The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Phenotyping for drought tolerance in grain crops: when is it useful to breeders?
Abstract
Breeding for drought tolerance in grain crops is not a generic issue. Periods of drought vary in length, timing and intensity and different traits are important with different types of drought. The search for generic drought tolerance using single-gene transformations has been disappointing. It has typically concentrated on survival of plants suffering from severe water stress, which is rarely an important trait in crops. More promising approaches that target complex traits tailored to specific requirements at the different main stages of the life of a crop, during: establishment, vegetative development, floral development and grain growth are outlined. The challenge is to devise inexpensive and effective ways of identifying promising phenotypes with the aim of aligning them with genomic information to identify molecular markers useful to breeders. Controlled environments offer the stability to search for attractive phenotypes or genotypes in a specific type of drought. The recent availability of robots for measuring large number of plants means that large numbers of genotypes can be readily phenotyped. However, controlled environments differ greatly from those in the field. Devising pot experiments that cater for important yield-determining processes in the field is difficult, especially when water is limiting. Thus, breeders are unlikely to take much notice of research in controlled environments unless the worth of specific traits has been demonstrated in the field. An essential link in translating laboratory research to the field is the development of novel genotypes that incorporate gene(s) expressing a promising trait into breeding lines that are adapted to target field environments. Only if the novel genotypes perform well in the field are they likely to gain the interest of breeders. High throughput phenotyping will play a pivotal role in this process.
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.
Integrity signals
Registry
Cites
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.
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
Phenomics – technologies to relieve the phenotyping bottleneck
Furbank, R. T., & Tester, M. (2011) · Trends in Plant Science · doi:10.1016/j.tplants.2011.09.005 · Cited by 1,675
Shinozaki, K., & Yamaguchi-Shinozaki, K. (2000) · Current Opinion in Plant Biology · doi:10.1016/s1369-5266(00)80068-0 · Cited by 1,628
Breeding for high water-use efficiency
Condon, A. G. (2004) · Journal of Experimental Botany · doi:10.1093/jxb/erh277 · Cited by 1,208
Genomic selection: prediction of accuracy and maximisation of long term response
Goddard, M. (2009) · Genetica · doi:10.1007/s10709-008-9308-0 · Cited by 1,089
Pot size matters: a meta-analysis of the effects of rooting volume on plant growth
Poorter, H., Bühler, J., van Dusschoten, D., Climent, J., & Postma, J. A. (2012) · Functional Plant Biology · doi:10.1071/fp12049 · Cited by 834
Yamaguchi‐Shinozaki, K. (2000) · Current Opinion in Plant Biology · doi:10.1016/s1369-5266(00)00067-4 · Cited by 814
Breeding for Yield Potential and Stress Adaptation in Cereals
Araus, J. L., Slafer, G. A., Royo, C., & Serret, M. D. (2008) · Critical Reviews in Plant Sciences · doi:10.1080/07352680802467736 · Cited by 806
Hammer, G. L., Dong, Z., McLean, G., Doherty, A., Messina, C., Schussler, J., Zinselmeier, C., Paszkiewicz, S., & Cooper, M. (2009) · Crop Science · doi:10.2135/cropsci2008.03.0152 · Cited by 745
Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field
Trachsel, S., Kaeppler, S. M., Brown, K. M., & Lynch, J. P. (2011) · Plant and Soil · doi:10.1007/s11104-010-0623-8 · Cited by 731
Breeding and Cereal Yield Progress
Fischer, R. A. (., & Edmeades, G. O. (2010) · Crop Science · doi:10.2135/cropsci2009.10.0564 · Cited by 681
Cited by
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction
Cooper, M., Messina, C. D., Podlich, D., Totir, L. R., Baumgarten, A., Hausmann, N. J., Wright, D., & Graham, G. (2014) · Crop & Pasture Science · doi:10.1071/cp14007 · Cited by 411
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., & Hawkesford, M. J. (2016) · Functional Plant Biology · doi:10.1071/fp16163 · Cited by 382
A multisite managed environment facility for targeted trait and germplasm phenotyping
Rebetzke, G. J., Chenu, K., Biddulph, B., Moeller, C., Deery, D. M., Rattey, A. R., Bennett, D., Barrett-Lennard, E. G., & Mayer, J. E. (2012) · Functional Plant Biology · doi:10.1071/fp12180 · Cited by 123
Uncovering hidden genetic variation in photosynthesis of field‐grown maize under ozone pollution
Choquette, N. E., Ogut, F., Wertin, T. M., Montes, C. M., Sorgini, C. A., Morse, A. M., Brown, P. J., Leakey, A. D. B., McIntyre, L. M., & Ainsworth, E. A. (2019) · Global Change Biology · doi:10.1111/gcb.14794 · Cited by 41
Metadata
- Authors
- Passioura, J. B.
- Venue
- Functional Plant Biology
- Publisher
- CSIRO Publishing
- Published
- 2012-09-25
- Volume
- 39
- Issue
- 11
- Pages
- 851-859
- ISSN
- 1445-4408 (ISSN-L)
- 1445-4416
- OpenAlex
- W1981179689
- Cited by
- 306
- References
- 64
- FWCI
- 19.47
- Type
- journal article
- Language
- en
- Open access
- closed
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: Phenotyping for drought tolerance in grain crops: when is it useful to breeders? https://app.cochat.ai/papers/10.1071/fp12079
- The paper
- Passioura, J. B. (2012). Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Functional Plant Biology. https://doi.org/10.1071/fp12079
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.