Crop simulation modelling

Whilst the SOI forecast system predicts irrigation requirements, for tactical decision making around stock levels and the need for additional brought in feed, an understanding of the yield outcome from an irrigation requirement is also required. Dry matter production can be predicted on the basis that an irrigation requirement is the cumulative use of water achieved under a scheduling practice (Fig. 5.3A) which can be translated into DM returns through the use of a biophysical crop model (Fig. 5.2A). The capacity of crop models to integrate soil and atmospheric dynamics through the effect on DM yield provide a time and cost-efficient alternative to experimentation on the physical system for testing alternative management strategies and resource issues (Meinke et al. 2001).

Representing the effects of rainfall through biological indices such as plant growth days or yield often improves the forecast skill compared with where only rainfall is used (Keogh et al. 2004a; McIntosh et al. 2005; Meinke & Stone 2005). Furthermore, considering that DM yield is a key driver in dairy profitability (Armstrong et al. 2010; Chapman et al. 2008), forecast information based on production indicators rather than crude rainfall probabilities may be more relevant and therefore easier to integrate into farmers‟ responses to seasonal climate risk (Ash et al. 2007). In the current study, pasture DM consequences are

incorporated in the decision-support system by optimising the choice of the irrigation scheduling practice according to the practice that maximises yield for the least amount of irrigation.

Production risk associated with using a particular scheduling practice in a given Phase is also provided by the 20th percentile yield minimum; however this could be adjusted according to the risk level of the farmer and enterprise. Production risk from using the Forecast system occurs because whilst using the maximum observed irrigation amount in a Phase for the selected practice meets the requirement in 100 % of years, yield will not necessarily be the maximum achievable for that season had another scheduling practice that uses more water been implemented. This can be understood by the different yield distributions of each

116 practice in Figure 5.2A. Thus information provided by the Forecast system in combination with simulation modelling, includes an irrigation amount (Table 5.1), a scheduling practice (Table 5.2), and a potential DM yield (Table 5.1).

5.4.3 User value

The value of forecast information is often assessed in terms of changes to gross margins, farm profit and/or utility (Abawi et al. 1995; Hammer et al. 1996; Ritchie et al. 2004). Such an economic analysis has not been performed in the current study. However gains in DM yield and savings to irrigation amount have been used to assess the user value of the Forecast system compared with where a best-bet Fixed strategy is followed each year. This has been assessed under two water availability scenarios – where the water allocation is non-limiting, and where cumulative water use cannot exceed 250 mm.

Under maximum water availability, the greatest improvement to irrigation efficiency was observed in the Fixed-phase decision-support system, where the practice was fixed (Practice 3) but the irrigation requirements differentiated according to the SOI Phases (Table 5.4A). Compared to a Fixed strategy where Practice 3 was used, requiring 276 mm annually, the Fixed-phase strategy reduced irrigation requirements by 8.5 % (Table 5.4A), resulting in an 0.5 t DM/ha increase in irrigation efficiency (Table 5.5). However, in situations where maximising yield is a higher priority over irrigation use, for example when irrigation availability is not restricted or is cost viable, then the Flexi-phase forecast system is recommended (Fig. 5.7), due to the gains in DM yield obtained from using Practice 2

following Phases 1 and 2 in October (Table 5.1A). That is, in using the Fixed-phase system, improved irrigation efficiency was achieved at the cost of DM yield. However the value of this reduction is likely to vary depending on whether feed or irrigation is more limiting (Armstrong 2004; Ho et al. 2007). Interestingly, Practice 1, which is the current industry recommended practice applied at Elliott, was not required to maximise DM yield, suggesting that rainfall utilisation was increased where alternative practices were selected for during the optimisation process (Table 5.2A).

As the irrigation allocation decreases and gets closer to the minimum irrigation requirements of the environment, less differentiation in water requirements (and therefore potential

savings) from a Forecast system is expected due to convergence between the Phase distributions (Fig. 5.4). This was the case under a 250 mm allocation constraint, with Practices 1-3 using the full allocation in each Phase (Fig. 5.3B). As such, the Forecast

117 strategies were the same as the Fixed strategy (Table 5.4B), negating the need for pre-season information under constraint conditions, and limiting the opportunity to improve irrigation efficiency (Table 5.5). Furthermore, as water availability becomes limiting to growth, the yield distributions converge between practices (Figure 5.2B). Compared with Practices 3 and 4 where there was no change between allocation scenarios, Practice 1 observed a significant change in the median and shape of the distribution (P < 0.001), depicting increased

variability in DM yield (Figure 5.2). The different effects on DM yield reflect the trade-off between short-term stress and end-of-season stress where water has run out before the end of the season. In the case of Practice 1 there was increased risk associated with maintaining short-interval schedules that refill the soil profile to field capacity, over ensuring longer-term water availability through practising a deficit irrigation strategy (Practices 2-4). This is the basis for supplementary irrigation to stabilise yields in Mediterranean climates (Oweis et al.

1998) and can also be applied to the rationale of irrigating over a larger area at a lower irrigation application rate compared with maximising water use over a smaller area to increase the marginal response of irrigation water (Kirda 2002; Rawnsley et al. 2009).