Sampling frequency affects estimates of annual nitrous oxide fluxes

Quantifying nitrous oxide (N(2)O) fluxes, a potent greenhouse gas, from soils is necessary to improve our knowledge of terrestrial N(2)O losses. Developing universal sampling frequencies for calculating annual N(2)O fluxes is difficult, as fluxes are renowned for their high temporal variability. We demonstrate daily sampling was largely required to achieve annual N(2)O fluxes within 10% of the best estimate for 28 annual datasets collected from three continents, Australia, Europe and Asia. Decreasing the regularity of measurements either under- or overestimated annual N(2)O fluxes, with a maximum overestimation of 935%. Measurement frequency was lowered using a sampling strategy based on environmental factors known to affect temporal variability, but still required sampling more than once a week. Consequently, uncertainty in current global terrestrial N(2)O budgets associated with the upscaling of field-based datasets can be decreased significantly using adequate sampling frequencies.

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.

Nitrification (DMPP) and urease (NBPT) inhibitors had no effect on pasture yield, nitrous oxide emissions nor nitrate leaching under irrigation in a hot-dry climate

Modern dairy farming in Australia relies on substantial inputs of fertiliser nitrogen (N) to underpin economic production. However, N lost from dairy systems represents an opportunity cost and can pose a number of environmental risks. Nitrogen cycle inhibitors can be co-applied with N fertilisers to slow the conversion of urea to NH4+ to reduce losses via volatilisation, and slow the conversion of NH4+ to NO3- to minimize leaching of NO3- and gaseous losses via nitrification and denitrification. In a field campaign in a high input ryegrass-kikuyu pasture system we compared the soil N pools, losses and pasture production between a) urea coated with the nitrification inhibitor (3,4-dimethyl pyrazole phosphate - DMPP) b) urea coated with the urease inhibitor (N-(n-butyl) thiophosphoric triamide - NBPT) and c) standard urea. There was no treatment effect (P>0.05) on soil mineral N, pasture yield, N2O flux nor leaching of NO3- cf. standard urea. We hypothesise that at our site, because gaseous losses were highly episodic (rainfall was erratic and displayed no seasonal rainfall nor soil wetting pattern) that there was a lack of coincidence of N application and conditions conducive to gaseous losses, thus the effectiveness of the inhibitor products was minimal and did not result in an increase in pasture yield. There remains a paucity of knowledge on N cycle inhibitors in relation to their effective use in field system to increase N use efficiency. Further research is required to define under what field conditions inhibitor products are effective in order to be able to provide accurate advice to managers of nitrogen in production systems.

Development of salinity tolerance in rice by constitutive-overexpression of genes involved in the regulation of programmed cell death

Environmental factors contribute to over 70% of crop yield losses worldwide. Of these drought and salinity are the most significant causes of crop yield reduction. Rice is an important staple crop that feeds more than half of the world’s population. However among the agronomically important cereals rice is the most sensitive to salinity. In the present study we show that exogenous expression of anti-apoptotic genes from diverse origins, AtBAG4 (Arabidopsis), Hsp70 (Citrus tristeza virus) and p35 (Baculovirus), significantly improves salinity tolerance in rice at the whole plant level. Physiological, biochemical and agronomical analyses of transgenic rice expressing each of the anti-apoptotic genes subjected to salinity treatment demonstrated traits associated with tolerant varieties including, improved photosynthesis, membrane integrity, ion and ROS maintenance systems, growth rate, and yield components. Moreover, FTIR analysis showed that the chemical composition of salinity-treated transgenic plants is reminiscent of non-treated, unstressed controls. In contrast, wild type and vector control plants displayed hallmark features of stress, including pectin degradation upon subjection to salinity treatment. Interestingly, despite their diverse origins, transgenic plants expressing the anti-apoptotic genes assessed in this study displayed similar physiological and biochemical characteristics during salinity treatment thus providing further evidence that cell death pathways are conserved across broad evolutionary kingdoms. Our results reveal that anti-apoptotic genes facilitate maintenance of metabolic activity at the whole plant level to create favorable conditions for cellular survival. It is these conditions that are crucial and conducive to the plants ability to tolerate/adapt to extreme environments.

Engineered protease inhibitors based on sunflower trypsin inhibitor-1 (SFTI-1) provide insights into the role of sequence and conformation in Laskowski mechanism inhibition

Laskowski inhibitors regulate serine proteases by an intriguing mode of action that involves deceiving the protease into synthesizing a peptide bond. Studies exploring naturally occurring Laskowski inhibitors have uncovered several structural features that convey the inhibitor's resistance to hydrolysis and exceptional binding affinity. However, in the context of Laskowski inhibitor engineering, the way that various modifications intended to fine-tune an inhibitor's potency and selectivity impact on its association and dissociation rates remains unclear. This information is important as Laskowski inhibitors are becoming increasingly used as design templates to develop new protease inhibitors for pharmaceutical applications. In this study, we used the cyclic peptide, sunflower trypsin inhibitor-1 (SFTI-1), as a model system to explore how the inhibitor's sequence and structure relate to its binding kinetics and function. Using enzyme assays, MD simulations and NMR spectroscopy to study SFTI variants with diverse sequence and backbone modifications, we show that the geometry of the binding loop mainly influences the inhibitor's potency by modulating the association rate, such that variants lacking a favourable conformation show dramatic losses in activity. Additionally, we show that the inhibitor's sequence (including both the binding loop and its scaffolding) influences its potency and selectivity by modulating both the association and the dissociation rates. These findings provide new insights into protease inhibitor function and design that we apply by engineering novel inhibitors for classical serine proteases, trypsin and chymotrypsin and two kallikrein-related peptidases (KLK5 and KLK14) that are implicated in various cancers and skin diseases.

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.

Action regulation across the adult lifespan (ARAL): A metatheory of work and aging

The goals of this article are to integrate action regulation theory (ART) with the lifespan developmental perspective and to outline tenets of a new metatheory of work and aging. The action regulation across the adult lifespan (ARAL) theory explains how workers influence, and are influenced by, their environment across different time spans. First, the basic concepts of ART are described, including the sequential and hierarchical structure of actions, complete tasks and actions, foci of action regulation, and the action-regulating mental model. Second, principles of the lifespan developmental perspective are delineated, including development as a lifelong and multidirectional process, the joint occurrence of gains and losses, intraindividual plasticity, historical embeddedness, and contextualism. Third, propositions of ARAL theory are derived by analyzing workers’ action regulation from a lifespan developmental perspective (i.e., effects of aging on action regulation), and by analyzing aging and development in the work context from an ART perspective (i.e., effects of action regulation on age-related changes in cognition and personality). Fourth, we develop further propositions to integrate ART with lifespan theories of motivation and socioemotional experience. Finally, we discuss implications for future research and practice based on ARAL theory.