Urban agriculture : a growing field of research workshop at INTERACT 2013

Growing food presents diverse challenges and opportunities within the urban environment. As cities develop, population density rises, land prices rise, and the opportunity to use land for traditional farming and gardening diminishes. Counter to this trend there are a growing number of both community gardens, city farms, guerrilla gardening, rooftop and vertical gardens, pot plants, windowsill herbs, and other balcony or backyard gardens cropping up in different cities, all with a purpose to produce food. This workshop brings to-gether practitioners and researchers in the field of urban agriculture and Hu-man-Computer Interaction to explore and opportunities for technology design to support the different forms of growing practice and foster local food production in cities. This 1-day workshop will serve as an active forum for researchers and practi-tioners across various fields including, but not limited to, agriculture and gar-dening, education, urban planning, human-computer interaction, and communi-ty engagement. This workshop has three distinct points of focus: i) Individual and small-scale gardening and food production, and how to connect like minded people who are involved in these practices to share their knowledge ii) Com-munities involved in urban agriculture, either through community gardens, city farms, or grassroots movements, often dependant on volunteer participation, providing the challenge of managing limited resources iii) Environmental and sociocultural sustainability through urban agriculture. The participants will have an opportunity to present their own work. This will be followed by a visit to a nearby city farm, which will provide a local context for a group design exercise. Finally the workshop will conclude with panel dis-cussions to review opportunities for further research and collaborations beyond the conference. For more information, please visit the workshop website, at http://www.urbaninformatics.net/resources/interact2013cfp/

Designing a seasonality application to support urban agriculture practice

This position paper describes the work in progress towards the goal of building a technical prototype that enables users – those who have little or no knowledge and experience engaging in urban agriculture – to receive information personalised to their location and situation, and allow them to ask questions and share experiences with others. We describe the design process thus far, informed by a survey and a workshop with experts in the field, before concluding with the future direction of this work.

Semi-parametric forecasting of spikes in electricity prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

Forecasting the future risk of Barmah Forest Virus disease under climate change scenarios in Queensland, Australia

BACKGROUND Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. METHODS/PRINCIPAL FINDINGS We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. CONCLUSIONS/SIGNIFICANCE We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

Short-term traffic volume forecasting : a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm

To enhance the performance of the k-nearest neighbors approach in forecasting short-term traffic volume, this paper proposed and tested a two-step approach with the ability of forecasting multiple steps. In selecting k-nearest neighbors, a time constraint window is introduced, and then local minima of the distances between the state vectors are ranked to avoid overlappings among candidates. Moreover, to control extreme values’ undesirable impact, a novel algorithm with attractive analytical features is developed based on the principle component. The enhanced KNN method has been evaluated using the field data, and our comparison analysis shows that it outperformed the competing algorithms in most cases.

Multifunctional agriculture in policy and practice? A comparative analysis of Norway and Australia

Ideals of productivist agriculture in the Western world have faded as the unintended consequences of intensive agriculture and pastoralism have contributed to rural decline and environmental problems. In Norway and Australia, there has been an increasing acceptance of the equal importance of social and environmental sustainability as well as economic sustainability. Alongside this shift is a belief that primary production needs to move away from an intensive, productivist-based agriculture to one that may be defined as post-productivist. In this paper, we argue that the dualism of productivism and post-productivism as concepts on agricultural policy regimes are too simplistic and discuss whether multifunctional agriculture is a better concept for a comparison of rural primary production at two extreme points of the scale, the market-oriented, liberalistic Australian agriculture and the market-protected small-scale Norwegian agriculture. We argue that multifunctionality in Australia rates relatively weakly as an ideology or policy and even less as a discourse or practice and hence is situated toward a ‘weak’ end of a continuum of a level of multifunctional agriculture. In Norwegian agriculture, multifunctional agriculture has thrived within a protectionist setting with the support of the public, the state and agricultural actors. In this sense it is very clearly a policy, practice and discourse that aims to preserve and conserve rural spaces, the cultural landscape, the farming way of life and food safety. Norway is as such situated toward a ‘strong’ end of a continuum of a level of multifunctional agriculture.

The Impacts of Climate Change on Australia’s Food Production and Export

The global food system is undergoing unprecedented change. With population increases, demands for food globally will continue to rise at the same time that agricultural environments are compromised through urban encroachment, climate change and environmental degradation. Australia has long identified itself as an agricultural exporting nation—but what will its capacity be in feeding an increasing global population as it also comes to terms with extreme climatic events such as the floods, fires and droughts, and reduced water availability, experienced in recent decades? This chapter traces the history of Australian agricultural exports and evaluates its food production and export capacity against scientific predictions of climate change impacts. With the federal government forecasting declines in the production of wheat, beef, dairy and sugar, Australia’s key export commodities may well be compromised. Calls to produce more food using new technologies are likely to generate significant environmental problems. Yet, a radical reconfiguration of Australian agriculture which incorporates alternative approaches, such as agro-ecology, is rarely considered by government and industry.

Robotics for sustainable broad-acre agriculture

This paper describes the development of small low-cost cooperative robots for sustainable broad-acre agriculture to increase broad-acre crop production and reduce environmental impact. The current focus of the project is to use robotics to deal with resistant weeds, a critical problem for Australian farmers. To keep the overall system affordable our robot uses low-cost cameras and positioning sensors to perform a large scale coverage task while also avoiding obstacles. A multi-robot coordinator assigns parts of a given field to individual robots. The paper describes the modification of an electric vehicle for autonomy and experimental results from one real robot and twelve simulated robots working in coordination for approximately two hours on a 55 hectare field in Emerald Australia. Over this time the real robot 'sprayed' 6 hectares missing 2.6% and overlapping 9.7% within its assigned field partition, and successfully avoided three obstacles.

Novelty-based visual obstacle detection in agriculture

This paper describes a novel obstacle detection system for autonomous robots in agricultural field environments that uses a novelty detector to inform stereo matching. Stereo vision alone erroneously detects obstacles in environments with ambiguous appearance and ground plane such as in broad-acre crop fields with harvested crop residue. The novelty detector estimates the probability density in image descriptor space and incorporates image-space positional understanding to identify potential regions for obstacle detection using dense stereo matching. The results demonstrate that the system is able to detect obstacles typical to a farm at day and night. This system was successfully used as the sole means of obstacle detection for an autonomous robot performing a long term two hour coverage task travelling 8.5 km.

Selecting volatility forecasting models for portfolio allocation purposes

Techniques for evaluating and selecting multivariate volatility forecasts are not yet understood as well as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a set of competing forecasting models which are subsequently applied in a portfolio allocation context. It is found that a likelihood-based loss function outperforms its competitors, including those based on the given portfolio application. This result indicates that considering the particular application of forecasts is not necessarily the most effective basis on which to select models.

Modeling nitrous oxide emissions from irrigated agriculture : testing DayCent with high-frequency measurements

A unique high temporal frequency dataset from an irrigated cotton-wheat rotation was used to test the agroecosystem model DayCent to simulate daily N2O emissions from sub-tropical vertisols under different irrigation intensities. DayCent was able to simulate the effect of different irrigation intensities on N2O fluxes and yield, although it tended to overestimate seasonal fluxes during the cotton season. DayCent accurately predicted soil moisture dynamics and the timing and magnitude of high fluxes associated with fertilizer additions and irrigation events. At the daily scale we found a good correlation of predicted vs. measured N2O fluxes (r2 = 0.52), confirming that DayCent can be used to test agricultural practices for mitigating N2O emission from irrigated cropping systems. A 25 year scenario analysis indicated that N2O losses from irrigated cotton-wheat rotations on black vertisols in Australia can be substantially reduced by an optimized fertilizer and irrigation management system (i.e. frequent irrigation, avoidance of excessive fertiliser application), while sustaining maximum yield potentials.

Beyond point forecasting : evaluation of alternative prediction intervals for tourist arrivals

This paper evaluates the performances of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state space models for exponential smoothing, and Harvey’s structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and Australia. The mean coverage rates and widths of the alternative prediction intervals are evaluated in an empirical setting. It is found that all models produce satisfactory prediction intervals, except for the autoregressive model. In particular, those based on the biascorrected bootstrap perform best in general, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.

Vision based guidance for robot navigation in agriculture

This paper describes a novel vision based texture tracking method to guide autonomous vehicles in agricultural fields where the crop rows are challenging to detect. Existing methods require sufficient visual difference between the crop and soil for segmentation, or explicit knowledge of the structure of the crop rows. This method works by extracting and tracking the direction and lateral offset of the dominant parallel texture in a simulated overhead view of the scene and hence abstracts away crop-specific details such as colour, spacing and periodicity. The results demonstrate that the method is able to track crop rows across fields with extremely varied appearance during day and night. We demonstrate this method can autonomously guide a robot along the crop rows.