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Many farming systems in Australia are underperforming. For example, a recent analysis showed that only about 29% of current crop sequences in the northern grains region of Australia are achieving 80% of their water-limited yield potential (Hochman et al., 2014). This is compounded by tight profit margins and changing climate and market conditions. Available evidence also suggests that between 2013 and 2018, the cost of consumable inputs, such as fertiliser, has increased by 5.7% (ABARES, 2018). Also, over the past five years, the cost of agricultural machinery in Australia has increased by 13.4% (ABS, 2018). However, several farming system component analyses and simulations have predominantly focused on the impact of biophysical processes on farming system performance, including soil quality, water use efficiency, dynamics of nitrogen, crop yields, and disease and nematodes effects of farming practices at the paddock scale. While biophysical optimisation of the farming system may be possible to improve the efficiency of most farming systems, key elements that are often ignored is how the intensity and diversity of different cropping systems impact on whole-farm factors, such as labour and machinery resources. Far from being obvious, these input resources are critical because they modify farm productivity and profitability in the short and long term. Moreover, a consideration of these factors is crucial because they can influence the adoption of farm innovations. The central objective of this study is to examine farm resource constraints with a focus on machinery, labour requirements and fuel requirements as influenced by diverse crop rotations in the northern grain-growing region of Australia. Our analysis is based on three steps. First, we simulated different crop rotations over 112 years (i.e., 1900-2012) of historical climate records using the Agricultural Production Simulator (APSIM). These crop rotations were identified following focus group meetings with leading farmers and advisors throughout the northern cropping zone of Australia. Second, we obtained information on machinery and labour parameters from existing literature, local technical guides and through a consultation process with farm advisers and growers (N = 26 farmers). Finally, we combined the APSIM generated outputs with the machinery and labour data to comprehensively determine how different crop rotations affect labour and machinery requirements within the farming system using analysis of variance. Results showed that the low-intensity systems required 46% less labour per ha than the higher-intensive systems, while the less diverse systems required about 33% less labour per ha than the more diverse systems. Planting and spraying operations respectively represent about 27% and 37% of total fieldwork requirements. Also, the labour required per ha is less in bigger farms compared to smaller farms, which may be explained by the larger machines used by these larger farms. For all sequences considered, peak labour periods fell in July, October to November, while non-peak period is August to September and December to January, corresponding with the periods in which most farm production activities occur. We conclude that Diversified crop rotation systems had significant effect on labour and machinery requirements and differed significantly among rotations (P < 0.05). Also, diverse rotations may create higher labour demand and peak periods that might, in some cases, limit the adoption of diversified crop rotations in some farm businesses, suggesting that labour efficiency can be an important consideration in farming systems research and analysis. These findings will be explored further as part of the on-going development of a bio-economic modelling to explore the trade-offs and synergies between system performance objectives and impacts of innovations options at the whole-farm level.


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