Precision irrigation with soil moisture sensors

Heterogeneity of soil physical characteristics (Sadler et al. 2002) and water application distribution (Jimenez et al. 2010) are key causes of spatial variation in crop performance. Soil moisture sensors have a particular advantage over meteorological based water use estimates in this respect, as they provide the opportunity to practice “precision irrigation”, which aims to apply water only where, when and in the amount needed by the plant (Krum et al. 2010). Significant water-savings have been demonstrated from using soil sensors

89 compared with time-based scheduling (e.g. Blonquist et al. 2006; Hedley & Yule 2009; McCready et al. 2009; Pathan et al. 2007; Qualls et al. 2001). The increase in WUE of 0.24 t DM/ML across both the well-watered (Sens1) and deficit irrigated (Sens2) treatments where sensors were used to schedule irrigation events, equated to a water-saving of 20 and 33 %, respectively, compared with the current industry recommended practice (Evap1) (Table 4.2). Furthermore, a significant regression between DM yield and the average soil at the irrigation

trigger point demonstrates the potential benefits of precision irrigation combined with the use of WatermarkTM sensors (Fig. 4.5).

Soil matric potential measured by granular matrix sensors (GMS) reflects how tightly water is held within the soil and therefore is relative to the energy plants must expend to absorb water or the consequent plant water stress experienced by the plant. This is evidenced by strong correlation between leaf and soil (Intrigliolo & Castel 2004), and DM yield and soil

(Merot et al. 2008), though variation in the relationship was reportedly high. However, rather than just an indication of a sensor precision problem, variation in leaf with soil is likely to

reflect the influence of evaporative demand on the rate of water flow through the plant and the corresponding hydraulic flow resistances between the bulk soil and the leaf tissue (Jones 2004). As it is the change in tissue water status that many aspects of the plant‟s physiology respond to, rather than bulk soil water content, dynamic changes in leaf may partly explain

the variation in DM yield not accounted for in the regression of DM yield to soil (Fig. 4.5).

As the soil dried out, variation in leaf became particularly evident (Fig. 4.2), which is likely

to reflect the non-linearity in the relationship between water content and water potential in both the soil and plant, which becomes exacerbated under dry soil conditions especially where there is high spatial heterogeneity in soil hydraulic properties.

However, there are limitations to the use of GMSs. They require good soil contact as they work by equilibrating with the surrounding soil moisture, so in coarse textured soil (i.e. sand) reduced soil/sensor contact may lead to incorrect estimation of soil water tension (Irmak & Haman 2001). In addition, GMSs tend to exhibit hysteretic behaviour (Thompson et al.

2006) and a high variability of readings (Intrigliolo & Castel 2004). However, when compared to the performance of other sensors based on Frequency or Time Domain

Reflectrometry, GMSs were similarly able to describe general trends in soil moisture changes during the growing season (Leib et al. 2003), and are relatively cheap allowing the possibility to achieve higher spatial resolution through the use of multiple sensors. Over the 1 ha

90 experimental area within zone A, the overall spatial variation in soil moisture at an irrigation trigger point across 3 replicates per treatment ranged from 3.4-41.6 % (data not shown). Thus applying precision techniques to account for variation at the scale assessed in the current study may not be viable when applied across a whole farm with multiple paddocks, soil types and terrains. Furthermore, as an irrigation scheduling tool, reliance on one sensor placed within a paddock is unlikely to be sufficient for achieving improved WUE. A study by Sadler et al. (2002) demonstrated the magnitude of variability that can exist within soil classification units, and over comparatively short distances, highlighting the need for improved identification of management zones and greater flexibility in the capacity to

manage and apply irrigation at various rates in order to achieve optimum management (Evans & Sadler 2008; Green & Erskine 2004; Hedley & Yule 2009; Krum et al. 2010; Sonmez et al. 2008). However, of the few economic studies that evaluate precision irrigation, the general consensus is that it isn‟t feasible at current capital costs (DeJonge et al. 2007; Lu et al. 2005; Watkins et al. 2002).

4.5

Conclusion

This study demonstrated the potential use of soil granular matrix sensors to account for spatial and temporal variability in soil moisture across a paddock to improve the regulation of water use by plants and therefore the DM response to water applied. Further testing is

required to improve the predictability of the relationship between DM yield and soil if it is to

be used with variable rate irrigation control. But through strategic placement of sensors to improve the average WUE of a paddock it is likely to be a useful irrigation tool. Considering that few farmers currently use any form of objective irrigation decision measures (ABS 2010b), as a first step, utilising a low-cost water-balance approach to increase rainfall capture is still likely to be an improvement on a set-scheduled irrigation strategy, which tends to dominate current practice (Watson & Drysdale 2005).

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Chapter 5: The value of pre-season forecasts of irrigation