Aspects of maize modelling in eastern Canada

Abstract

Maize (Zea mays L.) is a crop of growing importance in Eastern Canada. Modelling the temperature effects on phenological development, crop architecture and disease infection in maize contributes to the development of well-adapted, early-maturing varieties. Details of modelling these three aspects of maize growth were presented. The first focussed on quantifying the effect of air or soil temperature on maize phenological development. Crop growth was divided into two periods: vegetative (planting to silking) and grain filling (silking to maturity). A third period (planting to emergence) was separated within the vegetative period. Heat unit systems based on daily temperature response functions were developed to produce the most consistent heat unit sums for each period. The best fits of these functions were determined by minimizing standard deviations and coefficients of variation of these sums for each thermal period over locations and years. Calculated temperature response functions estimated thermal periods more consistently than growing degree days (GDD) for all three periods. The largest improvement was made in the silking to maturity period.The second aspect was a study of crop architecture. Methods were developed to mathematically characterize the structure of a canopy in terms of leaf area and leaf angle distributions with crop height and across the row. These calculations, in turn, were input to a soil–plant–atmosphere model to calculate interception of photosynthetically active radiation (PAR). Model calculations of PAR interception compared well with measurements for a range of plant types and plant population densities (R2 = 0.76).The third aspect was quantifying growth of Fusarium in maize. Differential equations were used to relate Fusarium rates of growth in maize ears to air temperature, relative humidity and precipitation. Integration of these equations over time produced quantitative estimates of fungal infection. Model calculations were compared to visual ratings of fungal infection for two Fusarium species over three years (R2 = 0.92).In each of the three aspects of this study, modelling tested our understanding of the processes involved and the dominant factors controlling these processes. Thus, modelling was an integral part of the scientific approach, synthesizing experimental data in a quantitative conceptual framework and identifying dominant factors and parameters which required additional focussed experimental evaluation. Key words: Phenological development, crop architecture, Fusarium infection