MODWht3: A Development‐Driven Wheat Growth Simulation

Abstract

Crop growth simulations are needed to help organize our knowledge of plant response to the environment for the purpose of assisting growers in management decisions, predicting impacts of land use decisions, and predicting the consequences of probable climatic variation. This paper describes the derivation, combination, and (where necessary) calibration of the models required to produce a simulation of the seasonal development and growth of winter wheat. The simulation provides appearance time and size estimates for each aboveground plant part (leaves, stems, spikes, and kernels), length and mass of roots with depth, time of occurrence of phenological events (germination, emergence, tillering, single ridge, double ridge, jointing, boot, heading, anthesis, soft dough, mature, and harvest ripe), rate and amount of water evaporated and transpired, uptake of N from the soil profile, and estimates of daily net photosynthesis. Strengths of the simulation include that it predicts dates for all crop phenological stages, it estimates sizes for all plant parts on a daily basis for comparisons with almost any observed field data, it is organized according to the natural structure of the system being simulated, and it is designed to be changed. Weaknesses include current lack of ranges for parameter values for different cultivars, a simplistic soil water budget, and limitations in specifying nutrient distribution among plant organs. No weed, pathogen, or insect models are incorporated; however, the program that implements the simulation is specifically designed to be easily modified, so pest models could be added.

Mid-Season Recovery from Nitrogen Stress in Winter Wheat

“…In order to do this, OSU needs to continue to evaluate and improve this technology to fit farmers and the environmental needs of our world. This approach is consistent with work showing the relationship between above ground plant dry weight and cumulative GDD (Rickman et al, 1996). Further analyses showed that a reliable INSEY could be obtained by dividing NDVI by the days from planting to sensing date (where GDD > 0) (Raun et al, 2002;Mullen et al, 2003).…”

Assessment of crop growth and soil water modules in SWAT2000 using extensive field experiment data in an irrigation district of the Yellow River Basin

“…Another recurring quest in the wheat phenological research in recent decades has been to better understand the role of temperature and to improve the thermal time concept (Mcmaster, 1997). Most wheat phenological modeling is based on the concept of thermal time, which is further modified by factors such as photoperiod, water stress, nutrient stress, upper temperature thresholds, and varying base temperatures (Weir et al, 1984;Ritchie and Otter, 1985;Mcmaster et al, 1992;Rickman et al, 1996). Cabelguenne et al (1999) divided the wheat growth into 4 different phases in the EPICphase model to improve the simulation of biomass, HI, and other relevant processes.…”

Temperature response function for leaf appearance rate in wheat and corn

“…Several seeding date experiments conducted in the United Kingdom (Baker et al 1980) and in western Australia (Kirby and Perry 1987) have indicated that phyllochron was correlated with the rate of daylength change at emergence. This concept has been incorporated in some wheat simulation models (Weir et al 1984;Wilhelm et al 1993;Rickman et al 1996). However, more recently, researchers have found this correlation did not exist in growth rooms (Cao and Moss 1989b;Hay and Delecolle 1989) or in the field (Baker et al 1986;Hotsonyame and Hunt 1997).…”