Can soil nematode community structure be used to indicate soil carbon dynamics in horticultural systems?

There is a need to develop indicators that relate the dynamics of soil organic carbon (SOC) with changes in land management of horticultural production systems. Soil nematode communities have been shown to be sensitive to land management changes, but often do not include plant-parasites in the calculation of soil nematode community indices. The concept of nematode functional guilds was used to estimate the proportion of carbon entering the soil ecosystem through different channels, such as through decomposition of organic material, the detrital channel, through the roots of plants, the root channel or recycled through the activity of predators, a predation channel. Calculations of the indices were developed and validated using case studies in the north Queensland banana industry. Firstly, a survey of organic and conventional banana farms found a greater proportion of C entering the soil ecosystem through the detrital channel and a reduced proportion of C originating through the root channel at the organic sites relative to conventional sites. Secondly, a field experiment comparing compost amendments, found application of fresh compost significantly increased the proportion of C entering the soil ecosystem through the detrital channel and decreased proportion of C originating from the root channel. Thirdly, a field experiment comparing 'conventional' banana production to an 'alternative' system which incorporated organic matter, found the proportion of C entering the soil ecosystem through the root channel was significantly greater in the conventional systems relative to the alternative system. This research demonstrates that nematode indices can be used to assess horticultural systems, by indicating the origins of SOC.

Growth, yield and Fusarium wilt resistance of six FHIA tetraploid bananas (Musa spp.) grown in the Australian subtropics

Six tetraploid hybrids from Fundación Hondureña de Investigación Agrícola (FHIA) were evaluated in Australia over a five year period. They included three AAAA hybrids (FHIA-02, FHIA-17 and FHIA-23) and three AAAB hybrids (FHIA-01, FHIA-18 and SH-3640.10) and they were compared with industry standards, ‘Williams’ (AAA, Cavendish subgroup) and ‘Lady Finger’ (AAB, Pome subgroup). They were screened for their resistance to Fusarium wilt race 1 and subtropical race 4 caused by the pathogen Fusarium oxysporum f.sp. cubense and they were also grown for several cycles on farms not infested with Fusarium wilt to record their agronomic characteristics. The AAAB hybrids, all derived from female parent ‘Prata Anã’ (AAB, Pome subgroup) were the most resistant to both races of Fusarium wilt and were very productive in the subtropics. They were significantly more productive than ‘Lady Finger’, which was susceptible to both races of Fusarium wilt. The AAAA hybrids, with the exception of FHIA-02 which was very susceptible to Fusarium wilt and displayed the poorest agronomic traits of the six hybrids, produced bunch weights as good as Cavendish but were significantly slower to cycle. FHIA-17 and FHIA-23, both derived from the female parent ‘Highgate’ (AAA, Gros Michel subgroup), were also significantly more resistant to Fusarium wilt than ‘Gros Michel’, while FHIA-17 demonstrated a level of resistance similar to ‘Williams’ and FHIA-23 was intermediate between ‘Lady Finger’ and ‘Williams’

Cattle herd inspections and fly trapping for the detection of the Old World screw-worm fly (Chrysomya bezziana)

ObjectivesTo compare the sensitivity of inspections of cattle herds and adult fly trapping for detection of the Old World screw-worm fly (OWS). ProceduresThe incidence of myiases on animals and the number of OWS trapped with LuciTrap (R)/Bezzilure were measured concurrently on cattle farms on Sumba Island (Indonesia) and in peninsular Malaysia (two separate periods for the latter). The numbers of animal inspections and traps required to achieve OWS detection at the prevalent fly densities were calculated. ResultsOn Sumba Island, with low-density OWS populations, the sensitivity of herd inspections and of trapping for OWS detection was 0.30 and 0.85, respectively. For 95% confidence of detecting OWS, either 45 inspections of 74 animals or trapping with 5 sets of 4 LuciTraps for 14 days are required. In Malaysia, at higher OWS density, herd inspections of 600 animals (twice weekly, period 1) or 1600 animals (weekly, period 2) always detected myiases (sensitivity = 1), while trapping had sensitivities of 0.89 and 0.64 during periods 1 and 2, respectively. For OWS detection with 95% confidence, fewer than 600 and 1600 animals or 2 and 6 LuciTraps are required in periods 1 and 2, respectively. ConclusionsInspections of cattle herds and trapping with LuciTrap and Bezzilure can detect OWS populations. As a preliminary guide for OWS detection in Australia, the numbers of animals and traps derived from the Sumba Island trial should be used because the prevailing conditions better match those of northern Australia.

Controlling Pythium in Ginger: Phase 2

The fungal disease Pythium Soft Rot is regarded by the ginger industry as their most serious disease threat. This project was developed to further investigate the factors contributing to the persistence and spread of Pythium Soft Rot on ginger farms and to identify measures for their control. The study demonstrated that the pathogen capable of causing Pythium Soft Rot in ginger was spread in contaminated ‘seed’, soil and water and that it can be managed through a combination of strategies that rely on early detection, reducing pathogen levels in soils, preventing water logging and restricting movement of contaminated ‘seed’, soil and water. However, in order to have an effective level of control, all strategies need to be integrated in an effective manner.

APSIM – Evolution towards a new generation of agricultural systems simulation

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.

Quantitative methods to measure pigmentation variation in farmed Giant Tiger Prawns, Penaeus monodon, and the effects of different harvest methods on cooked colour

Cooked prawn colour is known to be a driver of market price and a visual indicator of product quality for the consumer. Although there is a general understanding that colour variation exists in farmed prawns, there has been no attempt to quantify this variation or identify where this variation is most prevalent. The objectives of this study were threefold: firstly to compare three different quantitative methods to measure prawn colour or pigmentation, two different colorimeters and colour quantification from digital images. Secondly, to quantify the amount of pigmentation variation that exists in farmed prawns within ponds, across ponds and across farms. Lastly, to assess the effects of ice storage or freeze-thawing of raw product prior to cooking. Each method was able to detect quantitative differences in prawn colour, although conversion of image based quantification of prawn colour from RGB to Lab was unreliable. Considerable colour variation was observed between prawns from different ponds and different farms, and this variation potentially affects product value. Different post-harvest methods prior to cooking were also shown to have a profound detrimental effect on prawn colour. Both long periods of ice storage and freeze thawing of raw product were detrimental to prawn colour. However, ice storage immediately after cooking was shown to be beneficial to prawn colour. Results demonstrated that darker prawn colour was preserved by holding harvested prawns alive in chilled seawater, limiting the time between harvesting and cooking, and avoiding long periods of ice storage or freeze thawing of uncooked product.

Networks of stored grain diseases and pests: Strategies for sampling and mitigation

Pathogens and pests of stored grains move through complex dynamic networks linking fields, farms, and bulk storage facilities. Human transport and other forms of dispersal link the components of this network. A network model for pathogen and pest movement through stored grain systems is a first step toward new sampling and mitigation strategies that utilize information about the network structure. An understanding of network structure can be applied to identifying the key network components for pathogen or pest movement through the system. For example, it may be useful to identify a network node, such as a local grain storage facility, through which grain from a large number of fields will be accumulated and move through the network. This node may be particularly important for sampling and mitigation. In some cases more detailed information about network structure can identify key nodes that link two large sections of the network, such that management at the key nodes will greatly reduce the risk of spread between the two sections. In addition to the spread of particular species of pathogens and pests, we also evaluate the spread of problematic subpopulations, such as subpopulations with pesticide resistance. We present an analysis of stored grain pathogen and pest networks for Australia and the United States.

Good science for improving policy: greenhouse gas emissions from agricultural manures

Australia’s and New Zealand’s major agricultural manure management emission sources are reported to be, in descending order of magnitude: (1) methane (CH4) from dairy farms in both countries; (2) CH4 from pig farms in Australia; and nitrous oxide (N2O) from (3) beef feedlots and (4) poultry sheds in Australia. We used literature to critically review these inventory estimates. Alarmingly for dairy farm CH4 (1), our review revealed assumptions and omissions that when addressed could dramatically increase this emission estimate. The estimate of CH4 from Australian pig farms (2) appears to be accurate, according to industry data and field measurements. The N2O emission estimates for beef feedlots (3) and poultry sheds (4) are based on northern hemisphere default factors whose appropriateness for Australia is questionable and unverified. Therefore, most of Australasia’s key livestock manure management greenhouse gas (GHG) emission profiles are either questionable or are unsubstantiated by region-specific research. Encouragingly, GHG from dairy shed manure are relatively easy to mitigate because they are a point source which can be managed by several ‘close-to-market’ abatement solutions. Reducing these manure emissions therefore constitutes an opportunity for meaningful action sooner compared with the more difficult-to-implement and long-term strategies that currently dominate agricultural GHG mitigation research. At an international level, our review highlights the critical need to carefully reassess GHG emission profiles, particularly if such assessments have not been made since the compilation of original inventories. Failure to act in this regard presents the very real risk of missing the ‘low hanging fruit’ in the rush towards a meaningful response to climate change

Anthelmintic resistance in ovine gastrointestinal nematodes in inland southern Queensland

Objective To establish the prevalence of anthelmintic resistance in ovine gastrointestinal nematodes in southern Queensland. Design An observational parasitological study using the faecal egg count reduction test. Methods Sheep farms (n = 20) enrolled in this study met the twin criteria of using worm testing for drench decisions and having concerns about anthelmintic efficacy. On each farm, 105 sheep were randomly allocated to one of six treatment groups or an untreated control group. Faecal samples were collected on day 0 and days 10–14 for worm egg counts and larval differentiation. Single- and multi-combination anthelmintics, persistent and non-persistent, oral liquid or capsule, pour-on and injectable formulations were tested. Monepantel was not tested. Farmers also responded to a questionnaire on drenching practices. Results Haemonchus contortus was the predominant species. Efficacy <95% was recorded on 85% of farms for one or more anthelmintics and on 10% of farms for six anthelmintics. No resistance was identified on three farms. The 4-way combination product was efficacious (n = 4 farms). Napthalophos resistance was detected on one farm only. Resistance to levamisole (42% of farms), moxidectin injection (50% of farms) and the closantel/abamectin combination (67% of farms) was identified. Moxidectin oral was efficacious against Trichostrongylus colubriformis, which was predominant on only one farm. Of the farms tested, 55% ran meat breeds, 60% dosed more than the recommended dose rate and 70% always, mostly or when possible practised a ‘drench and move’ strategy. Conclusion This level of anthelmintic resistance in southern Queensland will severely compromise worm control and force increased use of monepantel.

Karta Odesskoi gubernii Kolonizatsionnye fondy

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USSR Karta Dnipropetrovskoi oblasti

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Plan U.S.R.R. Melitopol'skogo okruga Genicheskogo raiona poluostrova Chongar vymezhevannogo v poriadke chastichnogo izmeneniia ustroistva kolfonda s ustroistvom poselkov, razbivkoiu na polia sevooborota, gony soglasno nakazu N.K.Z dnia 1927, n 52-2825. Izmerenie v nature proizvedeno i plan etot sostovlen X. partieiu po ustroistvu kolfondov

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Skhematicheskii plan uchastkov pereselencheskogo fonda v Krymu

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Explanations of the general sketches of settlements on the Chonhar peninsula

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Sostoianie posevov v raionakh evreiskogo zemledeliia na 15-oe iiunia 1927 goda

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