Authors: Bellamy et. al. Published In: Rangeland Journal
The introduction starts out auspiciously from the perspective of an HM practitioner:
“Improvement in land management practices has been identified as the most significant factor needed to achieve sustainable agriculture ideals. However, the lack of feedback mechanisms to alert producers to problems that may arise from their actions or inactions, and of strategies to deal with them within the time-frame of of on-farm decision making, are considered to represent critical barriers to the adoption of more sustainable practices.”
In so many ways, this is what HRM is designed to do; the development of the holisticgoal and the importance of monitoring and modifying management towards the achievement of that goal fills precisely the need described in the previous quotation.
The authors choose to emphasize the complex variability in abiotic and biotic factors within oftentimes large paddocks on the Australian landscape. This complexity results in a matrix of under and overutilized resources within a single paddock. Conceptually, at least, Holistic Planned Grazing (HPG) should be able to deal with this complexity.
In contrast, the authors’ solution to this problem is described as a Decision Support System, specifically that of Landassess DSS. The focus is threefold: a key understanding of ecosystem processes, the identification of early warning indicators, the availability of effective tools to evaluate management options.
The paper then goes on to describe the development of a DSS based on these criteria. The DSS is a undoubtedly a software-based approach, using complex modeling, database, knowledge management and GIS agents to assist in the management and decision-making framework. This framework is compelling and by no means mutually exclusive with HM and HPG; on the contrary, the system could be used to factor in elements that are critical to the HM framework, like grazing planning, managed stock-densities, land health monitoring, and precise control over paddock recovery periods.
In this case, the DSS was used to develop a complex management model for the following factors:
- Land units (paddocks)
- System states-state tranisitions to classify paddock conditions under 5 category classifications
- Soil erosion risk
- Regression models for predicting pasture production
- Animal production
- Preferences for pasture condition, which allowed for the assignation of stocking rates
- Economic model to extrapolate value from predicted animal production
Ultimately, what the DSS allows the land managers to do is to run through a series of “What If” scenarios; these scenarios will then predict a number of interesting outputs: animal live weight gain, pasture production, soil erosion risk, and paddock gross margins. The article presents a couple of real world examples for the application of their modeling software.
The author’s themselves acknowledge that these types of models depend highly on the quality of the data and the assumptions on which they are built. Little is offered in terms of a detailed evaluation of their modeling software. Keep in mind, as well, that this paper was published 12 years ago; surely much progress has been made since that time. Questions abound, for example: How well does it predict things like pasture degradation and/or gross margins in the real world?
For the purposes of HMI, a real case could be made to engage researchers involved in these types of modelling and decision support framework efforts. By taking elements of the HM framework and incorporating them into these powerful modelling tools, HRM could experience an upgrade that allows it to deal with complexity several orders of magnitude beyond current capacity. To be sure, complex models have little value for small and medium scale ranches; but government agencies and large land holders stand to benefit greatly. This is, moreover, a powerful way to “institutionalize” HRM at very large scale landscape levels.
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