Samuel Trachsel

2.6k total citations · 1 hit paper
32 papers, 1.8k citations indexed

About

Samuel Trachsel is a scholar working on Plant Science, Agronomy and Crop Science and Genetics. According to data from OpenAlex, Samuel Trachsel has authored 32 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Plant Science, 20 papers in Agronomy and Crop Science and 15 papers in Genetics. Recurrent topics in Samuel Trachsel's work include Crop Yield and Soil Fertility (20 papers), Genetics and Plant Breeding (16 papers) and Genetic Mapping and Diversity in Plants and Animals (15 papers). Samuel Trachsel is often cited by papers focused on Crop Yield and Soil Fertility (20 papers), Genetics and Plant Breeding (16 papers) and Genetic Mapping and Diversity in Plants and Animals (15 papers). Samuel Trachsel collaborates with scholars based in Mexico, Argentina and United States. Samuel Trachsel's co-authors include Shawn M. Kaeppler, Jonathan P. Lynch, Kathleen M. Brown, Andreas Hund, P. Stamp, Rainer Messmer, Juan Burgueño, Raman Babu, G. N. Atlin and Xuecai Zhang and has published in prestigious journals such as Nature Genetics, PLoS ONE and Journal of Agricultural and Food Chemistry.

In The Last Decade

Samuel Trachsel

32 papers receiving 1.8k citations

Hit Papers

Shovelomics: high throughput phenotyping of maize (Zea ma... 2010 2026 2015 2020 2010 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Samuel Trachsel Mexico 20 1.6k 523 493 171 125 32 1.8k
Brent Barrett New Zealand 20 1.0k 0.6× 544 1.0× 485 1.0× 199 1.2× 53 0.4× 67 1.8k
Leonardo Crespo‐Herrera Mexico 25 1.7k 1.1× 602 1.2× 384 0.8× 146 0.9× 147 1.2× 67 1.9k
Marie Coque France 13 738 0.5× 151 0.3× 450 0.9× 64 0.4× 92 0.7× 14 862
Daisuke Saisho Japan 21 1.7k 1.1× 331 0.6× 117 0.2× 707 4.1× 13 0.1× 34 1.9k
Sheeja George United States 18 344 0.2× 58 0.1× 136 0.3× 238 1.4× 152 1.2× 44 697
Réka Howard United States 14 685 0.4× 463 0.9× 157 0.3× 41 0.2× 34 0.3× 38 896
Ling Guan Canada 18 340 0.2× 300 0.6× 825 1.7× 315 1.8× 19 0.2× 37 1.3k
A. McKay Australia 23 1.1k 0.7× 40 0.1× 184 0.4× 206 1.2× 163 1.3× 64 1.4k
Robert A. Graybosch United States 22 1.3k 0.8× 126 0.2× 343 0.7× 216 1.3× 39 0.3× 82 1.7k
Marı́a Rosa Simón Argentina 23 1.4k 0.9× 167 0.3× 251 0.5× 137 0.8× 33 0.3× 80 1.5k

Countries citing papers authored by Samuel Trachsel

Since Specialization
Citations

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

Fields of papers citing papers by Samuel Trachsel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samuel Trachsel

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Trachsel. A scholar is included among the top collaborators of Samuel Trachsel 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 Samuel Trachsel. Samuel Trachsel 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.
González-Pérez, Lorena, et al.. (2022). QTL and genomic prediction accuracy for grain yield and secondary traits in a maize population under heat and heat-drought stresses. Journal of Crop Improvement. 37(5). 709–734. 3 indexed citations
2.
Dhliwayo, Thanda, et al.. (2020). Evaluation of U.S. inbred lines with expired plant variety protection for mid-altitude tropical maize breeding. Euphytica. 216(3). 8 indexed citations
3.
Liu, Xia, Yibing Yuan, Carlos Alberto Martínez, et al.. (2020). Identification of QTL for early vigor and leaf senescence across two tropical maize doubled haploid populations under nitrogen deficient conditions. Euphytica. 216(3). 5 indexed citations
5.
Dhliwayo, Thanda, et al.. (2019). Effects of Drought and Low Nitrogen Stress on Provitamin A Carotenoid Content of Biofortified Maize Hybrids. Crop Science. 59(6). 2521–2532. 24 indexed citations
6.
Cerrudo, Diego, Shiliang Cao, Yibing Yuan, et al.. (2018). Genomic Selection Outperforms Marker Assisted Selection for Grain Yield and Physiological Traits in a Maize Doubled Haploid Population Across Water Treatments. Frontiers in Plant Science. 9. 366–366. 69 indexed citations
7.
Massange‐Sánchez, Julio A., et al.. (2018). Genome-wide analysis of the invertase gene family from maize. Plant Molecular Biology. 97(4-5). 385–406. 41 indexed citations
8.
Willcox, Martha C., Juan Burgueño, M. Cinta Romay, et al.. (2017). A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nature Genetics. 49(3). 476–480. 188 indexed citations
9.
Trachsel, Samuel, Lorena González-Pérez, Juan Burgueño, et al.. (2017). Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield. Crop Science. 57(5). 2517–2524. 62 indexed citations
10.
Trachsel, Samuel, Dapeng Sun, Hongjian Zheng, et al.. (2016). Identification of QTL for Early Vigor and Stay-Green Conferring Tolerance to Drought in Two Connected Advanced Backcross Populations in Tropical Maize (Zea mays L.). PLoS ONE. 11(3). e0149636–e0149636. 47 indexed citations
11.
Zaidi, P.H., Mainassara Zaman‐Allah, Samuel Trachsel, et al.. (2016). Phenotyping for Abiotic Stress Tolerance in Maize : Heat Stress. A field manual. CIMMYT eBooks. 15 indexed citations
14.
Almeida, Gustavo Dias de, Sudha Nair, Aluízio Borém, et al.. (2014). Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize. Molecular Breeding. 34(2). 701–715. 63 indexed citations
15.
Grieder, Christoph, Samuel Trachsel, & Andreas Hund. (2013). Early vertical distribution of roots and its association with drought tolerance in tropical maize. Plant and Soil. 377(1-2). 295–308. 25 indexed citations
16.
Trachsel, Samuel, P. Stamp, & Andreas Hund. (2010). EFFECT OF HIGH TEMPERATURES, DROUGHT AND ALUMINUM TOXICITY ON ROOT GROWTH OF TROPICAL MAIZE (ZEA MAYS L.) SEEDLINGS. Maydica. 55. 249–260. 22 indexed citations
17.
Trachsel, Samuel, P. Stamp, & Andreas Hund. (2010). Growth of axile and lateral roots of maize: response to desiccation stress induced by polyethylene glycol 8000.. Maydica. 55(2). 101–109. 11 indexed citations
18.
Trachsel, Samuel, Shawn M. Kaeppler, Kathleen M. Brown, & Jonathan P. Lynch. (2010). Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant and Soil. 341(1-2). 75–87. 490 indexed citations breakdown →
19.
Trachsel, Samuel, Rainer Messmer, P. Stamp, & Andreas Hund. (2009). Mapping of QTLs for lateral and axile root growth of tropical maize. Theoretical and Applied Genetics. 119(8). 1413–1424. 80 indexed citations
20.
Trachsel, Samuel, Rainer Messmer, P. Stamp, Nathinee Ruta, & Andreas Hund. (2009). QTLs for early vigor of tropical maize. Molecular Breeding. 25(1). 91–103. 30 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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