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Soil erodibility is influenced by several soil properties including the extent to which the clay fraction will disperse in water. Because early methods for estimating soil erosion were empirical methods and did not utilize water‐dispersible clay as a parameter, few data have been collected. The recent development of the Water Erosion Prediction Project (WEPP) model, a process‐based model for predicting water erosion that uses water‐dispersible clay in the algorithm for computing interrill erodibility, resulted in an increased demand for these data. In order to accommodate this and similar models, a method for estimating the water‐dispersible clay content of soils based on existing information is needed. Data collected by the National Soil Survey Laboratory in support of the WEPP were used to identify soil properties that were significantly correlated with water‐dispersible clay and to develop equations to estimate the water‐dispersible clay content of soils based on those properties. The property most strongly correlated with water‐dispersible clay is total clay. Other properties significantly correlated with water‐dispersible clay are the water content at 1.5 MPa, dithionite‐citrate‐extractable Fe and Al, the coefficient of linear extensibility, Wischmeier's M, the very‐fine‐sand content, the ratio of cation‐exchange capacity (CEC) to total clay, Bouyoucos' clay ratio, and the CEC. A simple linear regression of water‐dispersible clay vs. total clay revealed that, for the soils included in this study, approximately one‐third of the total clay was water dispersible. However, the model only had an R2 of 0.604. When the ratio of the CEC corrected for organic carbon (CCEC) to total clay was included in the model, the R2 improved to 0.723. However, sorting the data by the ratio of CCEC to total clay instead of including it in the model improved the overall fit of the model and increased the R2 to 0.879.

Slope geometry and the associated variation in soil properties influence runoff, drainage, soil temperature, the extent of soil erosion and deposition, and crop yields. With the current emphasis on prescription farming, approaches are needed to more effectively match inputs to production system needs while accounting for variation in soil and water resources within a field. The objective of the study was to develop simplified regression models to predict soil properties on different landscape positions from observed values on the nearly level upper interfluve. Soil samples were taken from the upper and lower interfluve, shoulder, upper and lower linear, and footslope at each of four sites in eastern Nebraska. Predictive equations were developed for 20 soil properties using multiple linear regression. Independent variables included were observed values of the property being modeled from the upper interfluve, sampling depth, and an irrigation code. Of the 100 models developed, only eight included significant contributions from all three independent variables. Models for pH, organic matter, electrical conductivity, exchangeable K, base saturation percentage, and available P and K consistently had R2 values greater than 0.50. The upper interfluve contributed significantly to the prediction of each of these properties except electrical conductivity. A comparison between average observed and predicted values for each soil property at each sampling depth revealed that the observed values generally fell within a 95% confidence interval about the predicted values. The confidence interval half‐width was generally <10% of the mean for the observed values. Further evaluation with independent data sets could be used to help strengthen and refine such generalized or geographically based models.

Students in the two lecture sections of a course in introductory soil science were taught two topics-texture and structure-using alternate instruction models, a conventional lecture approach or unsystematic instruction (UI) and a systematic instruction model (SIM). Analysis of the two methods showed that the teacher talked 96% of the time when the UI model was used and 90% of the time when SIM was used. The reduction in teacher talk when using SIM was offset by increased silence and pupil talk. Results from quizzes, major exams, and final exams in the spring and fall of 1988 revealed that students taught with SIM generally answered more of the questions correctly than did those taught with UI. The students receiving SIM did significantly better at the 99% confidence level on the quiz on the soil texture topic in fall 1988 and on the soil structure topic in spring 1988. Students receiving SIM also did significantly better (95% confidence level) on the major exam for the structure topic in spring 1988. The percentage of correct answers for the structure topic by those students receiving SIM always exceeded that of students receiving unsystematic instruction (UI). When student answers on the major exam were analyzed based on instructional objectives for soil texture and structure topics, students receiving SIM did better than those receiving UI on those questions covering objectives of a higher degree of difficulty and complexity. Student evaluations revealed that students preferred the SIM. M.H. Milford, Dep. of Soil and Crop Sci., Texas A&M Univ., College Station, TX 77843-2474; S.C. Brubaker, USDA-ARS, 248 Chase Hall, Univ. of Neb., Lincoln, NE 68583; and O.K. Johnson, College Teaching and Research Cognate Areas in Educational Curriculum and Instruction, Texas A&M Univ., College Station, TX 77843.

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