Boosting the productivity and profitability of northern Australian beef enterprises: Exploring innovation options using simulation modelling and systems analysis

The financial health of beef cattle enterprises in northern Australia has declined markedly over the last decade due to an escalation in production and marketing costs and a real decline in beef prices. Historically, gains in animal productivity have offset the effect of declining terms of trade on farm incomes. This raises the question of whether future productivity improvements can remain a key path for lifting enterprise profitability sufficient to ensure that the industry remains economically viable over the longer term. The key objective of this study was to assess the production and financial implications for north Australian beef enterprises of a range of technology interventions (development scenarios), including genetic gain in cattle, nutrient supplementation, and alteration of the feed base through introduced pastures and forage crops, across a variety of natural environments. To achieve this objective a beef systems model was developed that is capable of simulating livestock production at the enterprise level, including reproduction, growth and mortality, based on energy and protein supply from natural C4 pastures that are subject to high inter-annual climate variability. Comparisons between simulation outputs and enterprise performance data in three case study regions suggested that the simulation model (the Northern Australia Beef Systems Analyser) can adequately represent the performance beef cattle enterprises in northern Australia. Testing of a range of development scenarios suggested that the application of individual technologies can substantially lift productivity and profitability, especially where the entire feedbase was altered through legume augmentation. The simultaneous implementation of multiple technologies that provide benefits to different aspects of animal productivity resulted in the greatest increases in cattle productivity and enterprise profitability, with projected weaning rates increasing by 25%, liveweight gain by 40% and net profit by 150% above current baseline levels, although gains of this magnitude might not necessarily be realised in practice. While there were slight increases in total methane output from these development scenarios, the methane emissions per kg of beef produced were reduced by 20% in scenarios with higher productivity gain. Combinations of technologies or innovative practices applied in a systematic and integrated fashion thus offer scope for providing the productivity and profitability gains necessary to maintain viable beef enterprises in northern Australia into the future.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

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 methodology 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 this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling 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 clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Scheduling techniques to optimise sugarcane rail transport systems

In Australia, railway systems play a vital role in transporting the sugarcane crop from farms to mills. In this paper, a novel job shop approach is proposed to create a more efficient integrated harvesting and sugarcane transport scheduling system to reduce the cost of sugarcane transport. There are several benefits that can be attained by treating the train scheduling problem as a job shop problem. Job shop is generic and suitable for all trains scheduling problems. Job shop technique prevents operating two trains on one section at the same time because it considers that the section or the machine is unique. This technique is more promising to find better solutions in reasonable computation times.

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.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Integrated Sensor Systems for Vessels of Opportunity, National Oceanographic Centre, Southampton, UK, October 10-12,2006 : workshop proceedings

The use of self-contained, low-maintenance sensor systems installed on commercial vessels is becoming an important monitoring and scientific tool in many regions around the world. These systems integrate data from meteorological and water quality sensors with GPS data into a data stream that is automatically transferred from ship to shore. To begin linking some of this developing expertise, the Alliance for Coastal Technologies (ACT) and the European Coastal and Ocean Observing Technology (ECOOT) organized a workshop on this topic in Southampton, United Kingdom, October 10-12, 2006. The participants included technology users, technology developers, and shipping representatives. They collaborated to identify sensors currently employed on integrated systems, users of this data, limitations associated with these systems, and ways to overcome these limitations. The group also identified additional technologies that could be employed on future systems and examined whether standard architectures and data protocols for integrated systems should be established. Participants at the workshop defined 17 different parameters currently being measured by integrated systems. They identified that diverse user groups utilize information from these systems from resource management agencies, such as the Environmental Protection Agency (EPA), to local tourism groups and educational organizations. Among the limitations identified were instrument compatibility and interoperability, data quality control and quality assurance, and sensor calibration andlor maintenance frequency. Standardization of these integrated systems was viewed to be both advantageous and disadvantageous; while participants believed that standardization could be beneficial on many levels, they also felt that users may be hesitant to purchase a suite of instruments from a single manufacturer; and that a "plug and play" system including sensors from multiple manufactures may be difficult to achieve. A priority recommendation and conclusion for the general integrated sensor system community was to provide vessel operators with real-time access to relevant data (e.g., ambient temperature and salinity to increase efficiency of water treatment systems and meteorological data for increased vessel safety and operating efficiency) for broader system value. Simplified data displays are also required for education and public outreach/awareness. Other key recommendations were to encourage the use of integrated sensor packages within observing systems such as 100s and EuroGOOS, identify additional customers of sensor system data, and publish results of previous work in peer-reviewed journals to increase agency and scientific awareness and confidence in the technology. Priority recommendations and conclusions for ACT entailed highlighting the value of integrated sensor systems for vessels of opportunity through articles in the popular press, and marine science. [PDF contains 28 pages]

A proposed integrated livestock-rice-poultry-cum-fish culture in enclosure system

Integrated agriculture-cum-fish farming has been practised profitably for ages in the Chinese small-scale farming system. There is a great potential for this system by utilizing the vast Nigerian flood plains (approx. 515,000 ha). Dogongari Bay in Lake Kainji Basin was identified as a suitable site for this system after some extensive fish culture trials. Polyculture of Clarias spp., Heterotis niloticus and Tilapia was proposed for integration with layers in the poultry house, 2-ha upland rain-fed rice farming and indirect cattle rearing in the 5-ha enclosure site. Cost benefit analysis showed that the system will consistently record profit as from the second year of operation. Various complex factors were identified to affect profitability of this mixed farming system. Concerted research approach is needed to fully understand the interrelationships of the various components of this integrated system. Generous funding of research activities is very crucial in this situation

Commercial aquaculture in Nigeria: a vertically integrated approach in the commercial freshwater farming of tilapia in floating bamboo net-hapas and cages systems

Tilapia once termed "poor man's" fish, still remains as the highly-priced food fish in many developing countries. The good attributes of this fish prompt its use in intensive aquaculture vertically integrated systems (VIS) which embrace broodstock development, hatchery/nursery and growout phase. Based on the series of studies carried out at Kainji Lake Research Institute, in New Bussa, Nigeria using Oreochromis (Tilapia niloticus) in floating bamboo hapas/cages, the recommended intensive modular systems were estimated to be capable of producing 4 million Tilapia fingerlings and 729 tons fish (Market-size) annually. Cost-benefit analysis showed the venture to have high prospects. It is recommended that priority be given to Tilapia cage culture at the national level in order to contribute immensely towards increased fish production

An integrated design approach to power systems : from power flows to electricity markets

Power system is at the brink of change. Engineering needs, economic forces and environmental factors are the main drivers of this change. The vision is to build a smart electrical grid and a smarter market mechanism around it to fulfill mandates on clean energy. Looking at engineering and economic issues in isolation is no longer an option today; it needs an integrated design approach. In this thesis, I shall revisit some of the classical questions on the engineering operation of power systems that deals with the nonconvexity of power flow equations. Then I shall explore some issues of the interaction of these power flow equations on the electricity markets to address the fundamental issue of market power in a deregulated market environment. Finally, motivated by the emergence of new storage technologies, I present an interesting result on the investment decision problem of placing storage over a power network. The goal of this study is to demonstrate that modern optimization and game theory can provide unique insights into this complex system. Some of the ideas carry over to applications beyond power systems.