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

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

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

RPSA, a candidate gene for isolated congenital asplenia, is required for pre-rRNA processing and spleen formation in Xenopus

A growing number of tissue-specific inherited disorders are associated with impaired ribosome production, despite the universal requirement for ribosome function. Recently, mutations in RPSA, a protein component of the small ribosomal subunit, were discovered to underlie approximately half of all isolated congenital asplenia cases. However, the mechanisms by which mutations in this ribosome biogenesis factor lead specifically to spleen agenesis remain unknown, in part due to the lack of a suitable animal model for study. Here we reveal that RPSA is required for normal spleen development in the frog, Xenopus tropicalis. Depletion of Rpsa in early embryonic development disrupts pre-rRNA processing and ribosome biogenesis, and impairs expression of the key spleen patterning genes nkx2-5, bapx1 and pod1 in the spleen anlage. Importantly, we also show that whereas injection of human RPSA mRNA can rescue both pre-rRNA processing and spleen patterning, injection of human mRNA bearing a common disease-associated mutation cannot. Together, we present the first animal model of RPSA-mediated asplenia and reveal a crucial requirement for RPSA in pre-rRNA processing and molecular patterning during early Xenopus development

Low leakage-current InAsSb nanowire photodetectors on silicon

Axially doped p–i–n InAs0.93Sb0.07 nanowire arrays have been grown on Si substrates and fabricated into photodetectors for shortwave infrared detection. The devices exhibit a leakage current density around 2 mA/cm2 and a 20% cutoff of 2.3 μm at 300 K. This record low leakage current density for InAsSb based devices demonstrates the suitability of nanowires for the integration of III–V semiconductors with silicon technology

Evaluating the taxa that provide shared pollination services across multiple crops and regions

Many pollinator species visit multiple crops in multiple regions, yet we know little about their pollination service provisioning at local and regional scales. We investigated the floral visitors (n = 13,200), their effectiveness (n = 1718 single visits) and response to landscape composition across three crops avocado, mango and macadamia within a single growing region (1 year), a single crop (3 years) and across different growing regions in multiple years. In total, eight wild visitor groups were shared across all three crops. The network was dominated by three pollinators, two bees (Apis mellifera and Tetragonula spp.) and a fly, Stomorhina discolor. The visitation network for the three crops was relatively generalised but with the addition of pollen deposition data, specialisation increased. Sixteen managed and wild taxa were consistently present across three years in avocado, yet their contribution to annual network structure varied. Node specialisation (d’) analyses indicated many individual orchard sites across each of the networks were significantly more specialised compared to that predicted by null models, suggesting the presence of site-specific factors driving these patterns. Identifying the taxa shared across multiple crops, regions and years will facilitate the development of specific pollinator management strategies to optimize crop pollination services in horticultural systems