A big data approach to predicting crop yield

Many broadacre farmers have a time series of crop yield monitor data for their paddocks which are often augmented with additional spatial data such as gamma radiometrics surveys or ECa (apparent soil electrical conductivity) from an electro-magnetic induction survey (EMI). In addition there are now readily available national and global datasets which can be used to represent the crop-growing environment. Rather than analysing one paddock at a time, there is an opportunity to explore the value of combining data over multiple paddocks and years into one dataset. Using these datasets in conjunction with machine learning approaches allows predictive models of crop yield to be built. In this study we explored this approach with a particular emphasis on the forecasting ability of the models based on pre- and mid-season information from predictor variables. Several large farms in Western Australia were used as a case study. Yield from wheat, barley and canola crops from 3 different seasons that covered ~15,000 hectares in each year were used. The yield data was processed to a 10 m grid, and for each observation we built an associated space-time cube of predictors. This consisted of grower collected data such as EM and gamma radiometrics surveys, and nationally available data such as MODIS NDVI, and rainfall. Random Forest models were used to predict crop yield of wheat, barley and canola using the space-time cube. Three models were created based on pre-sowing, mid-season and late-season conditions to explore the changes in the predictive ability of the model as more within-season information became available. These time points also coincide with points in the season when a management decision is made, such as the application of fertiliser. The models were evaluated using cross-validation based on paddocks and years and this was assessed at the spatial resolution of the paddock. The models performed better as the season progressed, largely because more information about within-season data became available (e.g. rainfall). Cross-validated results showed the models predicted yield very accurately, with an RMSE of 0.36 to 0.42 t/ha, and an LCCC of 0.89 to 0.92 at the paddock resolution. The more years of yield data that were available for a paddock, the better the predictions were. The generic nature of this method makes it possible to apply to other agricultural systems where yield monitor data is available. A data-driven approach to predicting crop yield as an alternative to using mechanistic models has several advantages. Future work should explore integration of more data sources into the models and focus on using the models to inform management decisions such as fertiliser applications

Whole breast and regional nodal irradiation in prone versus supine position in left sided breast cancer

Background: Prone whole breast irradiation (WBI) leads to reduced heart and lung doses in breast cancer patients receiving adjuvant radiotherapy. In this feasibility trial, we investigated the prone position for whole breast + lymph node irradiation (WB + LNI). Methods: A new support device was developed for optimal target coverage, on which patients are positioned in a position resembling a phase from the crawl swimming technique (prone crawl position). Five left sided breast cancer patients were included and simulated in supine and prone position. For each patient, a treatment plan was made in prone and supine position for WB + LNI to the whole axilla and the unoperated part of the axilla. Patients served as their own controls for comparing dosimetry of target volumes and organs at risk (OAR) in prone versus in supine position. Results: Target volume coverage differed only slightly between prone and supine position. Doses were significantly reduced (P < 0.05) in prone position for ipsilateral lung (Dmean, D2, V5, V10, V20, V30), contralateral lung (Dmean, D2), contralateral breast (Dmean, D2 and for total axillary WB + LNI also V5), thyroid (Dmean, D2, V5, V10, V20, V30), oesophagus (Dmean and for partial axillary WB + LNI also D2 and V5), skin (D2 and for partial axillary WB + LNI V105 and V107). There were no significant differences for heart and humeral head doses. Conclusions: Prone crawl position in WB + LNI allows for good breast and nodal target coverage with better sparing of ipsilateral lung, thyroid, contralateral breast, contralateral lung and oesophagus when compared to supine position. There is no difference in heart and humeral head doses

Damaged hardmen: organised crime and the half-life of deindustrialisation

Despite frequent associations, deindustrialization features rarely in studies of organized crime, and organized crime is at best a spectral presence in studies of deindustrialization. By developing an original application of Linkon's concept of the “half‐life,” we present an empirical case for the symbiotic relationship between former sites of industry and the emergence of criminal markets. Based on a detailed case‐study in the west of Scotland, an area long associated with both industry and crime, the paper interrogates the environmental, social, and cultural after‐effects of deindustrialization at a community level. Drawing on 55 interviews with residents and service‐providers in Tunbrooke, an urban community where an enduring criminal market grew in the ruins of industry, the paper elaborates the complex landscapes of identity, vulnerability, and harm that are embedded in the symbiosis of crime and deindustrialization. Building on recent scholarship, the paper argues that organized crime in Tunbrooke is best understood as an instance of “residual culture” grafted onto a fragmented, volatile criminal marketplace where the stable props of territorial identity are unsettled. The analysis allows for an extension of both the study of deindustrialization and organized crime, appreciating the “enduring legacies” of closure on young people, communal identity, and social relations in the twenty‐first century

Notes for genera: basal clades of Fungi (including Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota)

Compared to the higher fungi (Dikarya), taxonomic and evolutionary studies on the basal clades of fungi are fewer in number. Thus, the generic boundaries and higher ranks in the basal clades of fungi are poorly known. Recent DNA based taxonomic studies have provided reliable and accurate information. It is therefore necessary to compile all available information since basal clades genera lack updated checklists or outlines. Recently, Tedersoo et al. (MycoKeys 13:1--20, 2016) accepted Aphelidiomycota and Rozellomycota in Fungal clade. Thus, we regard both these phyla as members in Kingdom Fungi. We accept 16 phyla in basal clades viz. Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. Thus, 611 genera in 153 families, 43 orders and 18 classes are provided with details of classification, synonyms, life modes, distribution, recent literature and genomic data. Moreover, Catenariaceae Couch is proposed to be conserved, Cladochytriales Mozl.-Standr. is emended and the family Nephridiophagaceae is introduced