15 papers) and Bioenergy crop production and management (14 papers). I. M. Ray is often cited by papers focused on Wheat and Barley Genetics and Pathology (27 papers), Turfgrass Adaptation and Management (15 papers) and Bioenergy crop production and management (14 papers). I. M. Ray collaborates with scholars based in United States, Mexico and Oman. I. M. Ray's co-authors include M. Shaun Townsend, L.W. Murray, M. K. Sledge, John A. Henning, Guangliang Jiang, S. E. Smith, Christopher Pierce, Daniela Soleri, Yee Hui Robin Chang and J. D. Berdahl and has published in prestigious journals such as Scientific Reports, Theoretical and Applied Genetics and Crop Science.

In The Last Decade

I. M. Ray

43 papers receiving 665 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
I. M. Ray United States	17	599	261	220	72	61	45	711
Karen Cane Australia	14	623 1.0×	131 0.5×	224 1.0×	121 1.7×	22 0.4×	17	670
Gérard Doussinault France	20	1.3k 2.2×	262 1.0×	500 2.3×	140 1.9×	36 0.6×	51	1.5k
Prabin Bajgain United States	15	470 0.8×	227 0.9×	203 0.9×	82 1.1×	70 1.1×	39	652
Brandon Schlautman United States	19	485 0.8×	136 0.5×	118 0.5×	156 2.2×	26 0.4×	40	677
Christopher J. Lambrides Australia	16	503 0.8×	79 0.3×	101 0.5×	53 0.7×	156 2.6×	39	706
Steven Yates Switzerland	17	515 0.9×	143 0.5×	107 0.5×	227 3.2×	53 0.9×	32	681
Jean‐Paul Sampoux France	10	253 0.4×	86 0.3×	127 0.6×	25 0.3×	101 1.7×	17	381
P. W. Wilkins United Kingdom	15	484 0.8×	111 0.4×	384 1.7×	127 1.8×	288 4.7×	37	820
Arild Larsen Norway	12	294 0.5×	77 0.3×	90 0.4×	26 0.4×	122 2.0×	22	386
H. Z. Cross United States	12	498 0.8×	160 0.6×	278 1.3×	35 0.5×	10 0.2×	46	594

Countries citing papers authored by I. M. Ray

Since Specialization

Citations

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

Fields of papers citing papers by I. M. Ray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I. M. Ray. 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 I. M. Ray. The network helps show where I. M. Ray may publish in the future.

Co-authorship network of co-authors of I. M. Ray

This figure shows the co-authorship network connecting the top 25 collaborators of I. M. Ray. A scholar is included among the top collaborators of I. M. Ray 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 I. M. Ray. I. M. Ray 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

Thapa, Ranjita, Julie Hansen, Christopher Pierce, et al.. (2024). Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa. G3 Genes Genomes Genetics. 14(11).

Singh, Lovepreet, et al.. (2022). Validation of DNA marker‐assisted selection for forage biomass productivity under deficit irrigation in alfalfa. The Plant Genome. 15(1). e20195–e20195. 7 indexed citations

Medina, Cesar Augusto, Harpreet Kaur, I. M. Ray, & Long‐Xi Yu. (2021). Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.). Cells. 10(12). 3372–3372. 16 indexed citations

Pierce, Christopher, et al.. (2018). Genetic Mapping of Water Use Efficiency and Carbon and Nitrogen Metabolism in Drought‐Stressed Alfalfa. Crop Science. 59(1). 92–106. 4 indexed citations

Wang, Junjie, Yan Zhao, I. M. Ray, & Mingzhou Song. (2016). Transcriptome responses in alfalfa associated with tolerance to intensive animal grazing. Scientific Reports. 6(1). 19438–19438. 18 indexed citations

Bhandari, Hem S., et al.. (2011). Amplified Fragment Length Polymorphism Marker Diversity among High Yielding Perennial Alfalfa (Medicago sativa L.) Core Collection Accessions. Crop Science. 51(3). 1110–1121. 6 indexed citations

Ray, I. M.. (2010). DEVELOPING ALFALFA VARIETIES FOR A WATER-CHALLENGED FUTURE. 3 indexed citations

Pierce, Christopher, et al.. (2010). Combining Ability and Heterosis for Forage Yield among Elite Alfalfa Core Collection Accessions with Different Fall Dormancy Responses. Crop Science. 50(1). 150–158. 19 indexed citations

Ray, I. M., et al.. (2008). Identifying rapd markers linked to an erect glandular hair trait in alfalfa. Revista Fitotecnia Mexicana. 31(2). 89–94. 1 indexed citations

10.

Sledge, M. K., I. M. Ray, & Guangliang Jiang. (2005). An expressed sequence tag SSR map of tetraploid alfalfa (Medicago sativa L.). Theoretical and Applied Genetics. 111(5). 980–992. 73 indexed citations

11.

Murray, L.W., et al.. (2004). Population-based diallel analyses among nine historically recognized alfalfa germplasms. Theoretical and Applied Genetics. 109(8). 1568–1575. 45 indexed citations

12.

Ray, I. M., et al.. (2004). Diallel Analysis of Carbon Isotope Discrimination and Its Association with Forage Yield among Nine Historically Recognized Alfalfa Germplasms. Crop Science. 44(6). 1970–1975. 25 indexed citations

13.

Cantrell, R. G., et al.. (2003). AFLP-based assessment of genetic diversity among nine alfalfa germplasms using bulk DNA templates. Genome. 46(1). 51–58. 60 indexed citations

14.

Murray, Leigh W., et al.. (2003). Clarification and Reevaluation of Population‐Based Diallel Analyses. Crop Science. 43(6). 1930–1937. 32 indexed citations

15.

Bagga, Suman, I. M. Ray, Soumitra Ghoshroy, et al.. (2003). Genetic engineering ruminal stable high methionine protein in the foliage of alfalfa. Plant Science. 166(2). 273–283. 17 indexed citations

16.

Ray, I. M., M. Shaun Townsend, & John A. Henning. (1998). Variation for Yield, Water‐Use Efficiency, and Canopy Morphology among Nine Alfalfa Germplasm. Crop Science. 38(5). 1386–1390. 28 indexed citations

17.

Henning, John A., et al.. (1996). Variation in aphid alarm pheromone content among glandular and eglandular-hairedMedicago accessions. Journal of Chemical Ecology. 22(9). 1629–1638. 18 indexed citations

18.

Barker, Randolph, et al.. (1995). Registration of ND‐WWG931 and ND‐WWG932 Western Wheatgrass Germplasms. Crop Science. 35(1). 295–296. 1 indexed citations

19.

Ray, I. M., Albert B. Frank, & J. D. Berdahl. (1994). Genetic Variances of Agronomic Traits in Tetraploid Crested Wheatgrass under Competitive Conditions. Crop Science. 34(6). 1436–1439. 3 indexed citations

20.

Ray, I. M. & William B. Sisson. (1986). Nitrate Reductase Activity of Kleingrass (Panicum coloratum L.) during Drought in the Northern Chihuahuan Desert. Journal of Range Management. 39(6). 531–531.

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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