Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 195

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials

Chapman, Scott C.·2008·Euphytica
DOI 10.1007/s10681-007-9623-z
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10681-007-9623-z

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Research works printed but not linked: 35

  1. Bartels D, Furini A, Ingram J, Salamini F, Belhassen E (1996) Response of plants to dehydration stress: a molecular analysis. First INTERDROUGHT international conference 1995 Drought tolerance in higher plants: genetical, physiological and molecular biological analysis. CABI, Montpellier, pp 111–118 · DOI
  2. Bidinger FR, Hammer GL, Muchow RC (1996) The physiological basis of genotype by environment interaction in crop adaptation. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 329–348
  3. Campos H, Cooper A, Habben JE, Edmeades GO, Schussler JR (2004) Improving drought tolerance in maize: a view from industry. Field Crops Res 90:19–34 · DOI · Paper page
  4. Chapman SC, Crossa J, Edmeades GO (1997) Genotype by environment effects and selection for drought tolerance in tropical maize. 1. Two mode pattern analysis of yield. Euphytica 95:1–9 · DOI
  5. Chapman SC, Cooper M, Butler DG, Henzell RG (2000a) Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Aust J Agric Res 51:197–207 · DOI
  6. Chapman SC, Cooper M, Hammer GL, Butler DG (2000b) Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Aust J Agric Res 51:209–221 · DOI · Paper page
  7. Chapman SC, Mathews KL, Cooper M, Jensen N, Wang E, Butler D, Sheppard J, Sahama T (2001) Using environment characterization to interpret wheat yield in water-limited environments. In: Proceedings of the 10th wheat breeding assembly, Mildura, 16–21 September, 2001, pp 136–139
  8. Chapman SC, Hammer GL, Podlich DW, Cooper M (2002b) Linking biophysical and genetic models to integrate physiology, molecular biology and plant breeding. In: Kang MS (ed) Quantitative genetics, genomics, and plant breeding. CABI, Wallingford, pp 167–187 · DOI
  9. Chapman SC, Cooper M, Podlich D, Hammer GL (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95:99–113 · DOI · Paper page
  10. Comstock RE (1977) Quantitative genetics and the design of breeding programs. In: Proceedings of the international conference on quantitative genetics. Iowa State University Press, Ames, USA, August 16–21, 1976, pp 705–718
  11. Condon AG, Richards RA, Rebetzke GJ, Farquhar GD (2004) Breeding for high water-use efficiency. J Exp Bot 55:2447–2460 · DOI · Paper page
  12. Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci 37:1168–1176 · DOI · Paper page
  13. Cooper M, Podlich DW, Micallef KP, Smith OS, Jensen NM, Kruger NL (2002) Complexity, quantitative traits and plant breeding: A role for simulation modelling in the genetic improvement of crops. In: Kang MS (ed) Quantitative genetics, genomics, and plant breeding. CABI, Wallingford, pp 143–166 · DOI
  14. Cooper M, Podlich DW, Löffler CM, Van Eeuwijk F, Chapman SC (2006) Genotype-by-environment interactions under water-limited conditions. In: Ribaut J-M (ed) Drought adaptation in cereals. Food Products Press, New York, pp 51–96
  15. Cullis BR, Thomson FM, Fisher JA, Gilmour AR, Thompson R (1996) The analysis of the NSW wheat variety database. II. Variance component estimation. Theor Appl Genet 92:28–39 · DOI
  16. DeLacy IH, Basford KE, Cooper M, Bull JK, McLaren CG (1996) Analysis of multi-environment trials—an historical perspective. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 39–124
  17. Edmeades GO, Bolaños J, Chapman SC, Lafitte HR, Banziger M (1999) Selection improves drought tolerance in tropical maize populations: I. Gains in biomass, grain yield, and harvest index. Crop Sci 39:1306–1315 · DOI · Paper page
  18. Edmeades GO, McMaster GS, White JW, Campos H (2004) Genomics and the physiologist: bridging the gap between genes and crop response. Field Crops Res 90:5–18 · DOI
  19. Hammer GL, Jordan DR (2007) An integrated systems approach to crop improvement. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene–plant–crop relations. Springer, The Netherlands, pp 45–61 · DOI
  20. Hammer GL, Butler DG, Muchow RC, Meinke H (1996a) Integrating physiological understanding and plant breeding via crop modelling and optimization. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 419–441
  21. Hammer GL, Chapman SC, Muchow RC (1996b) Modelling sorghum in Australia: the state of the science and its role in the pursuit of improved practices. In: Proceedings of the third Australian sorghum conference. AIAS Occasional Publication, Tamworth, 20–22 February 1996, pp 43–61
  22. Hammer GL, van Oosterom E, Chapman SC, Mclean G (2001) Supply and demand economics applied to crop growth. In: Borrell AK, Henzell RG (eds) Proceedings of the fourth Australian sorghum conference. CD-rom format. Range Media Pty Ltd. ISBN: 0–7242–2163–8, Kooralbyn, 5–8 February 2001
  23. Hammer GL, Chapman S, Van Oosterom E, Podlich DW (2005) Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Aust J Agric Res 56:947–960 · DOI · Paper page
  24. Hsiao TC (1973) Plant responses to water stress. Ann Rev Plant Physiol 24:519–570 · DOI · Paper page
  25. Loffler CM, Wei J, Fast T, Gogerty J, Langton S, Bergman M, Merrill B, Cooper M (2005) Classification of maize environments using crop simulation and geographic information systems. Crop Sci 45:1708–1716 · DOI
  26. Ludlow MM, Muchow RC (1990) A critical evaluation of traits for improved crop yields in water-limited environments. Adv Agron 43:107–153 · DOI · Paper page
  27. Malosetti M, Visser RGF, Celis-Gamboa C, van Eeuwijk FA (2006) QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato. Theor Appl Genet 113:288–300 · DOI
  28. (ed) Plant breeding for the 11th millennium proceedings 12th Australasian plant breeding conference. Perth, 15-20 September 2002, pp 650-654
  29. Podlich D, Cooper M (1998) QU-GENE: a simulation platform for quantitative analysis of genetic models. Bioinformatics 14:632–653 · DOI
  30. Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131:664–675 · DOI · Paper page
  31. Rizza F, Badeck FW, Cattivelli L, Lidestri O, Di Fonzo N, Stanca AM (2004) Use of a water stress index to identify barley genotypes adapted to rainfed and irrigated conditions. Crop Sci 44:2127–2137 · doi:10.2135/cropsci2004.2127
  32. Saulescu NN, Kronstad WE (1995) Growth simulation outputs for detection of differential cultivar response to environmental factors. Crop Sci 35:773–778 · doi:10.2135/cropsci1995.0011183x003500030023x
  33. Thornley JHM, France J (2004) Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences, 2nd edn. CABI Pub, Wallingford
  34. Voltas J, Van Eeuwijk FA, Araus JL, Romagosa I (1999) Integrating statistical and ecophysiological analyses of genotype by environment interaction for grain filling of barley II. Grain growth. Field Crops Res 62:75–84 · DOI
  35. Wang E, Robertson MJ, Hammer GL, Carberry PS, Holzworth D, Meinke H, Chapman SC, Hargreaves JNG, Huth NI, McLean G (2002) Development of a generic crop model template in the cropping system model APSIM. Eur J Agronomy. Elsevier Science B.V., Amsterdam Netherlands, pp 121–140 · DOI · Paper page

Cited by

See all 195

Metadata

Authors
Chapman, S. C.
Venue
Euphytica
Publisher
Springer Science and Business Media LLC
Published
2007-12-04
Volume
161
Issue
1-2
Pages
195-208
ISSN
  • 0014-2336 (ISSN-L)
  • 1573-5060
DOI
10.1007/s10681-007-9623-z
OpenAlex
W2083426970
Cited by
195
References
7
FWCI
11.17
Type
journal article
Language
en
Open access
closed
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. https://app.cochat.ai/papers/10.1007/s10681-007-9623-z
The paper
Chapman, S. C. (2008). Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica. https://doi.org/10.1007/s10681-007-9623-z

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.