نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار، گروه زراعت، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 استاد، گروه زراعت، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

3 کارشناس ارشد، گروه زراعت، دانشگاه ولیعصر رفسنجان

چکیده

اندازه‌گیری‌های دقیق سطح برگ برای مطالعات زراعی و فیزیولوژیک گیاه مهم هستند. به‌منظور یافتن روش سریع و مطمئن برای تخمین سطح برگ در گلرنگ، آزمایش مزرعه‌ای با چهار رقم (411، سینا، محلی اصفهان و صفه) در 3 تاریخ کاشت (17 فروردین و 6 و 27 اردیبهشت) به‌صورت آزمایش فاکتوریل در قالب طرح بلوک‌های کامل تصادفی انجام شد. در این مطالعه، از مدل‌های رگرسیونی مختلفی برای تخمین سطح برگ (LA) از طریق اندازه‌گیری‌های انجام‌شده برای تعداد برگ در ساقه اصلی (MSLN)، تعداد گره در ساقه اصلی (MSNN)، وزن خشک برگ (LDW)، وزن خشک ساقه (SDW) و وزن خشک رویشی (VDW) استفاده شدند. بر اساس RMSD (جذر میانگین مربعات) و r (ضریب همبستگی) بین سطح برگ مشاهده‌‌شده و پیش‌بینی‌شده، LDW بهترین متغیر مستقل برای تخمین سطح برگ معرفی شد. رابطه بین LA و LDW توسط مدل خطی توصیف شد. مقادیر RMSD در ارقام مختلف بین 39/9 تا 61/4 متغیر بود و مقدار r بیش از 0/96 بود که حاکی از دقت نسبتاً خوب مدل در پیش‌بینی LA از طریق LDW است. ازآن‌جا که هیچ اختلاف معنی‌داری بین ضرایب مدل خطی برازش یافته بر داده‌های LA و LDW ارقام مورد مطالعه وجود نداشت، داده‌های مربوط به همه ارقام باهم ادغام شدند و یک مدل کلی برای تخمین سطح برگ توسط LDW برای رقم گلرنگ به‌دست آمد(LDW 102/2+ 10/6=LA ) که در این مدل RMSD برابر 52 و r برابر 0/96 بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimating of Leaf Area in Safflower Using Vegetative Characteristics

نویسندگان [English]

  • b T 1
  • A S 2
  • H S 3

چکیده [English]

Background and Objectives
Accurate measurements of leaf area are important for agronomic and physiological studies. Leaf area can be calculated in two ways: direct and indirect. Direct methods for determining leaf area are a destructive method and need a planimeter to measure the total leaf area attached to shoots which are all time-consuming and tedious approaches. All direct methods are similar in that they are difficult, extremely labor-intensive, require many replicates to account for spatial variability in the canopy, and are therefore costly in terms of time and money and also destructive. In the indirect methods, leaf area was estimated through some crop vegetative characteristics which are less costly and time-consuming. Therefore, the objective of the present study is to examine different relationships between leaf area and some vegetative characteristics in safflower and to select the best characteristic for estimating leaf area.
Materials and Methods
The field experiment was conducted with four safflower cultivars (411, Sina, Esfahan and Sofeh) in three planting dates (4April, 25 April and 16 May 2012) as arrangement factorial in randomized complete blocks design. During the growing season, every 5 or 10 days, the leaf area was measured by the planimeter (T Devices, Cambridge, UK). At the same time, leaf dry weight (LDW), stem dry weight (SDW), vegetative dry weight (VDW), node number on main stem (MSNN) and leaf number on main stem (MSLN) were measured. The relationships between leaf area and these vegetative characteristics were examined by the regression. Consequently, the best characteristic for estimating leaf area was selected by the high R2, r and low RMSD.
Results
In this study, regression models were developed for estimating leaf area (LA) from measurements of main stem leaf number (MSLN), and main stem node number (MSNN), leaf dry weight (LDW), stem dry weight (LDW) and vegetative dry weight (VDW). On the basis of RMSD and coefficient correlation (r) between predicted and observed leaf area, LDW was found to be the best independent characteristic for determining the leaf area by the linear model. The values of RMSD varied from 39.9 to 61.4 and the values of r were higher than 0.96. Once there was no significant difference between model coefficients across cultivars we pooled all data and obtained a generalized linear model for estimating LA in all cultivars (LA=10.6+102.2LDW) with RMSD=52 and r=0.96.
Discussions
Overall, this study suggests that in the absence of planimeter device, it may be appropriate to estimate leaf area using the relationship between LA and LDW. Also, because of the close relationship between LDW and LA than other relationships, this relationship can be used to estimate leaf area in simulation models.
 
 
 

کلیدواژه‌ها [English]

  • Prediction
  • Allometric Relationships
  • Model
  1. Akram-Ghaderi, F. and Soltani, A. 2007. Leaf area relationships to plant vegetative characteristics in cotton (Gossypium hirsutum) grown in a temperate sub-humid environment. International Journal of Plant Production, 1: 63-71.
  2. Antunes, W.C., Pompelli, M.F., Carretero, D.M., and DaMatta, F.M. 2008. Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora). Annals of Applied Biology, doi:10.1111/j.1744-7348.2008.00235.x.
  3. Blanco, F.F. and Folegatti, M.V. 2005. Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Scientia Agricola, 62: 305-309.
  4. Chanda, S.V. and Singh, Y.D. 2002. Estimation of leaf area in wheat using linear measurements. Plant Breeding Seed Science, 46: 75-79.
  5. Chen, J.M., Rich, P.M., Gower, S.T., Norman, J.M., and Plummer, S. 1997. Leaf area index of boreal forests: theory, techniques, and measurements. Journal of Geophysics Research, 102: 29429-29443.
  6. Gower, S.T., Kucharik, C.J., and Norman, J.M. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing and Environment, 70: 29-51.
  7. Hammer, G.L., Hill, K., and Schrodter, G.N. 1987. Leaf area production and senescence of diverse grain sorghum hybrids. Field Crops Research, 17: 305-317.
  8. Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., and Baret, F. 2004. Review of methods for in situ leaf area index determination. I: Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121: 19-35.
  9. Kumar, R., and Sharma, S. 2010. Allometric model for nondestructive leaf area estimation in clary sage (Salvia sclarea ). Photosynthetica, 48: 313-316.
  10. Lieth, J.H., Reynolds, J.F., and Rogers, H.H. 1986. Estimation of leaf area of soybeans grown under elevated carbon dioxide levels. Field Crops Research, 13: 193-203.
  11. Ma, L., Gardener, F.P., and Selamat, A. 1992. Estimation of leaf area from leaf and total mass measurements in peanut. Crops Science, 32: 461-471.
  12. Payne, W.A., Wendt, C.W., Hossner, L.R., and Gates, C.E. 1991. Estimating pearl millet leaf area and specific leaf area. Agronomy Journal, 83: 937-941.
  13. Pourreza, J., Soltani, A., Naderi, A., and Aynehband, A. 2009. Modeling leaf production and senescence in wheat. American-Eurasian Journal of Agricultural and Environmental Science, 6: 498-507. [In Farsi]
  14. Soltani, A., Robertson, M.J., Mohammad-Nejad, Y., and Rahemi-Karizaki, A. 2006. Modeling chickpea growth and development: leaf production and senescence. Field Crops Research, 99: 14-23.
  15. Sonnentag, O., Talbot, J., Chen, J.M. and Roulet, N.T. 2007. Using direct and indirect measurements of leaf area index to characterize the shrub canopy in an ombrotrophic peatland. Agricultural and Forest Meteorology, 144: 200-212.
  16. Tsialtas, J.T. and Maslaris, N. 2008. Leaf allometry and prediction of specific leaf area (SLA) in a sugar beet (Beta vulgaris ) cultivar. Photosynthetica, 46: 351-355.