Kouhi Chellehkaran, N., d, H. (2017). Simulation of Leaf Area Index for Corn, cv. Single Cross 704, in Drought Stress Conditions. , 31(3), 383-491. doi: 10.22092/jwra.2017.113681
Nader Kouhi Chellehkaran; h d. "Simulation of Leaf Area Index for Corn, cv. Single Cross 704, in Drought Stress Conditions". , 31, 3, 2017, 383-491. doi: 10.22092/jwra.2017.113681
Kouhi Chellehkaran, N., d, H. (2017). 'Simulation of Leaf Area Index for Corn, cv. Single Cross 704, in Drought Stress Conditions', , 31(3), pp. 383-491. doi: 10.22092/jwra.2017.113681
Kouhi Chellehkaran, N., d, H. Simulation of Leaf Area Index for Corn, cv. Single Cross 704, in Drought Stress Conditions. , 2017; 31(3): 383-491. doi: 10.22092/jwra.2017.113681
Simulation of Leaf Area Index for Corn, cv. Single Cross 704, in Drought Stress Conditions
1Assistant Prof. of Agricultural Engineering Research Department, Kerman Agricultural and Resource Research Center
2Associate Professor, Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Abstract
Crop growth models facilitate management of irrigation water and fertilizer because less on-site (filed) visits and direct measurements are required. On the other hand, these models are complex and difficult to be understood and require input data that is not available, thus, using them in management decisions, which should be done before cultivating season, will be difficult. The objective of this study was to develop a model for grain yield prediction of Corn (Zea maize) based on simulated leaf area index (LAI) under water stress conditions. In this study, corn LAI was simulated based on cumulative degree-days and water stress index. The model used crop and soil as well as meteorological data including daily maximum and minimum air temperature (oC), precipitation (mm), and solar radiation (MJ.m-2d-1). The model was developed based on the 2013 growing season data for single cross 704 cultivar under full irrigation and water stress conditions, and was validated with 2014 growing season data. The highest values of simulated LAI in the 100% water requirement (WR) treatment were between 6.14 and 5.78, in the 80% WR between 5.63 and 5.4, and in the 60% WR was between 4.11 and 3.47, which varied by 0.13 (2%), 0.14 (2.5%) and 0.29 (6.6%), respectively. In the mid- stage of growth, the estimated LAI was more consistent with measured values (2%). In treatments under stress (except for the fourth stage of sampling), the LAI estimated by the model was 6.6% higher than the values observed. In all treatments, the high correlations (R2) between the values of the simulated LAI and observed LAI in both years of the experiment were between 0.9 and 0.99.
کوچکی، ع. و خواجه حسینی، م. 1387. زراعت نوین. انتشارات جهاد دانشگاهی مشهد. 704 ص.
کیانی، ع.، کوچکی، ع، ر.، بنایان، م. و نصیری محلاتی، م. 1382. ارزیابی مدل CERES-Wheat در دو نقطه متفاوت اقلیمی در استان خراسان. مجله پژوهشی بیابان. جلد 8 شماره 2، ص 264- 275.
اندرزیان، ب.، بخشنده، ع.، بنایان اوّل، م. و امام، ی. 1387. ارزیابی مدل شبیه سازی CERES-Wheat در شرایط اقلیمی اهواز. نشریه پژوهشهای زراعی ایران. جلد 6 شماره 1، ص 11- 22.
Chaudhuri, N.V. and E.T.Kanemasa. 1982. Effects of water gradient on sorghum growth, water relations and yield. Can J. Plant Sci. 62:599-607.
Khaledian M.R., Mailhol J.C., Ruelle P., and Rosique P. 2009. Adapting PILOTE model for water and yield management under direct seeding system: the case of corn and durum wheat in a Mediterranean context. Agricultural Water Management, 96(5): 757 – 770.
Ko, J., Piccinni, G., and Steglich, E .2009.Using EPIC model to manage irrigated cotton and maize. Agricultural Water Management 96 (2009) 1323–1331.
Inman-Bamber, N.G., Muchow, R.C., and Robertson, M.J. 2002. Dry matter partitioning of sugarcane in Australia and South Africa. Field Crops Res.76: 71-84.
Mailhol J.C., Olufayo A.A., and Ruelle P. 1997. Sorghum and sunflower evapotranspiration and yield from simulated leaf area index. Agricultural Water Management, 35: 167-182.
Moll RH and EJ Kamparth, 1977.Effect of population density up on agronomic traits associated
with genetic increases in yield of Zea mays L. Agron. J, 69: 81-84.
Nouri azhar, J., and Ehsanzedeh, P. 2007. Study of relationship of some gtowth indices and yield of five corn hybrids at two irrigation regim in Esfahan region. J. Sci. and Tech. 41: 261-272.
Pandey, R.K., Maranville, J.W., and Chetima, M.M. 2000a. Deficit irrigation and nitrogen effects on maize in a Sahelian environment. II. Shoot growth. Agric. Water Manage. 46: 15–27.
Saberali, S.F., Sadatnouri, S.A., Hejazi, A., and Zand, E. 2007. influence of plant density and planting pattern of corn on its growth and yield under competition with common Lambesquarters (Chenopodium album). J. Res. Prod. 74: 143-152.
Sinclair T.R., Gilbert R.A., Perdomo R.E., Shine Jr. J.M., Powell G., and Montes G. 2004. Sugarcane leaf area development under field conditions in Florida, USA. Field Crops Research, 88: 171–178.
Seetseng K.A. 2008. Effect of water application and plant density on canola (Brassica napus L.) in the free state. M.S. thesis, University of the Free State Bloemfontein.
Wolfe D.W., Henderson D.W., Hsiao T.C., and Alvino A. 1988. Interactive water and nitrogen effects on
senescence of maize: I. Leaf area duration, nitrogen distribution, and yield. Agronomy Journal, 80: 859–864.
