Block-level macadamia yield forecasting using spatio-temporal datasets

Early crop yield forecasts provide valuable information for growers and industry to base decisions on. This work considers early forecasting of macadamia nut yield at the individual orchard block level with input variables derived from spatio-temporal datasets including remote sensing, weather and elevation. Yield data from 2012–2019, for 101 blocks belonging to 10 orchards, was obtained. We forecast yield on each test year from 2014–2019 using models trained on data from years prior to the test year. Forecasts are generated in January, for the coming harvest in March–September. A linear model using ridge regularized regression produced consistently good predictions compared with other machine learning algorithms including lasso, support vector regression and random forest. Adding meteorological variables offered little improvement over using only remote sensing variables. The 2019 forecast root mean square error at the block level was 0.8 t/ha, and mean absolute percentage error was 20.9%. When block level predictions were aggregated across the multiple orchards per region, production prediction errors were between 0–15% from 2016–2019. The ridge regression model can be easily implemented in GIS platforms to deliver block-level yield forecast maps to end users

Macadamia Orchard Planting Year and Area Estimation at a National Scale

Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold

Improving survival outcomes in lung transplant recipients through early detection of bronchiolitis obliterans: Daily home spirometry versus standard pulmonary function testing

Bronchiolitis obliterans syndrome (BOS) is a fibrotic inflammatory process that manifests in a significant number of lung transplant recipients within five years of transplant. Left unchecked, the disease process, which has been linked to almost all chronic rejections, leads to irreversible damage, progressing to long-term sequelae and, eventually, respiratory failure. This systematic review examined the outcomes of eight randomized controlled trials that fulfilled specific selection criteria to determine whether home spirometry could be used as a BOS detection tool

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km² area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently

Accumulation and toxicity of monoaromatic petroleum hydrocarbons in early life stages of cod and haddock

A multitude of recent studies have documented the detrimental effects of crude oil exposure on early life stages of fish, including larvae and embryos. While polycyclic aromatic hydrocarbons (PAHs), particularly alkyl PAHs, are often considered the main cause of observed toxic effects, other crude oil derived organic compounds are usually overlooked. In the current study, comprehensive two-dimensional gas chromatography coupled to mass spectrometry was applied to investigate the body burden of a wide range of petrogenic compounds in Atlantic haddock (Melanogrammus aeglefinus) and cod (Gadus morhua) embryos that had been exposed to sublethal doses of dispersed crude oil. Several groups of alkylated monoaromatic compounds (e.g. alkyl tetralins, indanes and alkyl benzenes), as well as highly alkylated PAHs, were found to accumulate in the fish embryos upon crude oil exposure. To investigate the toxicity of the monoaromatic compounds, two models (1-isopropyl-4-methyltetralin and 1-isopropyl-4-methylindane) were synthesized and shown to bioaccumulate and cause delayed hatching in developing embryos. Minor developmental effects, including craniofacial and jaw deformations and pericardial edemas, were also observed at the highest studied concentrations of the alkylindane.acceptedVersio

Effects of ultrafast laser energy deposition on a hypervelocity boundary layer

This paper presents a preliminary study of hypersonic boundary layer phenomena resulting from the energy deposition of an ultrafast laser pulse in proximity to the tip of a 7° half-angle axisymmetric cone within the Oxford High Density Tunnel (HDT) facility. An ultrafast Ti:Sapphire laser was integrated into the facility’s systems, providing temporally precise and synchronous delivery of a single tightly focussed laser pulse to the target location in the HDT test section. This investigation independently assessed the variation of the freestream unit Reynolds number (Reunit,∞) on the disturbed boundary layer for laminar to turbulent conditions bound by the extrema unit Reynolds numbers 5.7 and 24.1 ± 0.9 × 106/m, while keeping laser settings constant. For all test conditions, the boundary layer state was characterised using high-speed schlieren imaging at 1 MHz for visualising the flow field, focussed laser differential interferometry (FLDI) to assess small density fluctuations, and surface-mounted highfrequency bandwidth pressure transducers (PCBs 132A31 and 132B32). Flow features associated with the energy deposition in the boundary layer, included the formation of a spherical shock wave that expanded radially and decayed, an elliptical high-temperature ‘hot spot’ region, and a trailing turbulent wake. The hot spot and turbulent wake density gradients increased linearly with unit Reynolds number, suggesting a relation to the local mean density or pressure. Normalising these values by mean density gave an estimate of turbulence intensity, which appeared independent of unit Reynolds number. The size of the hot spot decreased with unit Reynolds number, which is hypothesised to be caused by the higher mean pressure compressing the hot spot. The increasing instability of the boundary layer with unit Reynolds number led to longer duration turbulent wakes before the laminar boundary layer re-establishes

Study Protocol:Understanding SARS-Cov-2 infection, immunity and its duration in care home residents and staff in England (VIVALDI)

Global infection and mortality rates from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are disproportionately high in certain populations, including amongst older people. Care home residents are frequently exposed to infection due to contact with staff and other residents, and are highly susceptible to infection due to their age and co-morbidity. In England, official statistics suggest that at least 25% of all deaths in care home residents since the start of pandemic are linked to coronavirus disease 2019 (COVID-19), but limited testing for SARS-CoV-2 early in the pandemic means estimates of the true burden of disease are lacking. Additionally, little is known about patterns of transmission between care homes, the community and hospitals, or the relationship between infection and immunity in care home staff and residents. The VIVALDI study plans to address these questions. VIVALDI is a prospective cohort study aiming to recruit 6,500 staff and 5000 residents from 105 care homes across England. Successive rounds of testing for infection will be performed over a period of 12 months. Nasopharyngeal swabs will detect evidence of viral RNA and therefore active infection (accompanied by collection of data on symptoms), whereas blood tests will detect antibodies and evidence of cellular immunity to SARS-CoV-2. Whole genome sequencing of viral isolates to investigate pathways of transmission of infection is planned in collaboration with the COVID-19 Genomics UK Consortium. Qualitative interviews with care home staff will investigate the impact of the pandemic on ways of working and how test results influence infection control practices and behaviours. Data from residents and staff will be linked to national datasets on hospital admissions, antibody and PCR test results, mortality and care home characteristics. Data generated will support national public health efforts to prevent transmission of COVID-19 and protect care home staff and residents from infection.</p

The exposure of the Great Barrier Reef to ocean acidification

The Great Barrier Reef (GBR) is founded on reef-building corals. Corals build their exoskeleton with aragonite, but ocean acidification is lowering the aragonite saturation state of seawater (Omega(a)). The downscaling of ocean acidification projections from global to GBR scales requires the set of regional drivers controlling Omega(a) to be resolved. Here we use a regional coupled circulation-biogeochemical model and observations to estimate the Omega(a) experienced by the 3,581 reefs of the GBR, and to apportion the contributions of the hydrological cycle, regional hydrodynamics and metabolism on Omega(a) variability. We find more detail, and a greater range (1.43), than previously compiled coarse maps of Omega(a) of the region (0.4), or in observations (1.0). Most of the variability in Omega(a) is due to processes upstream of the reef in question. As a result, future decline in Omega(a) is likely to be steeper on the GBR than currently projected by the IPCC assessment report

Skilful seasonal predictions of Global Monsoon summer precipitation with DePreSys3

We assess skill of the Met Office’s DePreSys3 prediction system at forecasting summer global monsoon precipitation at the seasonal time scale (2-5 month forecast period). DePreSys3 has significant skill at predicting summer monsoon precipitation (r=0.68), but the skill varies by region and is higher in the northern (r=0.68) rather than in the southern hemisphere (r=0.44). To understand the sources of precipitation forecast skill, we decompose the precipitation into several dynamic and thermodynamic components and assess the skill in predicting each. While dynamical changes of the atmospheric circulation primarily contribute to global monsoon variability, skill at predicting shifts in the atmospheric circulation is relatively low. This lower skill partly relates to DePreSys3’s limited ability to accurately simulate changes in atmospheric circulation patterns in response to sea surface temperature forcing. Skill at predicting the thermodynamic component of precipitation is generally higher than for the dynamic component, but thermodynamic anomalies only contribute a small proportion of the total precipitation variability. Finally, we show that the use of a large ensemble improves skill for predicting monsoon precipitation, but skill does not increase beyond 20 members