Precision Irrigation Strategies for Chili (Capsicum spp.): A Systematic Review and Meta-Analysis

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.