Abstract

Due to increasing climate variability, water scarcity, and global food demand, improving irrigation efficiency is critical for sustainable crop production. Volumetric soil water content (SVWC) sensors offer potential for precise irrigation scheduling, yet their yield benefits in sandy soils remain unclear. This study assessed the impact of sensor-based irrigation on corn (Zea mays L.) grain yield under varying nitrogen (N) and seeding rates in southeastern Virginia from 2022 to 2024. A Split-Split-Plot design was implemented at the Tidewater Agricultural Research and Extension Center (TAREC) in Suffolk, VA, with main plots consisting of three irrigation methods: 36” subsurface drip irrigation (SDI), 36” SDI with SVWC sensors, and a non-irrigated control. Sub-plots included four seeding rates (59K, 74K, 89K, and 104K ha^-1) and four nitrogen rates (133, 200, 267, and 333 kg ha^-1). Results showed that grain yield was significantly influenced by irrigation method, nitrogen rate, and seeding rate (p < 0.0001). The highest yields were observed with 36” dripline (11,466 kg ha^-1) and sensor-controlled dripline (11,051 kg ha^-1), both significantly outperforming the non-irrigated control (7,359 kg ha^-1). Although sensor scheduling did not statistically surpass conventional drip irrigation, it maintained comparable stable yields, suggesting potential efficiency benefits. Yield increased with seeding rate, peaking at 42K plants/acre (10,368 kg ha^-1), and with nitrogen rate, reaching a maximum at 333 kg ha^-1 (10,448 kg ha^-1). Sensor-based irrigation supported high yields across all N and seeding combinations, demonstrating its utility in optimizing input use. In conclusion, while sensor-based irrigation did not significantly increase yields over traditional SDI, it consistently outperformed non-irrigated systems and may enhance water use efficiency in sandy soils under future climate and resource constraints.