Dynamics of plant populations in Heteropogon contortus(black speargrass) pastures on a granite landscape in southern Queensland. 2. Seed production and soil seed banks of H. contortus

Seed production and soil seed hanks of H. contortus were studied in a subset of treatments within an extensive grazing study conducted in H. contortus pasture in southern Queensland between 1990 and 1996. Seed production of H. contortus in autumn ranged from 260 to 1800 seeds/m2 with much of this variation due to differences in rainfall between years. Seed production was generally higher in the silver-leaved ironbark than in the narrow-leaved ironbark land class and was also influenced by a consistent stocking rate x pasture type interaction. Inflorescence density was the main factor contributing to the variable seed production and was related to the rainfall received during February. The number of seeds per inflorescence was unaffected by seasonal rainfall, landscape position, stocking rate or legume oversowing. Seed viability was related to the rainfall received during March. Soil seed banks in spring varied from 130 to 520 seeds/m2 between 1990 and 1995 with generally more seed present in the silver-leaved ironbark than in the narrow-leaved ironbark land class. There were poor relationships between viable seed production and the size of the soil seed bank, and between the size of the soil seed bank and seedling recruitment. This study indicates that H. contortus has the potential to produce relatively large amounts of seed and showed that the seasonal pattern of rainfall plays a major role in achieving this potential

Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.