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

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.

Interactive effects of carbon dioxide, low temperature, and ultrav｜olet-B radiation on cotton seedling root and shoot morphology and growth

Interactive effects of multiple environmental stresses are predicted to have a negative effect on cotton growth and development and these effects will be exacerbated in the future climate. The objectives of this study were to test the hypothesis that cotton cultivars differ in their responses to multiple environmental factors of （CO2） [400 and 750 μmol.mol 1 （＋（CO2）], temperature [28/20 and 20/12℃ （-T）], and UV-B radiation [0 and 10 kJ. m2. d ^-1（＋ UV- B）]. A genetic and molecular standard （TM-1） and three modern cotton cultivars （DP1522B2XE PHY496W3R, and ST4747GLB2） were grown in eight sunlit, controlled environment chambers with control treatment 400 μmol.mol^-1 [CO2], 28/21℃ temperature, and 0 kJ UV B. The results showed significant differences among the cultivars for most of the shoot and root parameters. Plants grown under low temperature alone or as a combination with ＋ UV B treatment caused more detrimental effects on root and shoot vigor. Although the elevated CO2 treatments weakened the damaging effects of higher UV-B levels on cotton growth on all cultivars, increased CO2 could not mask the negative effects of low temperature. When comparing all cultivars, genetic standard TM-1 produced the smallest values for the majority of traits under CO2, UV-B, and low temperature either alone or in combination with other treatments. Based on principal component analysis, the four cultivars were classified as tolerant （DP1522B2XF）, intermediate （PHY496W3R and ST4747GLB2） and sensitive （TM-1） to multiple environmental stresses.Low temperature was identified as the most damaging treatment to cotton early seedling vigor while elevated CO2 caused the least. Existing variability of cotton cultivars in response tomultiple environmental stresses could allow for selection of cultivars with the best coping ability and higher lint yield for future climate change environments.