CIVL3431 [6431] – Land Surface Processes and Management
Assignment 3 – Irrigation Scheduling – 10 % [8%]
Due Date: 1st Nov 2019, 5 pm
This assignment is to determine the crop water requirement using the 'Kc-ETo' approach and construct an irrigation scheduling routine based on a well-known water balance framework.
In the 'Kc-ETo' approach, described in detail by the United Nations Food and Agriculture Organisation Paper 56 (FAO56), differences in the crop canopy and aerodynamic resistance relative to the reference crop are accounted for within the crop coefficient. The Kc coefficient serves as an aggregation of the physical and physiological differences between crops. We will use the approach that integrates the relationships between evapotranspiration of the crop and the reference surface into a single Kc coefficient and the Penman-Monteith method to estimate the reference evapotranspiration (???? ) and determine the crop water requirement (i.e. actual ET).
Based on the water balance approach, you are asked to estimate the amount of water deficit and hence the amount of irrigation needed to support the crop growth.
A time series of irrigation data measured daily is provided as a validation dataset with your developed irrigation method.
Supplied files:
(1) Tabulated Kc value: 150629_Crop_Kc_values.pdf
(2) Almond farm
Almond_Met_Grid_-34.65_138.70.txt: meteorological information
Almond_NDVI.txt: NDVI time series
Almond_NAP_irrigation.xlsx: a time series of irrigation data
(3) SARDI Report: Pitt, T., Cox, J., Phogat, V., Fleming, N. and Grant, C. (2015). Methods to increase the use of recycled wastewater in irrigation by overcoming the constraint of soil salinity, Australian Water Recycling Centre of Excellence, Brisbane Australia.
Irrigation Scheduling
Part 1:
Your task is to use the framework set out in the FAO56 document to conduct an irrigation scheduling routine over an almond orchard in South Australia.
The required meteorological data has been downloaded from SILO and is supplied.
SILO is a database of Australian climate data from 1889 to the present. Details on SILO climate variables are discussed here: https://www.longpaddock.qld.gov.au/silo/about/climate-variables/.
The tabulated Kc values for South Australia are supplied.
Estimate crop water use, work out the water deficit created to support for the growth (i.e. irrigation water depth), considering effective precipitation and the water balance equation, and estimate the irrigation water depth. Then quantify errors between estimated irrigation water uses and delivered (field reported) water uses.
Tips:
• Ensure each year of irrigation calculations is separated per season (the season should be based on the tabulated Kc values supplied). Performing these calculations in the off season will cause very unusual values.
• Details on soil properties can be found in the report describing the two farms, which is provided with the assignment and referenced below (Pitt et al., 2015).
• The FAO56 report will describe in detail all aspects of irrigation scheduling, while the Victoria Agriculture website will give a more practical description of how to perform these calculations. Western Australia Agriculture may also be helpful. These sources combined should give you enough information to complete the irrigation scheduling calculations. Links to these pages are provided at the end of the assignment.
• You can set up the moisture balance sheet using excel, similar to Table 4 in by Agriculture Victoria. [the link to website:
http://agriculture.vic.gov.au/agriculture/horticulture/vegetables/vegetable-growing-andmanagement/estimating-vegetable-crop-water-use]
• Explain your water balance approach, explaining water requirements for production, and hence work out the amount of water needed for irrigation. Clarify your assumptions in this approach.
Part 2:
Now perform the same calculations as above but using a remote sensing based approach to calculate a Kc and quantify the errors between estimated and observed irrigation water uses. This is based on the Normalised Difference Vegetation Index (NDVI), a time series for each farm is provided.
The time series comes from Landsat 5, 7 and 8 satellites and has been compiled together. To convert this to a Kc value you should use the following equation:
?? 1.37 ???????? 0.086
This relationship is derived from previous studies relating NDVI and Kc over a variety of crops. It is currently used in a remote sensing cloud based irrigation scheduling tool called IrriSAT (links to this are below).
Tips:
• You can set up the moisture balance sheet based on the NDVI based Kc value (as compared to the tabulated Kc).
• Think about what NDVI is - what it is showing and how this can be used to estimate the crop’s growing stages?
• Compare how NDVI based Kc is different from the tabulated Kc and how this difference contributes to different estimation of AET and water requirement for vegetable crop production.
Submission Checklist
After completing the analytical work above, answer the following questions about your work and irrigation scheduling:
1. Write a methodology of your irrigation scheduling routine and present a monthly time series of both of your simulated irrigation depths against the monthly metered values.
Additionally, calculate a statistical evaluation of each method (e.g. Root Mean Square Error or similar).
• Structure this so that all of your working is presented as a report. E.g. explain all the equations you used and why before presenting the time series and statistical evaluation you calculated. Use the Word equation feature to write the equations you have used.
2. Which of your methods performs better? Use calculations you have performed in your answer.
3. What does an NDVI show? How is this related to a Kc and what it shows?
4. What are possible reasons why the tabulated and remote sensing methods produce different results?
5. If you were a farmer, would you implement a meteorologically based irrigation scheduling routine over your field? Or would you look for another option such as soil moisture probes
(field measurement of soil moisture)? Justify your answer.
6. Based on the positives and negatives of each method (e.g. in-situ soil moisture probes, tabulated Kc approach, NDVI Kc approach or others), which would you recommend to a government water authority, or similar, for monitoring irrigation water use (i.e. water accounting)? Why?
Ensure you back your answers up with references were applicable. Feel free to use headings to structure your response to these questions. The Tips written for Part 1 and Part 2 above, may help you in thinking about your responses to these questions. Keep responses to within 2 pages, Q1 and any references will not be counted towards this limit.
Please submit as a report in word format in both hard copy and soft copy. Include reliable citations in the responses to the questions. Also attach the excel spread sheet (or equivalent) used for calculations via an online submission.
Identified cases of plagiarism will be dealt with formally through University procedures.
Useful Links
FAO56 Report: http://www.fao.org/3/X0490E/X0490E00.htm
Victoria Agriculture: http://agriculture.vic.gov.au/agriculture/horticulture/vegetables/vegetablegrowing-and-management/estimating-vegetable-crop-water-use
Western Australia Agriculture: https://www.agric.wa.gov.au/water-management/evaporationbased-irrigation-scheduling
IrriSAT: https://irrisat-cloud.appspot.com/
Useful readings
Bretreger, D., et al. (2019). -Monitoring irrigation water use over paddock scales using climate data and landsat observations.- Agricultural Water Management 221: 175-191.
Hornbuckle, J., et al. (2016). IrriSAT Technical Reference. Deakin University, CSIRO Land & Water, NSW DPI, Australia.
McMahon, T. A., et al. (2013). -Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis.- Hydrology and Earth System Sciences 17(4): 1331-1363.
Pereira, L. S., et al. (2015). -Crop evapotranspiration estimation with FAO56: Past and future.- Agricultural Water Management 147: 4-20.
Pitt, T., et al. (2015). Methods to increase the use of recycled wastewater in irrigation by overcoming the constraint of soil salinity. Adelaide, South Australia, South Australian Research and Development Institute (SARDI).
Trout, T. J. and L. F. Johnson (2007). Estimating Crop Water Use From Remotely Sensed NDVI, Crop Models, and Reference ET. USCID 4th International Conference on Irrigation and Drainage. Sacramento, California: 275-285.