Development of a decision support system to determine the best maize (Zea mays. L) hybrid-planting date option under typical New Zealand management systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Science at Massey University, Palmerston North, New Zealand

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A study was conducted with the aim of developing a decision support tool in the form of a crop simulation model, to help New Zealand (NZ) farmers make informed hybrid-specific decisions to optimise maize (Zea mays L.) yields through selection of the best hybrid for a given planting date (PD) and location. Field experiments were established (2006-2007) in four environments (ENVs) to generate data to modify and evaluate the CERES-Maize model. Planting between 20 September and 13 October (Waikato) or 6 November (Manawatu) maximised grain yields while the respective PDs for achieving highest silage yields were 9-15 October or 23 October. Optimum PDs varied seasonally. For instance, a 10C mean temperature (spring) decrease advanced optimum PD by 1-2 wk. A base temperature of 80C (Tb8) led to adequate estimates of thermal durations for the pre-flowering phase while Tb0 was more satisfactory during grain filling. After minor model modifications using Waikato and Manawatu field data, CERES-Maize was successfully adapted for NZ conditions. Maize yields were simulated across eight contrasting ENVs using 31 yr weather data (1978-2009). High irradiance and moderate temperatures during grain filling resulted in the highest yields. This coincided with 1-18 October PDs. Temperatures <180C and >250C and irradiance <17 MJ m-2 d-1 during grain filling significantly reduced yields. Low spring temperatures also reduced leaf expansion, minimising source capacity. Planting date windows to achieve ≥95% of yield maxima ranged from 1-7 wk. Silage crops, warmer ENVs or early hybrids had wider planting windows and less crop failure risk when planted late. With early or late planting, yield reductions were greater in higher latitude ENVs where spring and autumn temperatures and radiation were much lower. Due to higher assimilate demand, late hybrids were generally more stress prone, whereas early hybrids were sink limited. A multiple-linear regression equation based on temperature and relative humidity was established to estimate field grain drydown. Another relationship based on the Gompertz model was also developed to estimate silage maturity using thermal time. These functions were used to enhance CERES-Maize‟s ability to predict harvest maturity. To simplify data collection for the model, linear and non-linear models for relationships between tassel initiation and leaf number; total plant leaf area and area of the largest leaf; and leaf tip number and fully expanded leaves were also established.
CERES maize computer model, Crop simulation, Grain yields