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Bu çalışmanın amacı, Çarşamba Ovası’nda yetiştirilen soya bitkisinin verim parametreleriyle (bitki boyu, bin tane ağırlığı ve tane verimi) toprakların bazı fiziksel ve kimyasal özellikleri arasındaki korelasyon ilişkilerine dayanarak, bu özellikler arasında regresyon modellerin oluşturulması ve elde edilen modellerin ova topraklarında bitki veriminin tahmininde uygulanabilirliğinin belirlenmesidir. Bu amaçla, ovanın çiftçiler tarafından soya tarımı yapılan arazilerinden toprak ve bitki örnekleri alınmıştır. Bitki boyu ile kil, hacim ağırlığı (Db), elektriksel iletkenlik (EC), organik madde (OM), azot (N), potasyum (K), bitkiye yarayışlı su (BYS), solma noktası (SN) parametreleri arasındaki regresyon modeli istatistiksel olarak anlamlı (p=0.091), belirleme katsayısı (R= 0.766) yüksek; bin tane ağırlığı ile OM, Db, kum, silt, kireç (CaCO3), katyon değişim kapasitesi (KDK), N, fosfor (P), K, sodyum (Na), tarla kapasitesi (TK), SN parametreleri arasındaki model istatistiksel olarak anlamlı farklılık saptanmamış, belirleme katsayısı (R= 0.782) yüksek; tane verimi ile OM, KDK, N, P, bakır (Cu), kil, kum, BYS, TK parametreleri arasındaki modelin performansı çok yüksek (R= 0.853; p= 0.029) olarak belirlenmiştir. Verim parametreleri ile toprakların fiziksel ve kimyasal özellikleri arasındaki regresyon modellerin geçerliliğinin belirlenmesinde belirleme katsayısı (R), hata kareler ortalamasının karekökü (HKOK), uygunluk indeksi , modelin etkinliği (ME) birlikte değerlendirilmiştir. Elde edilen regresyon modellerin, ova topraklarında yetiştirilen soya bitkisinin verim parametrelerinin tahmin edilmesinde uygulanabilirliği mümkün gözükmektedir.
The aim of this study is to establish regression models between the creation of these characteristics and the determination of the applicability of the models obtained in the prediction of the plant productivity in the soya plant (plants length, a thousand weight and one productivity) based on the correlation between the parameters of productivity (plants length, a thousand weight and one productivity). For this purpose, land and plant samples have been taken from the soya farms made by the farmers of the ocean. Regression model between plant length, volume weight (Db), electrical conductivity (EC), organic substance (OM), nitrogen (N), potassium (K), plant beneficial water (BYS), swelling point (SN) parameters is statistically meaningful (p=0.091), determination ratio (R=0). 766) high; with a thousand weights OM, Db, sand, silk, silk (CaCO3), kation exchange capacity (KDK), N, phosphorus (P), K, sodium (Na), field capacity (TK), model statistically significant differences between SN parameters have not been identified, determination rate (R= 0.782) high; with one performance OM, KDK, N, P, baker (Cu), silk, sand, BYS, TK parameters have been determined as very high (R= 0.853; p= 0.029) performance of the model. In determining the validity of the regression models between the performance parameters and the physical and chemical characteristics of the soil, the determination rate (R), the square corner of the error square average (HKOK), the conformity index, the effectiveness of the model (ME) have been evaluated together. The achieved regression models appear to be applicable in the prediction of the performance parameters of the soya plant grown in the oval soil.
The aim of this study was to develop regression models on the basis of correlation between yield parameters of soybean (plant height, thousand seed weight, and yield) and some physical and chemical characteristics of soils and to determine applicability of obtained models in estimation of plant yield grown in soils of Çarşamba Plain. Soil and plant samples were taken from farmer’s fields in the plain. Regression models between soybean plant height and soil properties of bulk density (BD), electrical conductivity (EC), organic matter (OM), nitrogen (N), potassium (K), wilting point (WP), available water content (AWC) and clay yielded high coefficient determination (R= 0.766) and was significant (p=0.091); Model between thousand seed weight and OM, BD, sand, silt, lime (CaCO3), cation exchange capacity (CEC), N, phosphorus (P), K, sodium (Na), field capacity (FC) and WP, resulted in R=0.782 and was insignificant; and model between seed yield and OM, CEC, N, P, copper (Cu), clay, sand, AWC, and FC yielded R= 0.853 and it was highly significant (p= 0.029). Determination coefficient (R), root mean square error (RMSE), index of agreement (d), model efficiency (ME) were evaluated together to determine the validity of regression models. In general, statistical parameters were within validity limits. The results suggested that the developed regression models can be applied in the estimation of yield parameters in soybean grown in study area.
Alan : Ziraat, Orman ve Su Ürünleri
Dergi Türü : Ulusal
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