Evaluation of Nutritional Status of Quince Trees (Cydonia oblonga Mill.) by Compositional Nutrient Diagnosis Method (CND) in Isfahan Province

آب و خاک

Article 7, Volume 37, Issue 2 - Serial Number 88, May and June 2023, Pages 281-293   

Document Type: Research Article

DOI: 10.22067/jsw.2023.79923.1231

Authors

Akbar Gandomkar¹; Zahra Khanmohammadi^{* 1}; M. Basirat²

¹Departments of Soil and Water Research, Isfahan Agricultural and Natural Resources Research and Training Center, AREEO, Isfahan, Iran

²Soil and water research institute, AREEO, Karaj, Iran

Abstract

Introduction
Quince with the scientific name "Cydonia oblonga Mill." is one of the most important horticultural products in the word including Iran. According to the average production from 1994 to 2020, Iran was the fourth largest quince producer in the world. Isfahan province is one of the most important centers of high quality quince production with 2432 hectares of cultivated area and annual production of 25986 tons. Most of the quince orchards are located in the cities of Natanz and Isfahan. Plant nutrition as an important factor in growth, is a function of nutrients and environmental conditions interactions. Assessing the nutritional status of plants is based on precise determination of nutrients and appropriate application method to diagnosis and interpret the results. Various methods have been used to evaluate the nutritional status of the plant, such as the Critical Value Approach (CVA), the Deviation from Optimum Percentage (DOP), the Diagnosis and Recommendation Integrated System (DRIS) and the Compositional Nutrient Diagnosis (CND). The CND method expresses interactions by considering the ratio of one element to the geometric mean of all elements. Then high and low functional groups are separated, by using mathematical and statistical methods and application of cumulative function of the variance ratio of nutrients and the chi-square distribution function. Finally, CND nutrients norms and indices such balance index are calculated step by step. Therefore, considering the importance of the quince production in the country and the lack of sufficient knowledge to determine its nutritional status, the present study was conducted with the aim of investigating the nutritional status of quince trees using the CND method and determining the nutrients norms for this product.
Materials and Methods
 In order to evaluate the nutritional status of quince trees using the CND method, 28 orchards were selected in the cities of Isfahan and Natanz. The orchards were selected such a way that they had different ranges of yield. The geographical location was recorded for each orchard. Then random and composite sampling of leaves was done from branches without fruit in July 2018. Concentration of nitrogen phosphorous, potassium, calcium, magnesium, iron, manganese, zinc, copper and boron was measured in quince leaves. At the end of season, the yield was determined for each orchard. The orchards divided into two groups based on high and low yields. The CND norms, CND nutritional index and nutritional balance index (r²) were computed based on steps of Parent and Dafir. The balance index of nutritional elements (r²) was calculated by Keith-Nilson method based on the Chi-square statistical distribution function (K²) in Excel software.
 
Results and Discussion
According to results of cumulative distribution function of nutrient variance and considering the yield of 23 tons per hectare as the intermediate yield, 25% of the studied orchards were in the high yield group and 75% of the orchards were in the low yield group. After solving the third– rank cumulative function equations of the studied nutrients, the highest yield was obtained for potassium Fci (VK) = 21.98 and the lowest value was for nitrogen Fci (VN) = 15.37. CND standard norms of nutrients and residual value were described as: V*N= 2.91, V*P= 1.39, V*K= 2.91, V*Ca= 2.13, V*Mg= 1.35, V*Fe= -2.01, V*Mn= -3.12, V*Zn= -3.97, V*Cu= -4.85, V*B= -3.51 and V*Rd= 6.78. The CND nutrient index revealed that potassium and nitrogen had the most negative index among macronutrients in the low-yield orchard group. The low amount of soil organic matter and the high presence of sand can contribute to the negative nitrogen index. Among the micronutrients, the iron index was negative in 67.7% of the low-yield orchards. Zinc and copper had the next highest nutritional requirements in most orchards. The presence of calcareous conditions in the soil of the studied orchards may be one of the reasons for this observation. The estimation of the nutritional balance index indicated that the r2 value in orchards with low yield was 60.3% higher than that in high-yield orchards.
Conclusion
 CND nutritional balance index (r²), specially in orchards with low yield was more than zero (20.85), indicating nutritional imbalance in these orchards. Proper management and balanced application of chemical fertilizers should be considered. This can increase the yield and quality of quince production.

Keywords

Nutritional balance; Quince tree; Standard norms

References

Anonymous. (2021). Agricultural statistics. Third volume: Crops Jihade-Agriculture Ministry, Tehran, Iran. P: 25-26.

2. Anonymous. (2016). Performance analysis report Seed and Plant Certification and Registration Institute.
3. Babalar, M., Mohebi, M., Askari, M.A., & Talaee, A. (2015). Effect of iron and nitrogen application on quantitative and qualitative characteristics of apple “cv. Fuji”. Iranian Journal of Horticultural Science, 46(3), 399-407. (In Persian with English abstract). https://doi.org/10.22059/ijhs.2015.55861
4. Basirat, M., Akhiani, A., & Daryashenas, A. (2016). Estimating sufficiency norms in compositional nutrient diagnosis (CND1) method for Shahroudi table grape. Iranian Journal of Soil Research, 30(1), 1-11. (In Persian with English abstract)

5- Basirat, M., Haghighatnia, H., & Mousavi, S.M. (2018). Evaluation and determination of the nutritional status of valencia orange orchards in south of Fars province. Journal of Water and Soil, 32(1), 143-154. (In Persian with English abstract). https://doi.org/10.22067/JSW.V32I1.67597

6. Bremner, J.M., & Mulvaney, C.S. (1982). Nitrogen-Total. p. 595–622. In: Page A.L., Miller R.H., & Keeney D.R., (eds.), Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
7. Castr, J., & Sotomayor, C. (1997). The influence of boron and zinc sprays bloomtime on almond fruit set. Acta Hortic 470: 402-405. https://doi.org/10.17660/ActaHortic.1998.470.55
8. Chakerolhosseini, M.R., Khorassani, R., Fotovat, A., & Basirat, M. (2016). Determination of norms and limitation of nutrients for orange by the compositional nutrient diagnosis method. Journal of Soil Management and Sustainable Production, 6(3), 161-172. (In Persian with English abstract)
9. Daryashenas, M., & Dehghani, F. (2006). Determination of DRIS reference norms for pomegranate in Yazd province. Iranian Journal of Soil Research, 20(1), 1-9. (In Persian with English abstract). https://doi.org/10.22092/IJSR.2006.127142.
10. Daryashenas, M., & Saghafi, K. (2011). Compositional nutrient diagnosis in sugar beet. Iranian Journal of Soil Research, 25(1), 1-12. (In Persian with English abstract). https://doi.org/10.22092/IJSR.2011.126454.
11. Daryashenas, M., & Rezai, H. (2011). Determination of DRIS reference norms for autumn sugar beet in Khuzestan province. Journal of Sugar Beet, 26(2), 185-204. (In Persian with English abstract). https://doi.org/10.22092/ JSB.2011.945.
12. Feyzizadeh, M., & Samadi, A. (2016). Comparing of deviation from optimum percentage (DOP) method and diagnostic recommendation integrated system (DRIS) for nutritional balances of onion (Allium cepa L.). Water and Soil Science, 26(2/3), 271-286. (In Persian with English abstract)
13. Gee, G.W., & Bauder, J.W. (1982). Particle size Analysis. p. 404–408. In: Page, A.L., Miller R.H., & Keeney D.R., (eds.), Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
14. Geiklooi, A., Reyhanitabar, A., & Najafi, N. (2021). Critical indexes of compositional nutrient diagnosis (CND) and its validation in wheat fields. Revista de la Facultad de Agronomía de la Universidad del Zulia, 38(3), 480-504. https://doi.org/10.47280/RevFacAgron(LUZ).v38.n3.02.
15. Hernandez-Caraballo, E.A., Rodriguez-Rodriguez, O., & Rodriguez-Perez, 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(3), 225-231. https://doi.org/10.1016/j.envexpbot.2008.05.010.
16. Hosseini, Y., Saleh, J., & Chakerolhosseini, M.R. (2020). Evaluation of nutritional status of lime orchards in Hormozgan province of Iran using compositional nutrient Diagnosis Method. Journal of Crop Production and Processing, 10(2), 81-92. (In Persian with English abstract). https://doi.org/10.47176/jcpp.10.2.34401.
17. Issac, A. Robert. (1990). Associate chapter Editor, Methods of Plant Analysis, Official Methods of Analysis of the A.O.A.C.
18. Khanmohammadi, Z., Khoshgoftarmanesh, A.H., & Melali, A.H. (2010). Methods of Plant Analysis. Academic Jihad Publishing Center, Isfahan Industrial Unit.
19. Khiari, L., Parent, L.E., & Tremblay, N. (2001a). Critical compositional nutrient indexes for sweet corn at early growth stage. Agronomy Journal, 93, 809-814. https://doi.org/10.2134/agronj2001.934809x.
20. Khiari, L., Parent, L.E., & Tremblay, N. (2001b). The Phosphorus compositional nutrient diagnosis range for potato. Agronomy Journal, 93, 815-819. https://doi.org/10.2134/agronj2001.934815x.
21. Knudsen D., Peterson G.A., & Pratt P.F. (1982). Lithium, sodium, and potassium. p. 225–246. In: Page A.L., Miller R.H., & Keeney D.R., (eds.), Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
22. Lindsay, W.L., & Norvell, W.A. (1978). Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Science Society of American Journal, 42, 421–428.
23. Motalebifard, R. (2022). Evaluation of nutritional status of Hamedan province grape fields by compositional nutrient Diagnosis Method. Journal of Water and Soil, 36(3), 365-375. (In Persian with English abstract). https://doi.org/10.22067/jsw.2022.74703.1137.
24. Nelson, R.E. (1982). Carbonate and Gypsum. p. 181–196. In: Page A.L., Miller R.H., & Keeney D.R., (eds.), Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
25. Nelson, D.W., & Sommers, L.E. (1982). Total carbon, organic carbon and organic matter. p. 539–579. In: Page A.L., Miller R.H., & Keeney D.R., (eds.), Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
26. Nyomora, A.M.S., Brown, P.H., & Freeman, M. (1997). Fall foliar applied boron increases boron concentration and nut set of almond. Journal of American Society for Horticultural Science, 122(3), 405-410. https://doi.org/10.21273/ JASHS.122.3.405.
27. Olsen, S.R., & Sommers, L.E. (1982). Phosphorus. p. 403–430. In: Page A.L., Miller R.H., & Keeney D.R., (eds.), Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
28. Page, A.L., Miller, R.H., & Keeney, D.R. (1982). Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. American Society of Agronomy, Inc. Soil Science of America, Inc. Madison, Wisconsin, USA.
29. Parent, L.E., & Dafir, M. (1992). A theoretical concept of compositional nutrient diagnosis. Journal of American Society Horticulture Science, 117, 239-242. https://doi.org/10.21273/JASHS.117.2.239.
30. Postman, J.D. (2012). Quince (Cydonia oblonga Mill.) center of origin provides sources of disease resistance. Acta Horticulturae, 948, 229-234. https://doi.org/10.17660/ActaHortic.2012.948.26.
31. Samadi, A., & Azizi, M. (2011). Norms establishment of the Diagnosis and recommendation intergrated system (DRIS) and comparison with DOP approach for nutritional Diagnosis of seedless grape (Sultana, cv) in Western Azarbaijan province, Iran. Iranian Journal of Soil Research, 24(2), 89-105. (In Persian with English abstract). https://doi.org/10.22092/IJSR.2010.126553.
32. Sharifmand, M., Sepehr, E., & Bybordi, A. (2018). Evaluation of nutritional status of squash by Compositional nutrient diagnosis (CND) method in Khoy region. Iranian Journal of Soil and Water Research, 48(5), 1007-1013. (In Persian with English abstract). https://doi.org/10.22059/IJSWR.2018.225775.667619.
33. Seedkolai, F., Sadeghi, H., & Moradi, H. (2015). Effects of foliar applications of nitrogen, boron and zinc on auxin contents, fruit set and fruit drop in orange (Citrus sinensis) cv. Thompson Navel. Iranian Journal of Horticultural Science, 46(3), 367-378. (In Persian with English abstract). https://doi.org/10.22059/ijhs.2015.55858.
34. Taheri, M., Vahedi, S., & Abasi, M. (2010). Comparison of nutrient status in different olive varieties with nutrition indicators. Pomology Research Scientific Journal, 5(1), 44-59. (In Persian with English abstract)
35. Zandi, S., Fatemi, A., Saiedi, M., & Hamedi, F. (2021). Investigation of different fertilizer management effect on nutritional status of apple by compositional nutrient diagnosis. Journal of Soil Management and Sustainable Production, 2(11), 91-107. (In Persian with English abstract). https://doi.org/10.22069/EJSMS.2021.18030.1951.