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Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species. [ABSTRACT FROM AUTHOR]

Herbicide resistance is an important weed management issue. Glyphosate is the most dominant herbicide, which controls a broad spectrum of weeds, including grasses such as Lolium rigidum. Lolium rigidum is a major weed of winter crops in Australia that is developing glyphosate resistance in increasing numbers of populations and has been observed growing throughout summer in recent years. Three L. rigidum populations, one summer-emerging glyphosate-resistant (GR), one winter-emerging glyphosate-susceptible (GS), and one winter-emerging population with unknown resistance status (CC04), were analyzed for target-site resistance to glyphosate after confirming their resistance or susceptibility to glyphosate in a dose–response experiment. Population GR was obtained from plants that emerged in summer and contained plants that survived all rates of glyphosate applied (0 to 4560 g a.e. ha−1). It was found to be 6.1 and 4.4 times more resistant than population GS across two experiments. Population CC04 was identified as susceptible. Plants from each population were analyzed for the presence of target-site mutations in the conserved region of the EPSPS gene; however, no mutations were identified that could cause resistance, suggesting non-target-site resistance in population GR. The effectiveness of alternative herbicides was also analyzed for each population. Paraquat was the most effective herbicide, with 0% survival across all populations. The ACCase-inhibiting herbicide clethodim was also highly effective (0 to 8% survival across populations). Other ACCase-inhibiting herbicides, propaquizafop, haloxyfop, and pinoxaden, were effective at controlling the two susceptible populations, CC04 and GS (0 to 36% and 0 to 20% survival, respectively), but were only moderately effective for controlling GR (28 to 51% survival). Paraquat and clethodim may be alternative options for controlling GR populations of L. rigidum and could be effective for use in management programs to slow the development of GR populations. [ABSTRACT FROM AUTHOR]

The legacy of deliberate and accidental introductions of invasive alien species to Australia has had a hefty economic toll, yet quantifying the magnitude of the costs associated with direct loss and damage, as well as for management interventions, remains elusive. This is because the reliability of cost estimates and undersampling have not been determined. We provide the first detailed analysis of the reported costs associated with invasive species to the Australian economy since the 1960s, based on the recently published InvaCost database and supplementary information, for a total of 2078 unique cost entries. Since the 1960s, Australia has spent or incurred losses totalling at least US$298.58 billion (2017 value) or AU$389.59 billion (2017 average exchange rate) from invasive species. However, this is an underestimate given that costs rise as the number of estimates increases following a power law. There was an average 1.8-6.3-fold increase in the total costs per decade since the 1970s to the present, producing estimated costs of US$6.09-57.91 billion year-1 (all costs combined) or US$225.31 million-6.84 billion year-1 (observed, highly reliable costs only). Costs arising from plant species were the highest among kingdoms (US$151.68 billion), although most of the costs were not attributable to single species. Of the identified weedy species, the costliest were annual ryegrass (Lolium rigidum), parthenium (Parthenium hysterophorus) and ragwort (Senecio jacobaea). The four costliest classes were mammals (US$48.63 billion), insects (US$11.95 billion), eudicots (US$4.10 billion) and monocots (US$1.92 billion). The three costliest species were all animals - cats (Felis catus), rabbits (Oryctolagus cuniculus) and red imported fire ants (Solenopsis invicta). Each State/Territory had a different suite of major costs by species, but with most (3-62%) costs derived from one to three species per political unit. Most (61%) of the reported costs applied to multiple environments and 73% of the total pertained to direct damage or loss compared to management costs only, with both of these findings reflecting the availability of data. Rising incursions of invasive species will continue to have substantial costs for the Australian economy, but with better investment, standardised assessments and reporting and coordinated interventions (including eradications), some of these costs could be substantially reduced. [ABSTRACT FROM AUTHOR]

A farm systems experiment comparing five pasture-based dairy systems was conducted in south-western Australia over 4 years on an annual ryegrass:subterranean clover sward. Fertiliser N inputs (0, 0.5, 1, 1.5 and 2 kg N ha−1day−1) and stocking rate (SR; 1.25, 1.50, 1.75, 2.0 and 2.25 milking cows ha−1) increased concurrently for each treatment (F1–F5, respectively). By design, milk yield per cow did not differ between treatments; therefore, the response was measured in terms of changes in herbage dry matter (DM) utilisation and nutritive characteristics, as well as differences in feed input and imported forage required. Total annual pasture utilisation (t DM ha−1) increased from treatment F1–F3 (5.1, 5.9 and 7.3 t DM−1ha−1, respectively), but responses beyond treatment F3 were not significant. Mean total N response efficiency was 9, 13, 8 and 7 kg DM per kg applied N for F2–F5, respectively, while mean marginal N response efficiency was 9, 17, −2 and 3 kg DM per kg applied N for F2–F5, respectively. The most efficient use of N fertiliser was achieved on treatment F3, where annual pasture utilisation levels in excess of 7 t DM ha−1yr−1were consistently demonstrated under the management strategies outlined, substantially greater than the local industry average. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (PRG) is an important forage grown on dairy farms in temperate regions globally, including south-eastern Australia. A forage value index (FVI) providing information on the seasonal production of commercially available PRG cultivars is currently available. Despite the importance of the nutritive value of pasture in dairy farm systems, the nutritive characteristics of PRG cultivars are not currently included in the FVI as they are not routinely measured in cultivar evaluation trials. This study investigated differences between cultivar functional groups (diploid and tetraploid). It also examined differences between individual cultivars within seasons at four locations in south-eastern Australia and examined how trial location affects cultivar ranking. Samples were collected from existing cultivar evaluation trials over a 3-year period and analysed for nutritive characteristics. There were differences (p < 0.05) between diploids and tetraploids for metabolisable energy (ME) and neutral detergent fibre (NDF) in each season at each location with a few exceptions in summer and autumn. Crude protein (CP) differed between functional groups in some seasons at some sites. Spearman rank correlations within season were strong for ME between trial locations (r = 0.78–0.96), moderate to high for NDF (0.51–0.86) and variable for CP (−0.69–0.56). These findings provide guidance on methods for implementing nutritive value testing in cultivar evaluation trials and support the imminent inclusion of ME in the Australian FVI. The ranking of cultivars for ME was more consistent across trial sites compared to NDF and CP, suggesting the latter two traits, in particular CP, are more sensitive to environmental influences. Based on these results, we do not recommend the inclusion of CP as an individual trait in the Australian FVI. A significantly larger dataset and further research on the genotype by environment interactions would be needed to reconsider this. The addition of ME in the Australian FVI will lead to better cultivar choices by farmers and could lead to more targeted perennial ryegrass breeding programs. [ABSTRACT FROM AUTHOR]

Soil phosphorus (P) concentrations in excess of those required to maximise pasture production impose unnecessary costs on farmers and potentially increase the risk of P runoff to waterways. The relationship between soil available P concentration and pasture production was examined on an Oxyaquic Hydrosol. Mean annual pasture production ranged from 18,415 to 21,834 kg DM/ha over the 3-year sampling period. There was no significant difference in the annual DM production at Olsen P concentrations of 23-81 mg/kg measured from the 0-75 mm sampling depth. Results show that there was no increase in pasture production when soil Olsen P concentrations exceeded 23 mg/kg (0-75 mm). The optimum soil Olsen P ranges determined in the current study are well below the mean paddock concentrations previously measured in northwest Tasmania (54 mg/kg) and suggest that such high concentrations are not justified in terms of optimising pasture growth. [ABSTRACT FROM AUTHOR]

In Melbourne, Australia, airborne grass pollen is the predominant cause of hay fever (seasonal rhinitis) during late spring and early summer, with levels of airborne grass pollen also influencing hospital admissions for asthma. In order to improve predictions of conditions that are potentially hazardous to susceptible individuals, we have sought to better understand the causes of diurnal, intra-seasonal and inter-seasonal variability of atmospheric grass pollen concentrations (APC) by analysing grass pollen count data for Melbourne for 16 grass pollen seasons from 1991 to 2008 (except 1994 and 1995). Some of notable features identified in this analysis were that on days when either extreme (>100 pollen grains m) or high (50-100 pollen grains m) levels of grass pollen were recorded the winds were of continental origin. In contrast, on days with a low (<20 pollen grains m) concentration of grass pollen, winds were of maritime origin. On extreme and high grass pollen days, a peak in APC occurred on average around 1730 hours, probably due to a reduction in surface boundary layer turbulence. The sum of daily APC for each grass pollen season was highly correlated ( r = 0.79) with spring rainfall in Melbourne for that year, with about 60% of a declining linear trend across the study period being attributable to a reduction of meat cattle and sheep (and hence grazing land) in rural areas around Melbourne. Finally, all of the ten extreme pollen events (3 days or more with APC > 100 pollen grains m) during the study period were characterised by an average downward vertical wind anomaly in the surface boundary layer over Melbourne. Together these findings form a basis for a fine resolution atmospheric general circulation model for grass pollen in Melbourne's air that can be used to predict daily (and hourly) APC. This information will be useful to those sectors of Melbourne's population that suffer from allergic problems. [ABSTRACT FROM AUTHOR]

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors. [ABSTRACT FROM AUTHOR]

Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model's prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha − 1 ). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha − 1 ). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of Australia and New Zealand. However, it can have poor persistence due to a low tolerance to both abiotic and biotic stresses. A major challenge in measuring persistence in pasture breeding is that the assessment of pasture survival depends on ranking populations based on manual ground cover estimation. Ground cover measurements may include senescent and living tissues and can be measured as percentages or fractional units. The amount of senescent pasture present in a sward may indicate changes in plant growth, development, and resistance to abiotic and biotic stresses. The existing tools to estimate perennial ryegrass ground cover are not sensitive enough to discriminate senescent ryegrass from soil. This study aimed to develop a more precise sensor-based phenomic method to discriminate senescent pasture from soil. Ground-based RGB images, airborne multispectral images, ground-based hyperspectral data, and ground truth samples were taken from 54 perennial ryegrass plots three years after sowing. Software packages and machine learning scripts were used to develop a pipeline for high-throughput data extraction from sensor-based platforms. Estimates from the high-throughput pipeline were positively correlated with the ground truth data (p < 0.05). Based on the findings of this study, we conclude that the RGB-based high-throughput approach offers a precision tool to assess perennial ryegrass persistence in pasture breeding programs. Improvements in the spatial resolution of hyperspectral and multispectral techniques would then be used for persistence estimation in mixed swards and other monocultures. [ABSTRACT FROM AUTHOR]