Rp Rawnsley

Climate change effects on pasture systems in south-eastern Australia.

Climate change projections for Australia predict increasing temperatures, changes to rainfall patterns, and elevated atmospheric carbon dioxide (CO2) concentrations. The aims of this study were to predict plant production responses to elevated CO2 concentrations using the SGS Pasture Model and DairyMod, and then to quantify the effects of climate change scenarios for 2030 and 2070 on predicted pasture growth, species composition, and soil moisture conditions of 5 existing pasture systems in climates ranging from cool temperate to subtropical, relative to a historical baseline. Three future climate scenarios were created for each site by adjusting historical climate data according to temperature and rainfall change projections for 2030, 2070 mid-and 2070 high-emission scenarios, using output from the CSIRO Mark 3 global climate model. In the absence of other climate changes, mean annual pasture production at an elevated CO2 concentration of 550 ppm was predicted to be 24-29% higher than at 380 ppm CO2 in temperate (C-3) species-dominant pastures in southern Australia, with lower mean responses in a mixed C-3/C-4 pasture at Barraba in northern New South Wales (17%) and in a C-4 pasture at Mutdapilly in south-eastern Queensland (9%). In the future climate scenarios at the Barraba and Mutdapilly sites in subtropical and subhumid climates, respectively, where climate projections indicated warming of up to 4.4 degrees C, with little change in annual rainfall, modelling predicted increased pasture production and a shift towards C-4 species dominance. In Mediterranean, temperate, and cool temperate climates, climate change projections indicated warming of up to 3.3 degrees C, with annual rainfall reduced by up to 28%. Under future climate scenarios at Wagga Wagga, NSW, and Ellinbank, Victoria, our study predicted increased winter and early spring pasture growth rates, but this was counteracted by a predicted shorter spring growing season, with annual pasture production higher than the baseline under the 2030 climate scenario, but reduced by up to 19% under the 2070 high scenario. In a cool temperate environment at Elliott, Tasmania, annual production was higher than the baseline in all 3 future climate scenarios, but highest in the 2070 mid scenario. At the Wagga Wagga, Ellinbank, and Elliott sites the effect of rainfall declines on pasture production was moderated by a predicted reduction in drainage below the root zone and, at Ellinbank, the use of deeper rooted plant systems was shown to be an effective adaptation to mitigate some of the effect of lower rainfall.

Simulating pasture growth rates in Australian and New Zealand grazing systems

DairyMod, EcoMod, and the SGS Pasture Model are mechanistic biophysical models developed to explore scenarios in grazing systems. The aim of this manuscript was to test the ability of the models to simulate net herbage accumulation rates of ryegrass-based pastures across a range of environments and pasture management systems in Australia and New Zealand. Measured monthly net herbage accumulation rate and accumulated yield data were collated from ten grazing system experiments at eight sites ranging from cool temperate to subtropical environments. The local climate, soil, pasture species, and management (N fertiliser, irrigation, and grazing or cutting pattern) were described in the model for each site, and net herbage accumulation rates modelled. The model adequately simulated the monthly net herbage accumulation rates across the range of environments, based on the summary statistics and observed patterns of seasonal growth, particularly when the variability in measured herbage accumulation rates was taken into account. Agreement between modelled and observed growth rates was more accurate and precise in temperate than in subtropical environments, and in winter and summer than in autumn and spring. Similarly, agreement between predicted and observed accumulated yields was more accurate than monthly net herbage accumulation. Different temperature parameters were used to describe the growth of perennial ryegrass cultivars and annual ryegrass; these differences were in line with observed growth patterns and breeding objectives. Results are discussed in the context of the difficulties in measuring pasture growth rates and model limitations.

Dynamic cattle behavioural classification using supervised ensemble classifiers

Cattle behavioural classification using cattle tag and supervised ensemble classifiers.Unsupervised hybrid clustering used to study inherent natural grouping in data set.Best classification accuracy was 96% using the bagging ensemble with Tree learner.A mechanism for the early detection and quantitative assessment of animal health. In this paper various supervised machine learning techniques were applied to classify cattle behaviour patterns recorded using collar systems with 3-axis accelerometer and magnetometer, fitted to individual dairy cows to infer their physical behaviours. Cattle collar data was collected at the Tasmanian Institute of Agriculture (TIA) Dairy Research Facility in Tasmania. In the first stage of analysis a novel hybrid unsupervised clustering framework, comprised of probabilistic principal component analysis, Fuzzy C Means, and Self Organizing Map network algorithms was developed and used to study the natural structure of the sensor data. Findings from this unsupervised clustering were used to guide the next stage of supervised machine learning. Five major behaviour classes, namely, Grazing, Ruminating, Resting, Walking, and other behaviour were identified for the classification trials. An ensemble of classifiers approach was used to learn models of cow behaviour using sensor data and ground truth behaviour observations acquired from the field. Ensemble classification using bagging, Random Subspace and AdaBoost methods along with conventional supervised classification methods, namely, Binary Tree, Linear Discriminant Analysis classifier, Naive Bayes classifier, k-Nearest Neighbour classifier, and Adaptive Neuro Fuzzy Inference System classifier were compared. The highest average correct classification accuracy of 96% was achieved using the bagging ensemble classification with Tree learner, which had 97% sensitivity, 89% specificity, 89% F1 score and 9% false discovery rate. This study has shown that cattle behaviours can be classified with a high accuracy using supervised machine learning technique. As dairy and beef systems become more intensive, the ability to identify the changes in the behaviours of individual livestock becomes increasingly difficult. Accurate behavioural monitoring through sensors provides a significant potential in providing a mechanism for the early detection and quantitative assessment of animal health issues such a lameness, informing key management events such as the identification of oestrus, or informing changes in supplementary feeding requirements.

Effect of defoliation interval on water-soluble carbohydrate and nitrogen energy reserves, regrowth of leaves and roots, and tiller number of cocksfoot (Dactylis glomerata L.) plants

This study investigated the influence of leaf stage-based defoliation interval on water-soluble carbohydrate and nitrogen energy reserve status, regrowth of leaves and roots, and tiller number of cocksfoot (Dactylis glomerata L.) cv. Kara plants up to 24 days (3.5-leaf stage) following defoliation. Treatments were based on defoliation intervals of 1-, 2-, and 4-leaf stages of regrowth, with treatments terminated when the 1-leaf defoliation interval had been completed 4 times, the 2-leaf interval 2 times, and the 4-leaf interval once. Selected plants were destructively harvested prior to commencement of treatments (H0), immediately following cessation of treatments (H1), and at 5 days (H2), 10 days (H3), and 24 days (H4) following H1. Leaf, root, and tiller dry matter yield were determined at each harvest event, as well as tiller number/plant. Levels of water-soluble carbohydrate and nitrogen reserves in plant stubble and roots were determined at each destructive harvest. Initiation and death of daughter tillers were monitored from H0 to the completion of the study. More frequent defoliation of cocksfoot plants resulted in reduced water-soluble carbohydrate assimilation and therefore leaf, root, and tiller dry matter accumulation during the subsequent recovery period. Defoliation at the 1-leaf stage severely limited the regrowth potential of cocksfoot plants, whereas defoliation at the 2-leaf stage was adequate for plant recovery, but did not maximise regrowth. The results of this study showed that a defoliation interval based on the 4-leaf stage maximises water-soluble carbohydrate reserves, tillering, and leaf and root dry matter yields. The priority sequence for allocation of water-soluble carbohydrate reserves followed the order of leaf growth, root growth, and tillering during the regrowth period. Nitrogen energy reserves were found to play a minor role in the regrowth of cocksfoot plants following defoliation. Additional keyword: leaf stage.

Complementary forages – integration at a whole-farm level

A high proportion of the Australian and New Zealand dairy industry is based on a relatively simple, low input and low cost pasture feedbase. These factors enable this type of production system to remain internationally competitive. However, a key limitation of pasture-based dairy systems is periodic imbalances between herd intake requirements and pasture DM production, caused by strong seasonality and high inter-annual variation in feed supply. This disparity can be moderated to a certain degree through the strategic management of the herd through altering calving dates and stocking rates, and the feedbase by conserving excess forage and irrigating to flatten seasonal forage availability. Australasian dairy systems are experiencing emerging market and environmental challenges, which includes increased competition for land and water resources, decreasing terms of trade, a changing and variable climate, an increasing environmental focus that requires improved nutrient and water-use efficiency and lower greenhouse gas emissions. The integration of complementary forages has long been viewed as a means to manipulate the home-grown feed supply, to improve the nutritive value and DM intake of the diet, and to increase the efficiency of inputs utilised. Only recently has integrating complementary forages at the whole-farm system level received the significant attention and investment required to examine their potential benefit. Recent whole-of-farm research undertaken in both Australia and New Zealand has highlighted the importance of understanding the challenges of the current feedbase and the level of complementarity between forage types required to improve profit, manage risk and/or alleviate/mitigate against adverse outcomes. This paper reviews the most recent systems-level research into complementary forages, discusses approaches to modelling their integration at the whole-farm level and highlights the potential of complementary forages to address the major challenges currently facing pasture-based dairy systems.

Potential of deficit irrigation to increase marginal irrigation response of perennial ryegrass (Lolium perenne L.) on Tasmanian dairy farms

In the cool temperate dairy regions of Tasmania, there is heavy reliance on irrigation to maximise pasture performance by ensuring that plants do not suffer water stress. Consequently, irrigation water has often been applied at a greater amount than plant water requirements, resulting in low efficiencies. An irrigation experiment was undertaken in north-western Tasmania between October 2007 and April 2008, examining the effect of deficit irrigation treatments on pasture growth and water-use efficiency. A rainfall deficit (potential evapotranspiration minus rainfall) of 20 mm was implemented to schedule irrigation, at which point 20, 16, 12, 8, or 0 mm of irrigation water was applied, referred to as treatments I100%, I80%, I60%, I40%, and I0%, respectively. The trial was a randomised complete block design with 4 replications. There were 21 irrigation events between October and April. The experimental area was grazed by 60 Holstein Friesian heifers at a grazing interval coinciding with emergence of 2.5–3.0 new ryegrass leaves/tiller of the I100% treatment. Cumulative pasture consumption for the irrigated period was 9.2, 8.9, 7.6, 6.9, and 3.7 t dry matter (DM)/ha for the I100%, I80%, I60%, I40%, and I0% treatments, respectively. The resulting marginal irrigation water-use index (MIWUI; marginal production due to irrigation) was 1.29, 1.54, 1.55, and 1.87 t DM/ML, for the I100%, I80%, I60%, and I40% treatments, respectively. The results of this study were modelled using the biophysical model DairyMod, with strong agreement between observed and modelled data. DairyMod was then used to simulate the MIWUI for 5 differing dairy regions of Tasmania using 40 years of climatic data (1968–2007) under 3 differing nitrogen management strategies by the 5 irrigation treatments. The modelling indicated that a MIWUI greater than 2 t DM/ML can be achieved in all regions. The current study has shown that the opportunity exists for irrigated pastoral systems to better manage an increasingly scarce resource and substantially improve responses to irrigation.

Resistance of pasture production to projected climate changes in south-eastern Australia

Abstract. Climate change impact analysis relies largely on down-scaling climate projections to develop daily time-step, future climate scenarios for use in agricultural systems models. This process of climate down-scaling is complicated by differences in projections from greenhouse gas emission pathways and, in particular, the wide variation between global climate model outputs. In this study, a sensitivity analysis was used to test the resistance of pasture production to the incremental changes in climate predicted over the next 60 years in southern Australia. Twenty-five future climate scenarios were developed by scaling the historical climate by increments of 0, 1, 2, 3 and 4°C (with corresponding changes to atmospheric carbon dioxide concentrations and relative humidity) and rainfall by +10, 0, –10, –20 and –30%. The resistance of annual and seasonal pasture production to these climatic changes was simulated at six sites in south-eastern Australia. The sites spanned a range of climates from high rainfall, cool temperate in north-west Tasmania to the lower rainfall, temperate environment of Wagga Wagga in southern New South Wales. Local soil and pasture types were simulated at each site using the Sustainable Grazing Systems Pasture model. Little change or higher annual pasture production was simulated at all sites with 1°C warming, but varying responses were observed with further warming. In a pasture containing a C4 native grass at Wagga Wagga, annual pasture production increased with further warming, while production was stable or declined in pasture types based on C3 species in temperate environments. In a cool temperate region pasture production increased with up to 2°C warming. Compared with the historical baseline climate, warmer and drier climate scenarios led to lower pasture production, with summer and autumn growth being most affected, although there was some variation between sites. At all sites winter production was increased under all warming scenarios. Inter-annual variation in pasture production, expressed as the coefficient of variation, increased in the lower rainfall scenarios where production was simulated to decline, suggesting that changing rainfall patterns are likely to affect the variability in pasture production more than increasing temperatures. Together the results indicate that annual pasture production is resistant to climatic changes of up to 2°C warming. The approach used in this study can be used to test the sensitivity of agricultural production to climatic changes; however, it does not incorporate changes in seasonal and extreme climatic events that may also have significant impacts on these systems. Nonetheless, the approach can be used to identify strategies that may increase resilience of agricultural systems to climate change such as the incorporation of C4 species into the pasture base.

Yield and water-use efficiency of contrasting lucerne genotypes grown in a cool temperate environment

In Tasmania, Australia, forage production is maximised by the use of irrigation. However, availability of water for irrigation is often limited, making the water-use efficiency (WUE) of a species/genotype an important consideration when designing forage systems. Field experimentation and an associated modelling study was undertaken to determine the WUE and environmental factors influencing WUE for contrasting lucerne (Medicago sativa) genotypes across six dairying regions within Tasmania. In the field experiment a significant genotype influence on WUE was identified under irrigated conditions and modelling identified a genotype influence on WUE in three out of six regions. WUE was related to the amount of water received (irrigation plus rainfall). The marginal response to the application of irrigation water (MWUE) was greatest for the highly winter-active genotype in the field experiment; however, modelling did not identify a consistent genotype influence on MWUE across regions. MWUE was negatively associated with the amount of deep drainage. The present study identified that lucerne has the potential to improve the WUE of forage systems across six different Tasmanian regions. The linkage of MWUE and deep drainage highlights that deficit irrigation practices could further improve the WUE of this forage crop, particularly in environments prone to deep drainage.

Evaluating the accuracy of the Agricultural Production Systems Simulator (APSIM) simulating growth, development, and herbage nutritive characteristics of forage crops grown in the south-eastern dairy regions of Australia

Abstract. Pasture-based dairy farms are a complex system involving interactions between soils, pastures, forage crops, and livestock as well as the economic and social aspects of the business. Consequently, biophysical and farm systems models are becoming important tools to study pasture-based dairy systems. However, there is currently a paucity of modelling tools available for the simulation of one key component of the system—forage crops. This study evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) in simulating dry matter (DM) yield, phenology, and herbage nutritive characteristics of forage crops grown in the dairy regions of south-eastern Australia. Simulation results were compared with data for forage wheat (Triticum aestivum L.), oats (Avena sativa L.), forage rape (Brassica napus L.), forage sorghum (Sorghum bicolor (L.) Moench), and maize (Zea mays L.) collated from previous field research and demonstration activities undertaken across the dairy regions of south-eastern Australia. This study showed that APSIM adequately predicted the DM yield of forage crops, as evidenced by the range of values for the coefficient of determination (0.58–0.95), correlation coefficient (0.76–0.94), and bias correction factor (0.97–1.00). Crop phenology for maize, forage wheat, and oats was predicted with similar accuracy to forage crop DM yield, whereas the phenology of forage rape and forage sorghum was poorly predicted (R2 values 0.38 and 0.80, correlation coefficient 0.62 and –0.90, and bias correction factors 0.67 and 0.28, respectively). Herbage nutritive characteristics for all crop species were poorly predicted. While the selection of a model to explore an aspect of agricultural production will depend on the specific problem being addressed, the performance of APSIM in simulating forage crop DM yield and, in many cases, crop phenology, coupled with its ease of use, open access, and science-based mechanistic methods of simulating agricultural and crop processes, makes it an ideal model for exploring the influence of management and environment on forage crops grown on dairy farms in south-eastern Australia. Potential future model developments and improvements are discussed in the context of the results of this validation analysis.

Whole-farm systems analysis of Australian dairy farm greenhouse gas emissions

The Australian dairy industry contributes ~1.6% of the nation’s greenhouse gas (GHG) emissions, emitting an estimated 9.3 million tonnes of carbon dioxide equivalents (CO2e) per annum. This study examined 41 contrasting Australian dairy farms for their GHG emissions using the Dairy Greenhouse Gas Abatement Strategies calculator, which incorporates Intergovernmental Panel on Climate Change and Australian inventory methodologies, algorithms and emission factors. Sources of GHG emissions included were pre-farm embedded emissions associated with key farm inputs (i.e. grains and concentrates, forages and fertilisers), CO2 emissions from electricity and fuel consumption, methane emissions from enteric fermentation and animal waste management, and nitrous oxide emissions from animal waste management and nitrogen fertilisers. The estimated mean (±s.d.) GHG emissions intensity was 1.04 ± 0.17 kg CO2 equivalents/kg of fat and protein-corrected milk (kg CO2e/kg FPCM). Enteric methane emissions were found to be approximately half of total farm emissions. Linear regression analysis showed that 95% of the variation in total farm GHG emissions could be explained by annual milk production. While the results of this study suggest that milk production alone could be a suitable surrogate for estimating GHG emissions for national inventory purposes, the GHG emissions intensity of milk production, on an individual farm basis, was shown to vary by over 100% (0.76–1.68 kg CO2e/kg FPCM). It is clear that using a single emissions factor, such as milk production alone, to estimate any given individual farm’s GHG emissions, has the potential to either substantially under- or overestimate individual farms’ GHG emissions.