نوع مقاله : مقاله کامل علمی پژوهشی
نویسندگان
1 دانشجوی دکتری فیزیولوژی گیاهان زراعی، دانشکده کشاورزی و منابع طبیعی دانشگاه گنبد کاووس، گنبد کاووس، ایران
2 نویسنده مسئول، دانشیار گروه تولیدات گیاهی و مسئول مکاتبه، دانشکده کشاورزی و منابع طبیعی دانشگاه گنبد کاووس، گنبد کاووس، ایران
3 استاد گروه تولیدات گیاهی، دانشکده کشاورزی و منابع طبیعی دانشگاه گنبد کاووس، گنبد کاووس، ایران
4 دانشیار گروه تولیدات گیاهی، دانشکده کشاورزی و منابع طبیعی دانشگاه گنبد کاووس، گنبد کاووس، ایران
5 استاد گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Background and objectives: Quinoa is a valuable food product that helps ensure food security worldwide. Accurate phenological predictions are essential for understanding a plant species' growth potential and performance. One of the most important studies required for this purpose is quantifying and estimating cardinal temperatures and day length parameters for the pollination stage, which is a crucial step in understanding the growth and performance of plants. Given that quantitative information on quinoa phenology is scarce, such data regarding pollination responses to temperature and day length can be useful in understanding the growth and performance of this plant. Therefore, the objectives of the present study are: (1) to develop a multiple model for predicting pollination in quinoa, and (2) to determine cardinal temperatures and day length parameters for pollination.
Materials and Methods: A field experiment was conducted with 12 planting dates from 2019 to 2021 on three quinoa varieties in Galugah city. In this study, 10 plants were randomly selected and marked in each subplot to record the pollination initiation time for each planting date. The time taken to reach the pollination initiation stage was also recorded, where at least 50% of the plants had at least one flower at any position on the plant. To quantify the pollination stage against temperature and day length, a multiple model using combined temperature and day length functions was used. The temperature function was represented as a piecewise function, and the day length function was represented exponentially. The model was executed using the Proc nlin procedure in SAS software. To evaluate the model's accuracy, criteria such as RMSD, R², comparison of 1:1 line coefficients with coefficients a and b from linear regression between predicted and observed values, and the correlation coefficient between them were used.
Results:The results showed that the RMSD values for different varieties ranged from 1.6 to 6.9 days, with lower values indicating higher model efficiency. The R² coefficient for describing the relationship between pollination rate and temperature and day length in the three quinoa varieties was estimated to be above 0.97. A high R² value indicates that the model is appropriate for describing the relationship between pollination rate, temperature, and day length. The base temperatures for the different varieties were estimated to be between 1.6 and 4.2 degrees Celsius, but no statistically significant differences were observed in the base temperatures of these three varieties. The optimal temperature estimated among the varieties ranged from 22.1 to 27.9 degrees Celsius. Further analysis using the error of estimating the optimal temperature revealed significant differences among the varieties regarding base temperature, with the Q12 variety having the highest optimal temperature value. The sensitivity coefficients to day length for varieties Q12, Titicaca, and Giz1 were estimated to be 0.025, 0.065, and 0.096 hours per day, respectively. Statistically significant differences were observed among the studied varieties with respect to sensitivity to day length. The biological day (the minimum number of days to pollination under optimal temperature and day length conditions) estimated through the model for the varieties Q12, Titicaca, and Giz1 were 31.7, 32.1, and 36.1 days, respectively.
Conclusion: The results showed that due to the low value of RMSD (less than 6.9 days) and high values of R2 (above 0.97) and r (above 0.95) in different cultivars, the bipartite-exponential model was able to describe the relationship between pollination well. Describe the temperature and length of the day. This model can be used to optimize management decisions, adjust phenological responses to diverse environmental conditions, and predict phenological responses to temperature and day length changes in the future.
Key Words: Temperature function, biological day, optimal temperature, phenology.
کلیدواژهها [English]
2.Abd El-Hakim, A. F., Mady, E., Abou Tahoun, A. M., Ghaly, M. S., & Eissa, M. A. (2022). Seed quality and protein classification of some quinoa varieties. Journal of Ecological Engineering, 23(1), 24-33. DOI: https://doi.org/10.12911/22998993/143866
3.Huang, J. Z., Shrestha, A., Tollenaar, M., Deen, W., Rajcan, I., Rahimian, H., & Swanton, C.J. (2001). Effects of temperature and photoperiod on the phenological development of wild mustard (Sinapis arvensis L.). Field Crops Research, 70, 75-86. DOI:10.1016/S0378-4290(00)00155-6.
4.Daba, K., Tar'an, B., Bueckert, R., & Warkentin, T. D., (2016). Effect of temperature and photoperiod on time to pollination in chickpea. Crop Science, 56(1), 200-208. https://doi.org/10.2135/cropsci2015.07.0445.
5.Soltani, A., Hammer, G.L., Torabi, B., Robertson, M.J., & Zeinali, E. (2006). Modeling chickpea growth and development: phenological development. Field Crops Research, 99, 1-13. DOI:10.1016/j.fcr.2006.02.004.
6.Torabi, B., Adibniya, M., & Rahimi, A. (2015). Seedling emergence response to temperature in safflower: measurements and modeling. Internationa Journal Plant Production, 9, 393-314. doi: 10.22069/ijpp.2015.2223.
7.Torabi, B., Adibnya, M., Rahimi, A., & Azari, A. (2020). Modeling pollination response to temperature and photoperiod in safflower. Industrial Crops and Products, 151, 112474. doi.org/10.1016/j.indcrop.2020.112474.
8.Torabi, B., Attarzadeh, M., & Soltani, A. (2013). Germination Response to Temperature in Different Safflower (Carthamus tinctorius) Cultivars. Seed Technology, 35, 47-59.
9.Torabi, B., Soltani, E., Archontoulis, S.V., & Rabii, A. (2016). Temperature and water potential effects on Carthamus tinctorius L. seed germination: measurements and modeling using hydrothermal and multiplicative approaches. Brazilian Journal of Botany, 39, 427-436. doi:10.1007/s40415-015-0243-x.
10.Hardegree, S.P. (2006a). Predicting germination response to temperature. I. Cardinal-temperature models and subpopulation-specific regression. Annals of Bottany. 97, 1115–1125. doi: 10.1093/aob/mcl071.
11.Hardegree, S.P., & Winstral, A.H. (2006). Predicting germination response to temperature. II. Three-dimensional regression, statistical gridding and iterative-probit optimization using measured and interpolated-subpopulation data. Annals of Bottany, 98, 403–410. doi:10.1093/aob/mcl112.
12.Hardegree, S.P. (2006b). Predicting germination response to temperature. III. Model validation under field-variable temperature conditions. Annals of Botany, 98, 827–834. doi: 10.1093/aob/mcl163.
13.Ellis, R.H., Hadley, P., Roberts, E.H., & Summerfield, R.J. (1990). Quantitative relations between temperature and crop development and growth, in: Jackson, M.T., Ford-Lloyd, B.V., Parry, M.L. (Eds.), Climatic change and plant genetic resources. London: Belhaven Press, 85-115.
14.Slafer, G.A., & Rawson, H.M. (1996). Responses to photoperiod change with phenophase and temperature during wheat development. Field Crops Research, 46, 1-13. doi:10.1016/0378-4290(95)00081-X.
15.Soltani, A., Torabi, B., Zeinali, E., & Sarparast, R. (2004). Response of chickpea to photoperiod as a qualitative long-day plant. Asian Journal Plant Science, 6, 705-708. doi:10.3923/ajps.2004.705.708.
16.Jensen, E., Robson, P., Norris, J., Cookson, A., Farrar, K., Donnison, I., & Clifton-Brown, J. (2013). Pollination induction in the bioenergy grass Miscanthus sacchariflorus is a quantitative short-day response, whilst delayed pollination under long days increases biomass accumulation. Journal of experimental Botany, 64, 541–552. doi:10.1093/jxb/ers346.
17.Mwale, S.S., Azam-Ali, S.N., Clark, J.A., Bradley, R.G., & Chatha, M.R. (1994). Effect of temperature on the germination of sunflower (Helianthus annuus L.). Seed Science Technology, 22, 565-571. doi: 10.1002/fsn3.1983.
18.Soltani, A., & Sinclair, T.R., 2012. Modeling Physiology of Crop Development, Growth and Yield. CABI International. 322 pp.
19.Archontoulis S.V., Miguez, F.E., & Moore, K.J. (2014). A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: application to soybean. Environmental Modeling & Software, doi:10.1016/j.envsoft.2014.04.009.
20.Yan, W. & Wallace, D. H. (1998). Simulation and prediction of plant phenology for five crops based on photoperiod×temperature interaction. Annals of Botany, 81: 705-716. doi:10.1006/anbo.1998.0625
21.Eghbali, Sh., Nassiri-Mahallati, M., Jahan, M., & Salehi, M. (2023). Quinoa Phenological Development Modeling Based on Field Data. Iranian Journal of Field Crops Research, 21(1), 47-60. https://doi.org/10.22067/jcesc.2022.74969.1145
22.Goadriaan, J. & Van Laar, H. H. (1994). Modeling potential crop growth processes. Klower Academic Public, Dordrecht, The Netherlands. 54(1), 127-129.
23.SAS Institute Inc. (1989). SAS user’ guide: Statics, Version 6, 4th editions, SAS Inst. Inc. Cary, N.C.
24.Bertero, H. D., King, R. W., & Hall, A. J. (1999). Modelling photoperiod and temperature responses of pollination in quinoa (Chenopodium quinoa Willd.) Field Crops Research, 63(1), 19-34. doi.org/10.1016/S0378-4290(99)00024-6.
25.Latif, T., Haider, Z., Ramzan, M., Akhter, M., Gull, S., Mahmood, A., Riaz, A., & Khan, R. (2019). Quantifying effects of photoperiod, temperature and humidity on pollination initiation in Basmati rice lines. American Journal of Plant Sciences, 10, 893-903. doi:10.4236/ajps.2019.106064.
26.Silim, S.N., & Omanga, P.A. (2001). The response of short-duration pigeonpea lines to variation in temperature under field condition in Kenya. Field Crops Research, 72, 97-108. doi:10.1016/S0378-4290(01)00167-8.
27.Ajam-Norouzi, H., & Soltani, A. (2008). Prediction of pollination occurrence in faba bean (Vicia faba L.). Journal of Agricultural Sciences and Natural Resources, 15, 65-77. doi:10.15835/nbha3915615.
28.Turpin, L.E., Robertson, M.J., Haire, C., Bellotti, W.D., Moore, A.D., & Rose, I. (2003). Simulating faba bean development, growth and yield in Australian. Australian Journal of Agricultural Research. 54, 39–52. doi:10.1071/AR02064.
29.Xue, Q. (2000). Phenology and gas exchange in winter wheat (Triticum aestivum L.) Ph.D. Dissertation, University of Nebraska-Lincoln, Lincoln.
30.Kamkar, B. (2005). Application of a system approach for evaluation of potential yield and yield gap of cumin and three millet genus (a case study in Northern, Razavi and Southern Khorasan provinces). Ph.D. thesis. Ferdowsi Univ Mashhad, 177p.
31.Folliard, A., Traore, P. C. S., Vaksmann, M. & Kouressy, M. (2004). Modeling of sorghum response to photoperiod: a threshold-hyperbolic approach. Field Crops Research, 89: 59-70. doi:10.1016/j.fcr.2004.01.006.
)