Simulating Timothy Growth and Nutritive Value with Observed and Synthetic Weather Data

Abstract

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 fi ve sites representing diff erent 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 fi ber [NDF] concentration and in vitro digestibility of NDF [dNDF]) of timothy (Phleum pratense L.) grown in cycles of fi ve 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, confi rming that they can be used with the CATIMO model to predict the impact of climate change at several sites in Canada.

Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada

“…Future climate scenarios were obtained through downscaling steps from global climate models to individual sites through a stochastic weather generator (AAFC-WG) based on climate change simulated by two global climate models under the forcing of two IPCC emission scenarios. The adequate performance of AAFC-WG to generate weather data for crop models was confirmed by comparing generated and observed weather data, and by considering the outputs of crop models for perennial grasses (CATIMO; Jing et al 2013b) and annual crops (DSSAT; Qian et al 2011). For comparative purposes, DM yield and nutritive value attributes of timothy with two harvests per year under baseline conditions (1961Á1990) were also simulated.…”

Timothy Yield and Nutritive Value under Climate Change in Canada

“…The stochastic weather generator AAFC‐WG, previously calibrated for the 30‐yr (1961‐1990) observed weather data at selected sites in Canada (Qian et al, 2010), was used to generate 300‐yr synthetic baseline daily weather data and four series of daily weather data for the period 2040 to 2069 at 10 sites across Canada. The adequate performance of AAFC‐WG to generate weather data for crop models was demonstrated by comparing projected and observed weather data, and by considering the outputs of crop models for perennial grasses (CATIMO; Jing et al, 2013b) and annual crops (DSSAT; Qian et al, 2011).…”

“…The 300‐yr weather data were grouped into 60 cycles in sequence, with each cycle containing 5 yr (Jing et al, 2013b). We recorded: (i) crop attributes: date of growth onset, date of first and second harvests, end of growing season, DM yield, NDF concentration, and dNDF; (ii) environmental factors: reduction factors for temperature [ f ( T )] and water [ f ( W )] stresses, precipitation during spring growth and summer regrowth, and cumulative GDD during the whole growing season; and (iii) the dynamics of leaf area index (LAI) and reserves to verify the response of regrowth processes to climate change.…”

Predicted Yield and Nutritive Value of an Alfalfa–Timothy Mixture under Climate Change and Elevated Atmospheric Carbon Dioxide

“…Because of its many specific characteristics, notably enhanced N fixation by the legume component (Nyfeler et al, 2011), a legume-grass forage mixture can be expected to behave differently than pure stands of the individual species under climate change. Many recent studies have used crop models to evaluate the potential effect of climate change on perennial forage crops (Thomson et al, 2005;Prato et al, 2010;Ruget et al, 2012;Höglind et al, 2013;Jing et al, 2013a;Prato and Qiu, 2014). Few studies, however, have considered the effect of climate change on legume-grass forage mixtures (Riedo et al, 1999;Lazzarotto et al, 2010), and none have been conducted in northern areas of North America.…”

“…As proposed by Qian et al (2011) andJing et al (2013a), statistical tests were first performed to verify that the 300-yr series of synthetic data correctly represented the 30-yr weather data observed for the reference period at the weather stations in QSW and QE. The weather generator was then used to generate a 300-yr series of synthetic data for future periods and RCPs by perturbing the weather generator parameters based on the simulated changes in climate parameters from the climate models (CanESM2, CanRCM4, and HadGEM2).…”