Dataset: Eleven commercial wheat cultivars for water and saline stress studies

Autores/as

DOI:

https://doi.org/10.46420/TAES.e240015

Palabras clave:

Triticum aestivum L., sustainability, food security, agriculture

Resumen

Wheat is a vital crop for global food security, but its productivity is increasingly threatened by water and salinity stress, exacerbated by climate change. These environmental challenges affect wheat growth at multiple stages, leading to reduced germination, stunted development, and lower yields. Given that over 20% of cultivated land worldwide is affected by salinity, there is an urgent need to develop saline stress-tolerant wheat cultivars. This study presents a dataset of 11 commercial wheat cultivars, with a focus on their responses to water and salinity stress. The dataset includes comprehensive measurements such as germination rate, shoot and root length, and biomass under control, water, and saline conditions. By analyzing these data, researchers can better understand the genetic and phenotypic traits associated with drought and salinity tolerance. This research offers valuable insights into breeding strategies aimed at enhancing stress resistance, contributing to the development of wheat varieties capable of withstand harsh environmental conditions and ensuring global food security. This dataset provides a foundation for further exploration into the mechanisms of stress tolerance in wheat and opens new avenues for improving agricultural sustainability.

Referencias

Bisong, E. (2019). Google Colaboratory. In Building Machine Learning and Deep Learning Models on Google Cloud Platform (pp. 59–64). Apress. https://doi.org/10.1007/978-1-4842-4470-8_7

Hasanuzzaman, M., Nahar, K., Rahman, A., Anee, T. I., Alam, M. U., Bhuiyan, T. F., Oku, H., & Fujita, M. (2017). Approaches to Enhance Salt Stress Tolerance in Wheat. In Wheat Improvement, Management and Utilization. InTech. https://doi.org/10.5772/67247

MAPA. (2009). Rules for seed analysis (in portuguese). In MAPA/ACS.

McKinney, W. (2010). Data Structures for Statistical Computing in Python. 56–61. https://doi.org/10.25080/Majora-92bf1922-00a

Nezhadahmadi, A., Prodhan, Z. H., & Faruq, G. (2013). Drought Tolerance in Wheat. The Scientific World Journal, 2013(1). https://doi.org/10.1155/2013/610721

Poudel, P. B., & Poudel, M. R. (2020). Heat Stress Effects and Tolerance in Wheat: A Review. Journal Biology Today’s World, 9(4), 217.

Sallam, A., Alqudah, A. M., Dawood, M. F. A., Baenziger, P. S., & Börner, A. (2019). Drought Stress Tolerance in Wheat and Barley: Advances in Physiology, Breeding and Genetics Research. International Journal of Molecular Sciences, 20(13), 3137. https://doi.org/10.3390/ijms20133137

Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace.

Yadav, M. R., Choudhary, M., Singh, J., Lal, M. K., Jha, P. K., Udawat, P., Gupta, N. K., Rajput, V. D., Garg, N. K., Maheshwari, C., Hasan, M., Gupta, S., Jatwa, T. K., Kumar, R., Yadav, A. K., & Prasad, P. V. V. (2022). Impacts, Tolerance, Adaptation, and Mitigation of Heat Stress on Wheat under Changing Climates. International Journal of Molecular Sciences, 23(5), 2838. https://doi.org/10.3390/ijms23052838

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Publicado

2024-11-30

Número

Sección

Seção Base de datos Científica