Introduction Albert

A with the introduction of many new With the development of automated weather station technologies, whether it be medical, mechanical, networks in the early 1980s, hourly values of temperaor Internet related, are promises; sometimes, too many ture, solar radiation, precipitation, relative humidity, promises are made about the potential capabilities of wind direction, and wind speed become available in these new technologies. After the initial period of euaddition to the long-established record of cooperative phoria is over, reality sets in. Then the hard work begins weather data usually consisting of maximum and miniof making promises meet expectations. Eventually, mum temperatures and precipitation. Satellite data have those technologies that narrow the difference between been used to model incoming solar irradiance. Models expectations and reality survive and flourish. Some techto predict weather parameters for nonuniform locations, nologies die and are forgotten, and others may become those with different azimuth and slope than a flat surthe butt of jokes. face, are available. In other words, the successful appliIn the mid-1960s, crop simulation modeling began cation of a crop simulation model is not limited by the with the pioneering work of C.T. de Wit (1965). Overly availability of weather data. Similarly, there are suffienthused scientists, engineers, and administrators raised cient soil data available, either through actual measurehigh expectations of the many possible uses of this new ments or from databases on the web, so these data are technology. In 1973, John Passioura’s article “Sense and not generally a factor that limits the successful applicaNonsense in Crop Simulation” was published in the tion of a crop simulation model. Managerial options Journal of the Australian Institute of Agricultural Scisuch as day of sowing, plant density, irrigation and fertilence, providing a counter balance to voices that were izer amounts and dates of application should be known. overly optimistic. A challenge that we constantly face Obviously, these options are not a limiting factor in is to inject healthy skepticism into our work, to keep us this situation. from becoming too comfortable. To quote from Daniel Model inputs to quantify plant responses to the enviBoorstin, noted historian and former head of the Library ronment, sometimes called genetic coefficients, usually of Congress, “The greatest obstacle to discovery is not represent optimum measures of time, how long the crop ignorance, but the illusion of knowledge.” will be in a specific developmental stage and, with reMy perception is that crop simulation modeling has gards to yield, what happens to the reproductive organs reached a relative plateau; there have been no large during this time. Going from these optimum inputs to changes in how these models operate in recent years the simulated actual plant responses is a major aspect but more of a refining of existing algorithms and the of any simulation model. The time component of these development of user-friendly interfaces. This is a dancoefficients can be determined by visible changes in the gerous situation as it can indicate a sense of arrogance plant, relatively easily, compared with what happens and complacency; it’s as good as it gets. physiologically to the reproductive organs. While iniOne way to gain insight into the current status of crop tially, time and yield components had to be quantified, simulation modeling is by looking at the inputs that are with the desire for model generality and the large effort common to the many types of crop simulation models. necessary to accurately determine these yield compoThese categories of inputs quantify weather, soil, mananents, it is my perception that it became easier to meagerial options, and plant responses to the environment. sure the time component and final yield and then use the model in an iterative procedure to determine the