The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Drought Resistance Improvement in Rice: An Integrated Genetic and Resource Management Strategy
Open accessAbstract
Drought is the major constraint to rice production in rainfed areas across Asia and sub-Saharan Africa. In the context of current and predicted water scarcity, increasing irrigation is generally not a viable option for alleviating drought problems in rainfed rice-growing systems. It is therefore critical that genetic management strategies for drought focus on maximum extraction of available soil moisture and its efficient use in crop establishment and growth to maximize biomass and yield. Extensive genetic variation for drought resistance exists in rice germplasm. However, the current challenge is to decipher the complexities of drought resistance in rice and exploit all available genetic resources to produce rice varieties combining drought adaptation with high yield potential, quality, and resistance to biotic stresses. The strategy described here aims at developing a pipeline for elite breeding lines and hybrids that can be integrated with efficient management practices and delivered to rice farmers. This involves the development of high-throughput, high-precision phenotyping systems to allow genes for yield components under stress to be efficiently mapped and their effects assessed on a range of drought-related traits, and then moving the most promising genes into widely grown rice mega-varieties, while scaling up gene detection and delivery for use in marker-aided breeding.
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
Quantitative Trait Loci and Crop Performance under Abiotic Stress: Where Do We Stand?: Table I.
Collins, N. C., Tardieu, F., & Tuberosa, R. (2008) · PLANT PHYSIOLOGY · doi:10.1104/pp.108.118117 · Cited by 559
Steele, K. A., Price, A. H., Shashidhar, H. E., & Witcombe, J. R. (2006) · Theoretical and Applied Genetics · doi:10.1007/s00122-005-0110-4 · Cited by 370
CHENU, K., CHAPMAN, S. C., HAMMER, G. L., MCLEAN, G., SALAH, H. B. H., & TARDIEU, F. (2008) · Plant, Cell & Environment · doi:10.1111/j.1365-3040.2007.01772.x · Cited by 144
Cited by
Phenotyping for drought tolerance of crops in the genomics era
Tuberosa, R. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00347 · Cited by 558
Root biology and genetic improvement for drought avoidance in rice
Gowda, V. R. P., Henry, A., Yamauchi, A., Shashidhar, H. E., & Serraj, R. (2011) · Field Crops Research · doi:10.1016/j.fcr.2011.03.001 · Cited by 465
Self-citationStacking for future: pyramiding genes to improve drought and salinity tolerance in rice
Shailani, A., Joshi, R., Singla‐Pareek, S. L., & Pareek, A. (2021) · Physiologia Plantarum · doi:10.1111/ppl.13270 · Cited by 65
Singh, R., Pandey, N., Kumar, A., & Shirke, P. A. (2016) · Protoplasma · doi:10.1007/s00709-015-0800-y · Cited by 31
Self-citation
Metadata
- Authors
- Serraj, R., McNally, K. L., Slamet-Loedin, I., Kohli, A., Haefele, S. M., Atlin, G., & Kumar, A.
- Venue
- Plant Production Science
- Publisher
- Informa UK Limited
- Published
- 2011-01
- Volume
- 14
- Issue
- 1
- Pages
- 1-14
- ISSN
- 1343-943X (ISSN-L)
- 1349-1008
- OpenAlex
- W2092138026
- Cited by
- 241
- References
- 3
- FWCI
- 16.35
- Type
- journal article
- Language
- en
- Open access
- gold
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
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.