Quinoa Phenological Development Modeling Based on Field Data

Document Type : Research Article

Authors

• Sh Eghbalishahabad ¹
• M Nasiri mahalati ²
• M Jahan ²
• M Salehi ³

¹ Ph.D. graduate, Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

² Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

³ National Salinity Research Center, Agricultural Research, Education and Extension Organization (AREEO), Yazd, Iran

10.22067/jcesc.2022.74969.1145

Abstract

Introduction
Climate change is rapidly degrading the conditions of crop production. For instance, increasing salinization and aridity is forecasted to increase in most parts of the world. As a consequence, new stress-tolerant species and genotypes must be identified and used for future agriculture. Stress-tolerant species exist but are actually underutilized and neglected. Quinoa, scientifically known as Chenopodium quinoa Willd. is a member of the Amaranthaceae family. Promoting the cultivation and nutrition of quinoa will diversify food products in the country, sustainable production, increase farmers' incomes and provide part of the community's food needs. Crop simulation models have been used for various studies such as selecting the appropriate cultivar, determining the best planting date, predicting the effect of diversity and climate change on growth. Field research requires a lot of time and money, while computer simulation models can save time and money by conducting extensive experimental simulations.
Materials and Methods
This research was conducted in two regions of Yazd province with 10 separate experiments in the form of a randomized complete block design with 3 replications. Experimental factors included 5 promising modified lines in Yazd Salinity Research Center with Titicaca cultivar. The lines consisted of four intermediate maturity lines, numbered 1 (NSRCQE), 2 (NSRCQC), 3 (NSRCQD), and 6 (NSRCQA), one late maturity line numbered 4 (NSRCQB), and the early maturity cultivar Titicaca numbered 5. Sampling and note-taking were performed regularly, once every three days, in proportion to the progress of the phenological stages of each line. A model based on degree-day-growth was prepared in FST language. In preparing the length table, due to the short day of Quinoa, for all lines in the model up to 12.5 hours, the development rate was one, and after 13.8 hours, the development rate was zero. The base temperature in the model was 2 °C. Then, the model was calibrated and evaluated with data taken from the field.
Results and Discussion
RMSE (CV) coefficient of variation between 7 to 12%, root mean square error (RMSE) between 4.4 to 6.4 days, Wilmot agreement index (d) between 0.99 to 1, model efficiency (ME) between 0.96  to 0.98, the mean deviation from the model (MB) was between 0.05 to 0.08 and the coefficient of determination (R²) was between 92 % to 98%. These values indicated a good estimate of the day to flowering of quinoa with the model written in FST language, and the values of day to flowering simulated gained the necessary validity. The coefficient of variation of nRMSE (CV) is between 6.8 to 8.6%, the root mean square error (RMSE) is between 6.2 and 8.7 days, the Wilmot agreement index (d) is between 0.75 and 0.92, The mean deviation from the model (MB) was between 0.05 to 0.08 and the coefficient of determination (R²) was between 92% and 98%. These values indicated a good estimate of the day to physiological maturity with the model written in the FST language, and the day values to the simulated physiological maturity gained the necessary validity. Calibration and evaluation of model efficiency using root mean square error (RMSE), coefficient of variation or nRMSE percentage (CV), Wilmot agreement index (d), model efficiency (ME), and mean model deviation (MB), coefficient of explanation (R²), line test 1: 1 day until germination, flowering and good physiological maturity was estimated.
Conclusion
The results of this study indicated that, the quinoa model prepared for quinoa in terms of degree-day-growth well predicts the developmental stages (emergence, flowering and maturation) of this plant in terms of maturity (early, medium and late) and can be its help determined the appropriate planting date in different areas. This calibrated sub-model can now be used to evaluate different temperature and photoperiod effects for decision making in a wide range of growth environments in quinoa cultivation systems in current and future climatic conditions. Therefore, this sub-model can be used in educational-research and applied work in the field.

Keywords

• FST
• Growing degree days
• Sowing date

20.1001.1.20081472.1402.21.1.4.0

Main Subjects

• Plant modeling

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Open Access

©2023 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Abasi, S., Cordnaeich A., & Bagheri, M. (2018). Evaluation of genetic diversity of new chenopodium quinoa (Chenopodium quinoa Willd.) cultivars based on agromorphological traits. 15^th National Iranian Congress Science Congress, 2-5 Sep. 2019. Karaj, Iran. (in Persian with English abstract).
2. Bertero, H. D., King, R. W., & Hall, A. J. (1999a). Modelling photoperiod and temperature responses of flowering in quinoa (Chenopodium quinoa). Field Crop Reserch, 63, 19-34.
3. Bertero, H. D., King, R. W., & Hall, A. J. (1999b). Photoperiod-sensitive evelopment phases in quinoa (Chenopodium quinoa). Field Crops Research 60, 231-243.
4. Eghbali, Sh., Jahan, M., Nassiri, M. M., & Salehi, M. (2021). The response of phenological stages of quinoa promising lines to temperature and photoperiod regimes. Iranian Journal of Field Crops Research, 19, 261-274. https://doi.org/10.22067/jcesc.2021.69051.1032
5. FAO. (2014). Food and nutrition in numbers. Food and Agriculture Organisation, Rome, Accessed 27 March 2015.
6. Fischer, S., Wilckens, R., Jara, J., Aranda, M., Valdivia, W., Bustamante, L., Graf, F., & Obal. I. (2017). Protein and antioxidant composition of quinoa (Chenopodium quinoa ) sprout from seeds submitted to water stress, salinity and light conditions. Industrial Crops & Products 107, 558-564. https://doi.org/10.1016/j.indcrop.2017.04.035
7. Hirich, A., Choukr-Allah, R., & Jacobsen, S. E. (2014). Quinoa in Morocco-Effect of sowing dates on development and yield. Journal of Agronomy and Crop Science, 23, 1-7. https://doi.org/10.1111/jac.12071
8. Matthews, R. B., Rivington, M., Muhammed, S., Newton, A. C., & Hallett, P. D. (2013). Adapting crops and cropping systems to future climates to ensure food security: the role of crop modelling. Global Food Security, 2, 24-28. https://doi.org/10.1016/j.gfs.2012.11.009
9. Nassiri, M. M. (2000). Modelling Potential crop growth processes. Jahad Daneshgahi Mashhad press.
10. Nassiri, M. M., Koocheki, A. R., Fallahpour, F., & Amiri, M. B. (2019). Optimization of Nitrogen Fertilizer and Irrigation in Wheat (Triticum aestivum) Cultivation by Central Composite Design. Journal of Agroecology, 11, 515-530. https://doi.org/10.22067/jag.v11i2.31912
11. Pourghasemian, N., Moradi, R., & Naghizadeh, M. (2018). Effect of Planting Time and Place on Quality of Some Brompt on Stock Varieties for Cultivation in Bardsir, Kerman. Crops Improvement, 20, 679-692. https://doi.org/10.22059/jci.2018.246733.1879
12. Präger, A., Boote, K. J., Munz, S., & Hönninger, S. G. (2019). Simulating growth and development processes of Quinoa (Chenopodium quinoa): adaptation and evaluation of the CSM-CROPGRO model. Agronomy, 9, 832. https://doi.org/10.3390/agronomy9120832
13. Qulipor, A., Golkhodani, K., Latifi, N., & Moqadam, M. (2003). Comparison of growth and yield of rapeseed varieties in rain fed conditions. Gorgan Agricultural Sciences and Natural Resources, 3(1), 111-121.
14. Rahban, S., Torabi, B., Soltani, S., & Zeinali, E. (2021). Using SSM-iCrop Model to Predict Phenology, Yield, and Water Productivity of Canola (Brassica napus) in Iran Condition. Journal of Agroecology, 13, 157-177. https://doi.org/10.22067/jag.v13i2.84057
15. Salehi, M., & Dehghani, F. (2017). Quinoa, suitable semi cereal for salt water resources. Report of Minisrty of Agricultural Jihad.
16. Salehi, M., Soltani, V., & Dehghany, F. (2019). The effect of planting date on phenological stages and yield of quinoa seeds in saline conditions. Environmental Stresses in Crop Sciences, 12, 923-932. https://doi.org/10.22077/escs.2019.1514.1341
17. Soltani, A., Robertson, M. J., Mohammad-Nejad, Y., & Rahemi-Karizaki, A. (2006). Modeling chickpea growth and development: leaf production and senescence. Field Crops Reserch 99, 14-23. https://doi.org/10.1016/j.fcr.2006.02.005
18. Vega-Gálvez, A., Miranda, M., Vergara, J., Uribe, E., Puente, L., & Martínez, E. A. (2010). Nutrition facts and functional potential of quinoa (Chenopodium quinoa), an ancient Andean grain: a review. The Journal of Agricultural Science, 90, 2541-2547. https://doi.org/10.1002/jsfa.4158
19. Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63, 1309-1313. https://www.jstor.org/stable/26222954
20. Yang, A., Akhtar, S. S., Amjad, M., Iqbal, S., & Jacobsen, S. E. (2016). Growth and Physiological Responses of Quinoa to Drought and Temperature Stress. Journal of Agronomy and Crop Science, 202, 445-453. https://doi.org/10.1111/jac.12167