VARIANCE AS A FACTOR EFFECT IN INTERDISCIPLINARY STUDIES OF AGRICULTURAL SYSTEMS

Studies of interrelationships among factors typically focus on factor effects related to the mean response. In some instances, response variances, as well as, or even rather than, response means, may be affected by the factors under consideration. In this paper, generalizations of Levene\u27s test and the Jackknife test to two-factor experimental designs are studied via simulation studies to assess their ability to identify differences in the variance as an interaction effect or as a factor main effect. These tests are then applied to a particular example where relationships between chile plants and two prominent pests of chile plants -nematodes and yellow nutsedge -- are under study. This example illustrates the utility of these tests in studying relationships among factors in agricultural systems

DATA STRUCTURE WITH RESPECT TO THE MAIN EFFECTS MODEL: A DISCUSSION MOTIVATED BY A META-ANALYSIS DATA SET

A discussion on data structure relative to the main effects model is motivated by a severely unbalanced meta-analysis data set. This data set is used to highlight the difficulty of assessing data structure when multiple factor data sets are severely unbalanced. Both theoretical results and numerical examples are used to establish that simple approaches to examining data structure using two-way tables provide easily assimilated information about the effect of data unbalance on main effect contrast variances. In addition, notions of balance, proportionality, unbalance, and missing cells with respect to the main effects model are defined in terms of the two-way tables and are related to main effect contrast estimate variances as assessed using the D-optimality criterion

Comparison of Some Approaches to Determine Spatial Dependence of Soil Properties

Knowledge of variability and spatial structure of soil properties is essential for optimal design for collecting soil samples and effectively applying management decisions in the field. The objective of this study is to compare some approaches for characterizing, and comparing spatial dependence of isotropic second-order stationary processes. The evaluated approaches are the nugget to sill ratio (NR), normalized (by fitted sill) semivariogram, correlograms, and two integral scales. Soil samples, collected at a regular 50 m × 50 m grid from 0-15 cm depths, were analyzed for sand and clay, bulk density (b), saturated hydraulic conductivity (Ks), wilting point, available water content (AWC), pH, electrical conductivity (EC), nitrate-nitrogen (NO3- N), and chloride (Cl) were determined. Geostatistical software (GS+, Gamma Design Software, Plainwell, MI) was used to estimate the variance structure of various measured soil properties. Analysis include using data on the spatial variability of various properties from four published studies. NR displayed spatial dependence ignoring the influence of range, normalized semivariogram and correlogram provided the visual comparison, and both integral scales incorporated the influence of range and provided single number spatial dependence summaries. Either of the integral scale formulations can be used to characterize the spatial dependence of soil properties from agricultural fields

Lesson Worksheets: A Tool for Developing Youth Weather and Climate Science Comprehension

At an Extension youth agricultural science center, our team developed and pilot tested a five-lesson weather and climate science curriculum for middle school–aged youths. As part of the endeavor, we conducted an item analysis of the five worksheets used across the lessons and determined relationships between worksheet scores and pretest/posttest science comprehension improvement scores. Results from 88 primarily Hispanic eighth graders indicated that worksheet performance was related to overall science comprehension, science knowledge, and weather and climate resiliency in agriculture and natural resources lesson improvement scores. Results support the use of formative scaffolding tools such as worksheets in Extension youth programming to improve youth science comprehension

Science Comprehension Retention Among Youth Agriscience Students Instructed in Weather and Climate

The purpose of this article is to examine the science comprehension retention of 8th-grade science students taught a new weather and climate curriculum. The students’ middle school is part of an innovative Extension youth agricultural science center that has a mission to develop and test new teaching and learning models and curricula in agriculture and natural resources. Our curriculum was developed following a science comprehension model we created and have been testing at the center. It contained lessons on the water cycle, the greenhouse effect, measuring and analyzing precipitation and temperature data, and mitigating and adapting to weather and climate extremes in agriculture and natural resources. For each lesson, students viewed introductory PowerPoint slides, participated in an activating strategy, set up an experiment or analyzed local precipitation or temperature data, formulated hypotheses, participated in a summary activity, and completed a worksheet. We pretested 81 students, taught the curriculum over a 6-day period, and gave the posttest. We returned 2 months later to administer a follow-up to check for science comprehension retention. The students’ overall science comprehension and science knowledge, science skills, and reasoning abilities subcomponent follow-up scores were lower than their post-program test scores. Both boys and girls declined in their overall post-program test gains over the 2 months. Students also declined in their preference for learning-by-doing from post-test to follow-up. Based on these results, we made changes to the curriculum consistent with the literature on learning retention before publishing it online for youth educators