Quantifying and Validating Soybean Seed Emergence Model as a Function of Temperature

• 作者: Bobby Golden Chathurika Wijewardana Firas Ahmed Alsajri J. Trenton Irby K. Raja Reddy L. Jason Krutz
• 来源期刊: 美国植物学期刊(英文)
• 学科: 医学
• 关键词: GROWING Degree DAYS MODEL SEED EMERGENCE Soybean TEMPERATURE
• 年，卷（期）: 2019,（1）
• 页码范围: 111-124
• 页数: 14页
• 分类号: R73

摘要

Developing a model for soybean seed emergence offers a tool producers could use for planting date options and in predicting seedling emergence. In this study, temperature effects on soybean seed emergence were quantified, modeled, and validated. The data for seed emergence model development was generated at varying temperatures, 20°C/12°C, 25°C/17°C, 30°C/22°C, 35°C/27°C, and 40°C/32°C, on two soybean cultivars, Asgrow AG5332 and Progeny P 5333 RY. Time for 50% emergence (t50%) was recorded, and seed emergence rate (SER) was estimated as reciprocal to time at each temperature in both the cultivars. No differences were observed between the cultivars in their response to temperature. A quadratic model (QM) best described the relationship between t50% and SGR and temperature (R2 = 0.93). Two sets of experiments were conducted to validate the model. In Experiment 1, 17 time-series planting date studies with the same cultivars were used by utilizing diurnal and seasonal changes in temperature conditions. In the second experiment, sunlit growth chambers with 3 different day/night temperatures, low—20°C/12°C, optimum—30°C/22°C, and high—40°C/32°C, and 64 soybean cultivars belonging MG III, IV, and V, were used. Air temperature and t50 were recorded, and SGR was estimated in all experiments. No differences were recorded among the cultivars for t50% and SGR, but differences were observed among seeding date and temperature experiments. We tested QM and traditionally used Growing Degree Days models against the data collected in validation experiments. Both the model simulations predictions agreed closely with the observed data. Based on model statistics, R2, root mean square errors (RMSE), and comparison of observations and predictions to assess model performance, the QM model performed better than the GDD model for soybean seed emergence under a wide range of cultivars and environmental conditions.