Directors and officers insurance – Recent critical issues in Australia and New Zealand

Directors and Officers Liability Insurance (“D&O insurance”) has grown and evolved rapidly over the past 80 years to assume an important position in most corporations’ corporate governance and risk management strategies. This article focuses upon certain topical matters of particular concern to directors and officers including the availability of defence costs where a D&O policy is subject to a statutory charge; the commercial desirability of stand-alone “A-side” coverage, being the cover provided directly to directors and officers for loss resulting from claims made against them for wrongful acts; the impact of fraud and/or dishonesty upon D&O cover; and disclosure of the nature and extent of D&O cover to the directors and officers themselves and to third parties – in the latter case such access frequently being necessary to determine the economic viability of pursuing a proposed action against a company and its directors and officers.

Legume pastures can reduce N2O emissions intensity in subtropical cereal cropping systems

Alternative sources of N are required to bolster subtropical cereal production without increasing N2O emissions from these agro-ecosystems. The reintroduction of legumes in cereal cropping systems is a possible strategy to reduce synthetic N inputs but elevated N2O losses have sometimes been observed after the incorporation of legume residues. However, the magnitude of these losses is highly dependent on local conditions and very little data are available for subtropical regions. The aim of this study was to assess whether, under subtropical conditions, the N mineralised from legume residues can substantially decrease the synthetic N input required by the subsequent cereal crop and reduce overall N2O emissions during the cereal cropping phase. Using a fully automated measuring system, N2O emissions were monitored in a cereal crop (sorghum) following a legume pasture and compared to the same crop in rotation with a grass pasture. Each crop rotation included a nil and a fertilised treatment to assess the N availability of the residues. The incorporation of legumes provided enough readily available N to effectively support crop development but the low labile C left by these residues is likely to have limited denitrification and therefore N2O emissions. As a result, N2O emissions intensities (kg N2O-N yield−1 ha−1) were considerably lower in the legume histories than in the grass. Overall, these findings indicate that the C supplied by the crop residue can be more important than the soil NO3− content in stimulating denitrification and that introducing a legume pasture in a subtropical cereal cropping system is a sustainable practice from both environmental and agronomic perspectives.

Trehalose accumulation triggers autophagy during plant desiccation

Global climate change, increasingly erratic weather and a burgeoning global population are significant threats to the sustainability of future crop production. There is an urgent need for the development of robust measures that enable crops to withstand the uncertainty of climate change whilst still producing maximum yields. Resurrection plants possess the unique ability to withstand desiccation for prolonged periods, can be restored upon watering and represent great potential for the development of stress tolerant crops. Here, we describe the remarkable stress characteristics of Tripogon loliiformis, an uncharacterised resurrection grass and close relative of the economically important cereals, rice, sorghum, and maize. We show that T. loliiformis survives extreme environmental stress by implementing autophagy to prevent Programmed Cell Death. Notably, we identified a novel role for trehalose in the regulation of autophagy in T.loliiformis. Transcriptome, Gas Chromatography Mass Spectrometry, immunoblotting and confocal microscopy analyses directly linked the accumulation of trehalose with the onset of autophagy in dehydrating and desiccated T. loliiformis shoots. These results were supported in vitro with the observation of autophagosomes in trehalose treated T. loliiformis leaves; autophagosomes were not detected in untreated samples. Presumably, once induced, autophagy promotes desiccation tolerance in T.loliiformis , by removal of cellular toxins to suppress programmed cell death and the recycling of nutrients to delay the onset of senescence. These findings illustrate how resurrection plants manipulate sugar metabolism to promote desiccation tolerance and may provide candidate genes that are potentially useful for the development of stress tolerant crops.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.