Sustainable turfgrass management model

With the range of equipment, materials and technologies as well as the time pressures on staff, and overall staffing levels, an interactive model which focusses on turfcultural outcomes will certainly aid in decision making for a groundsman or manager of turfgrass surfaces.

I suppose an initial question could be ‘Why bother?’
If someone is happy to continue as before, without giving much consideration to whether they are using their (and their employer’s) resources as effectively and efficiently as possible, and if budgets are of little concern (even for elite clubs this is still an important factor that grounds managers will need to consider – budgets are not unlimited) then I suppose they might not be bothered. Added to this I would also include their education and training and more importantly the subsequently learning that has taken place over their career is probably limited and there is an element of being exposed.

What should ideally occur is that there is an acknowledgement of areas for improvement and such an interactive model could be used to considerably assist in addressing these.

What is a model?
This in itself is an engaging question. A good description for a model is that it provides “abstract representations of the form and functioning of systems” (de Neufville and Stafford, p.257).

A particular requirement of a model is that it should reduce complexity and promote understanding, providing a learning experience that leads to reflection and further investigation (Pearson & Ison, p.14)

What I’m aiming to produce is a detailed representation of a turfgrass system which can be used to plan maintenance activities in achieving defined performance outcomes. The model will need to factor in the purpose of each activity. For example, is the purpose of fertiliser application to increase grass growth (not really) or is it to help develop a hard wearing sward (definitely). Clearly to create the latter there is going to be grass growth but the focus is really on ‘development’ and ‘hard wearing’ which is different from a specific aim of encouraging grass growth per se.

An important outcome of the project is that the model will be deployed online.

What impact will activities and associated soil environmental data also have on root growth, thatch development and soil bulk density in particular? These are all factors that an informed turf manager will want to know when considering any maintenance and management programme. How is the carrying capacity of the surface affected by the management practices undertaken? How is the quality of user experience affected by the range of variables? It would certainly be useful if these types of questions could all be answered, or at least have a high probability of accuracy, by such a model.

Three major types of models are identified by de Neufville and Stafford (p.261):
1. naive model – basic, to help develop ideas further and to demonstrate a concept;
2. simple correlative forecasting model – for basic forecasting based on some interconnected variables;
3. causal models based upon a priori understanding of a system – providing insight to the issue being investigated by factoring in cause and effect outcomes allowing for a more predictive approach to modelling to occur.

Background information

The complexity of the task to devise a sustainable turfgrass management (causal) model is not to be underestimated, however, this does not mean it is not something which should not be attempted. I expect a lot will be learnt as part of the ongoing development, and later on the refinement, of the model.

Modelling grass growth has proven challenging. A model which aims to incorporate a wide range of core technical data which a turf manager needs to know to most effectively and efficiently manage a turfgrass surface will present many more challenges, but will be of considerable value to managers and providers in particular.

I’ve trawled through a wide range of literature (and clearly there will be a lot more I haven’t looked at, but I’m aiming to get a reasonable overview not a definitive one due to time limitations) and have selected several (see the bibliography) which appear to cover different aspects of what I am looking at and which could assist in providing relevant insight into developing such a model. I’ll expand on this as I delve further into the concept.

To start with identifying some of the requirements for the model I drew up a chart which listed a number of core features which would need to be considered within a turf management system and then identified a selection of turf maintenance activities and provided a rating for how much of an impact the activities might have on the core features. I also provided an indication of the potential control a turf manager might have on the core features. I hadn’t included enhanced lighting, enriched CO2 or undersoil heating as potential ways in which a manager can influence the core features, but this is something that will be included as I progress the model.

Core features, activities and potential control by a turf manager

Core features, activities and potential control by a turf manager

For a model to reflect sustainability I will need to carry out an analysis of each activity (i.e. a function) and have included an example of an initial systems analysis of the mowing activity. Identifying the inputs needed to carry out the function, the constraints that might be imposed on it, the resources needed to achieve the activity and then the outputs from the activity. I’m thinking that each of these analyses will form a sub-set of the main model.

Mowing System Analysis

Mowing System Analysis

To help me visualise the concept further, but only at the ‘naive’ stage, I then created a simple spreadsheet which started to look at the core features, provided a monthly rating (1.0 to 0.0) for how the feature might influence a system. The graph that is illustrated shows how the variables influence the grass growth – pretty juvenile data analysis really as it is quite linear in calculation, but the point of the spreadsheet was to visualise theoretical impacts.

Concept Model Version 1

Concept Model Version 1

There is a wealth of research data available for the management of turfgrass surfaces but interpreting it so the outcomes of the research can be transferred to an holistic model is going to be an interesting learning journey.

I’ll be posting updates on my progress, no doubt highlighting the frustrations and limitations of the project. Early days, but I think a good start has been made.

Bibliography:

de Neufville, R. & Stafford, J.H. (1971), ‘System analysis for engineers and managers’, McGraw-Hill,

Maynard Smith, J. (1974), ‘Models in Ecology’, Cambridge University Press

Innis, G.S. (Ed), (1978), ‘ Grassland Simulation Model’, Springer-Verlag

I. R. JOHNSON, T. E. AMEZIANE and J. H. M. THORNLEY (1983), ‘A Model of Grass Growth’ Ann. Bot. 51, 599-609,

Pearson, C.J. & Ison, R.L. (1997), ‘Agronomy of Grassland Systems (2nd Edn)’, Cambridge University Press

Luo, Y. & Mooney, H.A. (1999), ‘Carbon Dioxide and Environmental Stress’, Academic Press

Michel Lafarge, Pierre Loiseau, (2002), ‘Tiller density and stand structure of tall fescue swards
differing in age and nitrogen level’, European Journal of Agronomy 17, 209–219

Michel Lafarge, Claude Mazel, David R.C. Hill, (2005), ‘A modelling of the tillering capable of reproducing the fine-scale horizontal heterogeneity of a pure grass sward and its dynamics’, Ecological Modelling 183, 125–141

YIQI LUO, DIETER GERTEN, GUERRIC LE MAIRE, WILLIAM J. PARTON, ENSHENG WENG, XUHUI ZHOU, CINDY KEOUGH, CLAUS BEIER, PHILIPPE CIAIS, WOLFGANG CRAMER,
JEFFREY S. DUKES, BRIDGET EMMETT, PAUL J. HANSON, ALAN KNAPP, SUNE LINDER, DAN NEPSTAD and LINDSEY RUSTAD, (2008), ‘Modeled interactive effects of precipitation, temperature, and [CO2] on ecosystem carbon and water dynamics in different climatic zones’, Global Change Biology 14, 1–14

M. Duru, M. Adam, P. Cruz, G. Martin, P. Ansquer, C. Ducourtieux, C. Jouany,  J.P. Theau, J. Viegas (2009) ‘Modelling above-ground herbage mass for a wide range of  grassland community types’, Ecological Modelling 220, 209–225

Cristina Hurtado-Uria, Deirdre Hennessy, Laurence Shalloo,  Declan O’Connor & Luc Delaby (2013) Relationships between meteorological data and  grass growth over time in the south of Ireland, Irish Geography, 46:3, 175-201, DOI: 10.1080/00750778.2013.865364

DAVID S. LEBAUER, DAN WANG, KATHERINE T. RICHTER, CARL C. DAVIDSON, AND MICHAEL C. DIETZE, (2013), ‘Facilitating feedbacks between field measurements  and ecosystem models’, Ecological Monographs, 83(2), pp. 133–154

Susanne Rolinski, Christoph Müller, Jens Heinke, Isabelle Weindl, Anne Biewald, Benjamin Leon Bodirsky, Alberte Bondeau, Eltje R. Boons-Prins, Alexander F. Bouwman, Peter A. Leffelaar,  Johnny A. te Roller, Sibyll Schaphoff, and Kirsten Thonicke, (2017), ‘Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6’, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2017-26

Chris Gray, 9th March, 2017