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

Effective control of Leptosphaeria maculans increases importance of L. biglobosa as a cause of phoma stem canker epidemics on oilseed rape

BACKGROUND: Phoma stem canker is a damaging disease of oilseed rape caused by two related fungal species, Leptosphaeria maculans and L. biglobosa. However, previous work has mainly focused on L. maculans and there has been little work on L. biglobosa. This work provides evidence of the importance of L. biglobosa to stem canker epidemics in the UK. RESULTS: Quantification of L. maculans and L. biglobosa DNA using species-specific quantitative PCR showed that L. biglobosa caused both upper stem lesions and stem base cankers on nine oilseed rape cultivars in the UK. Upper stem lesions were mainly caused by L. biglobosa. For stem base cankers, there was more L. maculans DNA than L. biglobosa DNA in the susceptible cultivar Drakkar, while there was more L. biglobosa DNA than L. maculans DNA in cultivars with the resistance gene Rlm7 against L. maculans. The frequency of L. biglobosa detected in stem base cankers increased from 14% in 2000 to 95% in 2013. Ascospores of L. biglobosa and L. maculans were mostly released on the same days and the number of L. biglobosa ascospores in air samples increased from the 2010/2011 to 2012/2013 growing seasons. CONCLUSION: Effective control of L. maculans increased infection by L. biglobosa, causing severe upper stem lesions and stem base cankers, leading to yield losses. The importance of L. biglobosa to phoma stem canker epidemics needs to target both L. maculans and L. biglobosa

Host pathogen interactions in relation to management of light leaf spot disease (caused by Pyrenopeziza brassicae) on Brassica species

Light leaf spot, caused by Pyrenopeziza brassicae, is currently the most damaging disease problem in oilseed rape in the UK. According to recent survey data, the severity of epidemics has increased progressively across the UK, with current yield losses of up to £160M per annum in England and more severe epidemics in Scotland. Light leaf spot is a polycyclic disease with primary inoculum consisting of air-borne ascospores produced on diseased debris from the previous cropping season. Splash-dispersed conidia produced on diseased leaves are the main component of the secondary inoculum. P. brassicae is also able to infect and cause considerable yield losses on vegetable brassicas, especially Brussels sprouts. There may be spread of light leaf spot among different brassica species. Since they have a wide host range, Pyrenopeziza brassicae populations are likely to have considerable genetic diversity and there is evidence suggesting population variations between different regions, which need further study. Available disease-management tools are not sufficient to provide adequate control of the disease. There is a need to identify new sources of resistance, which can be integrated with fungicide applications to achieve sustainable management of light leaf spot. Several major resistance genes and quantitative trait loci have been identified in previous studies, but rapid improvements in the understanding of molecular mechanisms underpinning B. napus – P. brassicae interactions can be expected through exploitation of novel genetic and genomic information for brassicas and extracellular fungal pathogens.Peer reviewe

Calix[4]arene-Based Porous Organic Nanosheets

Copyright © 2018 American Chemical Society. Calixarenes are a common motif in supramolecular chemistry but have rarely been incorporated in structurally well-defined covalent 2D materials. Such a task is challenging, especially without a template, because of the nonplanar configuration and conformational flexibility of the calixarene ring. Here, we report the first-of-a-kind solvothermal synthesis of a calix[4]arene-based 2D polymer (CX4-NS) that is porous, covalent, and isolated as few-layer thick (3.52 nm) nanosheets. Experimental and theoretical characterization of the nanosheets is presented. Atomic force microscopy and transmission electron microscopy results are consistent with the calculated lowest energy state of the polymer. In the lowest energy state, parallel layers are tightly packed, and the calixarenes adopt the 1,2-alternate conformation, which gives rise to a two-dimensional pattern and a rhombic unit cell. We tested the material's ability to adsorb I2 vapor and observed a maximum capacity of 114 wt %. Molecular simulations extended to model I2 capture showed excellent agreement with experiments. Furthermore, the material was easily regenerated by mild ethanol washings and could be reused with minimal loss of efficiency

DeepIaC: Deep learning-based linguistic anti-pattern detection in IaC

Linguistic anti-patterns are recurring poor practices concerning inconsistencies among the naming, documentation, and implementation of an entity. They impede readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in infrastructure as code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their names. To this end, we propose a novel automated approach that employs word embeddings and deep learning techniques. We build and use the abstract syntax tree of IaC code units to create their code embedments. Our experiments with a dataset systematically extracted from open source repositories show that our approach yields an accuracy between 0.785 and 0.915 in detecting inconsistencies

Carbenylative Amination and Alkylation of Vinyl Iodides via Palladium Alkylidene Intermediates

Most palladium-catalyzed reactions involving insertion of alkylidenes with α-hydrogens undergo β-hydride elimination from alkylpalladium­(II) intermediates to form alkenes. Vinyl iodides were shown to generate η³-allylpalladium intermediates that resist β-hydride elimination, preserving the sp³ center adjacent to the carbene moiety. Acyclic stereocontrol (<i>syn</i>/<i>anti</i>) for carbenylative amination and alkylation reactions was low, suggesting a lack of control in the migratory insertion step. Highly hindered carbene precursors inexplicably led to formation of <i>Z</i>-alkenes with high levels of stereocontrol