The effect of super absorbent on yield and yield components of chickpea under season terminal drought stress conditions

Document Type : Research Paper


1 Assistant Proffesor, Department of Agronomy, Payame Noor University, Iran

2 MSc, Department of Agronomy, Islamic Azad University, Sabzevar Branch, Iran


Water is the prime requirement for the existence of life. Due to the limited water resources, it is essential to save and economize the use of water resources. This can be achieved by applying proper water management including storage and maintaining soil water, improving soil water permeability and increasing water use efficiency. Due to the importance of chickpea plants as a source of protein and the other side, irreparable damage of terminal drought for chickpea, the achievement of strategies for drought tolerance can be very important and necessary. Super absorbent polymers of hydrocarbons can absorb and hold water, several times their weight and the polymer discharged at water deficit condition, gradually. The soil remains wet for a long time and again, don’t need to irrigation. The effectiveness of super absorbent soil sandy loam is higher than loam and clay soils. With increasing consumption, biomass and water use efficiency increases. An experiment on corn was found that the use of 0.05 percent super absorbent material in clay loam soil and 0.01 percent loam and 0.03 percent sand soil for best results in terms of dry matter production and water use efficiency. The objective of this study was to determine the right amount of super absorbent to achieve maximum chickpea yield and yield components under drought stress and also the most sensitive stages of chickpea growth to drought stress.
Materials and methods:
In order to evaluate effect of super absorbent and terminal drought stress on yield and yield components of chickpea (Hashem cultivar), a field experiment was conducted as split plot on randomized complete block design with four replications during 2014 in Chaghatay. Treatments included drought stress: cutting irrigation at flowering stage, cutting irrigation at the beginning poding stage and control condition (non stress), in the main plots and the use of super absorbent in three levels: 0, 50 and 100 kg.ha-1 subplots. Studied traits included plant height, pod number per plant, number seed per pod, protein percent, 1000 seed weight, economic and biological yield and harvest index.

Results indicated that yield, biological yield, number pod per plant, number grain per pod, grain weight and protein content were decreased with cutting of irrigation but plant height and harvest index were not significantly different. The use of super absorbent may moderate the effect of drought stress on yield and yield components were chickpeas. The maximum yield and yield component were obtained by the use of 100 kg.ha-1 super absorbent while no significant effect on the protein content, but the protein in cut irrigation at flowering stage with 115.23 kg/ha was more than the protein in cut irrigation at pod stage with 77.29 kg/ha.
To obtain optimum yield in drought stress condition, the best result was obtained of 100 kg.ha-1 super absorbent.


Main Subjects

1. Araya, A., Hoogenboom, G., Luedeling, E., Hadgue, K.M., Kisekkaf, I., and
Martorano, L.G. 2015. Assessment of maize growth and yield using crop
models underpresent and future climate in southwestern Ethiopia. Agr. Forest.
Meteorol., 214: 252-265.
2. Archontoulis, S.V., Miguez, F.E., and Moore, K.J. 2014. Evaluating APSIM
maize, soil water, soil nitrogen, manure, and soil temperature modules in the
Midwestern United States. Agron. J., 106(3): 1025-1040.
3. Borras, L., and Otegui, M.E. 2001. Maize kernel weight response to post–
flowering source – sink ratio. Crop Sci., 41: 1816 – 1822.
4. Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D.,
Sierra, J., Bertuzzi, P., Burger, P., Bussiere, F., Cabidoche, Y.M., Cellier, P.,
Debaeke, P., Gaudillere, J.P., Henault, C., Maraux, F., Seguin, B., and Sinoquet,
H. 2003. An overview of the crop model STICS. Eur. J. Agron., 18: 309– 332.
5. Choukan, R. 2013. Final report of yield trial and adaptability of late and
medium maturingpromising hybrids of maize (final stage). Seed and Plant
Improvement Institute. Iran, 50p.
6. Dehghanpour, Z., and Estakhr, A. 2010. Determination of the suitable planting
date for new early maturity maize hybrids in second cropping in temprate
regions in Fars province. Seed Plant Prod. J., 26 (2): 169-191. (In Persian)
7. Deihimfard, R., Mahallati, M.N., and Koocheki, A. 2015. Yield gap analysis in
major wheat growing areas of Khorasan province, Iran, through crop modelling.
Field Crops Res., 184: 28-38.
8. Dettori, M., Cesaraccio, C., Motroni, A., Spano, D., and Duce, P. 2011. Using
CERES-Wheat to simulate durum wheat production and phenology in Southern
Sardinia, Italy. Field Crops Res., 120: 179–188.
9. Diepenbrock, W. 2000. Yield analysis of winter oilseed rape (Brassica napus
L.): A review. Field Crops Res., 67: 35- 49.
10. Dwyer, L.M., Evanson, L., Hamilton, R.I. 2003. Maize physiological traits
related to grain yield and harvest moisture in mid-toshortseason environments.
Crop Sci., 34: 985-992.
11. Emam Y., Sedaghat, M., and Bahrani, H. 2013. Responses of maize (SC704)
yield and yield components to source restriction. Iran Agric. Res., 32(1): 31 -40.
12. Estakhr, A., and Choukan, R. 2011. Effect of planting date on grain yield and its
components and reaction to important maize viruses in Fars Province in some
exotic and Iranian maize hybrids. Seed Plant Prod. J., 27(3): 313-333. (In
13. FAO (Food and Agriculture Organization). 2014. FAOSTAT. Available online
14. Fosu-Mensah, B.Y., MacCarthy, D.S., Vlek, P.L.G., and Safo, E.Y. 2012.
Simulating impact of seasonal climatic variation on the response of maize (Zea
mays L.) to inorganic fertilizer in sub-humid Ghana. Nutr Cycl Agroecosyst.,
94: 255–271.
15. Goldani, M., Rezvani, M.P., Nassiri, M.M., and Kaffi, M. 2011. Radiation use
efficiency and phenological and physiological characteristics in hybrids of
maize (Zea may L.) on response to different densities. Iran. J. Agric. Res., 7(2):
595- 604. (In Persian)
16. Hammer, G.L., Van Oosterom, E., McLean, G., Chapman, S.C., Broad, I.,
Harland, P., and Muchow, R.C. 2010. Adapting APSIM to model the
physiology and genetics of complex adaptive traits in field crops. J. Exp. Bot.
61(8): 2185–2202.
17. Harris, D., Rashid, A., miraj, G., Arif, M., and Shah, H. 2007. On farm seed
priming with zinc sulphate solution: A cost effect way to increase the maize
yields of resource-poor farmers. Field Crops Res., 102: 119-127.
18. Hasanzadeh, M.H., and Dehghanpour, Z. 2010. Final report of the study of yield
and compatibility in early maturity maize hybrids. Center of Agriculture
research and nature resources of Khorasan Razavi province. Iran, 22p.
19. Jego, G., Pattey, E., Bourgeois, G., Drury, C.F., and Tremblay, N. 2011.
Evaluation of the STICS crop growth model with maize cultivar parameters
calibrated for Eastern Canada. Agr. Sust. Dev., 31: 557– 570.
20. Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt,
L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., and Ritchie, J.T. 2003. The
DSSAT cropping system model. Eur. J. Agron., 18: 235- 265.
21. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J.,
Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z.,
McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E.,
Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn,
D.M., and Smith, C.J. 2003. An overview of APSIM, a model designed for
farming systems simulation. Eur. J. Agron., 18: 267– 288.
22. Madadizadeh, M. 2016. Simulate growth and yield of different maize cultivars
in Kerman province using the APSIM model. Ph.D. Dissertation, Shahid
Beheshti University, Tehran, Iran. Unpublished. (In Persian)
23. Mahru, A.H., Soltani, A., Galeshi, S., and Kalate-Arabi, M. 2010. Estimates of
genetic coefficients and evaluation of DSSAT model for Golestan province.
EJCP., 3(2): 229-253. (In Persian)
24. Makowski, D., Naud, C., Jeffroy, M.H., Barbttin, A., and Monod, H. 2006.
Global sensitivity analysis for calculating the contribution of genetic parameters
to the variance of crop model prediction. Reliab. Eng. Syst. Safe., 91: 1142-
25. McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D., and Huth,
N.I. 1995. APSIM: an agricultural production system simulation model for
operational research. Math. Comput. Simulat., 39(3): 225-231.
26. Mesfin, T., Moeller, C., Parsons, D., and Meinke, H. 2015. Maize (Zea mays L.)
productivity as influenced by sowing date and nitrogen fertiliser rate at
Melkassa, Ethiopia: parameterisation and evaluation of APSIM-Maize.17nd
Australian Society of Agronomy Conference. Hobart. Sep., 20- 24. 1- 4.
27. Ministry of Agriculture Jihad, 2014. Final report of planting maize in 2014,
with province separation. Agronomy Department, Ministry of Agriculture Jihad,
Iran. (In Persian)
28. Moeinirad, A., Pirdashti, H., Eaghanehpour, F., and Mokhtarpour, H. 2013.
Effecct of sowing date and plant density on phenology, morphology and yield
of Maize cv. KSC704 in Gorgan. J. Res. Crop Sci., 19: 41- 56. (In Persian)
29. Monteith, J.L. 1986. How do crops manipulate water supply and demand?
Philos. Trans. A. Math. Phys. Eng. Sci., 316: 245–259.
30. Naderi, F., Siadat, S.A., and Rafiee, M. 2010. Effect of planting date and plant
density on grain yield and yield components of two maize hybrids as second
crop in Khorram Abad. Iran. J. Crop Sci., 12(1): 31- 41. (In Persian)
31. Nassiri, M.M. 2008. Modeling Crop Growth Processes. Mashhad Univ. Press,
280p. (In Persian)
32. Paliwal, R.L., Granados, G., Lafitte, H.R., Violic, A.D., and Marathée, J.P.
2000. Tropical Maize: Improvement and Production. FAO, Rome, Italy, 374p.
33. Rahimi Moghaddam, S. 2013. Determination of genetic coefficients of some
maize (Zea mays L.) cultivars in Iran to be applied in crop simulation models.
M.Sc. Thesis, Shahid Beheshti University, Tehran, Iran., 88p. (In Persian)
34. Rahimi Moghaddam, S., Deihimfard, R., Soufizadeh, S., Kambouzia, J.,
Nazariyan Firuzabadi, F., and Eyni Nargeseh, H. 2015a. The effect of sowing
date on grain yield, yield components and growth physiological indices of six
grain maize cultivars in Iran. J. Agroecol., 5(1): 72- 83. (In Persian)
35. Rahimi Moghaddam, S., Deihimfard, R., Soufizadeh, S., Kambouzia, J.,
Nazariyan Firuzabadi, F., and Eyni Nargeseh, H. 2015b. Determination of
genetic coefficients of some maize (Zea mays L.) cultivars of Iran for
application in crop simulation models. Iran. J. Field Crops Res., 13(2): 328-339
(In Persian)
36. Research Center for Agriculture and Natural Resources of Fars Province, 2016.
Series reports of yield and stability in early maturity maize hybrids in 2007,
2008, 2011 and 2012.
37. Saberi, A., Ghoshchi, F., Sirani, S., and Safahani, A. 2008. Effect of Plant
Density and Planting Pattern on Grain Yield of Maize cv. KSC704 in Gorgan.
Plant Ecosys. J., 19: 96-111. (In Persian)
38. Salehi, B. 2005. Effect of row spacing and plant density on grain yield and yield
components in maize (cv. Sc 704) in Miyaneh. Iran. J. Crop Sci., 6(4): 383-
395. (In Persian)
39. Seifert, E. 2014. OriginPro 9.1: Scientific Data Analysis and Graphing
Software—Software Review. J. Chem. Inf. Model., 54(5): 1552–1552.
40. Sinclair, T.R. 1986. Water and nitrogen limitations in soybean grain production
I. Model development. Field Crops Res., 15(2): 125-141.
41. Soltani, A., and Hoogenboom, G. 2007. Assessing crop management options
with crop simulation models based on generated weather data. Field Crops Res.,
103: 198- 207.
42. Soltani, A., Hammer, G.L., Torabi, B., Robertson, M.J., and Zeinali, E. 2006.
Modeling chickpea growth and development: phonological development. Field
Crops Res., 99: 1-13.
43. Srivas, S.K., and Singh, U.P. 2004, Genetic variability, character association
and path analysis of yield and its component traits in forage maize (Zea mays
L.). Range. Manag. Agrofores., 25: 149-153.
44. Wallach, D., and Goffinet, B. 1987. Mean squared error of prediction in models
forstudying economic and agricultural systems. Biometrics., 43: 561–576.
45. Wang, E., Robertson, M.J., Hammer, G.L., Carberry, P.S., Holzworth, D.,
Meinke, H., Chapman, S.C., Hargreaves, J.N.G., Huth, N.I., and McLean, G.
2002. Development of a generic crop model template in the cropping system
model APSIM. Eur. J. Agron., 18(1): 121-140.
46. Willmott, C.J. 1982. Some comments on the evaluation of model performance.
Bull. Am. Meteor. Soc., 63: 1309–1313.