Pen parameters for improving the performance of sheep imported from Australia to Persian Gulf feedlots

Summer in the Persian Gulf region presents physiological challenges for Australian sheep that are part of the live export supply chain coming from the Australian winter. Many feedlots throughout the Gulf have very high numbers of animals during June to August in order to cater for the increased demand for religious festivals. From an animal welfare perspective it is important to understand the necessary requirements of feed and water trough allowances, and the amount of pen space required, to cope with exposure to these types of climatic conditions. This study addresses parameters that are pertinent to the wellbeing of animals arriving in the Persian Gulf all year round. Three experiments were conducted in a feedlot in the Persian Gulf between March 2010 and February 2012, totalling 44 replicate pens each with 60 or 100 sheep. The applied treatments covered animal densities, feed-bunk lengths and water trough lengths. Weights, carcass attributes and health status were the key recorded variables. Weight change results showed superior performance for animal densities of ≥1.2 m2/head during hot conditions (24-h average temperatures greater than 33 °C, or a diurnal range of around 29–37 °C). However the space allowance for animals can be decreased, with no demonstrated detrimental effect, to 0.6 m2/head under milder conditions. A feed-bunk length of ≥5 cm/head is needed, as 2 cm/head showed significantly poorer animal performance. When feeding at 90% ad libitum 10 cm/head was optimal, however under a maintenance feeding regime (1 kg/head/day) 5 cm/head was adequate. A minimum water trough allowance of 1 cm/head is required. However, this experiment was conducted during milder conditions, and it may well be expected that larger water trough lengths would be needed in hotter conditions. Carcass weights were determined mainly by weights at feedlot entry and subsequent weight gains, while dressing percentage was not significantly affected by any of the applied treatments. There was no demonstrated effect of any of the treatments on the number of animals that died, or were classified as unwell. However, across all the treatments, these animals lost significantly more weight than the healthy animals, so the above recommendations, which are aimed at maintaining weight, should also be applicable for good animal health and welfare. Therefore, best practice guidelines for managing Australian sheep in Persian Gulf feedlots in the hottest months (June–August) which present the greatest environmental and physical challenge is to allow feed-bunk length 5 cm/head on a maintenance-feeding program and 10 cm/head for 90% ad libitum feeding, and the space allowance per animal should be ≥1.2 m2/head. Water trough allocation should be at least 1 cm/head with provision for more in the summer when water intake potentially doubles.

Benzyl isothiocyanate: maximising production in papaya tissue extracts

Abstract: Although mainly grown for its sweet flavoured fruit, papaya (Carica papaya) has also been used for pharmacological purposes for many years. The reasons for use are varied with one of the best known being its anti-fungal action. Benzyl isothiocyanate (BITC) is the constituent most often implicated in this activity. Isothiocyanates are formed when the enzyme myrosinase catalyses the hydrolysis of the non-bioactive glucosinolates. This occurs when cellular contents come into contact through chewing, cutting or during extraction processes in the laboratory. While this is common in Brassica vegetables, the glucosinolate-myrosinase system is rare in fruit, papaya being a notable exception. It contains benzyl glucosinolate (BG), the glucosinolate precursor of BITC, in significant quantities. Parameters that determine the amount of BITC formed are duration of hydrolysis, presence/absence of nitrile-specifier proteins and BG content of different cultivars and tissues. We experimented with differing BITC extraction solvents, with the intention of developing a low cost, natural anti-fungal extract based on under-utilised papaya tissues. The findings suggest that papaya seeds, particularly from quarter-ripe fruit, have the potential to produce the highest levels of BITC necessary. Furthermore, they compare well with the nitrile-specifier protein-containing garden cress seeds (Lepidium sativum). To utilise the papaya seeds as a BITC source, an organic solvent such as ethanol is required to extract the largely water-insoluble BITC from the hydrolysed papaya seed mixture.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.