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Background and aims: Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods: In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results: Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions: The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake. [ABSTRACT FROM AUTHOR]

This study analyzes socio-economic demographics (including Geomatic conzoom® segmented demographic variables) as well as building and property registration information as risk factors in relation to the prevalence of residential building fires within 100 m × 100 m cells. The logistic regression model achieved a receiver operating curve (ROC) of 0.74 and a precision-recall curve of 0.12 on the testing dataset. The model identifies 19 significant variables related to the risk of residential fire. The top 5 highest performing variables in our model and their odds ratios are the following: number of people (OR 1.25), Multi/family residence-building type (OR 1.20), number of buildings (OR 1.18), conzoom® Type C—Country/Rural Communities (OR 0.85), construction year (OR 0.87). These results indicate that socio-economic factors play a large role in influencing fire vulnerability within residential areas and can help prioritize resource allocation to reduce fire vulnerability in the identified risk factor groups. [ABSTRACT FROM AUTHOR]

The coffee research community has maintained a long ongoing debate regarding the implications of shade trees in coffee production. Historically, there has been contrasting results and opinions on this matter, thus recommendations for the use of shade (namely in coffee agroforestry systems) are often deemed controversial, particularly due to potential yield declines and farmers' income. This study is one of the first demonstrating how several Coffea arabica cultivars respond differently to shade with respect to yield. By standardising more than 200 coffee yield data from various in-field trials, we assembled the so-called "Ristretto" data pool, a one of a kind, open-source dataset, consolidating decades of coffee yield data under shaded systems. With this standardised dataset, our meta-analysis demonstrated significant genotypic heterogeneity in response to shade, showing neutral, inverted U-shaped and decreasing trends between yield and shade cover amongst 18 different cultivars. These findings encourage the examination of C. arabica at the cultivar level when assessing suitability for agroforestry systems. Comparison of productivity is also encouraged across a range of low to moderate shade levels (10–40%), in order to help elucidate potential unknown optimal shade levels for coffee production. [ABSTRACT FROM AUTHOR]

We determined how conidia of arthropod-pathogenic fungi on leaves affected the behavior of two predators—Orius majusculus (Hemiptera: Anthocoridae) and Phytoseiulus persimilis (Acari: Phytoseiidae)—when offered a choice between preying on two-spotted spider mites, Tetranychus urticae (Acari: Tetranychidae), in the presence or absence of infective conidia of Metarhizium brunneum (Ascomycota: Hypocreales) and Neozygites floridana (Entomophthoromycota: Neozygitaceae). The results indicate no significant relation between the presence of conidia and predator behavior. The only indication of interference is between the generalists O. majusculus and M. brunneum, with a trend towards more time spent feeding and more prey encounters turning into feeding events on leaf discs without conidia than on leaf discs with conidia. Our results show that the presence of fungal conidia does not alter the preying behavior of the predators, and using predators and fungi together is not limited by any interference between organisms in the short term. [ABSTRACT FROM AUTHOR]

The theoretical underpinnings of the assessment of invasive alien species impacts need to be improved. At present most approaches are unreliable to quantify impact at regional scales and do not allow for comparison of different invasive species. There are four basic problems that need to be addressed: (1) Some impacted ecosystem traits are spatially not additive; (2) invader effects may increase non-linearly with abundance or there may be effect thresholds impairing estimates of linear impact models; (3) the abundance and impact of alien species will often co-vary with environmental variation; and (4) the total invaded range is an inappropriate measure for quantifying regional impact because the habitat area available for invasion can vary markedly among invasive species. Mathematical models and empirical data using an invasive alien plant species ( Heracleum mantegazzianum) indicate that ignoring these issues leads to impact estimates almost an order of magnitude from the real values. Thus, we propose a habitat-sensitive formula for regional impact assessment that is unaffected by non-linearity. Furthermore, we make some statistical suggestions on how to assess invader effects properly and we discuss the quantification of the invaded range. These improvements are crucial for impact assessment with the overall aim of prioritizing management of invasive species. [ABSTRACT FROM AUTHOR]