/ Rapid growth of intensive animal industries in southeast Queensland, Australia, has led to large volumes of animal waste production, which posses serious environmental problems in the Murray Darling Basin (MDB). This study presents a method of selecting sites for the safe application of animal waste as fertiliser to agricultural land. A site suitability map for the Westbrook subcatchment within the MDB was created using a geographic information system (GIS)-based weighted linear combination (WLC) model. The factors affecting the suitability of a site for animal waste application were selected, and digital data sets derived from up to 1:50,000 scale maps were acquired. After initial preprocessing, digital data sets were clipped to the size of the delineated subcatchment boundary producing input factors. These input factors were weighted using the analytical hierarchy process (AHP) that employed an objectives-oriented comparison (OOC) technique to formulate the pairwise comparison matrix. The OOC technique, which is capable of deriving factor weight independently, formulated the weight derivation process by making it more logical and systematic. The factor attributes were classified into multiple classes and weighted using the AHP. The effects of the number of input factors and factor weighting on the areal extent and the degree of site suitability were examined. Due to the presence of large nonagricultural and residential areas in the subcatchment, only 16% of the area was found suitable for animal waste application. The areal extent resulting from this site suitability assessment was found to be dependent on the areal constraints imposed on each input factor, while the degree of suitability was principally a function of the weight distribution between the factors.

A geographic information system (GIS) based manure application plan has been developed for the site-specific application of animal waste to agricultural fields in the

-We have appraised the effectiveness of peer assessment of assignments in aiding student learning at the University of Southern Queensland. Each student was randomly allocated two peers' assignments for double-blind assessment. A marking rubric was provided. More than 95% of the class participated in the process. Students' peer-assessment work was evaluated by the instructor. Over 80% of the students assessed their peers satisfactorily. Students' learning experiences, attitudes and behavior towards the peer assessment system was surveyed. More than 60% of the students considered peer assessment a useful learning tool. However 25% remained unconvinced. Most of these students either fully or partially subscribed to William Perry's position of 'dualism'. About 55% found the feedback from their peer's useful. Surprisingly, >69% of the students believed that the peer assessment had nothing to contribute towards a students' community of practice.Index Terms -assignment, double-blind, marking rubric, peer assessment. BACKGROUND AND CONCEPT

Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of timeseries PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≥78.79%. For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area.

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