Simulating timothy growth and nutritive value with observed and synthetic weather data
Stochastic weather generators and crop growth models are used to explore the impact of climate change on crop development and yield. Synthetic weather data from the stochastic weather generator AAFC-WG have been evaluated with annual crop models but not with a perennial grass model. Our objective was to evaluate synthetic weather data from AAFC-WG using a perennial crop model, the Canadian Timothy Model (CATIMO), at five sites representing different agro-ecological regions of Canada. Synthetic 300-yr weather data from AAFC-WG and observed 30-yr weather data, both for the period 1961 to 1990, were used to simulate dates of growth onset and harvests, and yield and nutritive value (neutral detergent fiber [NDF] concentration and in vitro digestibility of NDF [dNDF]) of timothy (Phleum pratense L.) grown in cycles of five consecutive years before reseeding and with two harvests per year. Dates of growth onset and harvests simulated from synthetic weather data were close to those simulated from observed weather data, with normalized root mean square errors (NRMSEs) of 4.2% for dates of growth onset and <0.6% for harvest dates. Simulated dry matter yield (NRMSE < 4.5%), NDF concentration (NRMSE < 1.1%), and dNDF (NRMSE < 0.4%) with synthetic and observed weather data were also very close. Synthetic weather data generated by AAFC-WG accurately reproduced weather conditions of observed data for timothy development, yield, and nutritive value, confirming that they can be used with the CATIMO model to predict the impact of climate change at several sites in Canada. © 2013 by the American Society of Agronomy.
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