DEVELOPMENT OF WILD OAT SEED DISPERSAL DISTRIBUTIONS USING AN INDIVIDUAL-PLANT GROWTH SIMULATION MODEL

An individual-plant growth simulation model for quantifying competition between spring barley and wild oat has been previously described (price, Shafii, and Thill, 1994). Individual plants within a population were modeled independently and competition between plants was determined by resource demand within plant specific areas-of-influence. Calibration of the model to spring barley and wild oat biomass data was performed and shown to have a high degree of accuracy under mono culture conditions. The work presented here applies the specified model to a larger scale simulation for the purpose of demonstrating seed dispersal in wild oat. This is accomplished by breaking the annual cycle of wild oat seeds into the three integrated phases: Growth and development, dissemination, and dormancy. The growth and development phase is handled using the individual-plant growth model. The subsequent dispersal of seeds is described using two-dimensional stochastic processes. Finally, a life table analysis, based on predetermined transition probabilities, is used to establish the makeup of populations in the following season. A sensitivity analysis which examines various biological, ecological, and mechanical components over a 10 year period is carried out and the potential use in weed science education is demonstrated

AN INDIVIDUAL-PLANT GROWTH SIMULATION MODEL FOR QUANTIFYING PLANT COMPETITION

Plant competition models traditionally have used population or stand level parameters as a basis for modeling. While such models may be valid with regard to average responses, they fail to account for important factors such as within stand variability and spatial relationships. This translates to an assumption of uniformity in growth characteristics among individual plant,S as well as an equidistant spacing arrangement which are unlikely in real populations. One alternative is to model the growth characteristics of individual plants separately which, when combined as a system, will inherently have popUlation attributes related to competition. Competition models of this type allow for various combinations of growth patterns and spatial arrangements. An individual-plant based simulation model is introduced and the relationships of model parameters with existing concepts in plant competition are discussed. Models are calibrated to wild oat (Avenafatua) and spring barley (Hordeum vulgare) using data from replicated field experiments in Northern Idaho

Response of Spring Barley (Hordeum vulgare) to Herbicides

‘Karla’, ‘Klages\u27, ‘Morex’, and ‘Steptoe’ cultivars of spring barley (Hordeum vulgare L.) differed in susceptibility to postemergence recommended application rates of diclofop {(±)-2-[4-(2,4-dichlorophenoxy) phenoxy] propanoic acid}, difenzoquat [1,2-dimethyl-3,5-diphenyl-1H-pyrazolium], chlorsulfuron {2-chloro-N-[[(4-methoxy-6-methyl-1,3,5-triazin-2-yl)amino] carbonyl] benzenesulfonamide}, and metribuzin [4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4-triazin-5(4H)-one] in 1981 and 1982. Metribuzin injured Morex, and difenzoquat injured all cultivars within 2 weeks after herbicide application. Metribuzin reduced height and crop biomass compared to the hand-weeded control. Herbicide treatments did not affect grain yield at Moscow, ID, in either year. However, metribuzin reduced yield of Karla and Morex, and diclofop reduced yield of Karla compared to the hand-weeded control at Pullman, WA, in 1982. Barley injury and grain yield loss depended on herbicide treatment and cultivar. Early season herbicide injury to barley did not indicate grain yield response at harvest

ESTIMATION OF CARDINAL TEMPERATURES IN GERMINATION DATA ANALYSIS

Seed germination is a complex biological process which is influenced by various environmental and genetic factors. The effects of temperature on plant development are the basis for models used to predict the timing of germination. Estimation of the cardinal temperatures, including base, optimum, and maximum, is essential because rate of development increases between base and optimum, decreases between optimum and maximum, and ceases above the maximum and below the base temperature. Nonlinear growth curves can be specified to model the time course of germination at various temperatures. Quantiles of such models are regressed on temperature to estimate cardinal quantities. Bootstrap simulation techniques may then be employed to assure the statistical accuracy of these estimates and to provide approximate nonparametric confidence intervals. A statistical approach to modelling germination is presented and application is demonstrated with reference to replicated experiments designed to determine the effect of temperature gradient on germination of three populations of an introduced weed species common crupina (Crupina vulgaris Pers.)

ASSESSING VARIABILITY OF AGREEMENT MEASURES IN REMOTE SENSING USING A BAYESIAN APPROACH

Remote sensing imagery is a popular accessment tool in agriculture, forestry, and rangeland management. Spectral classification of imagery provides a means of estimating production and identifYing potential problems, such as weed, insect, and disease infestations. Accuracy of classification is traditionally based on ground truthing and summary statistics such as Cohen\u27s Kappa. Variability assessment and comparison of these quantities have been limited to asymptotic procedures relying on large sample sizes and gaussian distributions. However, asymptotic methods fail to take into account the underlying distribution of the classified data and may produce invalid inferential results. Bayesian methodology is introduced to develop probability distributions for Cohen\u27s Conditional Kappa that can subsequently be used for image assessment and comparison. Techniques are demonstrated on a set of images used in identifYing a species of weed, yellow starthistle, at various spatial resolutions and flying times

Chemical fallow weed control

3 p.: ill.; 28 cm