CND AND PCA APPROACHES FOR MULTIVARIATE DIAGNOSIS OF NUTRIENT IMBALANCE IN PAPAYA (CARICA PAPAYA L.)

Abstract

A survey was conducted in commercial papaya growing orchards for collection of petiole samples to establish nutrient concentrations vs. yield data bank. The data bank was used for developing multivariate compositional nutrient diagnosis (CND) norms for assessing the nutritional status of various orchards. The mean N, P and K concentrations were 1.14, 0.198 and 2.32% respectively. The mean Ca (2.95%) concentration was thrice higher compared to Mg (1.03%) concentration. The mean values of Fe, Mn, Zn and Cu were 139, 76, 56 and 8 ppm respectively. The CND norms for V N , V P and V K were 1.956, 0.16 and 2.59 respectively. The norm for Ca (V Ca 2.86) and Mg (V Mg 1.83) were much narrower compared to the absolute nutrient concentration. The norms for V Fe, V Mn , V Zn and V Cu were -3.273, -3.55, -4.21, and -5.23 respectively. The multivariate CND norms developed for ten nutrients proved to be an important tool for diagnosis of nutrient imbalance in papaya. However, large variations in absolute nutrient concentrations necessitated the application of PCA (principal component analysis), which explained the interaction among different nutrients. The PCA performed on log-transformed data extracted four significant PCs namely (P - K - Ca + Mg + Fe + Mn + Zn ), (N + P + Fe - Zn - R + ), (Mg + S - Cu + ) and (N + Cu + ), which explained nearly 66.33% variance. The interaction between P and K on one hand and Ca, Mg, Fe Mn and Zn on the other hand was evident in PC1. N and P together exhibited greater antagonistic effect with Fe and Zn as shown in PC2. Mg and S showed the tendency to buildup in opposite direction as indicated in PC3. Nitrogen relationship with Cu was isolated in PC4. The relationship extracted by PCA was helpful in understanding the mutual association among different nutrients. The nutrient indices developed indicated that Zn was the most common yield-limiting nutrient. The CND norms and the indices developed can be used for identifying the hidden hunger of various nutrients in papaya for evolving nutrient management strategies.