QUANTIFYING DEGRADATION RISK – INTEGRATING CLIMATE RISK INTO RANGELAND MANAGEMENT

This paper describes a modelling approach to quantifying pasture degradation risk using a decision tree model in conjunction with information generated from the AussieGRASS Environmental Calculator. Model development, calibration and validation are presented, and the limitations of the approach discussed. INTRODUCTION Australia has one of the most variable climates in the world. Pastoral agriculture in Australia, as a climate-dependent industry, operates under highly variable climatic conditions. Pastures are often over-utilised in dry years due to low pasture growth and failure to match livestock numbers to available feed. Heavy pasture utilisation in dry growing seasons has been identified as the major driver of pasture degradation and has caused serious pasture degradation events in Australia over the last 100 years or more (McKeon et al. 2004). The social, economic and environmental impacts of pasture degradation have been well documented in Australian history (McKeon et al. 2004). This paper describes the development of a modelling approach to quantifying degradation risk, some preliminary results and limitations of the approach. JUSTIFICATION AND APPROACH Results of grazing trials and grazier experience have indicated that the interaction of heavy pasture utilisation and drought is the major driver of pasture degradation (McKeon et al. 2004). The level of pasture degradation risk depends on the severity of drought, the level of overgrazing and the fragility/resilience of particular land types. To match livestock numbers to available feed during drought, it would be beneficial to have reliable and relatively long lead climate forecasting information. However, current seasonal forecasts are inherently probabilistic and not always well adopted in grazing land management. In lieu of seasonal forecasting being adopted in grazing land management, a “safe” (i.e. conservative) pasture utilisation rate has been advocated (e.g. Johnston et al. 2000). Previous studies have indicated that the “safe” utilisation rate is closely related to pasture growth rate, which is a function of vegetation, soil and climatic factors (McKeon et al. 1990). In the GRASP model (McKeon et al. 1990) and the spatial version of this model – the AussieGRASS Environmental Calculator, solar radiation, available soil moisture and temperature are expressed by a growth index. The growth index ranges from 0 to 1, indicating the worst to the best growing conditions for pastures. The percentage of days in a year with the growth index >0.05 (%GiDays) has been shown to provide a useful indication of “safe” pasture utilisation rate and animal performance at land type scale (Hall et al. 1998). For example “safe” pasture utilisation rate was expressed by a relationship: SU = -11.2 + 0.385 * %GiDays (n = 6, r = 0.853) equation (1) where SU is “safe” utilisation rate in percentage based on long-term average annual pasture growth, %GiDays is the percentage of days in a year with growth index >0.05 (Hall et al. 1998). The severity of drought can be directly indicated by the deficit of rainfall. However, pastures may produce different amounts of dry matter for same amount of rainfall, depending on the soil type, ground cover, topography, species composition and rainfall distribution. Therefore, pasture growth ranked against historical values (i.e. as a percentile) is a better way to indicate the severity of drought for the purpose of this study. A decision tree modelling approach (Zhang et al. 2005) was applied to quantify degradation risk. Initial input variables tested were actual utilisation rate (calculated from modelled annual pasture growth and estimated stocking rate), modelled “safe” utilisation rate, severity of drought (expressed as percentile pasture growth) and seasonal rainfall outlook based on Southern Oscillation Index (SOI). Other factors which we propose to test in future include ground cover, land type and pasture condition. Logical decision rules in this decision tree model were subjectively derived from past studies (e.g. Scanlan et al. 1994; Johnston et al. 1996; Day et al. 1997; Hall et al. 1998; McKeon et al. 2004) and were refined through model calibration by selecting different subsets of input variables and splitting points. Model calibration was carried out using the outputs generated by the AussieGRASS Environmental Calculator. A long-term (30 year: 1977–2007) median %GiDays spatial layer for Australia was calculated which was used to derive a spatial layer of “safe” utilisation rate based on equation (1). Time series (1880–2007) of stocking rate (beef equivalent [BE], 400kg live weight per 100ha), annual pasture growth (kg DM/ha), 24 month percentile pasture growth and forecast rainfall probability were generated across Australia on a 5km × 5km grid for each year ending September. These time series were then input to a decision tree model to predict degradation risk for each year from 1890 to 2007. Model calibration was conducted through iteratively refining the logical decision rules by comparing model predictions against eight well documented historical degradation episodes in six regions across Australia (Fig. 1) as described by McKeon et al. (2004). When the logical decision rules were constructed, an independent validation of the decision tree model was conducted. As all well-documented degradation episodes were incorporated in the model calibration, four areas located in Northern Territory, Queensland and New South Wales where pastures were considered to have never been subjected to severe degradation episodes (Fig. 1) were selected to conduct an initial model validation. Figure 2. A prototype of the decision tree model used to quantify potential degradation risk. Degradation risk is classified into five levels from 1 – 5 (low to extremely high) by different decision rules. For example, if current utilisation rate is < 2.0 times “safe” utilisation rate (safe UR) but ≥ 1.5 times “safe” utilisation rate, and pasture growth in last 24 month is lower than the 25th percentile of its long-term record, then the potential degradation risk is predicted as level 3 (high). Whether degradation occurs depends on individual property management in terms of stock numbers. Figure 1. Areas and regions used in model validation. Current utilisation rate pasture growth Last 24 month Current utilisation rate