A framework for optimising capital investment and operations in livestock logistics

Despite the longevity, scale and importance of northern Australia's beef industry, recent disruptions to external markets have demonstrated a degree of vulnerability to shocks in the supply chain. Matching the industry's long-evident resilience to climatic variability with resilience to changes in markets and supply chains requires careful planning. One component of this is how investments in infrastructure will need to be planned to facilitate adaptive responses to market changes. This paper provides an outline of a modelling framework that links strategic and operational dynamic models of logistics along the supply chain from the property to the abattoir or port. A novelty of the methodology is that it takes into account the high granularity of individual livestock transport vehicle movements and the ability to scale up to an almost complete view of logistics costs across the entire beef industry of northern Australia. The paper illustrates how the methodology could be used to examine the effects of changes in logistics infrastructure on efficiency and costs using examples from the states of Northern Territory, Western Australia and Queensland.

Atrazine degradation and transport in runoff on a Black Vertosol

In Australia communities are concerned about atrazine being detected in drinking water supplies. It is important to understand mechanisms by which atrazine is transported from paddocks to waterways if we are to reduce movement of agricultural chemicals from the site of application. Two paddocks cropped with grain sorghum on a Black Vertosol were monitored for atrazine, potassium chloride (KCl) extractable atrazine, desethylatrazine (DEA), and desisopropylatrazine (DIA) at 4 soil depths (0-0.05, 0.05-0.10, 0.10-0.20, and 0.20-0.30 m) and in runoff water and runoff sediment. Atrazine + DEA + DIA (total atrazine) had a half-life in soil of 16-20 days, more rapid dissipation than in many earlier reports. Atrazine extracted in dilute potassium chloride, considered available for weed control, was initially 34% of the total and had a half-life of 15-20 days until day 30, after which it dissipated rapidly with a half life of 6 days. We conclude that, in this region, atrazine may not pose a risk for groundwater contamination, as only 0.5% of applied atrazine moved deeper than 0.20 m into the soil, where it dissipated rapidly. In runoff (including suspended sediment) atrazine concentrations were greatest during the first runoff event (57 days after application) (85 μg/L) and declined with time. After 160 days, the total atrazine lost in runoff was 0.4% of the initial application. The total atrazine concentration in runoff was strongly related to the total concentration in soil, as expected. Even after 98% of the KCl-extractable atrazine had dissipated (and no longer provided weed control), runoff concentrations still exceeded the human health guideline value of 40 μg/L. For total atrazine in soil (0-0.05 m), the range for coefficient of soil sorption (Kd) was 1.9-28.4 mL/g and for soil organic carbon sorption (KOC) was 100-2184 mL/g, increasing with time of contact with the soil and rapid dissipation of the more soluble, available phase. Partition coefficients in runoff for total atrazine were initially 3, increasing to 32 and 51 with time, values for DEA being half these. To minimise atrazine losses, cultural practices that maximise rain infiltration, and thereby minimise runoff, and minimise concentrations in the soil surface should be adopted.

Runoff, soil loss, and nutrient transport from cropping systems on Red Ferrosols in tropical northern Australia.

Runoff, soil loss, and nutrient loss were assessed on a Red Ferrosol in tropical Australia over 3 years. The experiment was conducted using bounded, 100-m(2) field plots cropped to peanuts, maize, or grass. A bare plot, without cover or crop, was also instigated as an extreme treatment. Results showed the importance of cover in reducing runoff, soil loss, and nutrient loss from these soils. Runoff ranged from 13% of incident rainfall for the conventional cultivation to 29% under bare conditions during the highest rainfall year, and was well correlated with event rainfall and rainfall energy. Soil loss ranged from 30 t/ha. year under bare conditions to <6 t/ha. year under cropping. Nutrient losses of 35 kg N and 35 kg P/ha. year under bare conditions and 17 kg N and 11 kg P/ha. year under cropping were measured. Soil carbon analyses showed a relationship with treatment runoff, suggesting that soil properties influenced the rainfall runoff response. The cropping systems model PERFECT was calibrated using runoff, soil loss, and soil water data. Runoff and soil loss showed good agreement with observed data in the calibration, and soil water and yield had reasonable agreement. Longterm runs using historical weather data showed the episodic nature of runoff and soil loss events in this region and emphasise the need to manage land using protective measures such as conservation cropping practices. Farmers involved in related, action-learning activities wished to incorporate conservation cropping findings into their systems but also needed clear production benefits to hasten practice change.

Estimating daily methane production in individual cattle with irregular feed intake patterns from short-term methane emission measurements

Spot measurements of methane emission rate (n = 18 700) by 24 Angus steers fed mixed rations from GrowSafe feeders were made over 3- to 6-min periods by a GreenFeed emission monitoring (GEM) unit. The data were analysed to estimate daily methane production (DMP; g/day) and derived methane yield (MY; g/kg dry matter intake (DMI)). A one-compartment dose model of spot emission rate v. time since the preceding meal was compared with the models of Wood (1967) and Dijkstra et al. (1997) and the average of spot measures. Fitted values for DMP were calculated from the area under the curves. Two methods of relating methane and feed intakes were then studied: the classical calculation of MY as DMP/DMI (kg/day); and a novel method of estimating DMP from time and size of preceding meals using either the data for only the two meals preceding a spot measurement, or all meals for 3 days prior. Two approaches were also used to estimate DMP from spot measurements: fitting of splines on a 'per-animal per-day' basis and an alternate approach of modelling DMP after each feed event by least squares (using Solver), summing (for each animal) the contributions from each feed event by best-fitting a one-compartment model. Time since the preceding meal was of limited value in estimating DMP. Even when the meal sizes and time intervals between a spot measurement and all feeding events in the previous 72 h were assessed, only 16.9% of the variance in spot emission rate measured by GEM was explained by this feeding information. While using the preceding meal alone gave a biased (underestimate) of DMP, allowing for a longer feed history removed this bias. A power analysis taking into account the sources of variation in DMP indicated that to obtain an estimate of DMP with a 95% confidence interval within 5% of the observed 64 days mean of spot measures would require 40 animals measured over 45 days (two spot measurements per day) or 30 animals measured over 55 days. These numbers suggest that spot measurements could be made in association with feed efficiency tests made over 70 days. Spot measurements of enteric emissions can be used to define DMP but the number of animals and samples are larger than are needed when day-long measures are made.