Optimizing Organic Fertilizer Application for Enhanced Tomato Yield and Quality

Thesis Abstract

The productivity and quality of tomato crops are significantly influenced by fertilization practices, with a growing shift towards organic approaches that promote sustainable agriculture and environmental health. Despite the recognized benefits of organic fertilizers, optimal application rates and timing to maximize yield and fruit quality remain inadequately defined, leading to inconsistent outcomes among smallholder and commercial farmers. This study aims to determine the optimal organic fertilizer application regime for enhancing tomato yield and quality, with specific objectives to evaluate the effects of different organic fertilizer types and application rates, assess their influence on growth parameters and fruit nutrient composition, and establish a predictive model for best fertilization practices. The research adopts a randomized complete block design (RCBD) involving three organic fertilizer types—composted cattle manure, poultry manure, and vermicompost—and four application rates (50, 100, 150, and 200 kg/ha), totaling 12 treatment combinations. The experimental site is situated in a major tomato-producing region characterized by medium-textured soils and a subtropical climate. The population comprises smallholder farmers with existing tomato cultivation experience, with a sample size of 120 plots, each randomly assigned to treatment groups to ensure statistically robust comparisons. Data collection instruments include standardized questionnaires for soil and plant tissue analysis, portable spectrophotometers for nutrient content determination, and digital measurement tools for growth assessments. Data analysis employs analysis of variance (ANOVA) to identify significant differences among treatments, complemented by Tukey’s HSD test for multiple comparisons at a 5% significance level. Regression analysis is utilized to develop a predictive model linking fertilizer application parameters to crop yield and quality outcomes. Additionally, soil and plant tissue nutrient data are subjected to Principal Component Analysis (PCA) to elucidate nutrient dynamics, while thematic analysis is applied to farmer interviews assessing practical feasibility and adoption potential. Expected findings indicate that moderate application rates, particularly 100–150 kg/ha of poultry manure and vermicompost, significantly improve tomato yield and fruit nutrient content without causing nutrient imbalances. The study anticipates that specific fertilizer types will differentially influence growth metrics such as plant height, leaf area, and fruit weight, along with qualitative parameters including vitamin C content and lycopene concentration. The predictive model derived from the data is expected to serve as a practical decision-support tool for farmers aiming to optimize organic fertilization practices. This research contributes to the existing body of knowledge by systematically elucidating the relationship between organic fertilizer application parameters and tomato performance, grounded in the Theory of Planned Behavior (TPB) and the Resource-Based View (RBV) to understand farmer adoption and resource optimization dynamics. It advances sustainable crop management strategies by providing empirical guidelines for organic fertilizer application tailored to local conditions, promoting higher yields, improved fruit quality, and environmental sustainability. The study concludes that tailored organic fertilizer management enhances tomato productivity and quality, with recommendations for establishing region-specific fertilization protocols and integrating farmer training programs on organic input application. Future research should explore long-term soil health impacts, the integration of organic fertilizers with other cultural practices, and scalability assessments to broader agricultural systems. Overall, the findings aim to facilitate sustainable tomato production, contributing to food security and ecological resilience in smallholder farming systems.