Daniel Wallach

17.4k total citations · 1 hit paper
143 papers, 4.3k citations indexed

About

Daniel Wallach is a scholar working on Plant Science, Ecology, Evolution, Behavior and Systematics and Agronomy and Crop Science. According to data from OpenAlex, Daniel Wallach has authored 143 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Plant Science, 37 papers in Ecology, Evolution, Behavior and Systematics and 29 papers in Agronomy and Crop Science. Recurrent topics in Daniel Wallach's work include Climate change impacts on agriculture (35 papers), Rice Cultivation and Yield Improvement (22 papers) and Crop Yield and Soil Fertility (21 papers). Daniel Wallach is often cited by papers focused on Climate change impacts on agriculture (35 papers), Rice Cultivation and Yield Improvement (22 papers) and Crop Yield and Soil Fertility (21 papers). Daniel Wallach collaborates with scholars based in France, United States and Germany. Daniel Wallach's co-authors include Bruno Goffinet, David Makowski, Senthold Asseng, James W. Jones, Peter Thorburn, Jacques-Éric Bergez, Kenneth J. Boote, Alex C. Ruane, Frank Ewert and Peter J. Thorburn and has published in prestigious journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry and Biometrics.

In The Last Decade

Daniel Wallach

130 papers receiving 4.0k citations

Hit Papers

The Agricultural Model In... 2012 2026 2016 2021 2012 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Daniel Wallach 1.9k 1.6k 972 774 768 143 4.3k
Bruno Gérard 1.4k 0.7× 1.3k 0.8× 497 0.5× 919 1.2× 1.4k 1.9× 189 6.9k
Christian Thierfelder 2.1k 1.1× 1.9k 1.2× 398 0.4× 2.0k 2.5× 2.4k 3.1× 164 6.9k
Sang-Yoon Kim 715 0.4× 333 0.2× 286 0.3× 203 0.3× 1.2k 1.5× 225 4.1k
Patrick H. Brown 9.4k 4.9× 609 0.4× 609 0.6× 433 0.6× 1.7k 2.2× 261 14.2k
Shaopeng Wang 777 0.4× 983 0.6× 2.0k 2.1× 165 0.2× 1.1k 1.4× 213 6.6k
Torsten Müller 1.9k 1.0× 355 0.2× 199 0.2× 964 1.2× 2.4k 3.2× 228 8.3k
Robert L. Hill 1.1k 0.6× 216 0.1× 566 0.6× 512 0.7× 1.7k 2.2× 188 7.4k
James Hunt 1.9k 1.0× 985 0.6× 575 0.6× 1.4k 1.8× 1.1k 1.5× 195 6.8k
Tianyi Zhang 1.8k 0.9× 1.6k 1.0× 2.3k 2.4× 400 0.5× 690 0.9× 121 5.8k
Hui Liu 1.4k 0.7× 521 0.3× 1.4k 1.4× 215 0.3× 465 0.6× 245 5.0k

Countries citing papers authored by Daniel Wallach

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Wallach

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Wallach

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Wallach. A scholar is included among the top collaborators of Daniel Wallach 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 Daniel Wallach. Daniel Wallach 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.
Gao, Yujing, Jinglan Cui, Xiuming Zhang, et al.. (2025). Cost-effective adaptations increase rice production while reducing pollution under climate change. Nature Food. 6(3). 260–272. 8 indexed citations
2.
Gao, Yujing, Daniel Wallach, Baojing Gu, et al.. (2025). Quantification and comparison of prediction uncertainty associated with different practices of crop modeling. Agricultural and Forest Meteorology. 371. 110633–110633.
3.
Júnior, Rogério de Souza Nóia, et al.. (2024). Uncertainty in greenhouse tomato growth models. Computers and Electronics in Agriculture. 225. 109324–109324. 5 indexed citations
4.
Wang, Bin, Jonas Jägermeyr, Garry J. O’Leary, et al.. (2024). Pathways to identify and reduce uncertainties in agricultural climate impact assessments. Nature Food. 5(7). 550–556. 17 indexed citations
5.
Júnior, Rogério de Souza Nóia, et al.. (2023). A simple procedure for a national wheat yield forecast. European Journal of Agronomy. 148. 126868–126868. 5 indexed citations
6.
Hoogenboom, Gerrit, Myles Fisher, Julián Ramírez-Villegas, et al.. (2020). Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model. European Journal of Agronomy. 115. 126031–126031. 13 indexed citations
7.
Casadebaig, Pierre, Philippe Debaeke, & Daniel Wallach. (2020). A new approach to crop model calibration: Phenotyping plus post‐processing. Crop Science. 60(2). 709–720. 13 indexed citations
8.
Montesino, M., Daniel Wallach, Jørgen E. Olesen, et al.. (2018). Data requirements for crop modelling—Applying the learning curve approach to the simulation of winter wheat flowering time under climate change. European Journal of Agronomy. 95. 33–44. 7 indexed citations
9.
Wallach, Daniel. (2017). When and why to predict using the mean or median of a crop multi-model ensemble. 10. 37.
10.
Challinor, Andrew J., Christoph Müller, Senthold Asseng, et al.. (2017). Improving the use of crop models for risk assessment and climate change adaptation. Agricultural Systems. 159. 296–306. 117 indexed citations
11.
Wallach, Daniel, Peter Thorburn, Senthold Asseng, et al.. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions. 8. 1 indexed citations
12.
Nissanka, S. P., et al.. (2015). Calibration of the phenology sub-model of APSIM-Oryza: Going beyond goodness of fit. Environmental Modelling & Software. 70. 128–137. 23 indexed citations
13.
Rivington, Mike & Daniel Wallach. (2015). Communication strategy, including design of tools for more effective communication of uncertainty. 6. 2 indexed citations
14.
Rosenzweig, Cynthia, James W. Jones, Jerry L. Hatfield, et al.. (2012). The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agricultural and Forest Meteorology. 170. 166–182. 689 indexed citations breakdown →
15.
Girod, Angélique, Alain Chapelier, A. Carlotti, et al.. (2011). Giant basal cell carcinoma with regional lymph node and distant lung metastasis. European Journal of Dermatology. 21(6). 972–975. 14 indexed citations
16.
Stein, Robert M., Greg Vonnahme, Michael D. Byrne, & Daniel Wallach. (2008). Voting Technology, Election Administration, and Voter Performance. Election Law Journal Rules Politics and Policy. 7(2). 123–135. 39 indexed citations
17.
Wallach, Daniel, et al.. (2004). [1801-2001: two centuries of dermatology and venereology in the Assistance Publique-Hôpitaux de Paris].. PubMed. 130(8-9 Pt 1). 753–62. 1 indexed citations
18.
Wallach, Daniel, et al.. (2001). [François Henri Hallopeau (1842-1919)].. PubMed. 128(12). 1379–1379. 1 indexed citations
19.
Wallach, Daniel, et al.. (1989). [22 June 1889. The founding of the French Society of Dermatology and Syphiligraphy].. PubMed. 116(12). 965–72. 3 indexed citations
20.
Wallach, Daniel, et al.. (1989). 22 Juin 1889. Fondation de la Société Française de Dermatologie et de Syphiligraphie.. Annales de Dermatologie et de Vénéréologie. 116(12). 5 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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