The influence of water field capacity and fertilizer combinations on tomato under intelligent drip in greenhouse

Abstract

Tomato production is significant as the demand is increasing in time to meet food security and human nutrition as well. The purpose of the study was to investigate the effect of water and fertilizer application in greenhouse tomato growth index, yield and quality using an intelligent drip system to achieve improved yield by minimizing the fertigation. A randomized block design was used in ten treatments including control (CK-W₄N₄,K₄) consisting four level (W₁-65%, W₂-75%, W₃-85%, W₄-100%) each of water field capacity and four-level Urea-Potash (N₁,K₁-245,490, N₂,K₂-350,700, N₃,K₃-455,910, N₄,K₄-80,100 kg ha^-1) combinations. Data obtained were analyzed by a general linear model and developed a regression model for yield. The results showed, the highest tomato yield was 103.16 t ha^-1 in T₈-W₃N₂K₁ significantly influenced by the treatment, which is found 2% greater compared to the CK (100.92 t ha^-1). The highest leaf area index (5.21) was obtained with T₇-W₃N₁K₃ produced improved yield. The highest fruit weight (288.77 g fruit^-1) and fruit diameter (85.33 mm) obtained with T₂-W₁N₂K₂ had no significant influence on tomato yield. The model delivered a paramount prediction (r² = 0.82) of tomato yield. In conclusion, results showed the intelligent drip system could be used to minimize inputs to improve tomato production.