Effects of waterlogged soil on corn growth

Waterlogging is when soil becomes overly saturated with water, which can cause plant stress. Excessive moisture levels can cause soil to saturate with water and deplete the soil's oxygen content. Overirrigation, excessive rainfall, and other factors can cause waterlogging. In this study, the researchers wanted to understand the effects of waterlogging on corn growth and development, as well as soil oxygen levels.

Two corn hybrids, Pioneer P2089VYHR and Agrigold A6659, were grown in two separate studies under ambient sunlight and temperature conditions. A drip irrigation system allowed for precise control of soil moisture. Waterlogging was achieved by plugging drainage holes at the bottom of the pots with wooden plugs. Waterlogging durations were 0, 2, 4, 6, 8, 10, 12, and 14 days at the second leaf growth stage. Apogee’s SO-110 soil oxygen sensors were placed in three pots per treatment to continuously monitor soil oxygen levels and temperature. Corn growth and development information, as well as pigment estimation data, were collected 15 days after application (23 days after sowing).

During waterlogging, soil oxygen levels decreased rapidly, reaching zero in approximately 8 to 10 days. Oxygen levels returned to 0-day (control) levels within two days after waterlogging ceased. Whole plant dry weight decreased with increasing waterlogging duration, decreasing by 44% in Experiment 1 and 27% in Experiment 2 after 2 days of waterlogging. Leaf area and root volume also decayed exponentially. Leaf number and plant height were the least sensitive parameters measured in both experiments and decreased linearly. Root forks were the most sensitive parameter, decreasing by 83% and 80% in both experiments after 14 days of waterlogging. Treatments significantly reduced soil oxygen levels during the experimental period in both experiments.

Conclusions:

These data improve our understanding of how maize plants respond to increasing waterlogging durations. The functional relationships generated in this study can improve current maize simulation models for field applications.