The Stephen F. Austin Experimental Forest

On December 14, 1944, the Seventy-Eighth United States Congress passed a bill that authorized the transfer of 2,560 acres in Nacogdoches County, Texas, to the research branch of the United States Forest Service (USFS). This land became the Stephen F. Austin Experimental Forest (SFAEF) on September 19. 1945. One of eighty-one federal experimental forests and ranges nationally, it is the only one of its kind in Texas. Located seven miles west of Nacogdoches, three quarters of the Forest consists of bottomland hardwood forests along the Angelina River and the remainder of mixed pine and hardwood uplands

The Relationship Between Student Demographics and Student Engagement with Online Library Instruction Modules

Objective – To investigate whether there are any demographic trends affecting student engagement with online library instruction which might have implications for practice, the authors designed a case study to examine the relationship between student demographic characteristics and engagement with online library instruction modules in English 102 courses at a single university. Methods – The authors recruited 181 students from English 102 (ENG 102), a research-based composition course, to participate in the study. ENG 102 instructors asked all participants to complete an online library instruction module embedded in the university’s course management system, either before in-person library instruction or in lieu of face-to-face library instruction. No external incentive was provided for online module completion. The research team measured levels of student engagement by recording the amount of time students spent on each page of the online module. In collaboration with the Office of Institutional Research, the authors then pulled demographic data on each participant using the university’s student information system. Pearson chi-square tests were performed to determine whether there were any notable associations between levels of student engagement and student age, grade point average, gender, and race/ethnicity. Results – Observable trends tied age and higher grade point average to higher levels of engagement with online instruction. There was additionally a slight trend linking female participants to higher levels of engagement than their male peers. In the category of race/ethnicity, the two largest subgroups, Hispanic and Caucasian students, exhibited similar levels of engagement. Conclusions – The authors conclude that there may be demographic implications for practice in designing online library instruction programs, especially when considering student age and academic performance indicators. They also conclude that, owing to this case study’s limited sample size, further study is warranted to investigate these conclusions, and to further examine the possible impact of gender and race/ethnicity on engagement with online library instruction modules

Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder

The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to characterise this, and emphasise that future research should tackle this explicitly in realistic settings

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

ESTIMATING THE LIKELIHOOD OF YELLOW STARTHISTLE OCCURRENCE USING AN EMPIRICALLY DERIVED NONLINEAR REGRESSION MODEL

Yellow starthistle is a noxious weed common in the semiarid climate of Central Idaho and other western states. Early detection of yellow starthistle and predicting its infestation potential have important scientific and managerial implications. Weed detection and delineation are often carried out by visual observation or survey techniques. However, such methods may be ineffective in detecting sparse infestations. The distribution of yellow starthistle over a large region may be affected by various exogenous variables such as elevation, slope and aspect. These landscape variables can be used to develop prediction models to estimate the potential invasion of yellow starthistle into new areas. A nonlinear prediction model has been developed based on a polar coordinate transformation to investigate the ability of landscape characteristics to predict the likelihood of yellow starthistle occurrence in North Central Idaho. The study region included the lower Snake river and parts of the Salmon and Clearwater basins encompassing various land use categories. The model provided accurate estimates of incidence of yellow starthistle within each specified land use category and performed well in subsequent statistical validations