Predictive modelling of coconut (Cocos nucifera L) growth parameters using linear regression: Insights into stem diameter, height, and chlorophyll content

This study developed a linear regression model to predict coconut stem diameter (D), height (H), and chlorophyll content (SPAD) based on environmental and treatment factors during two cultivation seasons (January-June 2023 and July-December 2023) at the Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia. The experiment utilized a growing media treatment (M3) composed of 50% soil, 30% cocopeat, and 20% perlite. Key predictor variables included time (W), nitrogen (N), potassium (K), moisture content (MC), wind speed (WS), and electrical conductivity (EC). Regression analysis revealed significant relationships: time (W) positively influenced stem diameter (0.3875) and height (0.3329), while nitrogen (N) had a positive effect on diameter (0.08827). In contrast, potassium (K) negatively impacted both stem diameter (-0.03461) and height (-0.0505), as did moisture content (-0.01561) and wind speed (-0.3872). For chlorophyll content, time (W) (2.399) and electrical conductivity (EC) (0.0193) were positive predictors, while potassium (-0.3063) and wind speed (-3.416) exerted negative influences. ANOVA confirmed the significance of time, potassium, moisture content, and wind speed on growth parameters. Time emerged as a critical factor for coconut development, highlighting the necessity of managing these variables to optimize growth and chlorophyll content. These findings provide valuable insights for enhancing coconut cultivation strategies.

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