Precision Irrigation Strategies for Chili (Capsicum spp.): A Systematic Review and Meta-Analysis
Description
This dataset was generated to test the hypothesis that precision irrigation systems—particularly drip and sensor-based irrigation—provide superior agronomic and resource-use performance in chili (Capsicum spp.) cultivation compared with conventional surface or furrow irrigation methods. The underlying assumption is that improved control of water and nutrient delivery enhances yield, water-use efficiency, and economic returns while reducing agronomic risks. The data comprise extracted quantitative variables from peer-reviewed studies included in a systematic review and random-effects meta-analysis. Variables include crop yield (t ha⁻¹), water use efficiency, benefit–cost ratio, irrigation typology, fertilization strategy (e.g., conventional fertilization, RDF-based fertigation), and contextual information such as location and experimental design where reported. Data were collected through a structured literature screening and extraction process following PRISMA guidelines. The dataset shows a clear performance hierarchy among irrigation technologies, with precision-based systems consistently achieving higher yields and efficiency than conventional methods. Integrated systems combining drip irrigation with fertigation or sensor-based control exhibit the largest agronomic and economic gains, while substantial heterogeneity across studies reflects strong contextual dependency related to management practices and agroecological conditions. These data can be used to reproduce the meta-analysis, evaluate comparative performance among irrigation technologies, explore moderator effects (e.g., fertilization strategy), and support future quantitative syntheses or modeling studies on sustainable irrigation management in horticultural systems. The dataset is intended for reuse in meta-analytical, comparative, or decision-support research rather than for primary experimental inference.
Files
Steps to reproduce
Literature identification and screening Identify peer-reviewed studies on chili (Capsicum spp.) irrigation using database searches following PRISMA guidelines. Apply predefined inclusion and exclusion criteria to select eligible studies. Data extraction Extract quantitative variables from each study, including yield (t ha⁻¹), irrigation typology, fertilization strategy (e.g., conventional, RDF-based fertigation), water-use efficiency, benefit–cost ratio, and relevant contextual information (location, experimental design) where available. Data compilation and preprocessing Compile extracted data into a structured spreadsheet. Standardize units, resolve inconsistencies, and group treatments into comparable irrigation technology categories (e.g., conventional, drip, sensor-based systems). Meta-analysis Perform a random-effects meta-analysis to estimate pooled yield effects across studies and technology groups. Calculate heterogeneity statistics (I²) to assess between-study variability. Subgroup and interaction analysis Conduct subgroup analyses by irrigation type and fertilization strategy, and evaluate interaction effects (e.g., drip irrigation × fertigation) to identify performance hierarchies. Bias assessment Assess potential publication bias using funnel plots and interpret asymmetry qualitatively in relation to study size and reported yield responses. Visualization and interpretation Generate forest plots, subgroup comparisons, and summary figures to interpret performance trends, emphasizing comparative outcomes rather than absolute yield estimates.
Institutions
- Universitas Gadjah Mada