S. J. Yeates

881 total citations
22 papers, 689 citations indexed

About

S. J. Yeates is a scholar working on Plant Science, Agronomy and Crop Science and Soil Science. According to data from OpenAlex, S. J. Yeates has authored 22 papers receiving a total of 689 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Plant Science, 4 papers in Agronomy and Crop Science and 4 papers in Soil Science. Recurrent topics in S. J. Yeates's work include Research in Cotton Cultivation (7 papers), Soybean genetics and cultivation (6 papers) and Agricultural pest management studies (5 papers). S. J. Yeates is often cited by papers focused on Research in Cotton Cultivation (7 papers), Soybean genetics and cultivation (6 papers) and Agricultural pest management studies (5 papers). S. J. Yeates collaborates with scholars based in Australia, United Kingdom and India. S. J. Yeates's co-authors include G. A. Constable, P. S. Carberry, M. E. Probert, M. J. Bell, M. J. Robertson, N.I. Huth, J. E. Turpin, Graeme C. Wright, R. B. Brinsmead and Perry Poulton and has published in prestigious journals such as Field Crops Research, Agricultural Systems and Communications in Soil Science and Plant Analysis.

In The Last Decade

S. J. Yeates

20 papers receiving 634 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. J. Yeates Australia 13 550 236 178 152 74 22 689
Allan Peake Australia 14 405 0.7× 283 1.2× 238 1.3× 150 1.0× 84 1.1× 21 581
Bonnie M. Flohr Australia 13 438 0.8× 316 1.3× 255 1.4× 103 0.7× 79 1.1× 18 620
Roberto Rizzalli Argentina 10 469 0.9× 433 1.8× 66 0.4× 158 1.0× 53 0.7× 15 614
E. Chakwizira New Zealand 12 277 0.5× 265 1.1× 80 0.4× 210 1.4× 68 0.9× 39 493
P. Martiniello Italy 15 391 0.7× 329 1.4× 66 0.4× 110 0.7× 38 0.5× 53 641
Safia Médiène France 11 343 0.6× 267 1.1× 131 0.7× 102 0.7× 57 0.8× 18 601
Chloe MacLaren United Kingdom 11 380 0.7× 241 1.0× 115 0.6× 133 0.9× 37 0.5× 22 609
Antoine Gardarin France 14 543 1.0× 308 1.3× 151 0.8× 72 0.5× 20 0.3× 28 680
Stefano Carlesi Italy 14 532 1.0× 328 1.4× 107 0.6× 146 1.0× 28 0.4× 37 680
Sylvain Vrignon‐Brenas France 9 366 0.7× 157 0.7× 79 0.4× 230 1.5× 115 1.6× 11 587

Countries citing papers authored by S. J. Yeates

Since Specialization
Citations

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

Fields of papers citing papers by S. J. Yeates

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. J. Yeates. 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 S. J. Yeates. The network helps show where S. J. Yeates may publish in the future.

Co-authorship network of co-authors of S. J. Yeates

This figure shows the co-authorship network connecting the top 25 collaborators of S. J. Yeates. A scholar is included among the top collaborators of S. J. Yeates 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 S. J. Yeates. S. J. Yeates 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.
2.
3.
Petheram, Cuan, et al.. (2015). Evaluation of the economic feasibility of water harvesting for irrigation in a large semi-arid tropical catchment in northern Australia. Agricultural Systems. 142. 84–98. 3 indexed citations
4.
Grundy, Paul, et al.. (2012). NORpak Cotton production and management guidelines for the Burdekin and north Queensland coastal dry tropics region. Queensland Department of Agriculture and Fisheries archive of scientific and research publications (Queensland Department of Agriculture and Fisheries). 2 indexed citations
5.
Yeates, S. J., et al.. (2010). Irrigated cotton in the tropical dry season. I: Yield, its components and crop development. Field Crops Research. 116(3). 278–289. 29 indexed citations
6.
Yeates, S. J., et al.. (2010). Irrigated cotton in the tropical dry season. II: Biomass accumulation, partitioning and RUE. Field Crops Research. 116(3). 290–299. 31 indexed citations
7.
Yeates, S. J., et al.. (2010). Irrigated cotton in the tropical dry season. III: Impact of temperature, cultivar and sowing date on fibre quality. Field Crops Research. 116(3). 300–307. 31 indexed citations
8.
Duggan, B. L., et al.. (2009). Phosphorus Fertilizer Requirements and Nutrient Uptake of Irrigated Dry‐Season Cotton Grown on Virgin Soil in Tropical Australia. Communications in Soil Science and Plant Analysis. 40(15-16). 2616–2637. 8 indexed citations
9.
Duggan, B. L., et al.. (2007). Phosphorus Fertilizer Requirements and Nutrient Uptake of Irrigated Dry‐Season Cotton Grown on Virgin Soil in Tropical Australia. Communications in Soil Science and Plant Analysis. 39(1-2). 282–301. 5 indexed citations
10.
Whish, Jeremy, Ian Broad, Peter Carberry, et al.. (2005). Modelling the effects of row configuration on sorghum yield reliability in north-eastern Australia. Australian Journal of Agricultural Research. 56(1). 11–23. 58 indexed citations
11.
Foale, M. A., M. E. Probert, P. S. Carberry, et al.. (2004). Participatory research in dryland cropping systems — monitoring and simulation of soil water and nitrogen in farmers' paddocks in Central Queensland. Australian Journal of Experimental Agriculture. 44(3). 321–331. 14 indexed citations
12.
Yeates, S. J., et al.. (2004). Cotton growth and yield after seed treatment with mepiquat chloride in the tropical winter season. Field Crops Research. 93(2-3). 122–131. 20 indexed citations
13.
Robertson, M. J., P. S. Carberry, N.I. Huth, et al.. (2002). Simulation of growth and development of diverse legume species in APSIM. Australian Journal of Agricultural Research. 53(4). 429–446. 266 indexed citations
14.
Yeates, S. J., et al.. (2002). Developing management options for mepiquat chloride in tropical winter season cotton. Field Crops Research. 74(2-3). 217–230. 27 indexed citations
15.
Yeates, S. J., R. J. Lawn, & S. W. Adkins. (2000). Prediction of weather damage of mungbean seed in tropical Australia. I. Relation between seed quality, weather, and reproductive development. Australian Journal of Agricultural Research. 51(5). 637–648. 5 indexed citations
16.
Yeates, S. J., R. J. Lawn, & S. W. Adkins. (2000). Prediction of weather damage of mungbean seed in tropical Australia. II. Model application. Australian Journal of Agricultural Research. 51(5). 649–656. 1 indexed citations
17.
Ellis, R. H., R. J. Lawn, R. J. Summerfield, et al.. (1994). Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. IV. Cultivated and Wild Mung Bean. Experimental Agriculture. 30(1). 31–43. 24 indexed citations
18.
Ellis, R. H., R. J. Lawn, R. J. Summerfield, et al.. (1994). Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. III. Cowpea Vigna unguiculata. Experimental Agriculture. 30(1). 17–29. 25 indexed citations
19.
Ellis, R. H., R. J. Lawn, R. J. Summerfield, et al.. (1994). Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. V. Chickpea (Cicer arietinum). Experimental Agriculture. 30(3). 271–282. 30 indexed citations
20.
Summerfield, R. J., R. J. Lawn, Aiming Qi, et al.. (1993). Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. II. Soyabean (Glycine Max). Experimental Agriculture. 29(3). 253–289. 62 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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