Evaluation of a Model to Simulate Wheat Growth and Development under Drought Stress Conditions

Document Type : Research Article

Authors

• F Hosseinpanahi ¹
• M Kafi ²
• M Parsa ²
• M Nasiri mahalati ²
• M Bannayan Aval ²

¹ University of Kurdistan

² Ferdowsi University of Mashhad

10.22067/gsc.v12i3.42215

Abstract

Modeling is one of the best tools for quantitative analyzing of biological systems that is helpful for understanding physiological basis of crops growth and development especially under limited water. In this study, a model was developed based on FAO Penman-Monteith model to simulate soil water balance (SWB) components and linked with wheat growth and development model, which was developed based on basic models SUCROS and LINTUL. A field experiment was conducted in order to presented model validation in 2010-2011 at the Ferdowsi University of Mashhad Research Field. Experimental design was split plots with 5 irrigation treatments (main plot), 2 cultivars (sub plot), and 3 replications. Irrigation treatments included irrigation based on full water requirement (FI), without irrigation during spring (NI), irrigation at the rate of 75% of water requirement (75% FI), irrigation at the rate of 50% of water requirement (50% FI) and irrigation at the rate of 25% of water requirement (25% FI) and subplots also included Pishgam (drought-resistant) and Gascogne (drought-susceptible) wheat cultivars. Then simulated results was validated with two methods: fitted linear regression between observed and simulated data and compare with 1:1 line and Root Mean Square Error in percent (RMSE %). The result of LAI-trend simulation was excellent for susceptible cultivar in non-stress condition (FI treatment) and medium for drastic stress condition (NI and 25%FI treatments). The simulation accuracy was good for other treatments. The LAI-trend simulation for resistant variety was good in all treatments. The model accuracy in maximum Leaf Area Index (LAImax) simulations and its day ripening was excellent for both varieties. Dry matter production of susceptible cultivar was simulated excellent only in FI treatment and good for other treatments. But the model accuracy was gained excellent for resistant variety in all treatments. The model accuracy in yield simulations also was excellent for both varieties. The simulated yield for NI, 25%FI, 50%FI, 75%FI and FI treatments was 1285, 3031, 4697, 6137 and 7649 kg.ha-1, respectively. The actual yield of Gascogne variety for the mentioned treatments was 1615, 2954, 4483, 5952 and 8132 kg.ha-1, and for Pishgam variety was 1758, 3652, 5071, 6064 and 7548 kg.ha-1, respectively. Overall, the results suggesting the effectiveness of presented model to predict wheat growth and development variation under different water supplies but should be revalidated with various experiments to reach better results.

Keywords

• Evapotranspiration
• FAO Penman-Monteith model
• LINTUL
• Modelling
• SUCROS

20.1001.1.20081472.1393.12.3.6.3

---

References

1- حسین پناهی، ف. 1391. بهره گیری از رهیافت مدل سازی در طراحی تیپ ایده آل گندم برای شرایط تنش خشکی در شرایط آب و هوایی مشهد. پایان نامه دکتری، دانشکده کشاورزی، دانشگاه فردوسی مشهد.

2- علیزاده، ا.، و غ. کمالی. 1387. نیاز آبی گیاهان در ایران. چاپ دوم. انتشارات آستان قدس رضوی، مشهد، 227 صفحه.

3- نصیری محلاتی، م. 1387. مدلسازی. در کتاب زراعت نوین. گردآورندگان: کوچکی، ع.، و خواجه حسینی، م. انتشارات جهاد دانشگاهی مشهد، مشهد، 704 صفحه.

4- Allen, R. G., L. S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage Paper 56. FAO, Rome.

5- Araus, J. L., G. A. Slafer, M. P. Reynolds, and C. Roya. 2002. Plant breeding and drought in C3 cereals: what should we breed for?. Annals of Botany, 89: 925-940.

6- Arora, V. K., and P. R. Gajri. 1998. Evaluation of a crop growth-water balance model for analyzing wheat responses to climate- and water-limited environments. Field Crops Research, 59: 213-224.

7- Arora, V. K., and P. R. Gajri. 2000. Assessment of a crop growth-water balance model for predicting maize growth and yield in a subtropical environment. Agricultural Water Management, 46: 157-166.

8- Asseng, S., B. A. Keating, I. R. P. Fillery, P. J. Gregory, J. W. Bowden, N. C. Turner, J. A. Palta, and D. G. Abrecht. 1998. Performance of the APSIM-wheat model in Western Australia. Field Crops Research, 57: 163-179.

9- Bannayan, M., and N. M. J. Crout. 1999. A stochastic modelling approach for real –time forecasting of winter wheat yield. Field Crops Research, 62: 85-95.

10- Behera, S. K., and R. K. Panda. 2009. Integrated management of irrigation water and fertilizers for wheat crop using field experiments and simulation modeling. Agricultural Water Management, 96: 1532–1540.

11- Bergjord, A. K., H. Bonesmo, and A. O. Skjelvag. 2008. Modelling the course of frost tolerance in winter wheat I. Model development. European Journal of Agronomy, 28: 321–330.

12- Biernath, C., S. Gayler, S. Bittner, C. Klein, P. Högy, A. Fangmeier, and E. Priesack. 2011. Evaluating the ability of four crop models to predict different environmental impacts on spring wheat grown in open-top chambers. European Journal of Agronomy, 35: 71–82.

13- Boote, K. J., M. J. Kropff, and P. S. Bindraban. 2001. Physiology and modelling of traits in crop plants: implications for genetic improvement. Agricultural Systems, 70: 395–420.

14- Chipanshi, A. C., E. A. Ripley, and R. G. Lawford. 1999. Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model. Agricultural Systems, 59: 57-66.

15- Eitzinger, J., M. Stastna, Z. Zalud, and M. Dubrovsky. 2003. A simulation study of the effect of soil water balance and water stress on winter wheat production under different climate change scenarios. Agricultural Water Management, 61: 195–217.

16- Eitzinger, J., M. Trnka, J. Hösch, Z. Žalud, and M. Dubrovský. 2004. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecological Modelling, 171: 223–246.

17- Faramarzi, M., H. Yang, R. Schulin, and K. C. Abbaspoura, 2010. Modeling wheat yield and crop water productivity in Iran: Implications of agricultural water management for wheat production. Agricultural Water Management, 97: 1861–1875.

18- Farshi, A.A., Feyen, J., Belmans, C., and Wijngaert, K. 1987. Modeling of yield of winter wheat as a function of soil water availability. Agricultural Water Management, 12: 323-339.

19- Foulkes, M. J., R. S. Bradley, R. Weightman, and J. W. Snape. 2007. Identifying physiological traits associated with improved drought resistance in winter wheat. Field Crops Research, 103: 11–24.

20- Goudriaan, J., and H. H. Van Laar. 1994. Modelling potential crop growth processes. Textbook with exercises. Kluwer Academic Publishers, Dordrecht, The Netherlands, 274 pp.

21- Hammer, G. L., M. J. Kropff, T. R. Sinclair, and J. R. Porter. 2002. Future contributions of crop modelling*/from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. European Journal of Agronomy, 18: 15-31.

22- Hanks, R. J., and V. P. Rassmussen. 1982. Predicting crop production as related to plant water stress. Advances in Agronomy, 35: 193-215.

23- Hunt, L. A., M. P. Reynolds, K. D. Sayre, S. Rajaram, J. W. White, and W. Yan. 2003. Crop modeling and the identification of stable coefficients that may reflect significant groups of genes. Agronomy Journal, 95: 20-31.

24- Jackson, P., M. Robertson, M. Copper, and G. L. Hammer. 1996. The role of physiological understanding in plant breeding: from a breeding perspective. Field Crops Research, 49: 11–37.

25- Jamieson, P. D., R. J. Martin, and G. S. Francis. 1995. Drought influences on grain yield of barley, wheat and maize. New Zealand Journal of Crop and Horticulture Science. 23: 55-66.

26- Jamieson, P. D., J. R. Porter, J. Gouadriaan, J. T. Ritchie, H. Van Keulen, and W. Stol. 1998a. A comparison of the models ARECWHEAT, CERES-wheat, Sirius, SUCROS2 and SWHEAT with measurement from wheat grown under drought. Field Crops Research, 55: 23-44.

27- Jamieson, P. D., M. A. Semenov, I. R. Brooking, and G. S. Francis. 1998b. Sirius: a mechanistic model of wheat response to environmental, variation. European Journal of Agronomy, 8: 161–179.

28- Jensen, M. E., J. L. Wright, and B. J. Pratt. 1971. Estimating soil moisture depletion from climate, crop and soil data. Trans. ASAE, 14: 954-959.

29- Landau, S., R. A. C. Mitchell, V. Barnett, J. J. Colls, J. Craigon, and R. W. Payne. 2000. A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and Forest Meteorology, 101: 151–166.

30- Loggini, B., A. Scartazza, E. Brugnoli, and F. Navari-Izzo. 1999. Antioxidant defense system, pigment composition, and photosynthetic efficiency in two wheat cultivars subjected to drought. Plant Physiology, 119: 1091–1099.

31- Maraux, F., F. Lafolie, and L. Bruckler. 1998. Comparison between mechanistic and functional models for estimating soil water balance: deterministic and stochastic approaches. Agricultural Water Management, 38: 1-20.

32- Porter, J. R., and M. Gawith. 1999. Temperatures and the growth and development of wheat: a review. European Journal of Agronomy, 10: 23–36.

33- Semenov, M. A., P. Martre, and P. D. Jamieson. 2009. Quantifying effects of simple wheat traits on yield in water-limited environments using a modeling approach. Agricultural and Forest Meteorology, 149: 1095–1104.

34- Shibu, M. E., P. A. Leffelaar, H. Van Keulen, and P. K. Aggarwal. 2010. LINTUL3, a simulation model for nitrogen limited situations: Application to rice. European Journal of Agronomy, 32: 255-271.

35- Simane, B., H. Van Keulen, W. Stol, and P. C. Struik. 1994. Application of a Crop Growth Model (SUCROS-87) to Assess the Effect of Moisture Stress on Yield Potential of Durum Wheat in Ethiopia. Agricultural Systems, 44: 337-353.

36- Spitters, C. J. T., and A. H. C. M. Schapendonk. 1990. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation. Plant and Soil, 123: 193-203.

37- Timsina, J., D. Godwin, E. Humphreys, Y. Singh, B. Singh, S. S. Kukal, and D. Smith. 2008. Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model. agricultural water management, 95: 1099-1110.

38- Turner, N. C. 2004. Agronomic options for improving rainfall-use efficiency of crops in dryland farming systems. Journal of Experimental Botany, 55: 2413–2427.

39- Van Ittersum, M. K., P. A. Leffelaar, H. Van Keulen, M. J. Kropff, L. Bastiaans, and J. Goudriaan. 2003. On approaches and applications of the Wageningen crop models. European Journal of Agronomy, 18: 201-234.

40- Van Laar, H. H., J. Goudriaan, and H. Van Keulen. 1997. SUCROS97: Simulation of crop growth for potential and water-limited production situations. C.T. de Wit Graduate School for Production Ecology and Resource Conservation, Wageningen, The Netherlands, pp.52.

41- Villegas, D., J. Casadesus, S. G. Atienza, V. Martos, F. Maalouf, F. Karam, I. Aranjuelo, and S. Nogues. 2010. Tritordeum, wheat and triticale yield components under multi-local mediterranean drought conditions. Field Crops Research, 116: 68–74.

42- Whitmore, A. P., and W. R. Whalley. 2009. Physical effects of soil drying on roots and crop growth. Journal of Experimental Botany, 60: 2845–2857.

43- Zhang, L., W. Van DerWerf, W. Cao, B. Li, X. Pan, and J. H. J. Spiertz. 2008. Development and validation of SUCROS-Cotton: a potential crop growth simulation model for cotton. NJAS (Wageningen Journal of Life Sciences), 56: 59-83.

44- Zhang, Y., Q. Yu, C. Liu, J. Jiang, and X. Zhang. 2004. Estimation of winter wheat evapotranspiration under water stress with two semiempirical approaches. Agronomy Journal, 96: 159-168.

45- Zhu, X. G., G. L. Zhang, D. Tholen, Y. Wang, C. P. Xin, and Q. F. Song. 2011. The next generation models for crops and agro-ecosystems. Science China Information Science, 54: 589–597.

46- Ziaei, A. N., and A. R. Sepaskhah. 2003. Model for simulation of winter wheat yield under dryland and irrigated conditions. Agricultural Water Management, 58: 1-17.