Synergistic assessment of compost quality using a multifaceted approaches and machine learning: A research protocol

Abstract

• Merging farmers' insights with empirical data for a user-focused evaluation of compost quality, filling a gap in conventional compost studies. • Machine learning models will effectively predict crop performance and compost efficacy, offering advanced insights beyond traditional methods. • DNA evaluation will helps in understanding the biological processes that enhance compost quality and efficacy. • Evaluating compost quality by merging quantitative and qualitative data will provide a comprehensive understanding of compost’s effectiveness • Detailed physical and chemical analyses of compost will ensure a thorough evaluation of compost safety and quality. . Several approaches encompassing laboratory analyses, test crops and the use of models have been employed in the assessment of compost quality. However, employing either of these methods unitarily could pose some limitations. This protocol outlines a comprehensive study to evaluate the quality, efficacy, and perceptions of locally produced compost in agricultural practices. The study employs a multidimensional approach, including farmer questionnaires, empirical growth evaluations, and plant growth performance indices. Organic-base amenders and isolated fungi will amend compost quality, applied to tomatoes and lettuce in vessels. Concurrently, crops will be grown on soils enriched with inorganic fertilizer and unenriched soils for comparison. Growth periods will be divided into trios for precise observations and measurements. Twenty farmers will participate in assessing crop growth and performance, capturing diverse perspectives. Parameters such as plant height, leaf colour, root development, foliage density, pest resistance, and stress responses will be evaluated. Plant growth performance indices (GT, RLI, and GI) will quantify the impact of compost treatments. Deoxyribonucleic Acid (DNA) sequencing will determine the bacteria and fungi genomes present at the end of the composting process. Data analysis will include cleaning, coding, descriptive statistics, and inferential statistics (Chi-Square, t-tests, ANOVA, regression, and correlation). Integration of empirical field/laboratory and responses/observations from farmers will provide a comprehensive understanding. Machine learning techniques will enhance predictions and satisfaction assessments. The study will also include a long-term monitoring phase to evaluate the sustained impacts of compost on soil health and agricultural productivity. The study aims to provide valuable insights for policymakers, farmers, and researchers to promote sustainable agricultural practices, reduce chemical inputs, enhance soil health, and support food security.