Agricultural science directions

Specialists know more and more about less and less. Current specialisation in agricultural science has resulted in research within very narrow boundaries. This has induced linear, mechanistic thinking, which doesn’t allow room for synergies, and results in confusion between cause and effect.

Soils, for example, have become partitioned into separate isolated fields of chemistry, physics and biology, with further specialisation within each. Unfortunately, soil degradation and the issue of how to restore healthy soils cannot be solved with many individual research projects conducted by various specialists. It needs a big-picture approach. In nature everything is linked with everything else. These circular, web-of-life phenomena have to guide our applied field research.

Much current ‘sustainability’ research is fiddling at the margins of entrenched methods, working on symptoms rather than the primary cause of problems – as evidenced by appearance of new problems after implementing ‘solutions. It is not simply a matter of doing better what we do. ‘Best practice’ locks us in status quo which is still not good enough!

If agricultural research is to deliver anything approaching sustainability, therefore, we need to change the science paradigm (Jackson 1985). Or as Dr Albert Einstein said: “No problem will be solved with the same level of thinking that created it in the first place”.

Over generations research has become increasingly “reductionist”, that is, reducing and outlining systematically the area of interest to be studied and the disciplines to be used. While this approach of fragmentation has delivered a lot of knowledge about the workings of particular crops, pastures, livestock, insect pests, chemicals, etc, focussing too intensely on closed systems with narrow boundaries – on single, isolated components of the bigger “real-world” system – means we are blind to larger cycles and patterns within which component parts exist (Stapper 2002).  In this way, the biological sciences themselves fragment our understanding by creating false divisions that break the cycle of life.{mospagebreak}

New problems keep emerging as each of them are dealt with as single issues, resulting in partial solutions that don’t necessarily solve the problem, for example, acidity (with lime) and salinity (with lowering ground water). Partial solutions tend to equate a single solution with the cause of the problem but lime and ground water, for example, are not always directly related with acidity (Anderson 2000) and dryland salinity (Jones 2001, 2006), respectively. Soil management related causes for dryland salinity have been derived from practical experiences in, for example, New South Wales (Wagner 2005, Andrews 2006, Newell 2006), Victoria (Nathan 1999) and Western Australia (Paulin 2002).

Experimental results dealing with isolated individual components are thus difficult to apply to paddocks, which are complex systems in time and space. What does an ‘average’ mean in a paddock? Other management factors are likely to be working against the application of individual research results, thereby inhibiting change. Hence, problems continue to emerge in agricultural production systems. Science is now proposing genetic engineering as ‘the’ solution for many of these problems – risking yet another oversimplification in our fragmented agricultural science (Stapper 2002), a ‘techno-fix’ with more band-aids over the real cause of our problems – degrading soils.

The standard multi-factorial research methodology seems ill-suited to studying complex biological systems where everything is linked with everything else. To obtain functional outcomes, no factors may be considered ‘constant’ in trials while varying a few ‘important’ factors to quantify their impact.

Also the boundary conditions of research objects chosen by specialists (e.g. pots and small plots in a growth chamber, green house or research station) are often not appropriately representative of real ecosystems (especially microclimate) and generate results not transferable to the farming-system level. Comparative analysis is needed on a commercial production scale. Questions arising from such studies then need answers through reductionist science.{mospagebreak}

New methodologies and directions of research are required in the search for resilience, to achieve reproducible and predictable outcomes in farming systems across agroecological zones. Such research needs to be planned, executed and analysed by a transdisciplinary team working across ecosystems at representative scales, that is, in agroecology (Gliessman 2000, Altieri 2006). This is to allow observation and measurement of expressions of the multitude of interacting components within and between different scales of the farming system. Plant health (Anderson 2000) and animal health (Voison 1958), for example, are dependent on availability in the right balance of minerals, but this is still regarded as ‘alternative’ thinking.

To reach sustainability in agriculture we have to look at the whole system and develop holistic tools within agricultural science that bring together, from across disciplines, the knowledge obtained through analytic reductionism, without getting lost in small component details of ‘what single factor? – the how? and why?’

Such tools are unlikely to be quantitative, hard systems, as dynamic interactions by soil organisms are too complex and too affected by small spatial and temporal changes in management and climate. Therefore, a soft systems approach is required, synthesising knowledge into management guidelines for sustainable land use combined with careful monitoring of status.

Australia’s public R&D in this direction is minimal, and seems to be one of the lowest of OECD countries as was evident at the recent International Federation of Organic Agriculture Movements Congress in Adelaide (ISOFAR 2005). Nevertheless, we must search for productive agricultural systems with reduced usage of petrochemicals and energy, and not rely on ‘Techno-Fantasy’ to help us out. As we face a future without cheap oil, science must play a role in dealing with the profound socioeconomic change now gathering momentum around us (Heij 2006).

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