Diversity of Cicadellidae in agricultural production areas in the Ovens Valley, Northeast Victoria, Australia

There is a paucity of data on the distribution of Cicadellidae (leafhoppers) in Australia. This study quantifies the relative abundance, seasonal activity and diversity of leafhoppers in the Ovens Valley region of north-east Victoria, Australia. Species diversity and abundance was assessed at four field sites in and around the field borders of commercially grown tobacco crops using three sampling techniques (pan trap, sticky trap and sweep net). Over 51 000 leafhopper samples were collected, with 57 species from 11 subfamilies and 19 tribes identified. Greater numbers and diversity of leafhoppers were collected in yellow pan traps. The predominant leafhopper collected was Orosius orientalis (Matsumura). Twenty-three leafhopper species were recorded for the first time in Victoria and eight economically important pest species were recorded. Seasonal activity of selected leafhopper species, covering two sampling seasons, is presented.

Participatory design for technological disruption within the agricultural sector

Agricultural adoption of innovation has traditionally been described as slow to diffuse. This paper therefore describes a case study grounded in PD to address a disruptive technology/system within the livestock industry. Results of the process were positive, as active engagement of stakeholders returned rich data. The contribution of the work is also presented as grounds for further design research in the livestock industry.

How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO2 fluxes in agricultural soils?

Abstract As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5–10 years interval) are used with densities of one site per 10–1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO2 fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6–6.5 Mg C ha−1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha−1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach). Keywords Soil monitoring networks - Soil organic carbon - Modeling - Sampling design

Measuring and decomposing changes in agricultural productivity, nitrogen use efficiency and cumulative exergy efficiency: Application to OECD agriculture

This paper uses an aggregate quantity space to decompose the temporal changes in nitrogen use efficiency and cumulative exergy use efficiency into changes of Moorsteen–Bjurek (MB) Total Factor Productivity (TFP) changes and changes in the aggregate nitrogen and cumulative exergy contents. Changes in productivity can be broken into technical change and changes in various efficiency measures such as technical efficiency, scale efficiency and residual mix efficiency. Changes in the aggregate nitrogen and cumulative exergy contents can be driven by changes in the quality of inputs and outputs and changes in the mixes of inputs and outputs. Also with cumulative exergy content analysis, changes in the efficiency in input production can increase or decrease the cumulative exergy transformity of agricultural production. The empirical study in 30 member countries of the Organisation for Economic Co-operation Development from 1990 to 2003 yielded some important findings. The production technology progressed but there were reductions in technical efficiency, scale efficiency and residual mix efficiency levels. This result suggests that the production frontier had shifted up but there existed lags in the responses of member countries to the technological change. Given TFP growth, improvements in nutrient use efficiency and cumulative exergy use efficiency were counteracted by reductions in the changes of the aggregate nitrogen contents ratio and aggregate cumulative exergy contents ratio. The empirical results also confirmed that different combinations of inputs and outputs as well as the quality of inputs and outputs could have more influence on the growth of nutrient and cumulative exergy use efficiency than factors that had driven productivity change. Keywords: Nutrient use efficiency; Cumulative exergy use efficiency; Thermodynamic efficiency change; Productivity growth; OECD agriculture; Sustainability

Measuring and monitoring soil organic carbon stocks in agricultural lands for climate mitigation

Policies that encourage greenhouse-gas emitters to mitigate emissions through terrestrial carbon (C) offsets – C sequestration in soils or biomass – will promote practices that reduce erosion and build soil fertility, while fostering adaptation to climate change, agricultural development, and rehabilitation of degraded soils. However none of these benefits will be possible until changes in C stocks can be documented accurately and cost-effectively. This is particularly challenging when dealing with changes in soil organic C (SOC) stocks. Precise methods for measuring C in soil samples are well established, but spatial variability in the factors that determine SOC stocks makes it difficult to document change. Widespread interest in the benefits of SOC sequestration has brought this issue to the fore in the development of US and international climate policy. Here, we review the challenges to documenting changes in SOC stocks, how policy decisions influence offset documentation requirements, and the benefits and drawbacks of different sampling strategies and extrapolation methods.

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

Agricultural biodiversity, farm level technical efficiency and conservation benefits : an empirical investigation

The issues involved in agricultural biodiversity are important and interesting areas for the application of economic theory. However, very little theoretical and empirical work has been undertaken to understand the benefits of conserving agricultural biodiversity. Accordingly, the main objectives of this PhD thesis are to: (1) Investigate farmers’ valuation of agricultural biodiversity; (2) Identify factors influencing farmers’ demand for agricultural biodiversity; (3) Examine farmers’ demand for biodiversity rich farming systems; (4) Investigate the relationship between agricultural biodiversity and farm level technical efficiency. This PhD thesis investigates these issues by using primary data in small-scale farms, along with secondary data from Sri Lanka. The overall findings of the thesis can be summarized as follows. Firstly, owing to educational and poverty issues of those being interviewed, some policy makers in developed countries question whether non-market valuation techniques such as Choice Experiment (CE) can be applied to developing countries such as Sri Lanka. The CE study in this thesis indicates that carefully designed and pre-tested nonmarket valuation techniques can be applied in developing countries with a high level of reliability. The CE findings support the priori assumption that small-scale farms and their multiple attributes contribute positively and significantly to the utility of farm families in Sri Lanka. Farmers have strong positive attitudes towards increasing agricultural biodiversity in rural areas. This suggests that these attitudes can be the basis on which appropriate policies can be introduced to improve agricultural biodiversity. Secondly, the thesis identifies the factors which influence farmers’ demand for agricultural biodiversity and farmers’ demands on biodiversity rich farming systems. As such they provide important tools for the implementation of policies designed to avoid the loss agricultural biodiversity which is shown to be a major impediment to agricultural growth and sustainable development in a number of developing countries. The results illustrate that certain key household, market and other characteristics (such as agricultural subsidies, percentage of investment of owned money and farm size) are the major determinants of demand for agricultural biodiversity on small-scale farms. The significant household characteristics that determine crop and livestock diversity include household member participation on the farm, off-farm income, shared labour, market price fluctuations and household wealth. Furthermore, it is shown that all the included market characteristics as well as agricultural subsidies are also important determinants of agricultural biodiversity. Thirdly, it is found that when the efficiency of agricultural production is measured in practice, the role of agricultural biodiversity has rarely been investigated in the literature. The results in the final section of the thesis show that crop diversity, livestock diversity and mix farming system are positively related to farm level technical efficiency. In addition to these variables education level, number of separate plots, agricultural extension service, credit access, membership of farm organization and land ownerships are significant and direct policy relevant variables in the inefficiency model. The results of the study therefore have important policy implications for conserving agricultural biodiversity in Sri Lanka.

Resources for improving journalists' understanding of the economic and financial parameters of Australia's agricultural and commodity producers

The research addresses how an understanding of the fundamentals of economics will better inform general journalists who report on issues or events affecting rural and regional Australia. The research draws on practice-based experience of the author, formal economics studies, interviews with news editors from Australian television news organisations, and interviews from leading economists. A guidebook has also been written to help journalists apply economic theories to their reporting. The guidebook enables reporters to think strategically and consider the 'big picture' when they inform society about policies, commodity trade, the environment, or any issues involving rural and regional Australia.

Productivity differences, technology adoption and economic growth : the case of India

When the acronym of ëBRICí was coined in 2001 by Jim OíNeill of Goldman Sachs, it was expected that economic growth rates in India, Brazil and Russia would eventually catch up with that of China. However, China has continued to outperform the other economies in the group, even after it was renamed ëBRICSí to reflect the inclusion of South Africa in 2010. The focus of this chapter is on one of the BRICS economies, namely India. Its aim is to examine from an economic perspective, why Indiaís performance has not lived up to expectations, and comment on the key challenges it faces in meeting them. We begin with some descriptive statistics regarding the progress of the Indian economy since 1990. While it has been growing at a rapid rate since the reforms it introduced in the1990s, there has been a slowdown in its overall GDP growth rates since 2008. The rate of growth experienced in the period 2003ñ07 was an average of 10.5 per cent. However, since the recession following the Global Financial Crisis (GFC) of 2008, the growth rate has fallen. From the period 2008ñ12 it has only registered an average growth rate of 6.5 per cent (World Bank, 2013). This chapter suggests that one of the major factors underpinning this slowdown is the performance of Indiaís agricultural sector. The importance of the agricultural sector is highlighted by the following stylized facts.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

Developments in mud filtration technology in the sugarcane industry

Dirt collected with sugarcane is processed and separated from the juice in the sugar factory by filtration equipment for return to the cane fields. New technologies over the past decade have enabled performance improvements to be obtained for this key unit operation. Filter mud product still contains a reasonable amount of sugar and the transportation of high moisture mud product has considerable cost. Australia’s traditional approach has been to use Rotary Vacuum Filters for processing and separating mud and other impurities from juice, but in recent years there has been interest in reducing sugar losses and transportation costs through utilisation of new technologies such as Horizontal Bed Filters, Vacuum Belt Press Filters, Membrane Press Filters and Centrifuges. Increasingly, these alternative equipment are being installed in new factories. This chapter describes the general principles of mud filtration theory and mud conditioning followed by a detailed description and review of the various filtration technologies and analysis of the relative merits associated with the equipment.

Impact of agricultural chemicals on the environment and human health in Asia: Are there solutions?

Modern commercial agricultural practices in Asia during the last three to four decades involving chemicals (fertilisers and pesticides) have been associated with large increases in food production never witnessed before, especially under the Green Revolution technology in South Asia. This also involves large-scale increases in commercial vegetable crops. However, the high reliance on chemical inputs to bring about these increases in food production is not without problems. A visible, parallel correlation between higher productivity, high artificial input use and environmental degradation and human ill-health is evident in many countries where commercial agriculture is widespread. In this chapter, we focus on the impact of chemical inputs, in particular the impact of pesticides on the environment and on human health in South Asia with special reference to Sri Lanka...

Bringing the farmer perspective to agricultural robots

The research reported in this paper explores autonomous technologies for agricultural farming application and is focused on the development of multiple-cooperative agricultural robots (AgBots). These are highly autonomous, small, lightweight, and unmanned machines that operate cooperatively (as opposed to a traditional single heavy machine) and are suited to work on broadacre land (large-scale crop operations on land parcels greater than 4,000m2). Since this is a new, and potentially disruptive technology, little is yet known about farmer attitudes towards robots, how robots might be incorporated into current farming practice, and how best to marry the capability of the robot with the work of the farmer. This paper reports preliminary insights (with a focus on farmer-robot control) gathered from field visits and contextual interviews with farmers, and contributes knowledge that will enable further work toward the design and application of agricultural robotics.