Measurement of genetic and environmental variation in barley (Hordeum vulgare) grain hardness

In this study, we assessed a broad range of barley breeding lines and commercial varieties by three hardness methods (two particle size methods and one crush resistance method (SKCS—Single-Kernel Characterization System), grown at multiple sites to see if there was variation in barley hardness and if that variation was genetic or environmentally controlled. We also developed near-infrared reflectance (NIR) calibrations for these three hardness methods to ascertain if NIR technology was suitable for rapid screening of breeding lines or specific populations. In addition, we used this data to identify genetic regions that may be associated with hardness. There were significant (p<0.05) genetic effects for the three hardness methods. There were also environmental effects, possibly linked to the effect of protein on hardness, i.e. increasing protein resulted in harder grain. Heritability values were calculated at >85% for all methods. The NIR calibrations, with R2 values of >90%, had Standard Error of Prediction values of 0.90, 72 and 4.0, respectively, for the three hardness methods. These equations were used to predict hardness values of a mapping population which resulted in genetic markers being identified on all chromosomes but chromosomes 2H, 3H, 5H, 6H and 7H had markers with significant LOD scores. The two regions on 5H were on the distal end of both the long and short arms. The region that showed significant LOD score was on the long arm. However, the region on the short arm associated with the hardness (hordoindoline) genes did not have significant LOD scores. The results indicate that barley hardness is influenced by both genotype and environment and that the trait is heritable, which would allow breeders to develop very hard or soft varieties if required. In addition, NIR was shown to be a reliable tool for screening for hardness. While the data set used in this study has a relatively low variation in hardness, the tools developed could be applied to breeding populations that have large variation in barley grain hardness.

Development and Accreditation of an Applied Climate Education Unit for Sustainable Land Use in Australia

In recent years, there have been significant developments in climate science relevant to agriculture and natural resource management. Assessing impacts of climate variability and use of seasonal climate forecasts have become increasingly important elements in the management "toolkit" for many Australian farmers. Consideration of climate change further increases the need for improved management strategies. While climate risk extension activities have kept pace with advances in climate science, a national review of the Vocational Education and Training system in Australia in relation to "weather and climate" showed that these topics were "poorly represented" at the management level in the Australian Qualifications Framework, and needed increased emphasis. Consequently, a new Unit of Competency concerning management of climatic risk was developed and accredited to address this deficiency. The objective of the unit was to build knowledge and skills for better management of climate variability via the elements of surveying climatic and enterprise data; analysing climatic risks and opportunities; and developing climatic risk management strategies. This paper describes establishment of a new unit for vocational education that is designed to harness recent developments in applied climate science for better management of Australia's highly variable climate. The main benefits of the new unit of competency, "Developing climatic risk management strategies,"were seen as improving decisions in climate and agriculture, and reducing climate risk exposure to enhance sustainable agriculture. The educational unit is now within the scope of agricultural colleges, universities, and registered training organisations as an accredited unit.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Measurement and Interpretation of Salinity Tolerance in Four Perennial Grasses

Whilst the topic of soil salinity has received a substantive research effort over the years, the accurate measurement and interpretation of salinity tolerance data remain problematic. The tolerance of four perennial grass species (non-halophytes) to sodium chloride (NaCl) dominated salinity was determined in a free-flowing sand culture system. Although the salinity tolerance of non-halophytes is often represented by the threshold salinity model (bent-stick model), none of the species in the current study displayed any observable salinity threshold. Further, the observed yield decrease was not linear as suggested by the model. On re-examination of earlier datasets, we conclude that the threshold salinity model does not adequately describe the physiological processes limiting growth of non-halophytes in saline soils. Therefore, the use of the threshold salinity model is not recommended for non-halophytes, but rather, a model which more accurately reflects the physiological response observed in these saline soils, such as an exponential regression curve.

Water addition, evaporation and water holding capacity of poultry litter

Litter moisture content has been related to ammonia, dust and odour emissions as well as bird health and welfare. Improved understanding of the water holding properties of poultry litter as well as water additions to litter and evaporation from litter will contribute to improved litter moisture management during the meat chicken grow-out. The purpose of this paper is to demonstrate how management and environmental conditions over the course of a grow-out affect the volume of water A) applied to litter, B) able to be stored in litter, and C) evaporated from litter on a daily basis. The same unit of measurement has been used to enable direct comparison—litres of water per square metre of poultry shed floor area, L/m2, assuming a litter depth of 5 cm. An equation was developed to estimate the amount of water added to litter from bird excretion and drinking spillage, which are sources of regular water application to the litter. Using this equation showed that water applied to litter from these sources changes over the course of a grow-out, and can be as much as 3.2 L/m2/day. Over a 56 day grow-out, the total quantity of water added to the litter was estimated to be 104 L/m2. Litter porosity, water holding capacity and water evaporation rates from litter were measured experimentally. Litter porosity decreased and water holding capacity increased over the course of a grow-out due to manure addition. Water evaporation rates at 25 °C and 50% relative humidity ranged from 0.5 to 10 L/m2/day. Evaporation rates increased with litter moisture content and air speed. Maintaining dry litter at the peak of a grow-out is likely to be challenging because evaporation rates from dry litter may be insufficient to remove the quantity of water added to the litter on a daily basis.

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.

Field measurement of beef pen manure methane and nitrous oxide reveals a surprise for inventory calculations

Few data exist on direct greenhouse gas emissions from pen manure at beef feedlots. However, emission inventories attempt to account for these emissions. This study used a large chamber to isolate N2O and CH4 emissions from pen manure at two Australian commercial beef feedlots (stocking densities, 13-27 m(2) head) and related these emissions to a range of potential emission control factors, including masses and concentrations of volatile solids, NO3-, total N, NH4+, and organic C (OC), and additional factors such as total manure mass, cattle numbers, manure pack depth and density, temperature, and moisture content. Mean measured pen N2O emissions were 0.428 kg ha(-1) d(-1) (95% confidence interval [CI], 0.252-0.691) and 0.00405 kg ha(-1) d(-1) (95% CI, 0.00114-0.0110) for the northern and southern feedlots, respectively. Mean measured CH4 emission was 0.236 kg ha(-1) d(-1) (95% CI, 0.163-0.332) for the northern feedlot and 3.93 kg ha(-1) d(-1) (95% CI, 2.58-5.81) for the southern feedlot. Nitrous oxide emission increased with density, pH, temperature, and manure mass, whereas negative relationships were evident with moisture and OC. Strong relationships were not evident between N2O emission and masses or concentrations of NO3- or total N in the manure. This is significant because many standard inventory calculation protocols predict N2O emissions using the mass of N excreted by the animal.