Sustaining productivity of a Vertosol at Warra, Queensland, with fertilisers, no-tillage or legumes. 9. Production and nitrogen benefits from mixed grass and legume pastures in rotation with wheat

Reduced supplies of nitrogen (N) in many soils of southern Queensland that were cropped exhaustively with cereals over many decades have been the focus of much research to avoid declines in profitability and sustainability of farming systems. A 45-month period of mixed grass (purple pigeon grass, Setaria incrassata Stapf; Rhodes grass, Chloris gayana Kunth.) and legume (lucerne, Medicago sativa L.; annual medics, M. scutellata L. Mill. and M. truncatula Gaertn.) pasture was one of several options that were compared at a fertility-depleted Vertosol at Warra, southern Queensland, to improve grain yields or increase grain protein concentration of subsequent wheat crops. Objectives of the study were to measure the productivity of a mixed grass and legume pasture grown over 45 months (cut and removed over 36 months) and its effects on yield and protein concentrations of the following wheat crops. Pasture production (DM t/ha) and aboveground plant N yield (kg/ha) for grass, legume (including a small amount of weeds) and total components of pasture responded linearly to total rainfall over the duration of each of 3 pastures sown in 1986, 1987 and 1988. Averaged over the 3 pastures, each 100 mm of rainfall resulted in 0.52 t/ha of grass, 0.44 t/ha of legume and 0.97 t/ha of total pasture DM, there being little variation between the 3 pastures. Aboveground plant N yield of the 3 pastures ranged from 17.2 to 20.5 kg/ha per 100 mm rainfall. Aboveground legume N in response to total rainfall was similar (10.6 - 13.2 kg/ha. 100 mm rainfall) across the 3 pastures in spite of very different populations of legumes and grasses at establishment. Aboveground grass N yield was 5.2 - 7.0 kg/ha per 100mm rainfall. In most wheat crops following pasture, wheat yields were similar to that of unfertilised wheat except in 1990 and 1994, when grain yields were significantly higher but similar to that for continuous wheat fertilised with 75 kg N/ha. In contrast, grain protein concentrations of most wheat crops following pasture responded positively, being substantially higher than unfertilised wheat but similar to that of wheat fertilised with 75 kg N/ha. Grain protein averaged over all years of assay was increased by 25 - 40% compared with that of unfertilised wheat. Stored water supplies after pasture were < 134mm (< 55% of plant available water capacity); for most assay crops water storages were 67 - 110 mm, an equivalent wet soil depth of only 0.3 - 0.45 m. Thus, the crop assays of pasture benefits were limited by low water supply to wheat crops. Moreover, the severity of common root rot in wheat crop was not reduced by pasture - wheat rotation.

Differential expression profiling of components associated with exoskeletal hardening in crustaceans

Background: Exoskeletal hardening in crustaceans can be attributed to mineralization and sclerotization of the organic matrix. Glycoproteins have been implicated in the calcification process of many matrices. Sclerotization, on the other hand, is catalysed by phenoloxidases, which also play a role in melanization and the immunological response in arthropods. Custom cDNA microarrays from Portunus pelagicus were used to identify genes possibly associated with the activation pathways involved in these processes. Results: Two genes potentially involved in the recognition of glycosylation, the C-type lectin receptor and the mannose-binding protein, were found to display molt cycle-related differential expression profiles. C-type lectin receptor up-regulation was found to coincide with periods associated with new uncalcified cuticle formation, while the up-regulation of mannose-binding protein occurred only in the post-molt stage, during which calcification takes place, implicating both in the regulation of calcification. Genes presumed to be involved in the phenoloxidase activation pathway that facilitates sclerotization also displayed molt cycle-related differential expression profiles. Members of the serine protease superfamily, trypsin-like and chymotrypsin-like, were up-regulated in the intermolt stage when compared to post-molt, while trypsin-like was also up-regulated in pre-molt compared to ecdysis. Additionally, up-regulation in pre- and intermolt stages was observed by transcripts encoding other phenoloxidase activators including the putative antibacterial protein carcinin-like, and clotting protein precursor-like. Furthermore, hemocyanin, itself with phenoloxidase activity, displayed an identical expression pattern to that of the phenoloxidase activators, i.e. up-regulation in pre- and intermolt. Conclusion: Cuticle hardening in crustaceans is a complex process that is precisely timed to occur in the post-molt stage of the molt cycle. We have identified differential expression patterns of several genes that are believed to be involved in biomineralization and sclerotization and propose possible regulatory mechanisms for these processes based on their expression profiles, such as the potential involvement of C-type lectin receptors and mannose binding protein in the regulation of calcification.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

The climate change risk management matrix for the grazing industry of northern Australia

The complexity, variability and vastness of the northern Australian rangelands make it difficult to assess the risks associated with climate change. In this paper we present a methodology to help industry and primary producers assess risks associated with climate change and to assess the effectiveness of adaptation options in managing those risks. Our assessment involved three steps. Initially, the impacts and adaptation responses were documented in matrices by ‘experts’ (rangeland and climate scientists). Then, a modified risk management framework was used to develop risk management matrices that identified important impacts, areas of greatest vulnerability (combination of potential impact and adaptive capacity) and priority areas for action at the industry level. The process was easy to implement and useful for arranging and analysing large amounts of information (both complex and interacting). Lastly, regional extension officers (after minimal ‘climate literacy’ training) could build on existing knowledge provided here and implement the risk management process in workshops with rangeland land managers. Their participation is likely to identify relevant and robust adaptive responses that are most likely to be included in regional and property management decisions. The process developed here for the grazing industry could be modified and used in other industries and sectors. By 2030, some areas of northern Australia will experience more droughts and lower summer rainfall. This poses a serious threat to the rangelands. Although the impacts and adaptive responses will vary between ecological and geographic systems, climate change is expected to have noticeable detrimental effects: reduced pasture growth and surface water availability; increased competition from woody vegetation; decreased production per head (beef and wool) and gross margin; and adverse impacts on biodiversity. Further research and development is needed to identify the most vulnerable regions, and to inform policy in time to facilitate transitional change and enable land managers to implement those changes.