Precision irrigation is still in its infancy both in Australia and internationally. Despite the widespread  promotion and adoption of precision agriculture in dry land cropping systems, the concept of irrigation as a  component of precision agricultural systems has not been widely canvassed. Irrigation is commonly viewed as a less than precise activity and the potential for precision irrigation is yet to be adequately evaluated. This report is a review of relevant irrigation research, existing technologies and the use of precision irrigation. It includes an assessment of the role of current irrigation application technologies in precision irrigation, variable rate applications, adaptive control and the sensing and decision support requirements. The review provides a framework to guide research and development of precision irrigation and its associated sensing, control, and decision support technologies.

Precision farming requires a real-time knowledge regarding the processes which are limiting production at any time in all areas of the field. The experience from precision agriculture suggests that the variables controlling crop yield are those that require within season management (eg, water, nitrogen, pests and diseases), in other words those requiring an automatic response. It also suggests that the temporal variations (within and between seasons) are greater than the spatial variability that the variable rate technologies attempt to address.

Experience also suggests that the practice of precision agriculture might be far more effective when applied in irrigated rather than dry-land agricultural systems. It might also be possible that spatially varied inputs to production (other than water) will be less necessary for irrigated crops as the improved water management reduces the significance of other input interactions. The role of irrigation as a spatially varied input to production is a natural extension of its present and primary role of minimising the temporal variation

Irrigation aspires to be and should be a precise activity involving both the accurate assessment of the crop water requirements and the application of this volume at the required time. The traditional wisdom has been that irrigation should meet the needs of the crop in a timely manner and as efficiently and as spatially uniformly as possible. A measure of precision is also required in:

This implies a system that can adapt to the prevailing conditions. Also implied in this is the idea that the system will be managed to achieve a specific target which, for example, may be maximum water use efficiency, maximum yield or maximum profitability.

Precision irrigation requires that the operator has access to detailed data and response information regarding the crop, soil, weather, environment and other production inputs and that there is adequate knowledge regarding the interaction of these variables and the economic responses to these inputs at the relevant spatial scale. In this case, precision irrigation will maximise the value of the other crop inputs while minimising wastage and environmental impacts. This requires a holistic view of irrigation management that includes all of the factors needed to make irrigation an efficient activity as well as those required in precision agriculture.

A potential stumbling block to the introduction of effective precision irrigation is the necessary understanding of the crop production systems and the ability to identify the interactions between the various crop inputs,   productivity gains and operating constraints/costs. The relatively recent development of crop simulation models for crops provides the first step towards a framework which may enable the identification of optimal strategies. These models are an essential part of the real-time decision systems required for precision irrigation by incorporation into controllers on irrigation application systems. Limitations of these models aside, the lack of low-cost, non-invasive (proximal) sensors able to provide measures of crop and soil responses across entire fields at relevant spatial scales means that precision irrigation systems will have to rely on simulation for the foreseeable future.

In conceptualising how the current irrigation application systems can be reinvented as precision irrigation systems, four spatial scales are important. The first of these is the scale at which the irrigation applications can be controlled. This is clearly a characteristic of the application system and varies from about 1 m2 for a LEPA system up to about a hectare for bay irrigation. The second is the scale of the actual variability of the irrigation applications. In practice this will be the scale at which the variation of the actual applications can be measured or predicted. This will also be the scale at which the crop simulation model can determine the crop response to the irrigation and predict forward in time to predict the effect on yield and water use efficiency. The data at this scale is also used in planning the next irrigation. In the case of LEPA this will be the same as the control scale but for bay irrigation it could be 1 m length of the bay. The third scale is the scale of the crop variability which will be related to the root zone extent of the individual plants. The final scale is that associated with any sensing of crop or soil parameters. This will be the largest of the three scales and needs only to be sufficiently frequent to allow some ground truthing of the relevant simulation model.

It is significant that no systems were identified in this country that could truly be classified as precision irrigation systems. However research is active in a number of areas relevant to precision irrigation and many of the component tools and technologies have or are being developed. Examples of these are illustrated in the case studies included throughout the review.

While many of the tools and technologies that will comprise precision irrigation systems are currently available, substantial research and development is required before a truly precision system is available for testing and adoption by the irrigation community. The R & D opportunities that emerge from the review fall into four categories.

Integration of the various component technologies for precision irrigation stands out. Combining the crop and soil sensing with appropriate crop growth simulation models to provide the seasonal decision making model is a necessary first step for all of the major crops. Combining that with the system for the control and optimisation of the particular irrigation application system completes the PI system. Given the dominant  position in the irrigation sector occupied by the various forms of surface irrigation and the substantial gains possible in application efficiency and yield (and hence water use efficiency) this would seem the likely priority area.

The technical feasibility of PI needs to be established at two levels, conceptual and practical. At the conceptual level, simulation can establish the optimum spatial scales for the range of crops and application systems. This will account for the spatial limitations of the application system, the constraints imposed by the sensing needs and capability, and the ability of the simulation tools to accurately predict the affects on crop growth and yield of small variations in applied depths. This stage must also determine if the diagnostic tools needed to determine the causes of particular crop responses are available and sufficiently accurate. At the practical level, PI systems need to be proven and demonstrated in field trials across the breadth of the Australian irrigation sector.

Current and past work has established that there are benefits to be obtained from adoption of PI (including spatially varied irrigation applications). However it is far from clear if the benefits outweigh the costs by a sufficient margin to warrant the adoption. Work needs to be undertaken across a sufficient range of crops, soils and irrigation application systems to determine where the maximum benefit can be obtained and to direct the priorities for research investment. This will also establish the advantages of full versus staged or partial adoption.

Specifically, quantifying the costs/benefits of full automation of surface irrigation and the agronomic benefits of spatially varied applications for a range of crops appear to be of high priority. It also remains to be shown, via the mechanism of field trials rather than simulation, that adaptive systems can provide substantially greater benefits than simple automation and/or traditional irrigation scheduling.

Development of improved tools and technologies will need to be on-going. However there are some clear immediate needs for particular sensing and simulation tools for the PI systems currently under development. These are:

• Low-cost, spatially-distributed, non-invasive sensing of soil moisture and crop response;
• Development of a fully deterministic sprinkler pattern model for centre pivot and lateral move machines that can account accurately for varying sprinkler pressure and height, sprinkler pattern overlap, wind, and machine movement;
• Development of a hydraulic diagnostic model for drip irrigation systems capable of interaction with the system control to deliver spatially varied applications;

• Improved crop models sensitive to small variations in irrigation management and with a self learning capability; and

• Verification of the use of short range radar for the measurement of the spatial distribution of rainfall at the sub-field scale.