Impact of a change in tillage and crop residue management practice on soil chemical and microbiological properties in a cereal-producing red duplex soil in NSW, Australia

The effect of a change of tillage and crop residue management practice on the chemical and micro-biological properties of a cereal-producing red duplex soil was investigated by superimposing each of three management practices (CC: conventional cultivation, stubble burnt, crop conventionally sown; DD: direct-drilling, stubble retained, no cultivation, crop direct-drilled; SI: stubble incorporated with a single cultivation, crop conventionally sown), for a 3-year period on plots previously managed with each of the same three practices for 14 years. A change from DD to CC or SI practice resulted in a significant decline, in the top 0-5 cm of soil, in organic C, total N, electrical conductivity, NH4-N, NO3-N, soil moisture holding capacity, microbial biomass and CO2 respiration as well as a decline in the microbial quotient (the ratio of microbial biomass C to organic C; P <0.05). In contrast, a change from SI to DD or CC practice or a change from CC to DD or SI practice had only negligible impact on soil chemical properties (P >0.05). However, there was a significant increase in microbial biomass and the microbial quotient in the top 0-5 cm of soil following the change from CC to DD or SI practice and with the change from SI to DD practice (P <0.05). Analysis of ester-linked fatty acid methyl esters (EL-FAMEs) extracted from the 0- to 5-cm and 5- to 10-cm layers of the soils of the various treatments detected changes in the FAME profiles following a change in tillage practice. A change from DD practice to SI or CC practice was associated with a significant decline in the ratio of fungal to bacterial fatty acids in the 0- to 5-cm soil (P <0.05). The results show that a change in tillage practice, particularly the cultivation of a previously minimum-tilled (direct-drilled) soil, will result in significant changes in soil chemical and microbiological properties within a 3-year period. They also show that soil microbiological properties are sensitive indicators of a change in tillage practice.

Mechanisms of mono- and poly-ubiquitination : ubiquitination specificity depends on compatibility between the E2 catalytic core and amino acid residues proximal to the lysine

Ubiquitination involves the attachment of ubiquitin to lysine residues on substrate proteins or itself, which can result in protein monoubiquitination or polyubiquitination. Ubiquitin attachment to different lysine residues can generate diverse substrate-ubiquitin structures, targeting proteins to different fates. The mechanisms of lysine selection are not well understood. Ubiquitination by the largest group of E3 ligases, the RING-family E3 s, is catalyzed through co-operation between the non-catalytic ubiquitin-ligase (E3) and the ubiquitin-conjugating enzyme (E2), where the RING E3 binds the substrate and the E2 catalyzes ubiquitin transfer. Previous studies suggest that ubiquitination sites are selected by E3-mediated positioning of the lysine toward the E2 active site. Ultimately, at a catalytic level, ubiquitination of lysine residues within the substrate or ubiquitin occurs by nucleophilic attack of the lysine residue on the thioester bond linking the E2 catalytic cysteine to ubiquitin. One of the best studied RING E3/ E2 complexes is the Skp1/Cul1/F box protein complex, SCFCdc4, and its cognate E2, Cdc34, which target the CDK inhibitor Sic1 for K48-linked polyubiquitination, leading to its proteasomal degradation. Our recent studies of this model system demonstrated that residues surrounding Sic1 lysines or lysine 48 in ubiquitin are critical for ubiquitination. This sequence-dependence is linked to evolutionarily conserved key residues in the catalytic region of Cdc34 and can determine if Sic1 is mono- or poly-ubiquitinated. Our studies indicate that amino acid determinants in the Cdc34 catalytic region and their compatibility to those surrounding acceptor lysine residues play important roles in lysine selection. This may represent a general mechanism in directing the mode of ubiquitination in E2 s.

A study of the effects metal residues in poly(9,9-dioctylfluorene) have on field-effect transistor device characteristics

The amount of metal residues from organometallic reagents used in preparation of poly(9,9-dioctylfluorene) by palladium catalysed Suzuki and nickel-induced Yamamoto polycondensations have been determined, and their effect upon the behaviour of the polymer in field-effect transistors (FETs) has been measured. The metal levels from material polymerised by Suzuki method were found to be much higher than from that made by the Yamamoto procedure. Simple treatment of the polymers with suitable metal trapping reagents lowered the metal levels significantly, with EDTA giving best results for nickel and triphenylphosphine for palladium. Comparison of the behaviour of FETs using polyfluorenes with varying levels of metal contamination, showed that the metal residues have little effect upon the mobility values, but often affect the degree of hysteresis, possibly acting as charge traps. Satisfactory device performances were obtained from polymer with palladium levels of 2000 μg/g suggesting that complete removal of metal residues may not be necessary for satisfactory device performance.

A comparative analysis of relevant crop carbon footprint calculators, with reference to cotton production in Australia

An increasing concern over the sustainability credentials of food and fiber crops require that farmers and their supply chain partners have access to appropriate and industry-friendly tools to be able to measure and improve the outcomes. This article focuses on one of the sustainability indicators, namely, greenhouse gas (GHG) emissions, and nine internationally accredited carbon footprint calculators were identified and compared on an outcomes basis against the same cropping data from a case study cotton farm. The purpose of this article is to identify the most “appropriate” methodology to be applied by cotton suppliers in this regard. From the analysis of the results, we subsequently propose a new integrated model as the basis for an internationally accredited carbon footprint tool for cotton and show how the model can be applied to evaluate the emission outcomes of different farming practices.

Calcium phosphate flocs and the clarification of sugar cane juice from whole of crop harvesting

Sugar cane biomass is one of the most viable feedstocks for the production of renewable fuels and chemicals. Therefore, processing the whole of crop (WC) (i.e., stalk and trash, instead of stalk only) will increase the amount of available biomass for this purpose. However, effective clarification of juice expressed from WC for raw sugar manufacture is a major challenge because of the amounts and types of non-sucrose impurities (e.g., polysaccharides, inorganics, proteins, etc.) present. Calcium phosphate flocs are important during sugar cane juice clarification because they are responsible for the removal of impurities. Therefore, to gain a better understanding of the role of calcium phosphate flocs during the juice clarification process,the effects of impurities on the physicochemical properties of calcium phosphate flocs were examined using small-angle laser light scattering technique, attenuated total reflectance Fourier transformed infrared spectroscopy, and X-ray powder diffraction. Results on synthetic sugar juice solutions showed that the presence of SiO2 and Na+ ions affected floc size and floc structure. Starch and phosphate ions did not affect the floc structure; however, the former reduced the floc size, whereas the latter increased the floc size. The study revealed that high levels of Na+ ions would negatively affect the clarification process the most, as they would reduce the amount of suspended particles trapped by the flocs. A complementary study on prepared WC juice using cold and cold/intermediate liming techniques was conducted. The study demonstrated that, in comparison to the one-stage (i.e., conventional) clarification process, a two-stage clarification process using cold liming removed more polysaccharides (≤19%),proteins (≤82%), phosphorus (≤53%), and SiO2 (≤23%) in WC juice but increased Ca2+ (≤136%) and sulfur (≤200%)

Effects of sewer conditions on the degradation of selected illicit drug residues in wastewater

The stability of five illicit drug markers in wastewater was tested under different sewer conditions using laboratory-scale sewer reactors. Wastewater was spiked with deuterium labelled isotopes of cocaine, benzoyl ecgonine, methamphetamine, MDMA and 6-acetyl morphine to avoid interference from the native isotopes already present in the wastewater matrix. The sewer reactors were operated at 20 °C and pH 7.5, and wastewater was sampled at 0, 0.25, 0.5, 1, 2, 3, 6, 9 and 12 h to measure the transformation/degradation of these marker compounds. The results showed that while methamphetamine, MDMA and benzoyl ecgonine were stable in the sewer reactors, cocaine and 6-acetyl morphine degraded quickly. Their degradation rates are significantly higher than the values reportedly measured in wastewater alone (without biofilms). All the degradation processes followed first order kinetics. Benzoyl ecgonine and morphine were also formed from the degradation of cocaine and 6-acetyl morphine, respectively, with stable formation rates throughout the test. These findings suggest that, in sewage epidemiology, it is essential to have relevant information of the sewer system (i.e. type of sewer, hydraulic retention time) in order to accurately back-estimate the consumption of illicit drugs. More research is required to look into detailed sewer conditions (e.g. temperature, pH and ratio of biofilm area to wastewater volume among others) to identify their effects on the fate of illicit drug markers in sewer systems.

Legumes or nitrification inhibitors to reduce {N2O} emissions from subtropical cereal cropping systems in Oxisols?

The DAYCENT biogeochemical model was used to investigate how the use of fertilizers coated with nitrification inhibitors and the introduction of legumes in the crop rotation can affect subtropical cereal production and {N2O} emissions. The model was validated using comprehensive multi-seasonal, high-frequency dataset from two field investigations conducted on an Oxisol, which is the most common soil type in subtropical regions. Different N fertilizer rates were tested for each N management strategy and simulated under varying weather conditions. DAYCENT was able to reliably predict soil N dynamics, seasonal {N2O} emissions and crop production, although some discrepancies were observed in the treatments with low or no added N inputs and in the simulation of daily {N2O} fluxes. Simulations highlighted that the high clay content and the relatively low C levels of the Oxisol analyzed in this study limit the chances for significant amounts of N to be lost via deep leaching or denitrification. The application of urea coated with a nitrification inhibitor was the most effective strategy to minimize {N2O} emissions. This strategy however did not increase yields since the nitrification inhibitor did not substantially decrease overall N losses compared to conventional urea. Simulations indicated that replacing part of crop N requirements with N mineralized by legume residues is the most effective strategy to reduce {N2O} emissions and support cereal productivity. The results of this study show that legumes have significant potential to enhance the sustainable and profitable intensification of subtropical cereal cropping systems in Oxisols.

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.

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.