Adapting to change – adaptive management strategies

Farmers have always carried out adaptive changes to their businesses based on the weather. They respond in the short-term by altering cropping patterns and

management practices (Iglesias, 2007; Anwar et al., 2012). Pannell (2010) argued that farmers faced with a changing climate will successfully adapt their systems by means of successive small, short-term changes in management practice, a concept supported by a number of authors including McCarl (2007). However, Moore (2012) stated that this depends on two assumptions; first, that the feasible rate of on-farm practice change is greater than the rate at which changing climate will alter the production environment; and second, that farmers’ perceptions of current conditions will be sufficiently accurate to allow them to adjust their management strategies. It seems highly unlikely that even well planned incremental innovation will suffice in response to some of the future projected climate changes (Ash et al., 2008).

Climate change adaptation options for mixed farmers are relatively well documented. (Smit and Skinner, 2001; Kingwell, 2006; Howden et al., 2010; Stokes et al., 2010; ACG, 2005; Iglesias, 2007; Crimp et al., 2008b; Moore, 2012). For example, options for

Broadacre Farmers Adapting To A Changing Climate 38

adapting to climate change at farm level in the broadacre sector include strategies such as, diversification of crop varieties, species change, shifting planting seasons, changing crop management practices, i.e. tillage spacings, rotations, nutrient and salinity

management, moisture conservation, pest management and taking advantage of season forecasting (Kokic et al., 2005; Howden et al., 2006). Livestock adaptations at farm level include increasing soil fertility to increase water use efficiency for pasture growth, ongoing genetic improvement, using summer-active perennials and using confinement feeding. Management of new pests and diseases, linked to a changing climate, also will be required (Moore, 2012). Longer-term adaptation strategies include changing the enterprise mix, diversifying into off farm employment, investing in off-farm assets and migrating to new industries and regions (Kokic et al., 2005).

Adaptation decisions are continuous with individual decisions being influenced by internal stimuli to the farm household, such as risk of income loss, changed perceptions of the farm environment, and changes in macro-economic policies or industry policies such as grain market deregulation (Chiotti and Johnston, 1995). Adaptations can be characterised as on-farm production practice management and farm financial management i.e. insurance and risk management (Joint working party on agriculture and the environment, 2011).

Kurukulasuriya and Rosenthal (2003) characterised adaptation strategies into micro- level, market responses, institutional changes and technological developments. A similar approach was used by Ash et al. (2008) who presented a conceptual map of coordinated adaptation for Australian agriculture across nested scales. However, there seems to be little evidence of these types of frameworks being adopted in the literature. Perhaps this is because it is recognised that there are practical limitations for

identifying and evaluating particular adaptation measures, given their huge variety, their peculiarities in particular applications and the importance of ongoing decision processes. The IPCC suggested that a useful alternative is to work towards enhancing adaptive capacity (Smit and Skinner, 2001; Kokic, 2005; McCarl et al., 2007).

An approach which is more common is to develop models to understand the impacts of climate change on the system, either at large scale or more localised specific systems. Examples of this approach have been discussed throughout this review (e.g. John et al., 2005; Crimp et al. 2008; Challinor et al., 2009; Moore, 2012; Rötter et al., 2011, Malcolm et al., 2012). These studies often examine climate change impacts by contrasting impacts ‘with’ and ‘without’ climate change.

Pearson et al. (2008) reviewed and documented the available climate model tools available for assessing vulnerability and they found the majority were biophysical agricultural models rather than bio-economic models. Rodriguez et al. (2011) criticised the predominant focus on biophysical modelling rather than bioeconomic modelling such as that by John et al. (2005).

Often biophysical studies are undertaken at an individual crop level where crop yield is used as the scale for reporting. The neglect of economic measures is a serious short- coming of these studies as farm managers and policy makers typically support their decisions with information on impacts on farm business profits and risks.

Broadacre Farmers Adapting To A Changing Climate 39 However, there is a range is of computer based tools emerging, developed by

scientists to provide farmers with information and procedures to assist with

management decisions, collectively known as decision support systems or tools. But despite the ease of accessing these models, their adoption by farmers has been very slow and low in most instances which is thought to be due to the models’ complexity, their lack of extension and marketing support and the lack of confidence in the models’ outputs (Loch et al, 2012).

Studies such as Hogan et al. (2011) and Greenhill et al. (2009) have found that financial viability is one of the key issues for farmers so the lack of uptake of

biophysical-based decision models is not surprising. The role and value of decision support tools is discussed in much greater detail by McCown et al. (2006).

The lack of studies that relate climate change production impacts to their economic ramifications has been is identified by Antle and Capalbo (2000) and Agrawala (2011). No studies found, except Lawes and Kingwell (2012), take a longitudinal approach and certainly none integrate biophysical considerations with socio-economic and

behavioural decision making, despite a number of authors identifying the need (Rotham and Robinson, 1997; Smit, 2001; Kurukulasuriya and Rosenthal, 2003; Fϋ ssel, 2005; Kokic et al., 2005; Howden, 2007; Twyman et al., 2011). Furthermore, there is an almost complete lack of literature on the cost side of the adaptation equation for agriculture, with the exception of the study by McCarl (2007) and Nelson et al. (2009) This review confirms this finding.

McCarl (2007) estimated that to respond to climate change, additional investments are required in research (such as drought resistant seed varieties), agricultural extension, and physical capital (such as irrigation infrastructure) for agriculture, forestry and fisheries. The cost is estimated to be USD 14.23 billion per year by 2030. Nelson et al. (2009) recommended an increase of at least $7 billion per year investment in

community-based adaptation programs. However, Agrawala (2011) claimed that strong assumptions on adaptation responses raise questions about the reliability of the results in McCarl (2007).

However, one of the difficulties acknowledged by McCarl (2007) is that the agricultural sector regularly adapts to forces such as development of pest resistance to treatment methods, development of irrigation facilities, invasive species, consumer diet

preferences, income effects on dietary choices, competition for water from municipal and industrial sectors, and changes in government policies among numerous others. Determining the costs associated with just those associated with climate change becomes problematic in such a complex and dynamic system with so many different interactions.