ارزیابی سازگاری ارقام مختلف کلزا(Brassica napus L.) تحت شرایط آب و هوایی منطقه شیروان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 فارغ التحصیل دکتری زراعت

2 عضو هیئت علمی

3 استادیار دانشگاه گنبد کاووس

4 هیات علمی

چکیده

چکیده
مقدمه
کلزا مهمترین دانه روغنی است که در بین دانههای روغنی جهان بیش‌ترین رشد را در دهههای اخیر داشته و امروزه مقام سوم را پس از سویا و نخل روغنی در فراوردههای روغن نباتی احراز کرده است (7). عملکرد کلزا در سال 2003 میلادی در جهان و ایران به ترتیب 1567 و 1592 بوده که در سال 2014 به 2043 و 2125 کیلوگرم در هکتار افزایش یافته است (15). متخصصان فیزیولوژی گیاهان زراعی میبایست شاخصهای فیزیولوژیک مهمی را که در گذشته باعث افزایش عملکرد شدهاند و در آینده نیز میتوانند به پیشرفت بهنژادی در افزایش کمی و کیفی کمک کنند، شناسایی نمایند (3). بنابراین این مطالعه با هدف بررسی صفات فیزیولوژیکی مرتبط با بهبود عملکرد ارقام کلزا انجام گرفت.
مواد و روش‌ها
این آزمایش در قالب طرح بلوک‌های کامل تصادفی در چهار تکرار در طی سال‌های زراعی 94-1393 و 95-1394 به اجرا درآمد. تیمارهای آزمایشی شامل 20 رقم و لاین کلزا بود. ثبت مراحل فنولوژیک به روش سیلوستر- برادلی (1984) صورت گرفت. به منظور اندازه‌گیری سطح برگ و زیست توده اولین نمونه‌براری تخریبی در نیمه دوم اسفند ماه (قبل از به ساقه رفتن ارقام) صورت گرفت و به فاصله 8 الی 10 روز نمونه‌برداری‌های بعدی تا رسیدگی فیزیولوژیک ادامه یافت. در پایان فصل رشد (مرحله رسیدگی برداشت) عملکرد و اجزای عملکرد اندازه‌گیری شدند. به منظور بررسی شاخص‌های رشد، ارقام بر اساس تجزیه کلاستر به سه گروه با عملکرد بالا (Bilbao)، متوسط (کرج 3) و پایین ( ساری‌گل) تقسیم شدند و از هر گروه یک نماینده انتخاب گردید.
نتایج و بحث
نتایج نشان داد که بین ارقام مورد مطالعه از لحاظ صفات فنولوژیک تفاوت معنی‌داری وجود داشت. به‌طوری که ارقام ساری‌گل، طلایه، شیرالی، ظفر و زرفام زودتر از سایر ارقام به مرحله رسیدگی فیزیولوژیک رسیدند. همبستگی مثبت و معنی‌دار دوام گل‌دهی با عملکرد (**66/0=r) و تعداد خورجین در بوته (**88/0=r) اهمیت این دوره را در تعیین عملکرد نشان داد. همچنین مهمترین مرحله تعیین کننده عملکرد (دوام گل‌دهی) تحت تأثیر شرایط محیطی همانند دما، تشعشع و بارندگی قرار گرفت. رقم Bilbao نسبت به دو رقم کرج 3 و ساری‌گل دارای شاخص سطح برگ بالاتری بود. همچنین رقم ساری‌گل زودتر از دو رقم دیگر به حداکثر شاخص سطح برگ دست یافت. همبستگی قوی بین عملکرد و حداکثر تجمع ماده خشک وجود داشت (**81/0=r). همزمانی شاخص سطح برگ حداکثر با تشعشع خورشیدی بیشتر برای دست‌یابی به حداکثر عملکرد بسیار مهم می‌باشد. مقدار CGR در سال اول به‌طور معنی‌داری از سال دوم بیش‌تر بود. وجود شرایط تشعشی دریافتی مطلوب‌تردر سال اول و هوای بارانی و ابری بیش‌تر در سال دوم علت تفاوت شاخص‌های رشد در دو سال بود. تعداد غلاف در بوته، تعداد دانه در غلاف، شاخص برداشت، درصد روغن، عملکرد دانه و عملکرد بیولوژیک در سال اول نسبت به سال دوم بیشتر بود. بخشی از تفاوت عملکرد بین دو سال با کاهش شاخص‌های رشد مرتبط بوده و بخش دیگر کاهش شاخص برداشت در سال دوم آزمایش می‌باشد. به‌طور کلی، عملکرد بیولوژیک، شاخص برداشت، روز تا رسیدن به شاخص سطح برگ حداکثر و تعداد خورجین در بوته 99 درصد از تغییرات مرتبط با عملکرد را توجیه نمودند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of adaptation of different varieties of Canola(Brassica napus L.) under the climatic conditions of Shirvan

نویسندگان [English]

  • abbas biabani 2
  • ali rahemi karizaki 3
2 دانشگاه گنبد کاووس
چکیده [English]

Introduction
Canola (Brassica napus L.) is one of the most important oil crops in the world. It has placed in third rank after Soybean and Palm and it has had the fastest of growth rate among oil seed in recent decades too. Canola yield was 1592 and 1567 kg.ha-1 in Iran and the world in 2003 respectively. It has increased 2125 and 2043 kg.ha-1 in Iran and the world in 2014 respectively. Crop physiologist should investigate the important physiological parameters which in the past have increased yield and can help to increase the quality and quantity of crop yield in the future. Therefore, the current study was carried out to evaluate the physiological traits associated with canola (Brassica napus L.) genotypes yield improvement.
Materials and Methods
Experiment was conducted as randomized complete block design with four replications at Higher Education Complex of Shirvan during growing seasons 2014-2015 and 2015-2016. Treatments were included 20 varieties and lines of rapeseed. The record of phonological stages was done based on Sylvester-Bradley (1984)’s method. Before the plants showed elongation, aboveground biomass and LAI were measured from destructive sampling and it has been continuing at intervals of 6 to 10 d until physiological maturity. Yield and components were measured at the end of the growing season. In order to investigation of growth indices, we have divided the varieties to three group based on cluster analysis and is select a variety as group representative. The groups are include high yield (Bilbao), medium yield (Karaje 3) and low yield (Sarigol).
Results and Discussion
Results indicated that there was significant differences among studied varieties in terms of phonological traits. So that Sarigol, Talatee, Shirali, Zafar and Zarfam were achieved earlier than others to physiological maturity. Positive and significant correlation of flowering duration with yield (r=0.66**) and the number of pod.plant-1 (r=0.88**) has showed its importance in determination of yield. Also, the most important of stage at making yield affected by environmental conditions such as temperature, radiation and rainfall. LAI for Bilbao was higher than Sarigol and Karaje 3. Also, Sarigol was achieved maximum LAI earlier than two other varieties. There was strong correlation between yield and maximum dry matter accumulation (r=0.81**). The synchronization of maximum LAI with more solar radiation was much more important to achieve maximum yield. In the first year of experiment CGR and RGR were higher than second. There were more solar radiation in first year that it was increased growth indices, also, harvest index. The number of pod.plant-1, seed.pod-1, harvest index, biological yield and seed yield were higher in the first year of experiment. The part of difference of yield between two years associated with reduced growth indices and another reason was decreased harvest index. Generally, biological yield, harvest index, days to achieve maximum leaf area index and number of pods per plant displayed 99% of the total yield- related changes.

کلیدواژه‌ها [English]

  • Harvest index
  • Leaf area index
  • Phonological characteristics
  • Seed yield
  • Solar radiation
  1. Ahmadi, J., Khatibi, M., Amirshekari, H., and Amini Dehagi, M. 2011. Evaluation of the effective morpho-physiological indices on the yield of spring wheat (Triticum aestivum L.) using multivariate statistical methods. Agron. J. 4: 55-66. (In Persian).
  2. Alavipanah, K. 2006. Thermal remote sensing and its application in earth sciences. Tehran University Press. 524p. (In Persian).
  3. Al-Gaadi, K.A., Hassaballa, A.A., Tola, E., Kayad, A.G., Madugundu, R., Alblewi, B., and Assiri, F. 2016. Prediction of potato crop yield using precision agriculture techniques. PLoS One. 11: 9. 1-16.
  4. Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., and Royo, C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agro. J. 92: 1. 83-91.
  5. Asrar, G., Fuchs, M., Kanemasu, E., and Hatfield, J. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76: 300-306.
  6. Bannari, A., Morin, D., Bonn, F., and Huete, A. 1995. A review of vegetation indices. Remote Sens. Reviews. 13: 95- 120.
  7. Baret, F., and Guyot, G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 35: 161-173.
  8. Bausch, W.C., Halvorson, A.D., and Cipra, J. 2008. Quick bird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots. Biosyst Eng. IOI. 306-315.
  9. Campbell, J.B. 2011. Introduction to Remote Sensing. Guilford Press. 667p.
  10. Gitelson, A.A. 1996. Remote estimation of leaf area index and green leaf biomass in maize canopies. J. Plant Physiol. 161: 165-173.
  11. Goel, N.S., and Qin, W. 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulation. Remote Sens. Reviews, 10: 309- 347.
  12. Hoffmann, C., and Blomberg, M. 2004. Estimation of leaf area index of Beta vulgaris L. based on optical remote sensing data. J. Agro. Crop Sci. 190: 197-204.
  13. Huete, A.R. 1988. A soil adjusted vegetation index (SAVI). Remote Sens. Environ. 25: 295-309.
  14. Huete, A.R., Liu, H., Batchily, K., and Leeuwen, W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59: 3. 440-451.
  15. Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yan, G., and Zhang, X. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101: 366-378.
  16. Jiang, Z., Huete, A.R., Didan, K., and Miura, T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ.112: 3833-3845.
  17. Jimen´ez-Mu˜noz, J.C., Sobrino, J.A., Plaza, A., Guanter, L., Moreno, J., and Martinez, P. 2009. Comparison between fractional vegetation cover retrievals from vegetation indices and mixture analysis: case study of PROBA/CHRIS data over an Agricultural area. Sensors. 9: 768-793.
  18. Jordan, C.F. 1969. Derivation of leaf area index from quality of light on forest floor. Ecol. J. 50: 663-666.
  19. Kamali, G.A., Momenzadeh, H., and Vazifeh Doust, M. 2011. Study of wheat yield production over Esfahan province during periods of dry and wet years using MODIS satellite data. J. Agro. 2: 181-190. (In Persian).
  20. Li-Hong, X., Wei-Xing, C., and Lin-Zhang, L. 2007. Predicting grain yield and protein content in winter wheat at different supply levels using canopy reflectance spectra. Published by Elsevier Limited Science Press. 17: 5. 646-653.
  21. Lopresti, M.F., Di Bella, C.M., and Degioanni, A.J. 2015. Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina. Inform. Process. Agri. 2: 73-84.         
  22. Mohammadi Ahmad Mahmoudi, E., Kamkar, B., and Abdi, O. 2015. Comparison land- statistic methods and the use of remote sensing data to predict wheat yield in some stage of growth (Case study: field lands of Golestan province). J. Crop Prod. 8: 2. 51-76. (In Persian).
  23. Mosleh Ghahfarokhi, Z. 2016. Soil digital mapping, land suitability and optimization cultivation model for major products plains of Shahrekord. PhD thesis pedology, Faculty of Agriculture, University of Shahrekord, Iran. (In Persian)
  24. Pinter, P., Jackson, R., Idso, S., and Reginato, R. 1981. Multidate spectral reflectance as predictors of yield in water stressed wheat and barley. Int. J. Remote Sens. 2: 1. 43-48.  
  25. Raun, W.R., Solie, J.B., Stone, M.L., Lukina, E.V., Thomason, W.E., and Schepers, J.S. 2001. In season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93: 131-138.
  26. Rondeaux, G., Steven, M. and Baret, F. 1996. Optimization of soil- adjusted vegetation indices. Remote Sens. Environ. 55: 98-107.
  27. Roujean, J.L., and Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 51: 375-384.
  28. Rouse, J.W. 1974. Monitoring the vernal advancement of retrogration of natural vegetation. NASA/GSFS Type III, Final Report, Greenbelt, MD.
  29. Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N., and Ghaemi, M. 2014. Wheat yield estimation using landsat images and field observation: A case study in Mashhad. J. Plant Prod. 20: 4. 45-63. (In Persian).
  30. Serrano, L., Filella, I., and Penuelas, J. 2000. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci. 40: 3. 723-731.  
  31. Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.M., Schlemmer, M.R., and Major, D.J. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93: 583-589.
  32. Siyal, A.A., Dempewolf, J., and Becker-Reshef, I. 2015. Rice yield estimation using Landsat ETM+ Data. J. Appl. Remote Sens. 9: 1-16.
  33. Solaimani, K., Shokrian, F., Tamartash, R., and Banihashemi, M. 2011. Landsat ETM+ based assessment of vegetation indices in highland environment. J. Adv. Res. Dev. 2: 1. 5-13.
  34. Tucker, C.J., Holben, B.N., Elgin, Jr, J. H., and McMurtrey, J.E. 1981. Remote sensing of total dry -matter accumulation in winter wheat. Remote Sens. Environ. 11: 171-189.
  35. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127-150.
  36. Tucker, C.J., Holben, B.N., Elgin Jr, J.H., and McMurtrey III, J.E. 1980. Relationship of spectral data to grain yield variation. Photogramm. Photogramm. Eng. Rem S. 46: 657-666.
  37. Webster, R. and Oliver, M.A. 1990. Statistical methods in soil and land resource survey. Spatial Information Systems, Oxford University Press, Oxford.
  38. Wiegand, C.L., Richardson, A.J., Escobar, D.E. and Gerbermann, A.H. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 35: 105-119.
  39. Zahirnia, A.R. and Matinfar, H.R. 2016. Evaluate the yield of irrigated wheat fields on the basis of data obtained from Landsat 8 in the southwestern province of Khuzestan. First National Conference on Remote Sensing and GIS in the earth sciences. Atmospheric and Oceanic Sciences Research Center-in. College of Agriculture, Shiraz University. (In Persian).
  40. Zanter, K. 2015. Landsat8 (L8) data users handbook. Department of the Interior U.S. Geological
  41. Zeinvand, M., Matinfar, H.R. and Zahirnia, A.R. 2016. Alfalfa yield estimation based on data obtained from Landsat 8, in plain Jaydar Poldokhtar of Lorestan province. First National Conference on Remote Sensing and GIS in the earth sciences. Atmospheric and Oceanic Science Research Center-in. College of Agriculture, Shiraz University. (In Persian).