Growth and species interactions of Eucalyptus pellita in a mixed and monoculture plantation in the humid tropics of north Queensland

This study investigated whether mixed-species designs can increase the growth of a tropical eucalypt when compared to monocultures. Monocultures of Eucalyptus pellita (E) and Acacia peregrina (A) and mixtures in various proportions (75E:25A, 50E:50A, 25E:75A) were planted in a replacement series design on the Atherton Tablelands of north Queensland, Australia. High mortality in the establishment phase due to repeated damage by tropical cyclones altered the trial design. Effects of experimental designs on tree growth were estimated using a linear mixed-effects model with restricted maximum likelihood analysis (REML). Volume growth of individual eucalypt trees were positively affected by the presence of acacia trees at age 5 years and this effect generally increased with time up to age 10 years. However, the stand volume and basal area increased with increasing proportions of E. pellita, due to its larger individual tree size. Conventional analysis did not offer convincing support for mixed-species designs. Preliminary individual-based modelling using a modified Hegyi competition index offered a solution and an equation that indicates acacias have positive ecological interactions (facilitation or competitive reduction) and definitely do not cause competition like a eucalypt. These results suggest that significantly increased in growth rates could be achieved with mixed-species designs. This statistical methodology could enable a better understanding of species interactions in similarly altered experiments, or undesigned mixed-species plantations.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

Analysis and Modelling of the Effects of Water Stress on Maize Growth and Yield in Dryland Conditions.

It is essential to provide experimental evidence and reliable predictions of the effects of water stress on crop production in the drier, less predictable environments. A field experiment undertaken in southeast Queensland, Australia with three water regimes (fully irrigated, rainfed and irrigated until late canopy expansion followed by rainfed) was used to compare effects of water stress on crop production in two maize (Zea mays L.) cultivars (Pioneer 34N43 and Pioneer 31H50). Water stress affected growth and yield more in Pioneer 34N43 than in Pioneer 31H50. A crop model APSIM-Maize, after having been calibrated for the two cultivars, was used to simulate maize growth and development under water stress. The predictions on leaf area index (LAI) dynamics, biomass growth and grain yield under rain fed and irrigated followed by rain fed treatments was reasonable, indicating that stress indices used by APSIM-Maize produced appropriate adjustments to crop growth and development in response to water stress. This study shows that Pioneer 31H50 is less sensitive to water stress and thus a preferred cultivar in dryland conditions, and that it is feasible to provide sound predictions and risk assessment for crop production in drier, more variable conditions using the APSIM-Maize model.

Developmental and growth controls of tillering and water-soluble carbohydrate accumulation in contrasting wheat (Triticum aestivum L.) genotypes: can we dissect them?

In wheat, tillering and water-soluble carbohydrates (WSCs) in the stem are potential traits for adaptation to different environments and are of interest as targets for selective breeding. This study investigated the observation that a high stem WSC concentration (WSCc) is often related to low tillering. The proposition tested was that stem WSC accumulation is plant density dependent and could be an emergent property of tillering, whether driven by genotype or by environment. A small subset of recombinant inbred lines (RILs) contrasting for tillering was grown at different plant densities or on different sowing dates in multiple field experiments. Both tillering and WSCc were highly influenced by the environment, with a smaller, distinct genotypic component; the genotypeenvironment range covered 350750 stems m(2) and 25210mg g(1) WSCc. Stem WSCc was inversely related to stem number m(2), but genotypic rankings for stem WSCc persisted when RILs were compared at similar stem density. Low tilleringhigh WSCc RILs had similar leaf area index, larger individual leaves, and stems with larger internode cross-section and wall area when compared with high tilleringlow WSCc RILs. The maximum number of stems per plant was positively associated with growth and relative growth rate per plant, tillering rate and duration, and also, in some treatments, with leaf appearance rate and final leaf number. A common threshold of the red:far red ratio (0.390.44; standard error of the difference0.055) coincided with the maximum stem number per plant across genotypes and plant densities, and could be effectively used in crop simulation modelling as a ocut-off' rule for tillering. The relationship between tillering, WSCc, and their component traits, as well as the possible implications for crop simulation and breeding, is discussed.

Production attributes of Merino sheep genetically divergent for wool growth are reflected in differing rumen microbiotas

Divergent genetic selection for wool growth as a single trait has led to major changes in sheep physiology and metabolism, including variations in rumen microbial protein production and uptake of α-amino nitrogen in portal blood. This study was conducted to determine if sheep with different genetic merit for wool growth exhibit distinct rumen bacterial diversity. Eighteen Merino wethers were separated into groups of contrasting genetic merit for clean fleece weight (CFW; low: WG− and high: WG+) and fed a blend of oaten and lucerne chaff diet at two levels of intake (LOI; 1 or 1.5 times maintenance energy requirements) for two seven-week periods in a crossover design. Bacterial diversity in rumen fluid collected by esophageal intubation was characterized using 454 amplicon pyrosequencing of the V3/V4 regions of the 16S rRNA gene. Bacterial diversity estimated by Phylogenetic distance, Chao1 and observed species did not differ significantly with CFW or LOI; however, the Shannon diversity index differed (P=0.04) between WG+ (7.67) and WG− sheep (8.02). WG+ animals had a higher (P=0.03) proportion of Bacteroidetes (71.9% vs 66.5%) and a lower (P=0.04) proportion of Firmicutes (26.6% vs 31.6%) than WG− animals. Twenty-four specific operational taxonomic units (OTUs), belonging to the Firmicutes and Bacteroidetes phyla, were shared among all the samples, whereas specific OTUs varied significantly in presence/abundance (P<0.05) between wool genotypes and 50 varied (P<0.05) with LOI. It appears that genetic selection for fleece weight is associated with differences in rumen bacterial diversity that persist across different feeding levels. Moderate correlations between seven continuous traits, such as methane production or microbial protein production, and the presence and abundance of 17 OTUs were found, indicating scope for targeted modification of the microbiome to improve the energetic efficiency of rumen microbial synthesis and reduce the greenhouse gas footprint of ruminants.