Diagnosis of Macro and Micro Nutrients Balance in Sugar Beet Using ahalanobis Distance, Aitchison Distance, and Pan Balance

Document Type : Research Paper

Authors

1 Assistant Professor, Soil and Water Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

2 Senior Expert, Soil and Water Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

Abstract

Soil, water, and plant test data can be used to optimize fertilizers use, increase the quantity and quality of crops yields, and to protect the environment from the negative effects of excess fertilizers. To achieve these goals, the analysis of these data should be based on a systematic and comprehensive approach, especially in terms of nutrients’ interactions and separation of nutrients synergistic and antagonistic effects. DRIS and CNDclr can diagnose D number of components, while D-1 could be diagnosed in the D-compositional “Hilbert space” across CNDilr. The objective of this paper was to compare “Aitchison” and “Mahalanobis” distances (as a predictor) across ilr coordinates as measures of nutrient imbalance, as well as determination of macro and micro reference norms for sugar beet using CND-ilr and “Pan Balance ” technique for diagnosing nutrients status. We collected 170 root yield and foliar samples in fall sugar beet fields of Khuzestan province in Iran and analyzed seven nutrients in leaf (N, P, K, Fe, Mn, Zn, and Cu). Then, nutrients were arranged into ten balances: ilr1: [Fe|Cu, Zn, Mn], ilr2: [Mn|Zn, Mn], ilr3: [Zn|Cu], ilr4: [P | N], ilr5: [NP | K], ilr6: [Fe | Mn], ilr7: [Zn | CU], ilr8: [Fe,Mn| Zn,Cu], ilr9: [N,P,K|Mn,Zn,Fe,Cu], and ilr10: [FV|N,P,K,Mn,Zn,Fe,Cu], which were computed as isometric log ratios (ilr). Total population of observations’ classification performed by a customized <> procedure (ROC technique) showed that a critical “Mahalanobis” distance of 4.2 separated balanced (low yield) from imbalanced (high yield) specimens about yield cut-off of 60.32 t/ha with test performance of 85%, as measured by the area under the ROC curve for ilr4 to ilr10. Comparing the “Mahalanobis” distance with the “Aitchison” distance showed that they were similar. By using Pan balance technique, comparing total nutrient balance between reference (TN) and none reference (TP) group of total fields by Tukey’s test showed seven significant differences (P ≤ 0.05), except ilr7. Results showed that in order to increase sugar beet root yield in the study area, it was not necessary to use iron fertilizers and N-fertilization should be reduced, while potassium fertilizer should be increased. 

Keywords


  1. امامی، ع. 1375. روش‌های تجزیه گیاه. مؤسسه تحقیقات خاک و آب. نشریه فنی شماره 982، تهران، ایران.
  2. بصیرت، م. ، ا. اخیانی. ، ع. م دریاشناس 1395. برآورد اعداد مرجع برای انگور رقم شاهرودی به روش تشخیص چند گانه عناصر غذایی یا (CND). مجله پژوهش­های خاک، جلد 30، شماره 1. موسسه تحقیقات خاک و آب.
  3. بی نام.1382 . واژه‌ها و اصطلاحات وآماری، پژوهشکده آمار. چاپ دوم.
  4. حسینی یعغوب،. 1395. کاربرد روش دریس برای ارزیابی وضعیت تغذیه­ایی باغ­های لیمو ترش در استان هرمزگان،نشریه پژوهش­های خاک، جلد 30 ، شماره 4 . موسسه تحقیقات خاک و آب
  5. دریاشناس، ع. م ، م. بصیرت،ع. پاک‌نژاد، س. دریاشناس 1396 . روش تحلیل داده‌های ترکیبی برای تشخیص وضعیت عناصر غذایی کم‌مصرف با رویکرد تعادل عناصر در چغندر‌قند پاییزه. مجله پژوهش­های خاک، جلد 31، شماره، 4.  موسسه تحقیقات خاک و آب.
  6. دریاشناس، ع. و ح. رضایی. 1389‌. تعیین نرم‌های استاندارد دریس(DRIS) برای چغندر‌‌قند پاییزه در استان خوزستان. مجله چغندرقند، دوره 26، شماره 35 . موسسه تحقیقات خاک و آب.
  7. دریاشناس، ع. و ک. ثقفی. 1390. تشخیص چند گانه عناصر غذایی(CND)  برای چغندرقند. مجله پژوهش‏های خاک. دوره25.شماره1 . موسسه تحقیقات خاک و آب.
  8. رضائی، ع. 1376. مفاهیم آمار و احتمالات، نشر مشهد.
  9. ملکوتی م. ج.، پ. کشاورز، و ن. ج. کریمیان. 1387. روش جامع تشخیص و ضرورت مصرف بهینه کود برای کشاورزی پایدار. انتشارات دانشگاه تربیت مدرس، تهران، ایران .
  10. 10.یاسری م.، م. س. یکانی نژاد، ا. پاکپورحاجی آقا، س. رحمانی، ج. رنگین و آ. اکابری.1391. خودآموز مفاهیم ارزیابی آزمون­های تشخیصی به روش تصویری حساسیت، ویژگی، ارزش اخباری مثبت و ارزش اخباری منفی. مجله دانشگاه علوم پزشکی خراسان شمالی، 1391، ( 4)2. صفحه 275 تا282.
  11. Aitchison J. and M. Greenacre. 2002. “Biplots of Compositional Data,” Journal of the Royal Statistical Society Series C Applied, Vol. 51, No. 4, pp. 375-392.
  12. Bates, T.E. 1971. Factors affecting critical nutrient concentrations in plant and their evaluation: A review. Soil Sci. 112:116–130.
  13. Baxter I. R., Vitek O., Lahner B., Muthukumar B., Borghi M., Morrissey J., et al. 2008. The leaf ionome as a multivariable system to detect a plant’s physiological status. Proc Natl Acad Sci U S A. 105(33):12081-6.
  14. Beaufils E. R. 1973. “Diagnosis and recommendation integrated system (DRIS),” in Soil Science, Bulletin, 1 (Pietermaritzburg: University of Natal), 1–132.
  15. Bergmann, W. 1988. Ernährungs-störungen bei Kulturpflanzen. 2. Auflage. Gustav Fischer.
  16. Draycott, A. Philip and Donald R. Christenson. 2003. Nutrients for sugar beet production page: 162-165.  CABI publishing.
  17. Draycott, A. Philip. 2006. Sugar beet, Blackwell publishing. PaCAge: 198
  18. Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., and Barceló-Vidal, C. 2003. Isometric log ratio transformations for compositional data analysis 1. Math. Geol. 35, 279–300.
  19. Malavolta, E. Manual de nutrição de plantas. 2006. Pav. Chimica, ESALQ and Ed. Agron. CERES, São Paulo, Brazil, 631 p.
  20. Marschner, P. 2011. Mineral Nutrition of Higher Plants, 3rd Edn. London: Academic Press.
  21. Matti Erjala, 1986. Control of manganese deficiency in sugar beet by placement. Journal of agriculture science in Finland, Vol. 58:215- 220.  
  22. Modesto Viviane Cristina, Serge-Étienne Parent, William Natale, Léon Etienne Parent. 2014. Foliar Nutrient Balance Standards for Maize (Zea mays L.) at High-Yield Level. American Journal of Plant Sciences, 2014, 5, 497-507.
  23. Nelson, L. A.; Anderson, R. L. 1984. Partitioning of soil test-crop response probability. p. 19-38in M. Stelly (Eds), Soil testing: Correlating and interpreting the analytical results. ASA Special Publication 29, ASA, Madison, WI.
  24. Parent SE, Parent LE, Rozane DE, Natale W .2013. Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica). Frontiers in Plant Science 4 :( article 449)1-12. [Online].Available at:://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824108/.
  25. Parent SÉ. Parent L. E., Rozane D. E., Hernandes A., Natale W. 2012a. “Nutrient balance as paradigm of plant and soil chemometrics,” in Soil Fertility, (ed.) Issaka R. N., editor. (New York: In Tech Publications), 83–114. [Online].Available at: http://dx.doi.org/10.5772/53343.
  26. Parent Serge-Étienne, Philip Barlow and Léon E. Parent1. 2012b. Balance-based Nutrient Diagnosis of New Zealand kiwifruit orchards. Available at:://www.biosoil.co.nz/vdb/document/6.
  27. Parent, L .E. and M. Dafir. 1992. A theoretical concept of compositional nutrient diagnosis. J. Am. Soc. Hortic. Sci. 117:239–242.
  28. Parent, L. E .2011. Diagnosis of the nutrient compositional space of fruit crops. Rev. Bras. Frutic. vol.33 no.1 Jaboticabal Mar. 2011. [Online].Available at: http://dx.doi.org/10.1590/S0100- 29452011000100041.
  29. Parent, L. E, Parent SE, V. Hebert-Gentile, K. Naess, L. Laponinte. 2013b. Mineral balance plasticity of cloudberry in Quebec-labrador bogs, American Journal of Plant Sciences, 2013, 4, 1508-1520.
  30. Rosanne Danilo Eduardo, Dirceu de mattos junior , Serge-Etienne Parent , William Natale , Leon Etienne Parent.2011.Compositional meta-analysis of Citrus varieties in the state of São Paulo, Brazil
  31. Walworth, J. L and M. E. Sumner. 1987. The diagnosis and recommendation integrated system (DRIS) Adv. In Soil Sci. Vol. 6: 149-188.