News

a.z
Daryashenas, A., Basirat, M., Paknejad, A., Daryashenas, S. (2018). Compositional Data Analysis Method for Diagnosing Micronutrients Status of Fall Sugar Beet with the Approach of “Nutrients Balance”. , 31(4), 497-508. doi: 10.22092/ijsr.2018.115840
A. M. Daryashenas; M. Basirat; A. R. Paknejad; S. Daryashenas. "Compositional Data Analysis Method for Diagnosing Micronutrients Status of Fall Sugar Beet with the Approach of “Nutrients Balance”". , 31, 4, 2018, 497-508. doi: 10.22092/ijsr.2018.115840
Daryashenas, A., Basirat, M., Paknejad, A., Daryashenas, S. (2018). 'Compositional Data Analysis Method for Diagnosing Micronutrients Status of Fall Sugar Beet with the Approach of “Nutrients Balance”', , 31(4), pp. 497-508. doi: 10.22092/ijsr.2018.115840
Daryashenas, A., Basirat, M., Paknejad, A., Daryashenas, S. Compositional Data Analysis Method for Diagnosing Micronutrients Status of Fall Sugar Beet with the Approach of “Nutrients Balance”. , 2018; 31(4): 497-508. doi: 10.22092/ijsr.2018.115840

Compositional Data Analysis Method for Diagnosing Micronutrients Status of Fall Sugar Beet with the Approach of “Nutrients Balance”

پژوهش های خاک
Article 4, Volume 31, Issue 4, March 2018, Pages 497-508    PDF (3.23 M)
DOI: 10.22092/ijsr.2018.115840
Authors
A. M. Daryashenas* 1; M. Basirat2; A. R. Paknejad3; S. Daryashenas4
1Assistant professor, Soil and Water Research Institute of Iran , Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
2Assistant professor, Soil and Water Research Institute of Iran , Agricultural Research, Education and Extension Organization (AREEO), Tehran, Ira
3Research lecturer, Safiabad Agricultural Research and Education and Natural Resources Center
4Senior expert, Soil and Water Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
Abstract
Tissue analysis is a useful tool for evaluation and optimizing nutrients for fall sugar beet. Nutrient diagnostic tools are based on two methods, i.e. nutrient concentration (critical minimum value) and ratios (Diagnosis and Recommendation Integrated System or DRIS). However, those methods disregard two important factors which are, firstly, compositional nature of analytical data and, secondly, dealing with high number of ratios which makes our final decision biased. So, we shall try to limit those numbers of ratios that can be diagnosed independently in a given composition. The use of orthogonal balances, a compositional data analysis technique, avoids such biases. Our objective was to develop foliar nutrient balance standards i.e. DRIS and CND-clr to CND-ilr for fall sugar beet and determine CND-ilr reference norms. We collected 183 root and sugar yields and foliar samples in fall sugar beet fields of Khuzestan province and analyzed four nutrients [H1] in leaf tissue (Cu, Zn, Mn, and Fe). Nutrients were arranged into three balances ilr1: [Fe|Cu, Zn, Mn], ilr2: [Mn|Zn, Mn], ilr3: [Zn|Cu] and computed as Isometric Log Ratios (ilr). Total population of observations were divided into a high and low population on the basis of 60.32 t/ha root yield and 9.40 t/ha sugar yield (cut off yield). Results showed that a Critical Aitchison Distance of 0.3 (as a predictor) separated balanced from imbalanced samples through three balances i.e. ilr1, ilr2, and ilr3. Three ilr reference norms were derived. "Mobile and fulcrums balance system" was used for 33 fall sugar beet fields (root yield < 60 t.ha-1: TP quadrant) with 3 balances. Results showed that Fe, Mn, Zn, and Cu with the concentration of, respectively, 296, 120, 41, and 19 mg.kg-1 can be considered as reference concentrations for balance based diagnosis, because concentration values are compositional and subjected to interactions. Results also showed that to increase the quantity and quality of sugar beet it is not necessary to use iron fertilizers, and if any iron fertilization has been used, it should be reduced.



 [H1]لطفا کنترل منید. شما 4 عنصر را نوشته اید.
Keywords
Aitchison Distance; Isometric Log Ratios; Reference concentrations; Fulcrums balance system
References
  1. اسماعیلی، م.، ا. گلچین و م. س. درودی. 1379. تعیین حد متعادل عناصر غذایی در سیب به روش DRIS. مجله خاک و آب. جلد 12، شماره 8، صفحه 22.
  2. امامی، ع. 1375. روش‌های تجزیه گیاه. مؤسسه تحقیقات خاک و آب. نشریه فنی شماره 982، تهران، ایران.
  3. بی نام.1382 . واژه‌ها و اصطلاحات آماری، پژوهشکده آمار. چاپ دوم.
  4. دریاشناس، ع. و ع. پاک نژاد. 1384‌. تعیین نرم‌های استاندارد دریس برای چغندر‌‌قند پاییزه استان خوزستان. نهمین کنگره علوم خاک ایران، ایران، کرج، 6 تا 9 شهریور،1384.
  5. دریاشناس، ع. و ک. ثقفی. 1390. تشخیص چند گانه عناصر غذایی(CND)  برای چغندرقند. مجله پژوهش‏های خاک. دوره25.شماره1 . موسسه تحقیقات خاک و آب.
  6. رضائی، ع. 1376. مفاهیم آمار و احتمالات، نشر مشهد.
  7. طهرانی، م.، م. بلالی، ف. مشیری و  ع. دریاشناس .1391.توصیه و برآورد کود در ایران: چالشها و راهکارها. مجله پژوهش‌های خاک (علوم خاک و آب) : 1391، دوره 26،  شماره 2 الف.  
  8. 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 .
  9. 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.
  10. 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.
  11. Aitchison, J. 1986. Statistical analysis of compositional data. Chapman and Hall, New York.
  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. Blanco-Macías, F., Magallanes-Quintanar, R., Valdez-Cepeda, R.D., Vázquez-Alvarado, R., OlivaresSáenz, E., Gutiérrez-Ornelas, E., and Vidales-Contreras, J.A. 2009. Comparison between CND norms and boundary-line approach nutrient standards: Opuntia ficus indica L. case. Revista Chapingo Serie Horticultura 15(2): 217-223.
  17. Egozcue, J. J. And V. Pawlowsky-Glahn .2006b. Simplicial geometry for compositional data. In Buccianti, A., Mateu-Figueras, G., Pawlowsky-Glahn, V. (Editors). Compositional data analysis in the geosciences: from theory to practice. Geological Society, London, Special Publication 264, p. 145-159.
  18. Egozcue, J. J., and Pawlowsky-Glahn, V. 2011a. “Basic concepts and procedures,” in Compositional Data Analysis: Theory and Applications, (ed.) V. Pawlowsky-Glahn and A. Buccianti (New York, NY: John Wiley and Sons), 12–28.
  19. 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.
  20. - Geraldson CM. 1970. Intensity and balance concept as an approach to optimal vegetable production. Communications in Soil Science and Plant Analysis; 1(4) 187-196.
  21. - Geraldson CM. 1984. Nutrient intensity and balance. In Stelly, M. (ed.) Soil testing: Correlating and Interpreting Analytical Results. Madison WI: American Society of Agronomy Special Publication 29; 1984. p. 75-84.
  22. Huang, H., Xiao Hu, C., Tan, Q., Hu, X., Sun X., and Bi, L. 2012. Effects of Fe–EDDHA application on iron chlorosis of citrus trees and comparison of evaluations on nutrient balance with three approaches. Scientia Hort. 146:137–142.
  23. Malavolta, E. Manual de nutrição de plantas. 2006. Pav. Chimica, ESALQ and Ed. Agron. CERES, São Paulo, Brazil, 631 p.
  24. Marschner, P. 2011. Mineral Nutrition of Higher Plants, 3rd Edn. London: Academic Press.
  25. 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.
  26. 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.
  27. Parent SE, Parent LE, Rozane DE, Natale W .2013a. 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/.
  28.  Parent SE, Parent LE, Egozcue JJ, Rozane DE, Hernandes A, Lapointe L, Gentile VH, Naess K, Marchand S, Lafond J, Mattos Junior D, Barlow P, Natale W .2013b.The plant ionome revisited by the nutrient balance concept. Frontiers in Plant Science 4 :( article 39)1-10.
  29. 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.
  30. 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.
  31. Parent, LE .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.
  32. Parent, L.E., and M. Dafir. 1992. A theoretical concept of compositional nutrient diagnosis. J. Am. Soc. Hortic. Sci. 117:239–242.
  33. Pawlowsky-Glahn, J. J. Egozcue and R. Tolosana Delgado.2011. “Principal Balances,” In: 4th International Workshop on Compositional data analysis (Codawork 2011), San Feliu de Guixols, Spain, 2011. 
  34. Pawlosky-Glahn, V.; Egozcue, J.J. 2008.  Compositional data and Simpson's paradox. Codawork. In: Com positional analysis workshop, 3. 2008. Girona, Disponível. [Online]. Available at:  
  35. 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  Proceedings of the 4th International Workshop on Data (2011).
  36. Silva, G.G.C. da, Neves, J.C.L., Alvarez V.V.H., and Leite, F.P. 2004. Nutritional diagnosis for Eucalypt by DRIS, M-DRIS, and CND. Sci. Agric. (Piracicaba, Braz.) 61(5):507-515.
  37. Umesh, U.N.; Peterson, R.A.; Mccann-Nelson, M.; Vaidyanthan, R. 1996. Type IV error in marketing research. The investigation of ANOVA interactions. Journal of the Academy of Marketing Science, Greenvale, v. 24, p. 17-26, 1996. Verlag, Stuttgart, 762 p.
  38. Wairegi L. W. I. and P. J. A. Van Asten.  2012. Norms for Multivariate Diagnosis of Nutrient Imbalance in Arabica and Rosusta Coffee in the East African Highlands,” Experimental Agriculture, Vol. 48, No. 3, 2012, pp. 448-460.[online]. Available at: htt://dx.doi.org/10.1017/S0014479712000142.
  39. Walworth, J. L and M. E. Sumner. 1987. The diagnosis and recommendation integrated system (DRIS) Adv. In Soil Sci. Vol. 6: 149-188.
Statistics
Article View: 1,297
PDF Download: 826


counter
Journal management system. designed by sinaweb