Aiming Qi

2.9k total citations
94 papers, 2.0k citations indexed

About

Aiming Qi is a scholar working on Plant Science, Agronomy and Crop Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Aiming Qi has authored 94 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Plant Science, 18 papers in Agronomy and Crop Science and 15 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Aiming Qi's work include Plant Disease Resistance and Genetics (18 papers), Soybean genetics and cultivation (16 papers) and Plant Pathogens and Fungal Diseases (13 papers). Aiming Qi is often cited by papers focused on Plant Disease Resistance and Genetics (18 papers), Soybean genetics and cultivation (16 papers) and Plant Pathogens and Fungal Diseases (13 papers). Aiming Qi collaborates with scholars based in United Kingdom, United States and Australia. Aiming Qi's co-authors include K. W. Jaggard, Eric S. Ober, R. J. Summerfield, R. H. Ellis, Bruce D.L. Fitt, Ε. H. Roberts, Peter Craufurd, A. M. Dewar, G. M. Richter and Mohamed F. R. Khan and has published in prestigious journals such as PLoS ONE, The Science of The Total Environment and Scientific Reports.

In The Last Decade

Aiming Qi

92 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aiming Qi United Kingdom 26 1.6k 391 307 199 178 94 2.0k
Alain Ratnadass France 16 1.5k 1.0× 324 0.8× 399 1.3× 122 0.6× 290 1.6× 96 2.3k
Pietro P. M. Iannetta United Kingdom 27 1.5k 1.0× 369 0.9× 212 0.7× 162 0.8× 300 1.7× 103 2.3k
John Spink United Kingdom 27 2.1k 1.3× 1.3k 3.2× 318 1.0× 345 1.7× 279 1.6× 74 2.6k
David R. Cléments Canada 25 1.4k 0.9× 485 1.2× 600 2.0× 258 1.3× 154 0.9× 96 2.2k
Robin Duponnois France 35 3.0k 1.9× 288 0.7× 348 1.1× 389 2.0× 235 1.3× 186 3.5k
Philippe Tixier France 25 1.1k 0.7× 137 0.4× 577 1.9× 129 0.6× 162 0.9× 113 2.1k
Art Diggle Australia 25 1.9k 1.2× 433 1.1× 183 0.6× 414 2.1× 135 0.8× 61 2.2k
Alison J. Karley United Kingdom 30 2.1k 1.4× 644 1.6× 611 2.0× 286 1.4× 312 1.8× 93 3.1k
C. Wayne Smith United States 25 2.7k 1.7× 452 1.2× 161 0.5× 229 1.2× 316 1.8× 124 3.2k
J. M. Lenné United Kingdom 18 906 0.6× 232 0.6× 226 0.7× 75 0.4× 137 0.8× 93 1.3k

Countries citing papers authored by Aiming Qi

Since Specialization
Citations

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

Fields of papers citing papers by Aiming Qi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aiming Qi

This figure shows the co-authorship network connecting the top 25 collaborators of Aiming Qi. A scholar is included among the top collaborators of Aiming Qi 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 Aiming Qi. Aiming Qi 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.
Noel, K. Dale, David Hughes, Guilherme Targino Valente, et al.. (2024). Transcriptomics of temperature-sensitive R gene-mediated resistance identifies a WAKL10 protein interaction network. Scientific Reports. 14(1). 5023–5023. 5 indexed citations
2.
Elkot, Ahmed F., Yasser M. Shabana, M. A. A. Gadallah, et al.. (2024). Yield Responses to Total Water Input from Irrigation and Rainfall in Six Wheat Cultivars Under Different Climatic Zones in Egypt. Agronomy. 14(12). 3057–3057. 1 indexed citations
3.
Mendoza, Luis E. del Río, Aiming Qi, Dilip K. Lakshman, et al.. (2023). Resistance to QoI and DMI Fungicides Does Not Reduce Virulence of C. beticola Isolates in North Central United States. Plant Disease. 107(9). 2825–2829. 2 indexed citations
5.
Qi, Aiming, et al.. (2023). Can 100% Pasture-Based Livestock Farming Produce Enough Ruminant Meat to Meet the Current Consumption Demand in the UK?. MDPI (MDPI AG). 2(3). 185–206. 5 indexed citations
6.
Huang, Yongju, et al.. (2022). Effective control of Leptosphaeria maculans increases importance of L. biglobosa as a cause of phoma stem canker epidemics on oilseed rape. Pest Management Science. 80(5). 2405–2415. 4 indexed citations
7.
Qi, Aiming, et al.. (2021). Azoxystrobin is Needed Before Infection for Control of Rhizoctonia solani in Sugarbeet. 2021(2). 1–6. 1 indexed citations
8.
Richter, G. M., et al.. (2015). Assessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensing. Biomass and Bioenergy. 85. 252–261. 23 indexed citations
9.
Qi, Aiming, Eric S. Ober, & K. W. Jaggard. (2012). Benchmarking sugar beet yields and growers' performance. Rothamsted Repository (Rothamsted Repository). 1 indexed citations
10.
Jaggard, K. W., Aiming Qi, G. F. J. Milford, et al.. (2011). Determining the optimal population density of sugarbeet crops in England. University of Hertfordshire Research Archive (University of Hertfordshire). 5 indexed citations
11.
Jaggard, K. W., Aiming Qi, & Eric S. Ober. (2010). Possible changes to arable crop yields by 2050. Philosophical Transactions of the Royal Society B Biological Sciences. 365(1554). 2835–2851. 266 indexed citations
12.
Jaggard, K. W., et al.. (2009). Why was 2008 such a good year for beet yields?. Rothamsted Repository (Rothamsted Repository). 1 indexed citations
13.
Jaggard, K. W., Aiming Qi, & Mikhail A. Semenov. (2007). The impact of climate change on sugarbeet yield in the UK: 1976–2004. The Journal of Agricultural Science. 145(4). 367–375. 47 indexed citations
14.
Qi, Aiming, A. M. Dewar, & R. Harrington. (2005). Forecasting virus yellows incidence in sugar beet - the post-Gaucho era. Rothamsted Repository (Rothamsted Repository). 2 indexed citations
15.
Pidgeon, J. D., et al.. (2005). Using multi-environment sugar beet variety trials to screen for drought tolerance. Field Crops Research. 95(2-3). 268–279. 48 indexed citations
16.
Qi, Aiming & K. W. Jaggard. (2002). Uses of mathematical models in sugar beet agriculture. University of Hertfordshire Research Archive (University of Hertfordshire). 1 indexed citations
17.
Qi, Aiming, A. M. Dewar, A. R. Werker, & R. Harrington. (2001). Virus yellows forecasting in sugar beet and the impact of Gaucho. Rothamsted Repository (Rothamsted Repository). 1 indexed citations
18.
Wheeler, Tim, et al.. (1999). Selecting legume cover crops for hillside environments in Bolivia.. Mountain Research and Development. 19(4). 318–324. 5 indexed citations
19.
Keatinge, J. D. H., et al.. (1999). Potential annual sown legumes for low-income systems in the East African highlands of Southwestern Uganda. Mountain Research and Development. 19(4). 345–353. 2 indexed citations
20.
Keatinge, J. D. H., et al.. (1999). Annual legume species as green manures/cover crops in low-income farming systems of Nepal. Mountain Research and Development. 19(4). 325–332. 9 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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