1. Selection of irrigation duration for high performance furrow irrigation on cracking clay soils Rod Smith, Jasim Uddin, Malcolm Gillies

2. Research question Is there a simple objective way of estimating time to cut-off for furrows in real-time & that does not require substantial data or complex computation

3. Typical infiltration curves for a cracking clay soil

4. Irrigation performance – various flow rates – 5% runoff

6. Tco vs advance time to mid-way down furrow

8. Data for 4 furrows x 4 irrigations

9. Example application efficiencies (%) – one field – average of four furrows IrrigationFarmer Individual Optimum (5% runoff) Set distance Guide- lines ‘Autofurrow’ (5% runoff) 249.558.664.265.261.5 454.570.973.274.177.6 370.695.095.186.489.7 590.395.0 96.998.8 781.895.086.883.892.5 Mean69.482.9 81.384.0

10. Application efficiencies (%) – single furrows FurrowFarmerOptimum Set distance Guidelines #1252.192.279.371.7 #4127.994.895.396.9 #6163.675.482.8*74.0 #7487.8 93.7*79.6 #8725.783.472.771.6 #9175.993.269.986.0 Ba85.8*79.987.1*77.7 By56.793.494.099.5 F86.589.887.585.9 K66.392.894.699.8* * advance did not reach end of field

11. Summary Three methods compared:  ‘Autofurrow’  Set distance cut-off  Guidelines based on advance rate Common features  Data collected during an irrigation is used to control that irrigation  Speed of advance is a function of flow rate, soil properties, moisture deficit  Hence adapt to changes in those variables

12. Summary ‘ Autofurrow’ is a reliable predictor of Tco but is data and computationally intensive. The two simpler alternative methods give deliver performance generally equivalent to ‘Autofurrow’ and each other – but some variability All methods deliver better performance than the ‘average’ grower All three methods benefit from fine tuning, either manually or as self learning in automated systems