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Predicting time trend of dry matter accumulation and leaf area index of winter cereals under nitrogen limitation by non-linear models

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Abstract

Physiological importance of some non-linear regression models parameters (Beta 1, Beta 2, Logistic, Richards, Gompertz, Symmetrical sigmoid pattern, cut linear exponential, and Weibull) were studied in describing the time trend of accumulated dry matter and LAI of winter cereals under two nitrogen levels. Thus, a factorial experiment based on Randomized Complete Block design with four replications was performed. Treatments were zero, and optimum nitrogen levels (150, 120, 150, 120, 210 and 240 kg/ha for bread wheat, durum wheat, hull less barley, two-rowed barley, six-rowed barley and triticale, respectively), and winter cereals including durum and bread wheat (cv. Koohdasht), barley (Hordeum vulgare L.), two-rowed barley (cv. Khorram), six-rowed (cv. Sahra), hull less barley (line 17), and triticale (Triticum wittmak L.). The experiment was performed during the 2013/2014 and 2014/2015 seasons at the research field of Gonbad Kavous University, Iran. Results revealed for LAI that in Koohdasht cultivar, according to MAE, the prediction of both Logistic (0.32) and Beta (0.38) models in the zero nitrogen was better than nitrogen consumption while in wheat drum, no difference was observed between the models in both conditions. All models could describe time trend of accumulated dry matter under both fertilizer levels, but Gompertz, symmetrical expo linear models shown slightly better than others. Enhancement estimation of parameters of these models (maximum accumulated dry matter, RGR in linear phase, RGR in Expo linear phase, lost time to beginning of Expo linear phase, slope of dry matter and time of CGR max) are very crucial in modelling studies, cultivars comparison, growth analyses and simulation of growth and production of winter cereals.

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References

  • Akira, T., & Junichi, Y. (1972). Dry matter production, yield components and grain yield of the maize plant. Journal of the Faculty of Agriculture, Hokkaido University, 57(1), 71–132.

    Google Scholar 

  • Amanullah, A., Shaha, S., Shaha, Z., Khalali, S. K., Jan, A., Jan, M. T., Afzal, M., Akbar, H., Khan, H., Rahman, H., & Nawab, K. (2014). Effects of variable nitrogen source and rate on leaf area index and total dry matter accumulation in maize (Zea mays L.) genotype under calcareous soils. Turkish Journal of Field Crops., 19(2), 276–284. https://doi.org/10.17557/tjfc.90307

    Article  Google Scholar 

  • Archana, R., Sujit, S. R., & Girish, J. (2017). Physiological parameters leaf area index, crop growth rate, relative growth rate and net assimilation rate of different varieties of rice grown under different planting geometries and depths in SRI. International Journal of Pure & Applied Bioscience, 5(1), 362–367. https://doi.org/10.18782/2320-7051.2472

    Article  Google Scholar 

  • Betty, J. S., Shem, G. J., & Everline, O. I. (2017). The use of regression models to predict tea crop yield responses to climate change: A case of Nandi East, Sub-County of Nandi County Kenya. Journal of Climate, 5(54), 1–14. https://doi.org/10.3390/cli5030054

    Article  Google Scholar 

  • Brankovic, G., Dodig, D., Pajic, V., Kandic, V., Kenzevic, D., Duric, N., & Zivanovic, T. (2018). Genetic parameters of Triticum aestivum and Triticum durum for technological quality properties in Serbia. Zemdirbyste-Agriculture, 105(1), 39–45. https://doi.org/10.13080/z-a.2018.105.006

    Article  Google Scholar 

  • Challinor, A. J., Müller, C., Asseng, S., Deva, C., Nicklin, K. J., Wallach, D., Vanuytrecht, E., Whitfield, S., Villegas, J. R., & Koehler, A. (2018). Improving the use of crop models for risk assessment and climate change adaptation. Agricultural Systems, 159, 296–306. https://doi.org/10.1016/j.agsy.2017.07.010

    Article  PubMed  PubMed Central  Google Scholar 

  • FAO. (2020). Fertilizers vol. 41, Food and Agriculture Organization of the United Nations.

  • Ferrise, R., Triossi, A., Stratonovitch, P., Indi, M. B., & Martre, M. (2010). Sowing date and nitrogen fertilization effects on dry matter and nitrogen dynamics for durum wheat: An experimental and simulation study. Field Crops Res., 117, 245–257. https://doi.org/10.1016/j.fcr.2010.03.010

    Article  Google Scholar 

  • Ghadirian, R., Soltani, A., Zeinali, E., & Kalate-Arabi, M. (2011). Evaluation of non-linear regression models to use in wheat growth analysis. EJCPP., 4(3), 55–77. (In Persian with English abstract).

    Google Scholar 

  • Hocaoglu, O., & Coskun, Y. (2018). Evaluation of dry matter accumulation in triticale by different sigmoidal growth models in west Anatolia of Turkey. Genetika, 50(2), 561–574. https://doi.org/10.2298/GENSR1802561H

    Article  Google Scholar 

  • Jhony, T. T., Alessandro, C. G., & Weber, S. R. (2017). Comparing non-linear mathematical models to describe growth of different animals. Acta Scientiarum Animal Science, 39(1), 73–81. https://doi.org/10.4025/actascianimsci.v39i1.31366

    Article  Google Scholar 

  • Karadavut, U., Palta, C., Kokten, K., & Bakoglu, A. (2010). Comparative study on some non-linear growth models for describing leaf growth of maize. International Journal of Agriculture and Biology, 12(2), 227–230.

    Google Scholar 

  • Kasampalis, D. A., Alexandridis, T. K., Deva, C., Challinor, A., Moshou, D., & Zalidis, G. (2018). Contribution of remote sensing on crop models: A review. Journal of Imaging., 4(52), 1–19. https://doi.org/10.3390/jimaging4040052

    Article  Google Scholar 

  • Khamis, A., & Ismail, Z. (2004). Comparative study on non-linear growth model to tobacco leaf growth data. Journal of Agronomy, 3(2), 147–153. https://doi.org/10.3923/ja.2004.147.153

    Article  Google Scholar 

  • Khan, A., Kong, X., Najeeb, U., Zheng, J., Yuen, D. K., Akhtar, K., Munsif, F., & Zhou, R. (2019). Planting density induced changes in cotton biomass yield, fiber quality, and phosphorus distribution under beta growth model. Agronomy, 9(9), 500–518. https://doi.org/10.3390/agronomy9090500

    Article  CAS  Google Scholar 

  • Kiynaz, S., Karadavut, U., & Ertek, A. (2016). Leaf area estimation of the sugar beet at different irrigation regimes. TURKJANS, 3(1), 8–16.

    Google Scholar 

  • Lei, Y. C., & Zhang, S. Y. (2004). Features and partial derivatives of Bertalanffy–Richards growth model in forestry. Nonlinear Analysis: Modelling and Control, 9, 65–73.

    Article  Google Scholar 

  • Lithourgidis, A. S., Vlachostergios, D. N., Dordas, C. A., & Damalas, C. A. (2011). Dry matter yield, nitrogen content, and competition in pea–cereal intercropping systems. European Journal of Agronomy, 34, 287–294. https://doi.org/10.1016/j.eja.2011.02.007

    Article  Google Scholar 

  • Liu, X. J., Qiang, C. A. O., Yuan, Z. F., Xia, L. I. U., Wang, X. L., Tian, Y. C., Cao, W. X., & Yan, Z. H. U. (2018). Leaf area index based nitrogen diagnosis in irrigated lowland rice. Journal of Integrative Agriculture., 17(1), 111–121. https://doi.org/10.1016/S2095-3119(17)61714-3

    Article  CAS  Google Scholar 

  • Mao, L., Zhang, L., Sun, X., Werf, W., Evers, J. B., Zhao, X., Zhang, S., Song, X., & Li, Z. (2018). Use of the beta growth function to quantitatively characterize the effects of plant density and a growth regulator on growth and biomass partitioning in cotton. Field Crops Research, 224, 28–36. https://doi.org/10.1016/j.fcr.2018.04.017

    Article  Google Scholar 

  • Montoya, F., García, C., Pintos, F., & Otero, A. (2017). Effects of irrigation regime on the growth and yield of irrigated soybean in temperate humid climatic conditions. Agricultural Water Management, 193, 30–45. https://doi.org/10.1016/j.agwat.2017.08.001

    Article  Google Scholar 

  • Pirmoradian, N., & Sepaskhah, A. R. (2006). A very simple model for yield prediction of rice under different water and nitrogen application. Biosystems Engineering, 93(1), 25–34. https://doi.org/10.1016/j.biosystemseng.2005.09.004

    Article  Google Scholar 

  • Portes, T. A., & Melo, H. C. (2014). Light interception, leaf area and biomass production as a function of the density of maize plants analysed using mathematical models. Acta Scientiarum - Agronomy, 36(4), 457–463. https://doi.org/10.4025/actasciagron.v36i4.17892

    Article  Google Scholar 

  • Prasad, T. V. R., Krishnamurthy, K., & Kailasam, C. (1992). Functional crop and cob growth models of maize (Zea mays L.) cultivars. Journal of Agronomy and Crop Science, 168(3), 208–212. https://doi.org/10.1111/j.1439-037X.1992.tb01000.x

    Article  Google Scholar 

  • Puiatti, G. A., Cecon, P. R., Nascimento, M., Nascimento, A. C. C., Carneiro, A. P. S., Silva, F. F., Puiatti, M., & Oliveira, A. C. R. (2018). Quantile regression of non-linear models to describe different levels of dry matter accumulation in garlic plants. Ciencia Rural, 48(1), 1–6. https://doi.org/10.1590/0103-8478cr20170322

    Article  Google Scholar 

  • Rahemi-karizaki, A. (2005). Predicting interception and use of solar radiation in chickpea. Thesis of M.Sc., Gorgan University of Agricultural Sciences, p. 89.

  • Reis, R. M., Cecon, P. R., Puiatti, M., Finger, F. L., Nascimento, M., Silva, F. F., Carneiro, A. P., & Silva, A. R. (2014). Non-linear regression models applied to clusters of garlic accessions. Horticultura Brasileira, 32(2), 178–183.

    Article  Google Scholar 

  • Richards, F. J. (1959). A flexible growth functions for empirical use. Journal of Experimental Botany, 10(2), 290–301. https://doi.org/10.1093/jxb/10.2.290

    Article  Google Scholar 

  • Sabouri, A., & Alipour Estakhri, V. (2014). Fitting of growth pattern model according to sunflower Lakomka and Progress cultivars in dryland conditions. Journal of Agricultural Knowledge., 5(10), 76–65.

    Google Scholar 

  • SAS Institute. (1992). SAS/STAT user's guide. Cary: SAS Institute Inc.

  • Sepaskhah, A. R., Fahandezh-Saadi, S., & Zand-Parsa, S. (2011). Logistic model application for prediction of maize yield under water and nitrogen management. Agricultural Water Management, 99, 51–57. https://doi.org/10.1016/j.agwat.2011.07.019

    Article  Google Scholar 

  • Shi, P., Men, X., Sandhu, H. S., Chakraborty, A., Li, B., Ou-Yang, F., Sun, Y., & Ge, F. (2013). The “general” ontogenetic growth model is inapplicable to crop growth. Ecological Modelling, 266, 1–9. https://doi.org/10.1016/j.ecolmodel.2013.06.025

    Article  Google Scholar 

  • Silva, H. R. F., Melo, V. L., Pacheco, D. D., Assis, Y. J. M., & Sales, H. D. (2014). Dry matter and micronutrients accumulation in cassava intercropped with banana tree. Pesquisa Agropecuaria Tropical, 44(1), 15–23. https://doi.org/10.1590/S1983-40632014000100008

    Article  Google Scholar 

  • Sorrell, B. K., Tanner, C. C., & Brix, H. (2012). Regression analysis of growth responses to water depth in three wetland plant species. AoB Plants. https://doi.org/10.1093/aobpla/pls043

    Article  PubMed  PubMed Central  Google Scholar 

  • Su, L., Wang, Q., Wang, C., & Shan, Y. (2015). Simulation models of leaf area index and yield for cotton grown with different soil conditioners. PLoS ONE, 10(11), 1–19. https://doi.org/10.1371/journal.pone.0141835

    Article  CAS  Google Scholar 

  • Timmermans, B. G. H., Vos, J., VanNieuwburg, J., Stomph, T. J., & Van der Putten, P. E. L. (2007). Germination rates of Solanum sisymbriifolium: Temperature response models, effects of temperature fluctuations and soil water potential. Seed Sci Re., 17(3), 221–231. https://doi.org/10.1017/S0960258507785628

    Article  Google Scholar 

  • Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18, 293–297.

    Article  Google Scholar 

  • Weraduwage, S. M., Chen, J., Anozie, F. C., Morales, A., Weise, S. E., & Sharkey, T. D. (2015). The relationship between leaf area growth and biomass accumulation in Arabidopsis thaliana. Frontiers in Plant Science, 6, 1–21. https://doi.org/10.3389/fpls.2015.00167

    Article  Google Scholar 

  • Yin, X., Gouadrian, J., Latinga, E. A., Vos, J., & Spiertz, J. H. (2003). A flexible sigmoid growth function of determinate growth. Annals of Botany, 91(3), 361–371. https://doi.org/10.1093/aob/mcg029

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge the financial support of the project (Grant Number: 6/1187) by Gonbad Kavous University, Golestan province, Iran.

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Correspondence to Ali Rahemi-Karizaki.

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Rahemi-Karizaki, A., Khaliliaghdam, N. & Biabani, A. Predicting time trend of dry matter accumulation and leaf area index of winter cereals under nitrogen limitation by non-linear models. Plant Physiol. Rep. 26, 443–456 (2021). https://doi.org/10.1007/s40502-021-00597-x

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