Abstract. In gaining streams, groundwater seeps out into the streams. In losing streams, stream water moves into groundwater systems. The flow moving through the streambed sediments under these two types of hydrologic conditions is generally in opposite directions (upward vs. downward). The two opposite flow mechanisms affect the pore size and fine particle content of streambeds. Thus it is very likely that the opposite flow conditions affect the streambed hydraulic conductivity. However, comparisons of the hydraulic conductivity (K) of streambeds for losing and gaining streams are not well documented. In this study, we examined the K distribution patterns of sediments below the channel surface or stream banks for the Platte River and its tributaries in Nebraska, USA. Two contrasting vertical distribution patterns were observed from the test sites. In gaining reaches, hydraulic conductivity of the streambed decreased with the depth of the sediment cores. In losing reaches, hydraulic conductivity increased with the depth of the sediment cores. These contrasting patterns in the two types of streams were mostly attributed to flow directions during stream water and groundwater exchanges. In losing reaches, downward movement of water brought fine particle into the otherwise coarse sediment matrix, partially silting the pores. For gaining reaches, upward flow winnowed fine particles, increasing the pore spacing in the top parts of streambeds, leading to higher hydraulic conductivity in shallower parts of streambeds. These flux directions can impact K values to depths of greater than 5 m. At each study site, in situ permeameter tests were conducted to measure the K values of the shallow streambed layer. Statistical analyses indicated that K values from the sites of losing reaches were significantly different from the K values from the sites of gaining reaches.

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Team research and associated skills are taking on increasing importance in the workplace. However, students are normally not exposed to this experience in a formal class structure. The objectives of this project were to give students insight into the dynamics of team research and experience with crop model development and evaluation of model results. By mutual agreement between the instructor and students, the project consisted of using a C4 model of photosynthesis to scale up from leaf to canopy level in maize (Zea mays L.) and to study the influence of leaf N content and sky conditions on canopy gas exchange properties. Results from the simulations were in good agreement with available measurements. Students were initially frustrated because they had to define a problem and felt they did not have adequate prior knowledge. Unequal distribution of responsibilities and grading were other causes of frustration among the students. Suggestions to overcome these problems are given. From the instructor's perspective, this approach can cause some anxiety since the course syllabus cannot be completely det'med until the students have decided on a topic for the class project. Also, to provide a wide range of options for this team project, the instructor must be willing to acquire the background (in a short amount of time) to direct the selected project. Since no single individual could have achieved what the team did in the same amount of time, this project demonstrated the strength of team research.A RECOMMENDATION contained in the recent report on future directions of graduate education in the USA (COSEPUP, 1995) stressed the importance of improving communication (relating complex ideas to nonspecialists) and team skills. While this report has engendered controversy (Bloom, 1995), it is clear that these skills are very important to agricultural scientists. Anticipating these directions, a team research project was incorporated into the graduate course titled Crop Growth and Yield Modeling. The first five authors (in alphabetical order) were students in this class, the next author taught the class, and the last author acted as a consultant.Crop models by their very nature integrate knowledge from many subject matter areas (e.g., micrometeorology, plant physiology, soil physics, numerical methods) to create a representation of the physical, chemical, and biological processes in growing plants. Developing a crop model is an

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