بررسی وضعیت عنصرهای غذایی در تاکستان‌های شهرستان خدابنده با کاربرد شاخص‌های تغذیه‌ای

نوع مقاله: مقاله کامل

نویسنده

استادیار، بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان زنجان، سازمان تحقیقات، آموزش و ترویج کشاورزی، زنجان، ایران

چکیده

با توجه به افزایش نیاز و تقاضا به مواد غذایی بیشتر و کاهش منابع آب‌وخاک، حفظ اراضی و ارتقا کمی و کیفی عملکرد گیاهان در واحد سطح با تأمین مواد موردنیاز خاک و گیاه تحقق می­یابد. شناسایی وضعیت عنصرهای غذایی در گیاهان، راه­کاری مؤثر در تعیین الگوی مصرفی عنصرهای غذایی و افزایش عملکرد و بهبود کیفیت محصولات کشاورزی است. ازاین‌رو در این پژوهش با استفاده از نتایج شاخص­های DOP، DRIS و CND به تجزیه‌وتحلیل وضعیت عنصرهای غذایی در نمونه­های برگ تاکستان­های شهرستان خدابنده در استان زنجان پرداخته شد. نمونه­های برگ از 75 تاکستان گردآوری و تجزیه­های شیمیایی عنصرهای نیتروژن، فسفر، پتاسیم، منگنز، مس، روی و بُر و ویژگی‌های مربوط به عملکرد انجام شد. معیار­های بهینه با تقسیم تاکستان­ها به دو گروه با عملکرد بالا و پایین تعیین شد. نتایج نشان داد بیشتر تاکستان­های شهرستان خدابنده به‌طور میانگین از لحاظ میزان عنصر پتاسیم، روی و مس با کمبود روبه‌رو هستند. بااین‌حال عنصرهای نیتروژن، فسفر، بُر و منگنز در منطقه بیش­بود داشتند. همچنین نتایج هر یک از شاخص­های DOP، DRIS و CND با هم مقایسه شد. بنابراین نتایج میزان شاخص­ها در برخی از عنصرها تفاوت داشتند. هر سه شاخص DOP، DRIS و CND کمبود عنصر روی را در بیشتر تاکستان­ها تشخیص دادند. به‌طورکلی شاخص­های DRIS و CND روند تغییرپذیری عنصرهای غذایی را نسبت به شاخص DOP بهتر نشان دادند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of vineyards nutritional status using nutrition indices in Khodabande region

نویسنده [English]

  • Mehdi Taheri
Assistant Professor, Soil and Water Research Department, Zanjan Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zanjan, Iran
چکیده [English]

Considering the increased demand for more food, and reduction of reduce soil and water resources, protecting of land and improving the quality and quantity of yield per unit area is feasible with supplying soil and plant with nutrients needed. Diagnosis of plant nutrient status is an effective way to determine the pattern of nutrient intake and increase of the yield and improvement of the quality of agricultural products. Therefore, in this study using the DOP, DRIS and CND indices, nutrient status in leaf samples of vineyards in Khodabande from Zanjan Province were analyzed. Leaf samples were collected from 75 vineyards and then were done chemical analysis of N, K, P, Mn, Cu, Zn and B and the characteristics of yield. Optimization norms were determined by dividing vineyards into two groups of high and low performances. The results showed that most vineyards of Khodabande had K, Zn and Cu deficiencies. However, in this area, N, P, B and Mn are more than optimum norms. Also the results of DOP, DRIS and CND were compared with each other. Values of indices in some of the nutrients were different. Based on three Indices of DOP, DRIS and CND, Zn had deficiency in the most vineyards. DRIS and CND showed a trend of changes in nutrients better than DOP.

کلیدواژه‌ها [English]

  • CND
  • DOP
  • DRIS
  • grapes
  • leaf analysis
  1. Akhter, N. (2011). Comparison of DRIS and critical level approach for evaluating nutrition status of wheat in District Hyderabad, Pakistan. Ph. D. thesis, University of Bonn. pp.115.
  2. Beaufils, E. R. (1973) Diagnosis and Recommendation Integrated System (DRIS). Soil Science Bull. No. 1, University of Natal, S. Africa.
  3. Daryashenas, A. M. & Rezaei, H. (2011). Determination of DRIS reference norms for autumn sugar beet in Khuzestan Province. Journal of Sugar Beet. 26(2), 183- 204. (in Farsi)
  4. Daryashenas, A. M. & Saghafi, K. (2011). Compositional nutrient diagnosis in sugar beet. Iranian Journal of Soil research, 25(1), 1- 12. (in Farsi)
  5. Dordipour, E., Emami, P. & Daryashenas, A. M. (2012). Evaluation of nutritional balance in Peach orchards through Deviation from Optimum Percentage (DOP) method. Electronic Journal of Soil Management and Sustainable Production, 2(1), 79-94.
  6. Dordipour, E., Emami, P. & Daryashenas, A.M. (2012). Evaluation of nutritional balance in peach orchards through deviation from optimum percentage (DOP) method. Journal of Soil Management and Sustainable Production, 2(1), 79- 94. (in Farsi)
  7. Golmohammadi, M. & Mostashari, M. (2011). Recognition of nutritional anomalies and to determine the optimal concentration of nutrients in the trees, grapes, Qazvin. Research project. Jihad Agriculture Organization. (in Farsi)
  8. Hernandez-Caraballo, E. A., Rodr´ıguez-Rodr´ıguez, O. & Rodr´ıguez-P´erez, V. (2008). Evaluation of the Boltzmann equation as an alternative model in the selection of the high-yield subsample within the framework of the compositional nutrient diagnosis system. Environmental and Experimental Botany, 64, 225-231.
  9. Jimenez, S. J., Pinochet, Y., Gogorcena, J. A. & Betran, M. A. M. (2007). Influence of different vigour cherry root stocks on leaves and shoots mineral composition. Scientia Horticulturae, 112, 73-79.
  10. Khiari, L., Parent, L. E. & Tremblay, N. (2001a). Critical compositional nutrient indexes for sweet corn at early growth stage. Agronomy Journal, 93, 809-814.
  11. Khiari, L., Parent, L. E. & Tremblay, N. (2001b). The phosphorus compositional nutrient diagnosis range for potato. Agronomy Journal, 93, 815-819.
  12. Khiari, L., Parent, L. E. & Tremblay, N. (2001c). Selecting the high-yield subpopulation for diagnosing nutrient imbalance in crops. Agronomy Journal, 93, 802-808.
  13. Letzsch, W. S. & Sumner, M. E. (1984) Effect of population size and yield level in selection of Diagnosis and Recommendation Integrated System (DRIS) norms. Communications in Soil Science and Plant Analysis, 15, 997-1006.
  14. MacKay, D. (2003). Information Theory, Inference and Learning Algorithms. Chapter 20. An Example Inference Task: Clustering. Cambridge University Press. pp: 284-292.
  15. Malakouti, M. J. (2005). Understanding nutritional abnormalities, quality criteria and optimal nutrient concentration in fruit production in calcareous soils of Iran. Soil and Water Research Institute, Agriculture research and education organization, Sana Pub, Tehran, Iran. (in Farsi)
  16. Malakouti, M. J. (2011). Towards improving the quality of consumed breads in Iran: review. Iranian Journal of Food and Science and Thechnology, 8(31), 11-21. (in Farsi)
  17. Malavolta, E. & Malavolta, M. L. (1989). Diagnose foliar: princípios e aplicações. In: BULL, L.T., ROSOLEM, C.A. Interpretação de análise química de sol e planta para fins de adubação. Botucatu, Fundação de Estudos e Pesquisas Agrícolas e Florestais, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, 227-308. (in French)
  18. Milosevic, T. & Milosevic, N. (2011). Diagnose apricot nutritional status according to foliar analysis. Plant Soil Environ, 57(7), 301-306.
  19. Montañés, L., Heras, L. & Sanz, M. (1991). Deviation from optimum percentage (DOP): new index for the interpretation of plant analysis. Annales Aula Dei, 20, 93-107.
  20. Montanes, L., Heras, L., Abadia, J. & Sanz, M. (1993). Plant analysis interpretation based on a new index: deviation from optimum percentage (DOP). Journal of Plant Nutrition, 16, 1289-1308.
  21. Parent L. E., Cambouris, A. N. & Muhawenimana, A. (1994). Multivariate diagnosis of nutrient imbalance in potato crops. Soil Science Society of America Journal, 58, 1432-1438.
  22. Parent, L. E., Natale, W. & Ziadi, N. (2009). Compositional nutrient diagnosis of corn using the Mahalanobis distance as nutrient imbalance index. Canadian Journal of Soil Science, 89(4), 383-390.
  23. Raghupathi, H. B. & Srinivas, S. (2014). Spatial Variability Studies in Banana for Identification of Nutrient Imbalance Using Diagnosis and Recommendation Integrated System. Communications in Soil Science and Plant Analysis, 45, 1667-1686.
  24. René, W., Côté, B., Camiré, C., Burgess, M. & Fyles, J. W. (2013). Development and application of CVA, DRIS and CND norms for three hybrids of Populous Maximowiczii planted in southern Quebec. Journal of Plant Nutrition, 36(1), 118-142.
  25. Romero, I., Benito, A., Domonguez, N., Garcia-Escudero, E. & Martin, I. (2014). Leaf blade and petiole nutritional diagnosis for Vitis vinifera L. cv. 'Tempranillo' by deviation from optimum percentage method. Spanish Journal of Agricultural Research, 12(1), 206-214.
  26. Samadi, A. & Majidi, A. (2010). Norms establishment of the diagnosis and recommendation intergrated system (DRIS) And comparison with Dop approach for nutritional diagnosis of seedless grape in western Azarbaijan Province, Iran. Iranian Journal of Soil research (Formerly Soil and Water Sciences), 24(2), 89-106. (in Farsi)
  27. Sanz, M. (1999). Evaluation of interpretation of DRIS system during growing season of the peach tree: Comparison with DOP method. Soil Science and Plant Analysis Journal, 30(7&8), 1025-1036.
  28. Schaller, K. & Lohnertz, O. (1984). Accommodation of DRIS-system to grape nutrition. In: International Colloquium for the Optimization of Plant Nutrition, Montpellier, 4, 1255-1263.
  29. Sharma, J., Shikhamany, S. D., Singh, R. K. & Raghupathi, H. B. (2005). Diagnosis of nutrient imbalance in Thompson seedless grape grafted on Dog Ridge rootstock by DRIS. Commun Soil Science and Plant Analysis, 36, 2823-2838.
  30. Sharma, J., Shikhamany, S. D., Satisha, J. & Raghupathi, H. B. (2006). Diagnosis of nutrient imbalance in bunch stem necrosis affected Thompson Seedless grapevines grown on Dog Ridge rootstock using DRIS, Indian Journal of Horticulture, 63(2), 139-144.
  31. Taheri, M. (2013). Nutritional Survey of Vineyards of Khodabande. Soil and water Research Institute, Agricultural and Natural Resources Research Center of Zanjan province, Id: 14-47-10-9003-90004. (in Farsi)
  32. Wairegi, L. W. I. & Vanasten, P. J. A. (2012). Norms for multivariate diagnosis of nutrient imbalance in Arabica and East African highlands. Expl Agric Cambridge University Press, 48(3), 448-460.
  33. Walworth, J. L. & Sumner, M. E. (1987). The diagnosis and recommendation integrated system (DRIS). Adv Soil Science Journal, 6, 149-188.