BACKGROUND AND AIMS: Accurately representing development is essential for applying crop simulations to investigate the effects of climate, genotypes or crop management. Development in wheat (Triticum aestivum, T. durum) is primarily driven by temperature, but affected by vernalization and photoperiod, and is often simulated by reducing thermal-time accumulation using vernalization or photoperiod factors or limiting accumulation when a lower optimum temperature (T(optl)) is exceeded. In this study T(optl) and methods for representing effects of vernalization and photoperiod on anthesis were examined using a range of planting dates and genotypes. METHODS: An examination was made of T(optl) values of 15, 20, 25 and 50 degrees C, and either the most limiting or the multiplicative value of the vernalization and photoperiod development rate factors for simulating anthesis. Field data were from replicated trials at Ludhiana, Punjab, India with July through to December planting dates and seven cultivars varying in vernalization response. KEY RESULTS: Simulations of anthesis were similar for T(optl) values of 20, 25 and 50 degrees C, but a T(optl) of 15 degrees C resulted in a consistent bias towards predicting anthesis late for early planting dates. Results for T(optl) above 15 degrees C may have occurred because mean temperatures rarely exceeded 20 degrees C before anthesis for many planting dates. For cultivars having a strong vernalization response, anthesis was more accurately simulated when vernalization and photoperiod factors were multiplied rather than using the most limiting of the two factors. CONCLUSIONS: Setting T(optl) to a high value (30 degrees C) and multiplying the vernalization and photoperiod factors resulted in accurately simulating anthesis for a wide range of planting dates and genotypes. However, for environments where average temperatures exceed 20 degrees C for much of the pre-anthesis period, a lower T(optl) (23 degrees C) might be appropriate. These results highlight the value of testing a model over a wide range of environments.
BACKGROUND AND AIMS: Accurately representing development is essential for applying crop simulations to investigate the effects of climate, genotypes or crop management. Development in wheat (Triticum aestivum, T. durum) is primarily driven by temperature, but affected by vernalization and photoperiod, and is often simulated by reducing thermal-time accumulation using vernalization or photoperiod factors or limiting accumulation when a lower optimum temperature (T(optl)) is exceeded. In this study T(optl) and methods for representing effects of vernalization and photoperiod on anthesis were examined using a range of planting dates and genotypes. METHODS: An examination was made of T(optl) values of 15, 20, 25 and 50 degrees C, and either the most limiting or the multiplicative value of the vernalization and photoperiod development rate factors for simulating anthesis. Field data were from replicated trials at Ludhiana, Punjab, India with July through to December planting dates and seven cultivars varying in vernalization response. KEY RESULTS: Simulations of anthesis were similar for T(optl) values of 20, 25 and 50 degrees C, but a T(optl) of 15 degrees C resulted in a consistent bias towards predicting anthesis late for early planting dates. Results for T(optl) above 15 degrees C may have occurred because mean temperatures rarely exceeded 20 degrees C before anthesis for many planting dates. For cultivars having a strong vernalization response, anthesis was more accurately simulated when vernalization and photoperiod factors were multiplied rather than using the most limiting of the two factors. CONCLUSIONS: Setting T(optl) to a high value (30 degrees C) and multiplying the vernalization and photoperiod factors resulted in accurately simulating anthesis for a wide range of planting dates and genotypes. However, for environments where average temperatures exceed 20 degrees C for much of the pre-anthesis period, a lower T(optl) (23 degrees C) might be appropriate. These results highlight the value of testing a model over a wide range of environments.
Authors: Robyn L Cave; Graeme L Hammer; Greg McLean; Colin J Birch; John E Erwin; Margaret E Johnston Journal: Ann Bot Date: 2013-02-12 Impact factor: 4.357