A lack of predatory interaction between rumen ciliate protozoa and Shiga-toxin producing Escherichia coli

Aims: To investigate interactions between rumen protozoa and Shiga toxin-producing Escherichia coli (STEC) and to ascertain whether it is likely that rumen protozoa act as ruminant hosts for STEC. Methods and Results: The presence of stx genes in different microbial fractions recovered from cattle and sheep rumen contents and faeces was examined using PCR. In animals shedding faecal STEC, stx genes were not detected in the rumen bacterial or rumen protozoal fractions. Direct interactions between ruminal protozoa and STEC were investigated by in vitro co-incubation. Rumen protozoa did not appear to ingest STEC, a STEC lysogen or non-STEC E. coli populations when co-incubated. Conclusions: The ruminal environment is unlikely to be a preferred habitat for STEC. Bacterial grazing by rumen protozoa appears to have little, if any, effect on STEC populations. Significance and Impact of the Study: This study indicates that ruminal protozoa are unlikely to be a major factor in the survival of STEC in ruminants. They appear as neither a host that protects STEC from the ruminal environment nor a predator that might reduce STEC numbers.

Crop and environmental attributes underpinning genotype by environment interaction in synthetic-derived bread wheat evaluated in Mexico and Australia

Synthetic backcrossed-derived bread wheats (SBWs) from CIMMYT were grown in the north-west of Mexico (CIANO) and sites across Australia during 3 seasons. A different set of lines was evaluated each season, as new materials became available from the CIMMYT crop enhancement program. Previously, we have evaluated both the performance of genotypes across environments and the genotype x environment interaction (G x E). The objective of this study was to interpret the G x E for yield in terms of crop attributes measured at individual sites and to identify the potential environmental drivers of this interaction. Groups of SBWs with consistent yield performance were identified, often comprising closely related lines. However, contrasting performance was also relatively common among sister lines or between a recurrent parent and its SBWs. Early flowering was a common feature among lines with broad adaptation and/or high yield in the northern Australian wheatbelt, while yields in the southern region did not show any association with the maturity type. Lines with high yields in the southern and northern regions had cooler canopies during flowering and early grain filling. Among the SBWs with Australian genetic backgrounds, lines best adapted to CIANO were tall (>100 cm), with a slightly higher ground cover. These lines also displayed a higher concentration of water-soluble carbohydrates in the stem at flowering, which was negatively correlated with stem number per unit area when evaluated in southern Australia (Horsham). Possible reasons for these patterns are discussed. Selection for yield at CIANO did not specifically identify the lines best adapted to northern Australia, although they were not the most poorly adapted either. In addition, groups of lines with specific adaptation to the south would not have been selected by choosing the highest yielding lines at CIANO. These findings suggest that selection at CIMMYT for Australian environments may be improved by either trait based selection or yield data combined with trait information. Flowering date, canopy temperature around flowering, tiller density, and water-soluble carbohydrate concentration in the stem at flowering seem likely candidates.

Row spacing and planting density effects on the growth and yield of sugarcane. 3. Responses with different cultivars.

The promotion of controlled traffic (matching wheel and row spacing) in the Australian sugar industry is necessitating a widening of row spacing beyond the standard 1.5 m. As all cultivars grown in the Australian industry have been selected under the standard row spacing there are concerns that at least some cultivars may not be suitable for wider rows. To address this issue, experiments were established in northern and southern Queensland in which cultivars, with different growth characteristics, recommended for each region, were grown under a range of different row configurations. In the northern Queensland experiment at Gordonvale, cultivars Q187((sic)), Q200((sic)), Q201((sic)), and Q218((sic)) were grown in 1.5-m single rows, 1.8-m single rows, 1.8-m dual rows (50 cm between duals), and 2.3-m dual rows (80 cm between duals). In the southern Queensland experiment at Farnsfield, cvv. Q138, Q205((sic)), Q222((sic)) and Q188((sic)) were also grown in 1.5-m single rows, 1.8-m single rows, 1.8-m dual rows (50 cm between duals), while 1.8-m-wide throat planted single row and 2.0-m dual row (80 cm between duals) configurations were also included. There was no difference in yield between the different row configurations at Farnsfield but there was a significant row configuration x cultivar interaction at Gordonvale due to good yields in 1.8-m single and dual rows with Q201((sic)) and poor yields with Q200((sic)) at the same row spacings. There was no significant difference between the two cultivars in 1.5-m single and 2.3-m dual rows. The experiments once again demonstrated the compensatory capacity that exists in sugarcane to manipulate stalk number and individual stalk weight as a means of producing similar yields across a range of row configurations and planting densities. There was evidence of different growth patterns between cultivars in response to different row configurations (viz. propensity to tiller, susceptibility to lodging, ability to compensate between stalk number and stalk weight), suggesting that there may be genetic differences in response to row configuration. It is argued that there is a need to evaluate potential cultivars under a wider range of row configurations than the standard 1.5-m single rows. Cultivars that perform well in row configurations ranging from 1.8 to 2.0 m are essential if the adverse effects of soil compaction are to be managed through the adoption of controlled traffic.

Food-borne pathogens and pig waste products - the spoke in the wheel?

Food-borne pathogens are present in normal healthy pigs and thus are also present in pig wastes and by-products. The presence of these pathogens can be viewed negatively (i.e. 'a spoke in the wheel') or as simply another issue that requires the adoption of appropriate guidelines and management procedures. A key component in the development of appropriate, effective guidelines and management practices is a solid basis of knowledge on which pathogens are present as well as the levels of these pathogens. This paper reviews Australian Pork Limited (APL) funded projects carried out in our laboratories that have provided a solid base of Australian data for the pig industry. These data will ensure that pathogens are not 'a spoke in the wheel' but rather an issue - like many others that confront the industry - that can be managed to ensure that there is no unacceptable risk to either public health or the environment.

Quantifying surfactant interaction effects on soil moisture and turf quality

Soil water repellency occurs widely in horticultural and agricultural soils when very dry. The gradual accumulation and breakdown of surface organic matter over time produces wax-like organic acids, which coat soil particles preventing uniform entry of water into the soil. Water repellency is usually managed by regular surfactant applications. Surfactants, literally, are surface active agents (SURFace ACTive AgeNTS). Their mode of action is to reduce the surface tension of water, allowing it to penetrate and wet the soil more easily and completely. This practice improves water use efficiency (by requiring less water to wet the soil and by capturing rainfall and irrigation more effectively and rapidly). It also reduces nutrient losses through run-off erosion or leaching. These nutrients have the potential to pollute the surrounding environment and water courses. This project investigated potential improvements to standard practices (product combination and scheduling) for surfactant use to overcome localised dry spots on water repellent soils and thus improve turf quality and water use efficiency. Weather conditions for the duration of the trial prevented the identification of improved practices in terms of combination and scheduling. However, the findings support previous research that the use of soil surfactants decreased the time for water to infiltrate dry soil samples taken from a previously severely hydrophobic site. Data will be continually collected from this trial site on a private contractual basis, with the hope that improvements to standard practices will be observed during the drier winter months when moisture availability is a limiting factor for turfgrass growth and quality.

Quantifying surfactant interaction effects on soil moisture

Quantifying surfactant interaction effects on soil moisture and turf quality.

Interaction between production characteristics and postharvest performance and practices for fresh fruit

Significant interactions have been demonstrated between production factors and postharvest quality of fresh fruit. Accordingly, there is an attendant need for adaptive postharvest actions to modulate preharvest effects. The most significant preharvest effects appear to be mediated through mineral nutrition influences on the physical characteristics of fruit. Examples of specific influencers include fertilisers, water availability, rootstock, and crop load effects on fruit quality attributes such as skin colour, susceptibility to diseases and physiological disorders, and fruit nutritional composition. Also, rainfall before and during harvest can markedly affect fruit susceptibility to skin blemishes, physical damage, and diseases. Knowledge of preharvest-postharvest interactions can help determine the basis for variability in postharvest performance and thereby allow refinement of postharvest practices to minimise quality loss after harvest. This knowledge can be utilised in predictive management systems. Such systems can benefit from characterisation of fruit nutritional status, particularly minerals, several months before and/or at harvest to allow informed decisions on postharvest handling and marketing options. Other examples of proactive management practices include adjusting harvesting and packing systems to account for rainfall effects before and/or during harvest. Improved understanding of preharvest-postharvest interactions is contributing to the delivery of consistently higher quality of fruit to consumers. This paper focuses on the state of knowledge for sub-tropical and tropical fruits, in particular avocado and mango.

Interaction between production characteristics and postharvest performance and practices for fresh fruit

Significant interactions have been demonstrated between production factors and postharvest quality of fresh fruit. Accordingly, there is an attendant need for adaptive postharvest actions to modulate preharvest effects. The most significant preharvest effects appear to be mediated through mineral nutrition influences on the physical characteristics of fruit. Examples of specific influencers include fertilisers, water availability, rootstock, and crop load effects on fruit quality attributes such as skin colour, susceptibility to diseases and physiological disorders, and fruit nutritional composition. Also, rainfall before and during harvest can markedly affect fruit susceptibility to skin blemishes, physical damage, and diseases. Knowledge of preharvest-postharvest interactions can help determine the basis for variability in postharvest performance and thereby allow refinement of postharvest practices to minimise quality loss after harvest. This knowledge can be utilised in predictive management systems. Such systems can benefit from characterisation of fruit nutritional status, particularly minerals, several months before and/or at harvest to allow informed decisions on postharvest handling and marketing options. Other examples of proactive management practices include adjusting harvesting and packing systems to account for rainfall effects before and/or during harvest. Improved understanding of preharvest-postharvest interactions is contributing to the delivery of consistently higher quality of fruit to consumers. This paper focuses on the state of knowledge for sub-tropical and tropical fruits, in particular avocado and mango.

Complete genome sequence and integrated protein localization and interaction map for alfalfa dwarf virus, which combines properties of both cytoplasmic and nuclear plant rhabdoviruses

Summary We have determined the full-length 14,491-nucleotide genome sequence of a new plant rhabdovirus, alfalfa dwarf virus (ADV). Seven open reading frames (ORFs) were identified in the antigenomic orientation of the negative-sense, single-stranded viral RNA, in the order 3′-N-P-P3-M-G-P6-L-5′. The ORFs are separated by conserved intergenic regions and the genome coding region is flanked by complementary 3′ leader and 5′ trailer sequences. Phylogenetic analysis of the nucleoprotein amino acid sequence indicated that this alfalfa-infecting rhabdovirus is related to viruses in the genus Cytorhabdovirus. When transiently expressed as GFP fusions in Nicotiana benthamiana leaves, most ADV proteins accumulated in the cell periphery, but unexpectedly P protein was localized exclusively in the nucleus. ADV P protein was shown to have a homotypic, and heterotypic nuclear interactions with N, P3 and M proteins by bimolecular fluorescence complementation. ADV appears unique in that it combines properties of both cytoplasmic and nuclear plant rhabdoviruses.

Non-target species interaction with sodium fluoroacetate (1080) meat bait for controlling feral pigs (Sus scrofa)

Fresh meat baits containing sodium fluoroacetate (1080) are widely used for controlling feral pigs in Queensland, but there is a potential poisoning risk to non-target species. This study investigated the non-target species interactions with meat bait by comparing the time until first approach, investigation, sample and consumption, and whether dying bait green would reduce interactions. A trial assessing species interactions with undyed bait was completed at Culgoa Floodplain National Park, Queensland. Meat baits were monitored for 79 consecutive days with camera traps. Of 40 baits, 100% were approached, 35% investigated (moved) and 25% sampled, and 25% consumed. Monitors approached (P < 0.05) and investigated (P < 0.05) the bait more rapidly than pigs or birds, but the median time until first sampling was not significantly different (P > 0.05), and did not consume any entire bait. A trial was conducted at Whetstone State Forest, southern Queensland, with green-dyed and undyed baits monitored for eight consecutive days with cameras. Of 60 baits, 92% were approached and also investigated by one or more non-target species. Most (85%) were sampled and 57% were consumed, with monitors having slightly more interaction with undyed baits than with green-dyed baits. Mean time until first approach and sample differed significantly between species groups (P = 0.038 and 0.007 respectively) with birds approaching sooner (P < 0.05) and monitors sampling later (P < 0.05) than other (unknown) species (P > 0.05). Undyed bait was sampled earlier (mean 2.19 days) than green-dyed bait (2.7 days) (P = 0.003). Data from the two trials demonstrate that many non-target species regularly visit and sample baits. The use of green-dyed baits may help reduce non-target uptake, but testing is required to determine the effect on attractiveness to feral pigs. Further research is recommended to quantify the benefits of potential strategies to reduce the non-target uptake of meat baits to help improve the availability of bait to feral pigs.