Predicting spring barley yield from variety-specific yield potential, disease resistance and straw length, and from environment-specific disease loads and weed pressure

Abstract For low-input crop production, well-characterised varieties increase the possibilities of managing diseases and weeds. This analysis aims at developing a framework for analyzing grain yield using external varietal information about disease resistance, weed competitiveness and yield potential and quantifying the impact of susceptibility grouping and straw length scores (as a measure for weed competitiveness) for predicting spring barley grain yield under variable biotic stress levels. The study comprised 52 spring barley varieties and 17 environments, i.e., combinations of location, growing system and year. Individual varieties and their interactions with environments were analysed by factorial regression of grain yield on external variety information combined with observed environmental disease loads and weed pressure. The external information was based on the official Danish VCU testing. The most parsimonious models explained about 50% of the yield variation among varieties including genotypeenvironment interactions. Disease resistance characteristics of varieties, weighted with disease loads of powdery mildew, leaf rust and net blotch, respectively, had a highly significant influence on grain yield. The extend to which increased susceptibility resulted in increased yield losses in environments with high disease loads of the respective diseases was predicted. The effect of externally determined straw length scores, weighted with weed pressure, was weaker although significant for weeds with creeping growth habit. Higher grain yield was thus predicted for taller plants under weed pressure. The results are discussed in relation to the model ramework, impact of the considered traits and use of information from conventional variety testing in organic cropping systems

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods