The relation between yield indices of maize plant and soil physicochemical characteristics

Yıl 2020, , 52 - 59, 01.01.2020

Nalan Kars İmanverdi Ekberli

https://doi.org/10.18393/ejss.644038

Cited By: 3

Öz

The
aim of this study was to set regression models based on correlation between
yield parameters of maize plant (height, thousand seed weight and yield) and
physical and chemical characteristics of soils and to determine applicability
of obtained models in estimation of plant yield grown in soils of Çarşamba
Plain. Regression coefficient (R), root mean square error (RMSE), index of
agreement , model efficiency (ME)
were evaluated to determine the validity of regression models between the yield
components and physical and chemical characteristics of 40 soil samples taken
from root zone of cultivated farms. Model associated with the relation between
(i) plant height and bulk
density (BD),
field capacity (FC), clay and sand content wasn’t statistical significant (R=
0.53, p>0.05); (ii) thousand seed weight and soil electrical
conductivity (EC), organic matter (OM), lime (CaCO₃), nitrogen (N),
phosphorus (P), potassium (K), Ca + Mg was characterized with a moderate R
(R=0.79, p < 0.05), and (iii) seed yield and OM, N, P, K,
copper (Cu), cation exchange capacity (CEC), CaCO₃ indices has the
highest R (R = 0.87; p <0.01). In general, statistical
parameters were within the validity limits. The established regression models
can be applied for the predicting of yield parameters of maize plant grown in
the farmed areas of the region.

Anahtar Kelimeler

Soil physicochemical properties, plant height, regression models, seed yield

Kaynakça

• Alexandrov, V.A., Hoogenboom, G., 2000. The impact of climate variability and change on crop yield in Bulgaria. Agricultural and Forest Meteorology 104(4): 315-327. Angelov, K., 1994. Correlations between grain yield and certain plant and ear chracteristics in maize hybrids. Field Crop Abstracts. 47: 133.
• Anonymous, 2018. Directorate of Seed Registration and Certification. Available at [access date: 19.02.2019]: https://www.tarimorman.gov.tr/BUGEM/TTSM/Belgeler/Tescil/Teknik%20Talimatlar/S%C4%B1cak%20%C4%B0klim%20Tah%C4%B1llar%C4%B1/MISIR_TEKNIK_TALIMATI.pdf
• Anonymous, 2019. Turkish Statistical Institute Main Statistics. Available at [access date: 19.02.2019]: https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr
• Bayraklı, F., Ekberli, İ.A., Gülser, C., 1999. Azerbaycan Mil ovası topraklarının verimlilik düzeylerinin deneysel ve matematiksel olarak değerlendirilmesi. OMU Ziraat Fakültesi Dergisi 14(2): 138-153 [in Turkish].
• Black, C.A., 1965. Methods of Soil Analysis, Part II - Chemical and Microbiological Properties. Agronomy Monograph 9.1, American Society of Agronomy (ASA), Soil Science Society of America (SSSA), Madison, Wisconsin, USA.
• Bouma, J., van Lanen, H.A.J., 1987. Transfer functions and threshold values: from soil characteristics to land qualities. In: Proceedings of the International Workshop on Quantified Land Evaluation Procedures. Beek, K.J., Burrough, P.A., MacCormack, D.E. (Eds.). 27 April - 2 May 1986, Washington, D.C., USA, pp. 106-110.
• Bouyoucos, G.J., 1962. Hydrometer method improved for making particle size analyses of soils. Agronomy Journal 54(5): 464-465.
• Budka, A., Łacka, A., Gaj, R., Jajor, E., Korbas, K., 2015. Predicting winter wheat yields by comparing regression equations. Crop Protection 78: 84-91.
• Çolakoğlu, H., 1985. Gübre ve Gübreleme. Ege Üniversitesi Ziraat Fakültesi Yayınları, Teksir No:17, Bornova, İzmir, Turkey [in Turkish].
• Demiralay, İ., 1993. Toprak Fiziksel Analizleri. Atatürk Üniversitesi Ziraat Fakültesi Yayınları No:143, Erzurum, Turkey [in Turkish].
• Dengiz, O., Ekberli, İ., 2017. Bazı vertisol alt grup topraklarının fizikokimyasal ve ısısal özelliklerinin incelenmesi. Akademik Ziraat Dergisi 6(1): 45-52 [in Turkish].
• Dorsey, J.W., Hardy, L.C., 2018 Sustainability factors in dynamical systems modeling: Simulating the non-linear aspects of multiple equilibria. Ecological Modelling 368: 69-77.
• Dotlacil, L., Toman, K., 1991. Testability of the yield of different wheat varieties. Rostlinna Vyroba,37: 33-38.
• Ekberli, İ., Dengiz, O., 2016. Bazı inceptisol ve entisol alt grup topraklarının fizikokimyasal özellikleriyle isısal yayınım katsayısı arasındaki regresyon ilişkilerin belirlenmesi. Toprak Su Dergisi 5(2): 1-10 [in Turkish].
• Gülser, C., 2004. Tarla kapasitesi ve devamlı solma noktası değerlerinin toprakların fiziksel ve kimyasal özellikleriyle ilişkili pedotransfer eşitliklerle belirlenmesi. OMU Ziraat Fakültesi Dergisi 19(3): 19-23 [in Turkish].
• Gülser, C., Candemir, F., 2014. Using soil moisture constants and physical properties to predict saturated hydraulic conductivity. Eurasian Journal of Soil Science 3(1): 77-81.
• Gülser, C., Ekberli, I., Candemir F., 2016. Spatial variability of soil physical properties in a cultivated field. Eurasian Journal of Soil Science 5(3): 192-200.
• Carena, M.J., Hallauer, A.R., Miranda Filho, J.B., 1987. Quantitative Genetics in Maize Breeding. Springer, New York, USA.
• Huang, F., Zhan, W., Ju, W., Wang, Z., 2014. Improved reconstruction of soil thermal field using two-depth measurements of soil temperature. Journal of Hydrology 519: 711–719.
• Jin, X., Li, Z., Yang, G., Yang, H., Feng, H., Xu, X., Wang, J., Li, X., Luo, J., 2017. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS Journal of Photogrammetry and Remote Sensing 126: 24-37.
• Kacar, B., 1994. Bitki ve Toprağın Kimyasal Analizleri III. Toprak Analizleri. Ankara Üniversitesi Ziraat Fakültesi Eğitim Araştırma ve Geliştirme Vakfı Yayınları No:3. Ankara, Turkey [in Turkish].
• Karabulut, A., Ünver, İ., 2012. Çukurova’da alüvyal bir tarım arazisinde bazı toprak verimlilik parametrelerinin jeoistatistiksel modellemesi. Toprak Su Dergisi 1(2): 71-81 [in Turkish].
• Karadavut, U., Genç, A., Tozluca, A., Palta, Ç., 2010. Silajlık ve danelik mısırlarda kuru madde birikiminin bazı matematiksel büyüme modelleri ile analizi. Tarım Bilimleri Dergisi 16: 89-96 [in Turkish].
• Kars, N., Ekberli, İ., 2019a. Çarşamba ovasının buğday bitkisi altındaki topraklarının bazı fiziksel ve kimyasal özelliklerinin incelenmesi. Toprak Su Dergisi 8(1): 18-28 [in Turkish].
• Kars, N., Ekberli, İ., 2019b. Buğday bitkisinin verim parametreleri ile bazı toprak özellikleri arasındaki pedotransfer modellerin uygulanabilirliği. Türkiye Tarımsal Araştırmalar Dergisi 6(2): 153-164 [in Turkish].
• Kosheleva, N.E., Kasimov, N.S., Samonova, O.A., 2002. Regression models for the behavior of heavy metals in soils of the Smolensk-Moscow Upland. Eurasian Soil Science 35(8): 845-856.
• Kumar, A., Pandey, V., Shekh, A.M., Dixit, S.K., Kumar, M., 2008. Evaluation of cropgro-soybean (Glycine max. [l] (merrill) model under varying environment condition. American-Eurasian Journal of Agronomy 1(2): 34-40.
• Lindsay, W.L., Norvell, W.A., 1978. Development of a DTPA Test for zinc, iron, manganese and copper. Soil Science Society America Journal 42(3): 421-428.
• Maiti, R.K., Wersche-Ebeling, P., 1998. Maize Science. Science Publishers, Inc. Enfield, USA. 520 p.
• Malone, R.W., Ma, L., Karlen, D.L., Meade, T., Meek, D., Heilman, P., Kanwar, R.S., Hatfield, J.L., 2007. Empirical analysis and prediction of nitrate loading and crop yield for corn–soybean rotations. Geoderma 140(3): 223-234.
• Nelson, R.E., 1982. Carbonate and gypsum. In: Methods of Soil Analysis, Part 2, Chemical and microbiological properties, Second Edition. Number 9, Page, A.L., Keeney, D. R., Baker, D.E., Miller, R.H., Ellis, R. Jr., Rhoades, J.D. (Eds.). ASA-SSSA, Madison, Wisconsin, USA. pp. 181-198.
• Olsen, S.R., Cole, C.V., Watanabe, F.S., Dean, L.A., 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. U.S. Department of Agriculture, Circular No 939, USA, 19p.
• Overman, A.R., Scholtz, III R.V., 2002. Mathematical models of crop growth and yield. Marcel Dekker Inc. New York, USA. 325p.
• Pachepsky, Y. A., Rawls, W.J., 2004. Development of pedotransfer functions in soil hidrology. Develoment in Soil Science, Elsevier, Volume 30, 542p.
• Peterson, C.J., Graybosch, R.A., Baenziger, P.S., Grombacher, A.W., 1992. Genotype and environment effects on quality characteristics of hard red winter wheat. Crop Science 32(1): 98-103.
• Rowell, D.L., 1994. Soil science: methods and applications. Longman Group Ltd., London, UK. 350 p.
• Thiéry, D., Amraoui, N., Noyer, M.L., 2018. Modelling flow and heat transfer through unsaturated chalk – Validation with experimental data from the ground surface to the aquifer. Journal of Hydrology 556: 660-673.
• U.S. Salinity Laboratory Staff., 1954. Diagnosis and Improvement of Saline and Alkali Soils, Agriculture. Handbook No:60, United States Department of Agriculture, Washington DC, USA. 160p. Available at [access date: 19.02.2019]: https://www.ars.usda.gov/ARSUserFiles/20360500/hb60_pdf/hb60complete.pdf
• Usowicz, B., Lipiec, J., Usowicz, J.B., Marczewski, W., 2013. Effects of aggregate size on soil thermal conductivity: Comparison of measured and model-predicted data. International Journal of Heat and Mass Transfer 57(2): 536-541.
• Vereecken, H., Weynants, M., Javaux, M., Pachepsky, Y.A., Schaap, M.G., van Genuchten, M.T., 2010. Using pedotransfer functions to estimate the van Genuchten-Mualem soil hydraulic properties: A review. Vadose Zone Journal 9(4): 795–820.
• Walkley, A., Black, I.A., 1934. An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science 37(1): 29-37.
• Wang, L., Li, X., Chen, Y., Yang, K., Chen, D., Zhou, J., Liu, W., Qi, J., Huang, J. 2016. Validation of the global land data assimilation system based on measurements of soil temperature profiles. Agricultural and Forest Meteorology 218-219: 288-297.
• Whitman, C.E., Haffield, J.L., Reginato, R.J., 1985. Effect of slope position on the microclimate, growth and yield of barley. Agronomy Journal 77(5): 663-669.
• Willmott, C.J., Matsuura, K., 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research 30(1): 79-82.
• Willmott, C.J., Robeson, S.M., Matsuura, K., 2012. Short Communication, A refined index of model performance. International Journal of Climatology 32(13): 2088-2094.