Predicting the Hardness of Turf Surfaces from a Soil Moisture Sensor Using IoT Technologies

In horseracing, “the going” is a term to describe the racetrack ground conditions. In Ireland presently, a groundskeeper or course clerk walks the racecourse poking it with a blackthorn stick, assesses conditions, and declares the going – it is a subjective measurement. This thesis will propose using remote low-cost soil moisture sensors to gather high frequency data about the soil water content in the ground and to enable informed decisions to be made. This will remove the subjective element from the ground hardness, and look at the data in an objective way. The soil moisture sensor will systematically collect high frequency data from the ground and store the data in a remote database using Internet of Things (IoT) technologies such as Message Queuing Telemetry Transport (MQTT), InfluxDB and Node-RED. The database will hold soil moisture readings, their timestamp and GPS location. From this data and data from an industry-standard Clegg hammer, the soil sensor will be automatically calibrated for the soil that it is sitting in regardless of the soil make-up, the sensor model, and the drainage of the soil. The going of the soil will also be deduced. The primary soil saturation data is fused with secondary open source weather data. Weather forecast information is gathered spanning out 3 hours, 24 hours and 5 days, and estimates can be made regarding how the ground will behave. These estimates are automatically update every 3 hours. The data will also allow decisions to be made for irrigation planning. Finally, the data will be visually displayed in real-time enabling a clear view of the soil moisture, current ground hardness, the going, rainfall and their forecasts. The system will propose how conditions will change if irrigation is applied

Considerations for the interdisciplinary development of environmental system models

Effective decision making and policy development requires holistic consideration of the modelling context. This thesis explores how consideration of multiple disciplinary perspectives and concerns lead to an integrative model development process for the purpose of socio-environmental systems (SES) management. The research is presented through two frames: (1) Integrated Environmental Model (IEM) development through a System-of-Systems (SoS) approach, and (2) the socio-technical considerations within an interdisciplinary modelling process. The presented research incorporates the perspectives of the modelling, systems engineering, and software development paradigms. IEMs are developed for the purpose of integrating knowledge across the various disciplines involved, whereas traditional approaches focus on single systems within the SES, such as hydrology, economics, social dynamics, or climatic drivers. Use of IEMs allows for the consideration of the flow-on effects due to system changes and interaction, and how these may affect long-term SES behaviour. Pathways that are robust - i.e., lead to beneficial or desirable outcomes - under a range of plausible but uncertain conditions can then be identified and assessed. An interconnected network of system models thus makes up an SoS model allowing consideration of higher-order effects. In practice, however, the decisions and approaches taken in developing constituent models may influence integrated system behaviour once coupled. The socio-technical modelling concerns within the SoS/SES modelling context, including the methods to assess and manage model validity, complexity, and uncertainty, with respect to model purpose and intended outcomes are explored through a series of publications. This thesis contributes to the growing body of knowledge through: 1. An expansive overview of the currently available software for model uncertainty and sensitivity analysis, and the techniques they encompass 2. An integrated environmental model for the Lower Campaspe catchment in North-Central Victoria, Australia. The model explores long-term implications of water management decisions and potential policy changes (primarily through an agricultural lens), including conjunctive use of surface and groundwater under a range of uncertain futures. 3. Demonstration of a property-based sensitivity analysis approach to model diagnostics that combines software testing and sensitivity analysis to validate model behaviour. The approach is useful as a first-pass screening tool. Failure to reproduce expected model behaviour indicates issues with the model to be corrected and avoids the necessity of more computationally demanding diagnostics. 4. A pragmatic step-by-step framework for the sensitivity analysis of spatially distributed environmental models 5. Exploration and discussion of the modelling practices, issues and challenges that arise when dealing with the various influences and effects of scale within the interdisciplinary SoS context through a socio-technical lens. The discussion leads to a call for a grander vision for SoS-IEM modelling (and commensurate funding) to better enable interdisciplinary, and integrative, socio-environmental research to occur. 6. A shared reflexive account of two case studies that draws out the considerations and decisions regarding scale to arrive at five shared lessons learnt to foster an effective interdisciplinary modelling process. The key conclusion is the need for researchers involved in SoS modelling of SESs to actively consider and address cross-disciplinary concerns through improved interdisciplinary communication, documentation practices, and explicit consideration of the interplay between defined scales and resulting influence on uncertainty. Integrative consideration of these would then lower or avoid barriers that hamper the development and application of integrated environmental system models