Roberto Confalonieri

2.0k total citations
67 papers, 1.5k citations indexed

About

Roberto Confalonieri is a scholar working on Plant Science, Ecology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Roberto Confalonieri has authored 67 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Plant Science, 22 papers in Ecology and 20 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Roberto Confalonieri's work include Remote Sensing in Agriculture (21 papers), Climate change impacts on agriculture (19 papers) and Rice Cultivation and Yield Improvement (17 papers). Roberto Confalonieri is often cited by papers focused on Remote Sensing in Agriculture (21 papers), Climate change impacts on agriculture (19 papers) and Rice Cultivation and Yield Improvement (17 papers). Roberto Confalonieri collaborates with scholars based in Italy, France and Spain. Roberto Confalonieri's co-authors include Stefano Bocchi, Livia Paleari, Ermes Movedi, Luca Bechini, Mirco Boschetti, Fulvia Tambone, Fabrizio Adani, G. Cappelli, Gianni Bellocchi and Valentina Pagani and has published in prestigious journals such as The Science of The Total Environment, Scientific Reports and Global Change Biology.

In The Last Decade

Roberto Confalonieri

66 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roberto Confalonieri Italy 24 729 484 435 410 265 67 1.5k
Anna Dalla Marta Italy 23 824 1.1× 353 0.7× 372 0.9× 330 0.8× 337 1.3× 104 1.7k
Bappa Das India 25 733 1.0× 459 0.9× 402 0.9× 200 0.5× 319 1.2× 114 1.9k
Liang He China 22 605 0.8× 300 0.6× 522 1.2× 465 1.1× 293 1.1× 74 1.4k
P.A.J. van Oort Netherlands 21 726 1.0× 257 0.5× 313 0.7× 519 1.3× 365 1.4× 60 1.6k
Balwinder Singh India 23 852 1.2× 247 0.5× 374 0.9× 441 1.1× 715 2.7× 54 1.8k
Philippe Burger France 10 906 1.2× 602 1.2× 394 0.9× 322 0.8× 274 1.0× 12 1.5k
J. Schellberg Germany 29 766 1.1× 1.0k 2.1× 366 0.8× 270 0.7× 427 1.6× 121 2.1k
Haiyan Wei China 23 496 0.7× 509 1.1× 441 1.0× 304 0.7× 463 1.7× 147 2.3k
Louis Kouadio Australia 21 682 0.9× 362 0.7× 265 0.6× 289 0.7× 135 0.5× 59 1.4k
Fábio Ricardo Marin Brazil 26 1.2k 1.7× 239 0.5× 473 1.1× 322 0.8× 676 2.6× 129 1.8k

Countries citing papers authored by Roberto Confalonieri

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Confalonieri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roberto Confalonieri

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Confalonieri. A scholar is included among the top collaborators of Roberto Confalonieri 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 Roberto Confalonieri. Roberto Confalonieri 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.
Rusconi, Chiara, et al.. (2025). Porometer for estimating stomatal conductance in maize: Determination of trueness and precision according to ISO 5725. Biosystems Engineering. 250. 158–162. 1 indexed citations
2.
Paleari, Livia, Alessandro Tondelli, Luigi Cattivelli, et al.. (2025). Extending genomic prediction to future climates through crop modelling. A case study on heading time in barley. Agricultural and Forest Meteorology. 368. 110560–110560. 1 indexed citations
3.
Movedi, Ermes, et al.. (2023). Impacts of climate change on semi-natural alpine pastures productivity and floristic composition. Regional Environmental Change. 23(4). 4 indexed citations
4.
5.
Paleari, Livia, et al.. (2022). Decomposing complex traits through crop modelling to support cultivar recommendation. A proof of concept with a focus on phenology and field pea. Italian Journal of Agronomy. 17(1). 1998–1998. 1 indexed citations
6.
Paleari, Livia, Tao Li, Yubin Yang, et al.. (2022). A trait‐based model ensemble approach to design rice plant types for future climate. Global Change Biology. 28(8). 2689–2710. 19 indexed citations
7.
Paleari, Livia, et al.. (2021). Setting-up of different water managements as mitigation strategy of the environmental impact of paddy rice. The Science of The Total Environment. 799. 149365–149365. 25 indexed citations
8.
Nutini, Francesco, Roberto Confalonieri, Livia Paleari, et al.. (2021). Supporting operational site‐specific fertilization in rice cropping systems with infield smartphone measurements and Sentinel-2 observations. Precision Agriculture. 22(4). 1284–1303. 21 indexed citations
9.
Bacenetti, Jacopo, et al.. (2020). May smart technologies reduce the environmental impact of nitrogen fertilization? A case study for paddy rice. The Science of The Total Environment. 715. 136956–136956. 52 indexed citations
10.
Oijen, Marcel van, Zoltán Barcza, Roberto Confalonieri, et al.. (2020). Incorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap. Agronomy. 10(2). 259–259. 21 indexed citations
11.
Paleari, Livia, et al.. (2019). Tailoring parameter distributions to specific germplasm: impact on crop model-based ideotyping. Scientific Reports. 9(1). 18309–18309. 13 indexed citations
12.
Nettleton, David, et al.. (2019). Predicting rice blast disease: machine learning versus process-based models. BMC Bioinformatics. 20(1). 514–514. 44 indexed citations
13.
Paleari, Livia, Ermes Movedi, & Roberto Confalonieri. (2017). Trait-based model development to support breeding programs. A case study for salt tolerance and rice. Scientific Reports. 7(1). 4352–4352. 12 indexed citations
14.
Confalonieri, Roberto, et al.. (2017). Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change. Ecological Modelling. 368. 1–14. 37 indexed citations
15.
Orlando, Francesca, Ermes Movedi, Simone Parisi, et al.. (2016). Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App. Sensors. 16(12). 2004–2004. 35 indexed citations
16.
Orlando, Francesca, et al.. (2015). Estimating leaf area index in tree species using the PocketLAI smart app. Applied Vegetation Science. 18(4). 716–723. 21 indexed citations
17.
Stella, Tommaso, C. Francone, Sevim Seda Yamaç, et al.. (2015). Reimplementation and reuse of the Canegro model: From sugarcane to giant reed. Computers and Electronics in Agriculture. 113. 193–202. 14 indexed citations
18.
Confalonieri, Roberto. (2004). A jackknife-derived visual approach for sample size determination. 587(Pt 24). 5907–23. 5 indexed citations
19.
Boschetti, Mirco, et al.. (2004). Monitoring paddy rice crops through remote sensing: productivity estimation by light use efficiency model. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5568. 46–46. 4 indexed citations
20.
Confalonieri, Roberto, et al.. (2002). Analysis of temperature profiles in flooded rice field: preliminary results. Zenodo (CERN European Organization for Nuclear Research). 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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