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Yield and growth criteria of irrigated and rainfed sunflower Helianthus annus and chickpea Cicer arientinum were studied under simulated climate change for Tabriz in Iran. Monthly average temperature and rainfall under doubling CO 2 were predicted using Goddard Institute for Space Studies (GISS) and Geophysical Fluid Dynamics Laboratory (GFDL) models. The effects of climatic change on crop growth period, yield components and water requirements of those crops were evaluated by a crop simulation model (OSBOL). Based on GISS and GFDL predictions, with doubling CO 2 concentration, mean annual temperature for Tabriz increased by 4.6 and 4.3°C, respectively. The results obtained by GISS showed that doubling CO 2 will lead to an increase in mean temperature of 3.7°C, and a 40% increase in mean rainfall, during the first 5 mo of the year. However, GFDL predicted an increase of 4.7°C in mean temperature and a reduction of 12% in mean rainfall during the first 5 mo of the year. According to OSBOL under GDFL predictions, the period from sowing to maturity and harvest index were reduced by climate change, whereas yield, biomass and water requirement for irrigated sunflower were increased. The biomass for rainfed sunflower increased, while days to maturity and harvest index were reduced. Yield predictions of OSBOL based on GISS contrasted with those based on GFDL. Doubling CO 2 caused a reduction in days to maturity, biomass, yield and water requirements for irrigated chick pea. The number of days to maturity for rainfed chickpea was reduced and harvest index, biomass and yield were increased due to climate change.

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Crop phenology models that use constant temperature parameters across developmental stages may be less accurate and have temperature‐dependent systematic prediction error (bias). Using the DD10 model, we evaluated default and optimized (DD_Opt) temperature parameters using data from seven California rice (Oryza sativa L.) cultivars grown in six locations over 3 yr (2012–2014). Furthermore, we evaluated the effect of using stage‐dependent temperature parameters on model performance using two‐ and three‐stage optimization approaches. Optimized temperature parameters, or DD_Opt (RMSE: 2.3–5.4 d), performed better than DD10 (RMSE: 2.9–7.3 d). A temperature sensitivity analysis indicated that the time from planting to panicle initiation was most sensitive to temperature (every 1°C increase decreased days to panicle initiation by 1.8 d) while time from heading to R7 (marked by the appearance of one yellow hull on the main stem panicle) was not affected by temperature. Optimized temperature parameters varied between stages, with base temperature decreasing and optimum temperature increasing with plant development. Compared to the DD_Opt, two‐stage optimization (planting–heading and heading–R7) reduced the RMSE by 0.8 d and the systematic error by 0.6 d °C−1. Three‐stage optimization (planting–panicle initiation, panicle initiation–heading, and heading–R7) further reduced RMSE by 1.1 d and systematic error by 1.4 d °C−1 for preheading. These results demonstrate the importance of using stage‐dependent parameters to improve accuracy of phenological models, which may be important when models are used to study the crop response to climate change, field management options, ecosystem productivity, breeding, and yield gap analysis.