Skip to main content

Advertisement

SpringerLink
Log in

AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts

  • Original Paper
  • Published:
Irrigation Science Aims and scope Submit manuscript

Abstract

Peanut (Arachis hypogaea L.) is an economically important legume crop in irrigated production areas of northern Australia. Although the potential pod yield of the crop in these areas is about 8 t ha−1, most growers generally obtain around 5 t ha−1, partly due to poor irrigation management. Better information and tools that are easy to use, accurate, and cost-effective are therefore needed to help local peanut growers improve irrigation management. This paper introduces a new web-based decision support system called AQUAMAN that was developed to assist Australian peanut growers schedule irrigations. It simulates the timing and depth of future irrigations by combining procedures from the food and agriculture organization (FAO) guidelines for irrigation scheduling (FAO-56) with those of the agricultural production systems simulator (APSIM) modeling framework. Here, we present a description of AQUAMAN and results of a series of activities (i.e., extension activities, case studies, and a survey) that were conducted to assess its level of acceptance among Australian peanut growers, obtain feedback for future improvements, and evaluate its performance. Application of the tool for scheduling irrigations of commercial peanut farms since its release in 2004–2005 has shown good acceptance by local peanuts growers and potential for significantly improving yield. Limited comparison with the farmer practice of matching the pan evaporation demand during rain-free periods in 2006–2007 and 2008–2009 suggested that AQUAMAN enabled irrigation water savings of up to 50% and the realization of enhanced water and irrigation use efficiencies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56

  • Bell M (1986) The effect of foliar pathogens on growth of peanuts (Arachis hypogaea L.) in tropical northern Australia. Aust J Agric Res 37:31–42

    Article  Google Scholar 

  • Chauhan YS, Wright GC, Rachaputi Rao CN, Holzworth D, Broome A, Krosch S, Robertson MJ (2010) Application of a model to assess aflatoxin risk in peanuts. J Agric Sci 148:341–351

    Article  CAS  Google Scholar 

  • Craufurd PQ, Prasad PVV, Waliyar F, Taheri A (2006) Drought, pod yield, pre-harvest Aspergillus infection and aflatoxin contamination on peanut in Niger. Field Crops Res 98:20–29

    Article  Google Scholar 

  • Crosthwaite I (1994) Peanut growing in Australia. Queensland Department of Primary Industries, Brisbane, Queensland, p 93

  • Davidson JI Jr, Williams RG (2002) Irrigator-Pro—a peanut irrigation management system, Version 4.2. National Peanut Research Laboratory, 1011 Forrester Dr. SE Dawson, GA. 31742

  • Fernandez CL, Trolinger TN (2007) Development of a web-based decision support system for crop managers: structural consideration and implementation case. Agron J 99:730–737

    Article  Google Scholar 

  • Georgiev GA, Hoggenboom G (1999) Near real-time agricultural simulations on the web. Simulation 73:22–28

    Article  Google Scholar 

  • Gorbet DW, Rhoads FM (1975) Response of two peanut cultivars to irrigation and kylar. Agron J 67:373–376

    Article  CAS  Google Scholar 

  • Guerra LC, Hoogenboom G, Hook JE, Thomas DL, Broken VK, Harris KA (2005) Evaluation of on-farm irrigation applications using the simulations model EPIC. Irrig Sci 23:171–181

    Article  Google Scholar 

  • Harris GA (2002) Irrigation water balance scheduling. DPI Notes FS0546, Brisbane

  • Hochman Z, van Rees H, Carberry PS, Hunt JR, McCown RL, Gartmann A, Holzworth D, van Rees S, Dalgiesh NP, Long W, Peake AS, Poulton PL, McClelland T (2009) Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmer monitor and manage crops in a variable climate. Crop Pasture Sci 60:1057–1070

    Article  Google Scholar 

  • Inman-Bamber NG, Attard SA, Baillie C, Lawson D, Simpson L (2005) A web-based system for planning use of limited irrigation water in sugarcane. In: Hogarth DM (ed) Proceedings of the 2005 Conference of the Australian Society of Sugar Cane Technologists held at Bundaberg, Queensland, Australia, 3–6 May 2005. PK Editorial Services Pty Ltd. Australia, pp 170–181

  • Jensen AL, Boll PS, Thysen I, Pathak BK (2000) Pl@ntInfo–a web-based system for personalized decision support system in crop management. Comp Electron Agric 25:271–293

    Article  Google Scholar 

  • Jones HG (2004) Irrigation scheduling: advantages and pitfalls of plant-based methods. J Exp Bot 55:2427–2436

    Article  PubMed  CAS  Google Scholar 

  • Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM a model designed for farming systems simulation. Eur J Agron 18:267–288

    Article  Google Scholar 

  • Leib GB, Elliott TV, Matthews G (2001) WISE: a web-linked and producer oriented program for irrigation scheduling. Comp Electron Agric 32:1–6

    Article  Google Scholar 

  • Leib BG, Jabro JD, Mathews GR (2003) Field evaluation and performance of soil moisture sensors. Soil Sci 168:396–408

    CAS  Google Scholar 

  • Mackson J, Wright GC, Rachaputi NC, Krosch S, Tonks J (2001) Assessing maturity in dryland peanuts. Queensland DPI Crop Link Brochure. Agdex No 141/637. DPI Farming Systems Insititute, Dalby, p 12

    Google Scholar 

  • Mahan JR, Burke J, Wanjura DF, Upchurch DR (2005) Determination of temperature and time thresholds for BIOTIC irrigation of peanut on the Southern High Plains of Texas. Irrig Sci 23:145–152

    Article  Google Scholar 

  • Payero JO, Tarkalson DD, Davison D, Petersen JL (2008) Effect of irrigation amounts applied with subsurface drip irrigation on corn evapotranspiration, yield, water use efficiency, and dry matter production in a semiarid climate. Agril Water Manage 95:895–908

    Article  Google Scholar 

  • Paz JO, Fraisse CW, Hatch LU, Garcia y Garcia A, Guerra LC, Uryasev OJ, Bellow GJ, Jones W, Hoogenboom G (2007) Development of an ENSO-based irrigation decision support tool for peanut production in the southeastern US. Comp Electron Agric 55:28–35

    Article  Google Scholar 

  • Robertson MJ, Carberry PS, Huth NI, Turpin JE, Probert ME, Poulton PL, Bell M, Wright GC, Yeates SJ, Brinsmead RB (2002) Simulation of growth and development of diverse legume species in APSIM. Aust J Agric Res 53:429–446

    Article  Google Scholar 

  • Santos AM, Cabelguenne M, Santos FL, Oliveira MR, Serralheiro RP, Bica MA (2000) EPIC-PHASE: a model to explore irrigation strategies. J Agric Eng Res 75:409–416

    Article  Google Scholar 

  • Shorter R, Simpson BW (1987) Peanut yield and quality variation over harvest dates, and evaluation of some maturity indices in south eastern Queensland. Aust Exp Agric 27:445–453

    Article  Google Scholar 

  • Thysen I, Detlefsen NK (2006) Online decision support for irrigation for farmers. Agril Water Manage 86:269–276

    Article  Google Scholar 

  • Wilson DR, Cloughley CG, Sinton SM (2001) A decision support system for managing root carbohydrate in asparagus. ISHS Acta Horticulturae 589:51–58

    Google Scholar 

  • Wright GC, Hubick KT, Farquhar GD (1991) Physiological analysis of peanut cultivar response to timing and duration of drought stress. Aust J Agric Res 42:453–470

    Article  Google Scholar 

  • Wright GC, Rachaputi NC, White D, Robertson M, Tonks J, Burrill P, Ginns S, Bullen K (2001) Achieving the genetic potential of peanuts in irrigated production systems. (Published on Web site: www.regional.org.au/au/asa/2001/6/b/wright.htm). Accessed on 5 Jan 2011

  • Young JH, Person NK, Donald JO, Mayfield WE (1982) Harvesting, curing and energy utilization. In: Patte HE, Young CT (eds) Peanut Science and technology. American Peanut Research and Education Society Inc, Yoakum, pp 458–485

    Google Scholar 

  • Zhu M, Lamb C, Butts CL, Blankenship PD (2004) Improving yield and grade with sub-surface drip irrigation in undulating fields. Trans ASAE 47:99–106

    Google Scholar 

Download references

Acknowledgments

We would like to acknowledge the contribution of: (a) the Grains Research and Development Corporation (GRDC) for funding the development of AQUAMAN through the projects DAQ00091 and DAQ00123, (b) peanut consultants (Peter Hatfield, Ian Crosthwaite, Duane Evans, Patrick Jones and Tony Crowley) and staff from the Peanut Company of Australia (Pat Harden and Grant Baker) for their input in the extension of AQUAMAN to growers, (c) all growers who tested AQUAMAN, and (d) Bundaberg Sugar for sharing information on commercial pod yield and irrigation data for the 2006–2007 and 2008–2009 seasons.

Author information

Authors and Affiliations

  1. Department of Employment, Economic Development and Innovation (DEEDI), PO Box 23, Kingaroy, QLD, Australia

    Yashvir S. Chauhan

  2. Peanut Company of Australia, PO Box 26, Kingaroy, QLD, 4610, Australia

    Graeme C. Wright

  3. CSIRO, PO Box 102, Toowoomba, QLD, 4350, Australia

    Dean Holzworth

  4. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland (UQ), PO Box 23, Kingaroy, QLD, 4610, Australia

    Rao C. N. Rachaputi

  5. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland (UQ), PO Box 102, Toowoomba, QLD, 4350, Australia

    José O. Payero

Authors
  1. Yashvir S. Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Graeme C. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dean Holzworth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rao C. N. Rachaputi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José O. Payero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yashvir S. Chauhan.

Additional information

Communicated by P. Waller.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chauhan, Y.S., Wright, G.C., Holzworth, D. et al. AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts. Irrig Sci 31, 271–283 (2013). https://doi.org/10.1007/s00271-011-0296-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00271-011-0296-y

Keywords

Search

Navigation