Skip to main content
SpringerLink
Log in

Sugarcane and Precision Agriculture: Quantifying Variability Is Only Half the Story – A Review

  • Chapter
  • First Online:
Climate Change, Intercropping, Pest Control and Beneficial Microorganisms

Part of the book series: Sustainable Agriculture Reviews ((SARV,volume 2))

Abstract

The world’s population is expected to reach 13 billion people by 2065. Increasing food production and sustaining the environmental resource base on which agriculture depends will prove a significant challenge for humankind. Precision agriculture is about putting the right input, in the right amount, at the right place, in the right manner at the right time. Precision agriculture is a tool which manages the variability in crop and growing conditions for improved economic and environmental returns. For precision agriculture to be successful, industry must collect, analyse, process and synthesise large amounts of information from a range of integrated enabling technologies. However, quantifying variability alone will not constitute successful precision agriculture in sugarcane production. Success will be measured by the extent to which these technologies are adopted by industry. This review broadens the popular within-field definition of precision agriculture to encompass higher levels of variability present at the farm, mill and regional scale. We propose that managing all these levels of variability is important, although, many of the technologies available for within-field management require further research prior to operationalisation. While a discussion on the range of enabling technologies such as the global positioning system, global information system, proximal sensing, remote sensing and variable rate technology is essential, we emphasise the need to develop a participatory action research environment to facilitate the adoption of precision agriculture for the benefit of whole of the industry.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adamchuk, V.I., J.W. Hummel, M.T. Morgan, and S.K. Upadhyaya. 2004. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture 44:71–91.

    Article  Google Scholar 

  • Almeida, T.I.R., C.R. De Souza Filho, and R. Rossetto. 2006. ASTER and Landsat ETM+ images applied to sugarcane yield forecast. International Journal of Remote Sensing 27: 4057–4069.

    Article  Google Scholar 

  • Apan, A., A. Held, S. Phinn, and J. Markley. 2004. Detecting sugarcane “orange rust” disease using EI-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing 25:489–498.

    Article  Google Scholar 

  • ASA. American Society of Agronomy. 1989. Decision Reached on Sustainable Agriculture. Agronomy News, January 1989:15.

    Google Scholar 

  • Auernhammer, H. 2001. Precision farming – the environmental challenge. Computers and Electronics in Agriculture 30:31–43.

    Article  Google Scholar 

  • Bajawa, S.G., P. Bajcsy, P. Groves, and L.F. Tian. 2004. Hyperspectral image data mining for band selection in agricultural applications. Transactions of the ASAE 47:895–907.

    Google Scholar 

  • Basso, B., J.T. Ritchie, F.J. Pierce, R.P. Braga, and J.W. Jones. 2001. Spatial validation of crop models for precision agriculture. Agricultural Systems 68:97–112.

    Article  Google Scholar 

  • Batte, M.T., and M.W. Arnholt. 2003. Precision farming adoption and use in Ohio: case studies of six leading-edge adopters. Computers and Electronics in Agriculture 38:125–139.

    Article  Google Scholar 

  • Berry, J.K., J.A. Delgado, R. Khosla, and F.J. Pierce. 2003. Precision conservation for environmental sustainability (Research Editorial)(Editorial). Journal of Soil and Water Conservation 58:332(8).

    Google Scholar 

  • Blackmer, A.M., and S.E. White. 1998. Using precision farming technologies to improve management of soil and fertiliser nitrogen. Australian Journal of Agricultural Research 49:555–564.

    Article  Google Scholar 

  • Bongiovanni, R., and J. Lowenberg-Deboer. 2004. Precision agriculture and sustainability. Precision Agriculture 5:359–387.

    Article  Google Scholar 

  • Boote, K.J., J.W. Jones, and N.B. Pickering. 1996. Potential uses and limitations of crop models. Agronomy Journal 88:704–716.

    Google Scholar 

  • Bouman, B.A.M. 1995. Crop modeling and remote-sensing for yield prediction. Netherlands Journal of Agricultural Science 43:143–161.

    Google Scholar 

  • Bramely, R.G.V. 2007. Precision agriculture: An avenue for profitable innovation in the Australian sugar industry, or expensive technology we can do without? SRDC Technical Report 3/2007. Sugar Research and Development Corporation Brisbane.

    Google Scholar 

  • Bramely, R.G.V., and R.P. Quabba. 2002. Opportunities for improving the management of sugarcane production through the adoption of precision agriculture – An Australian perspective. International Sugar Journal 104:152–161.

    Google Scholar 

  • Braunack, M.V., and D. McGarry. 2006. Traffic control and tillage strategies for harvesting and planting of sugarcane (Saccharum officinarum) in Australia. Soil and Tillage Research 89: 86–102.

    Article  Google Scholar 

  • Brisson, N., C. Gary, E. Justes, R. Roche, B. Mary, D. Ripoche, D. Zimmer, J. Sierra, P. Bertuzzi, P. Burger, F. Bussière, Y.M. Cabidoche, P. Cellier, P. Debaeke, J.P. Gaudillère, C. Hénault, F. Maraux, B. Seguin, and H. Sinoquet. 2003. An overview of the crop model. European Journal of Agronomy 18:309–332.

    Article  Google Scholar 

  • Burrough, P.A. 2001. GIS and geostatistics: Essential partners for spatial analysis. Environmental and Ecological Statistics 8:361–377.

    Article  Google Scholar 

  • Campbell, H. 1994 How effective are GIS in practice? A case study of British local Government. International Journal of Geographical Information Systems 8:309–325.

    Article  Google Scholar 

  • Cerri, D.G.P., and P.S.G. Magalhaes. 2005. Sugar Cane Yield Monitor. American Society for Agricultural Engineers. Tampa, Florida

    Google Scholar 

  • Clay, D.E., S.A. Clay, and G. Carlson. 2006. Site-specific management from a cropping system perspective. In A. Srinivasan (ed.), Handbook of Precision Agriculture, pp. 431–462. The Haworth Press, Binghampton, New York.

    Google Scholar 

  • Clevers, J.G.P.W. 1997. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sensing of Environment 61:221–228.

    Article  Google Scholar 

  • Cook, S.E., and R.G.V. Bramley. 1998. Precision agriculture; opportunities, benefits and pitfalls of site-specific crop management in Australia. Australian Journal of Experimental Agriculture 38:753–763.

    Article  Google Scholar 

  • Cornwall, A., and R. Jewkes. 1995. What is participatory research? Social Science & Medicine 41:1667–1676.

    Article  CAS  Google Scholar 

  • Cox, D.R.V. 1997. Precision agriculture in sugarcane production: A view from the Burdekin. In R.G.V. Bramely, et al. (eds.), Precision Agriculture – What can it offer the Australian sugar industry? Proceedings of a workshop held in Townsville, 10–12 June, 1997.

    Google Scholar 

  • Crossley, R., and G. Dines. 2004. Integrating harvest GPS tracking data with a spatial harvest recording system. In D.M. Hogarth (ed.), Proceedings of the Australian Society of Sugar Cane Technologists held at Brisbane, Queensland, Australia, 4–7 May, Vol. 26.

    Google Scholar 

  • Daily, G., P. Dasgupta, B. Bolin, P. Crosson, J.D. Guerny, P. Ehrlich, C. Folke, A.M. Jansson, B.-O. Jansson, N. Kautsky, A. Kinzig, S. Levin, K.-G. Maler, P. Pinstrup-Andersen, D. Siniscalco, and B. Walker. 1998. GLOBAL FOOD SUPPLY:Food production, population growth, and the environment. Science 281:1291–1292.

    Article  PubMed  CAS  Google Scholar 

  • Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13:319–340.

    Article  Google Scholar 

  • Davis, R., R. Bartels, and E. Schmidt. 2007. Precision agriculture technologies – Relevance and application to sugarcane production. In R. Bruce (ed.), SRDC Technical Report 3/2007: Precision Agriculture Options for the Australian Sugarcane Industry SRDC, Brisbane, pp. 60–166.

    Google Scholar 

  • Delgado, J.A., and W.C. Bausch. 2005. Potential use of precision conservation techniques to reduce nitrate leaching in irrigated crops. Journal of Soil and Water Conservation 60:379(9).

    Google Scholar 

  • Denham, J., A. Whitlock, and D. Yule. 2006. Contemporary Position and Navigation Needs of Precision Agriculture and Victoria’s GPSnet CORS network. International Global Navigation Satellite Systems Society 2006, Surfers Paradise, Australia.

    Google Scholar 

  • Dijksterhuis, H.L., L.G. Van Willigenburg, and R.P. Van Zuydam. 1998. Centimetre-precision guidance of moving implements in the open field: a simulation based on GPS measurements. Computers and Electronics in Agriculture 20:185–197.

    Article  Google Scholar 

  • Doraiswamy, P.C., J.L. Hatfield, T.J. Jackson, B. Akhmedov, J. Prueger, and A. Stern. 2004. Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment 92:548–559.

    Article  Google Scholar 

  • Dorigo, W.A., R. Zurita-Milla, A.J.W. de Wit, J. Brazile, R. Singh, and M.E. Schaepman. 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation 9:165–193.

    Article  Google Scholar 

  • Everingham, Y.L., R.C. Muchow, R.C. Stone, and D.H. Coomans. 2003. Using southern oscillation index phases to forecast sugarcane yields: a case study for northeastern Australia. International Journal of Climatology 23:1211–1218.

    Article  Google Scholar 

  • Everingham, Y.L., K.H. Lowe, D.A. Donald, D.H. Coomans, and J. Markley. 2007. Advanced satellite imagery to classify sugarcane crop characteristics. Agronomy For Sustainable Development 27:11–117.

    Article  Google Scholar 

  • Everingham, Y.L., R.C. Muchow, R.C. Stone, N.G. Inman-Bamber, A. Singels, and C.N. Bezuidenhout. 2002. Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts. Agricultural Systems 74:459–477.

    Article  Google Scholar 

  • FAO. 2007. Food Outlook – November 2007 – Sugar [Online]. Available by http://www.fao.org/docrep/010/ah876e/ah876e07.htm#r2

  • Fountas, S., S. Blackmore, D. Ess, S. Hawkins, G. Blumhoff, J. Lowenberg-Deboer, and C.G. Sorensen. 2005. Farmer experience with precision agriculture in Denmark and the US Eastern Corn Belt. Precision Agriculture 6:121–141.

    Article  Google Scholar 

  • Fuelling, T., and R. Wright. 1997. GPS and GIS: Management Tools for Millers and Growers. Proceedings of the Australian Society of Sugar Cane Technologists.

    Google Scholar 

  • Galvao, L.S., A.R. Formaggio, and D.A. Tisot. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment 94:523–534.

    Article  Google Scholar 

  • Galvao, L.S., A.R. Formaggio, and D.A. Tisot. 2006. The influence of spectral resolution on discriminating Brazilian sugarcane varieties. International Journal of Remote Sensing 27: 769–777.

    Article  Google Scholar 

  • Giudici, P. 2004. Applied Data Mining: Statistical Methods for Business and Industry. John Wiley and Sons Ltd, West Sussex.

    Google Scholar 

  • Gobron, N., B. Pinty, M.M. Verstraete, and J.-L. Widlowski. 2000. Advanced vegetation indices optimized for up-coming sensor-design, performance, and applications. IEEE Transactions on Geoscience and Remote Sensing 38:2489–2505.

    Article  Google Scholar 

  • Godwin, R.J., T.E. Richards, G.A. Wood, J.P. Welsh, and S.M. Knight. 2003. An economic analysis of the potential for precision farming in UK cereal production. Biosystems Engineering 84:533–545.

    Article  Google Scholar 

  • Goward, Y.M., and D.L. Williams. 1997. Landsat and Earth system science: development of terrestrial monitoring. Photogrammetric Engineering and Remote Sensing 63:887–900.

    Google Scholar 

  • Griffin, T.W., and J. Lowenberg-DeBoer. 2005. Worldwide adoption and profitability of precision agriculture. Revista de Politica Agricola 14:20–38.

    Google Scholar 

  • Haboudane, D., J.R. Miller, N. Tremblay, P.J. Zarco-Tejada, and L. Dextraze. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81:416–426.

    Article  Google Scholar 

  • Higgins, A.J., R.C. Muchow, A.V. Rudd, and A.W. Ford. 1998. Optimising harvest date in sugar production: A case study for the Mossman mill region in Australia: I. Development of operations research model and solution. Field Crops Research 57:153–162.

    Article  Google Scholar 

  • Hurtado, E., M.M. Artigao, and V. Caselles. 1994. Estimating Maize (Zea mays) evapotranspiration from NOAA-AVHRR thermal data in the Albacete area, Spain. International Journal of Remote Sensing 15:2023–2037.

    Article  Google Scholar 

  • Inman-Bamber, N.G. 1991. A growth model for sugarcane based on a simple carbon balance and the CERES-Maize water balance. South African Journal of Plant and Soil 8:93–99.

    Google Scholar 

  • Inman-Bamber, N.G., R.C. Muchow, and M.J. Robertson. 2002. Dry matter partitioning of sugarcane in Australia and South Africa. Field Crops Research 76:71–84.

    Article  Google Scholar 

  • Irvine, J.E. 2004. Sugarcane Agronomy. In G. James (ed.), Sugarcane 2nd ed., pp. 143–159. Blackwell Science Ltd, Carlton, Australia.

    Google Scholar 

  • Jakku, E., P.J. Thorburn, Y.L. Everingham, and N.G. Inman-Bamber. 2007. Improving the participatory development of decision support systems for the sugar industry. Proceedings of the Australian Sugar Cane Technologists, Vol. 29.

    Google Scholar 

  • James, G. 2004. An introduction to sugarcane. In G. James (ed.), Sugarcane, pp. 1–19. Blackwell Science Ltd, Carlton, Australia.

    Chapter  Google Scholar 

  • Jhoty, I. 1995. Geographical Information System and related information technology for the management of sugar cane lands. In R. Antoine (ed.), Proceedings of the First Annual Meeting of Agricultural Scientists, pp. 33–36. Food and Agricultural Research Council, Reduit, Mauritius.

    Google Scholar 

  • Jhoty, I. 1999. Applications of the global positioning system in the Mauritian sugar industry. Annual Meeting of Agricultural Scientists, Food and Agriculture Research Council, Reduit, Mauritius.

    Google Scholar 

  • Jhoty, I. 2003. Application of the principles and techniques of precision agriculture to sugar cane. Revue Agricole et Sucriere de I'lle Maurice 82:87–91.

    Google Scholar 

  • Johnson, A.K.L., and D.H. Walker. 1996. Utilising GIS in the sugar industry – development of the Herbert resource information centre. Proceedings of the 18th Australian Society of Sugar Cane Technologists, Brisbane.

    Google Scholar 

  • Johnson, R.M., and E.P. Richard. 2005a. Utilisation of yield mapping and variable rate lime application in Louisiana sugarcane. Sugarcane International 23:8–14.

    Google Scholar 

  • Johnson, R.M., and E.P. Richard. 2005b. Sugarcane yield, Sugarcane quality, and soil variability in Louisiana. Agronomy Journal 97:760–771.

    Article  Google Scholar 

  • Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT cropping system model. European Journal of Agronomy 18:235–265.

    Article  Google Scholar 

  • Karnieli, A. 2003. Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments. International Journal of Biometeorology 47:179–187.

    Article  PubMed  Google Scholar 

  • Keating, B.A., M.J. Robertson, R.C. Muchow, and N.I. Huth. 1999. Modelling sugarcane production systems. I. Development and performance of the sugarcane module. Field Crops Research 61:253–271.

    Article  Google Scholar 

  • Keating, B.A., P.S. Carberry, G.L. Hammer, M.E. Probert, M.J. Robertson, D. Holzworth, N.I. Huth, J.N.G. Hargreaves, H. Meinke, Z. Hochman, G. McLean, K. Verburg, V. Snow, J.P. Dimes, M. Silburn, E. Wang, S. Brown, K.L. Bristow, S. Asseng, S. Chapman, R.L. McCown, D.M. Freebairn, and C.J. Smith. 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18:267–288.

    Article  Google Scholar 

  • Kitchen, N.R. 2008. Emerging technologies for real-time and integrated agriculture decisions. Computers and Electronics in Agriculture 61:1–3.

    Article  Google Scholar 

  • Kruger, G., R. Springer, and W. Lechner. 1994. Global Navigation Satellite Systems (GNSS). Computers and Electronics in Agriculture 11:3–21.

    Article  Google Scholar 

  • Lamb, D.W., P. Frazier, and P. Adams. 2008. Improving pathways to adoption: Putting the right P’s in precision agriculture. Computers and Electronics in Agriculture 61:4–9.

    Article  Google Scholar 

  • Lawes, R.A., M.K. Wegener, K.E. Basford, and R.J. Lawn. 2004. The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar. Australian Journal of Agricultural Research 55:335–344.

    Article  Google Scholar 

  • Lebourgeois, V., A. Begue, P. Degenne, and E. Bappel. 2007. Improving harvest and planting monitoring for smallholders with geospatial technology: the Reunion Island experience. International Sugar Journal 109:109–117.

    Google Scholar 

  • Lisson, S.N., M.J. Robertson, B.A. Keating, and R.C. Muchow. 2000. Modelling sugarcane production systems: II: Analysis of system performance and methodology issues. Field Crops Research 68:31–48.

    Article  Google Scholar 

  • Liu, D.L., and G. Kingston. 1995. QCANE: A simulation model of sugarcane growth and sugar accumulation. In M.J. Robertson (ed.), Research and Modelling Approaches to Assess Sugarcane Production Opportunities and Constraints, pp. 25–29. Workshop Proceedings, University of Queensland, St. Lucia, Brisbane.

    Google Scholar 

  • Liu, D.L., and K.R. Helyar. 2003. Simulation of seasonal stalk water content and fresh weight yield of sugarcane. Field Crops Research 82:59–73.

    Article  Google Scholar 

  • Liu, J., C.E. Goering, and L. Tian. 2001. A neural network for setting target corn yields. Transactions of the ASAE 44:705–713.

    Google Scholar 

  • Machado, S., E.D. Bynum, T.L. Archer, J. Bordovsky, D.T. Rosenow, C. Peterson, K. Bronson, D.M. Nesmith, R.J. Lascano, L.T. Wilson, and E. Segarra. 2002. Spatial and temporal variability of sorghum grain yield: Influence of soil, water, pests, and diseases relationships. Precision Agriculture 3:389–406.

    Article  Google Scholar 

  • Magalhaes, P.S.G., and D.G.P. Cerri. 2007. Yield monitoring of sugar cane. Biosystems Engineering 96:1–6.

    Article  Google Scholar 

  • Mallarino, A., and D. Wittry. 2004. Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precision Agriculture 5:131–144.

    Article  Google Scholar 

  • Marchant, J.A., R.D. Tillett, and R. Brivot. 1998. Real-time segmentation of plants and weeds. Real-Time Imaging 4:243–253.

    Article  Google Scholar 

  • Marchant, J.A., H.J. Andersen, and C.M. Onyango. 2001. Evaluation of an imaging sensor for detecting vegetation using different waveband combinations. Computers and Electronics in Agriculture 32:101–117.

    Article  Google Scholar 

  • Markley, J., A. Raines, and R. Crossley. 2003. The development and integration of remote sensing, GIS and data processing tools for effective harvest management. Australian Society of Sugar Cane Technologists 25:CD ROM.

    Google Scholar 

  • Martiné, J.F., and P. Todoroff. 2002. The growth model Mosicas and its simulation platform Simulex: assessment and perspectives. Revue Agricole et Sucrière de l'Île Maurice 81 133–147.

    Google Scholar 

  • McBratney, A.B., and M.J. Pringle. 1999. Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture 1:125–152.

    Article  Google Scholar 

  • McBratney, A., B. Whelan, T. Ancev, and J. Bouma. 2005. Future directions of precision agriculture. Precision Agriculture 6:7–23.

    Article  Google Scholar 

  • McCown, R.L., G.L. Hammer, J.N.G. Hargreaves, D.P. Holzworth, and D.M. Freebairn. 1996. APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems 50:255–271.

    Article  Google Scholar 

  • Mehrotra, R., and H.W. Siesler. 2003. Application of mid infrared/near infrared spectroscopy in sugar industry. Applied Spectroscopy Reviews 38:307–354.

    Article  CAS  Google Scholar 

  • Mo, X., S. Liu, Z. Lin, Y. Xu, Y. Xiang, and T.R. McVicar. 2005. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling 183:301–322.

    Article  Google Scholar 

  • Molin, J.P., and L.A.A. Menegatti. 2004. Field-Testing of a Sugar Cane Yield Monitor in Brazil American Society of Agricultural Engineers. Ottawa, Ontario, Canada.

    Google Scholar 

  • Moran, C.J., and E.N. Bui. 2002. Spatial data mining for enhanced soil map modelling. International Journal of Geographical Information Systems 16:533–549.

    Article  Google Scholar 

  • Moran, M.S., Y. Inoue, and E.M. Barnes. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment 61:319–346.

    Article  Google Scholar 

  • Muchow, R.C., A.J. Higgins, A.V. Rudd, and A.W. Ford. 1998. Optimising harvest date in sugar production: a case study for the Mossman mill region in Australia: II. Sensitivity to crop age and crop class distribution. Field Crops Research 57:243–251.

    Article  Google Scholar 

  • Mutanga, O., A.K. Skidmore, and H.H.T. Prins. 2004. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89:393–408.

    Article  Google Scholar 

  • Neale, T. 2007. Making new technology pay: A farm to industry perspective. In R.K. Jones (ed.), SRDC Technical Report. Research and Development Strategies to Advance the Australian Sugarcane Industry Vol. 1. Sugar Research and Development Corporation, Brisbane.

    Google Scholar 

  • Nelson, P.N., and G.J. Ham. 2000. Exploring the response of sugar cane to sodic and saline conditions through natural variation in the field. Field Crops Research 66:245–255.

    Article  Google Scholar 

  • O'Leary, G.J. 2000. A review of three sugarcane simulation models with respect to their prediction of sucrose yield. Field Crops Research 68:97–111.

    Article  Google Scholar 

  • Pagnano, N.B., and P.S.G. Magalhaes. 2001. Sugarcane Yield Measurement. 3rd European Conference on Precision Agriculture. Montpellier, France.

    Google Scholar 

  • Parkes, M., and R. Panelli. 2001. Integrating catchment ecosystems and community health: The value of participatory action research. Ecosystem Health 7:85–106.

    Article  Google Scholar 

  • Pelletier, G., and S.K. Upadhyaya. 1999. Development of a tomato load/yield monitor. Computers and Electronics in Agriculture 23:103–117.

    Article  Google Scholar 

  • Pierce, F.J., and P. Nowak. 1999. Aspects of precision agriculture. Advances in Agronomy 67: 1–85.

    Article  Google Scholar 

  • Pimentel, D., S. Cooperstein, H. Randell, D. Filiberto, S. Sorrentino, B. Kaye, C. Nicklin, J. Yagi, J. Brian, J. O’Hern, A. Habas, and C. Weinstein. 2007. Ecology of increasing diseases: population growth and environmental degradation. Human Ecology 35:653–668.

    Article  Google Scholar 

  • Price, R.R., J.L. Larsen, and A. Peters. 2007. Development of an optical yield monitor for sugar cane harvesting. ASABE Annual International Meeting, Minneapolis, Minnesota. 17–20 June 2007.

    Google Scholar 

  • Pringle, M.J., A.B. McBratney, B.M. Whelan, and J.A. Taylor. 2003. A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data. Agricultural Systems 76:273–292.

    Article  Google Scholar 

  • Rao, N., P. Garg, and S. Ghosh. 2007. Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture 8:173–185.

    Article  Google Scholar 

  • Rogers, E.M. 2003. Diffusion of Innovations 5th ed. The Free Press, New York.

    Google Scholar 

  • Roloff, G., and D. Focht. 2006. Brazil. In A. Srinivasan (ed.), Handbook of Precision Agriculture: Principles and Applications, pp. 635–656. The Haworth Press, Binghampton, New York.

    Google Scholar 

  • Saraiva, A.M., A.R. Hirakawa, C.E. Cugnasca, M.A. Pierossi, and S.J. Hassuani. 2000. A weighing system for grab loaders for sugar cane yield mapping. Precision Agriculture 2:293–309.

    Article  Google Scholar 

  • Schmidt, E.J., C. Gers, G. Narciso, and P. Frost. 2001. Remote sensing in the South African sugar industry. Proceedings of the 24th Congress of the International Society of Sugar Cane Technologists ASSCT, Brisbane.

    Google Scholar 

  • Simbahan, G.C., A. Dobermann, and J.L. Ping. 2004. Screening yield data improves grain yield maps. Agronomy Journal 96:1091–1102.

    Article  Google Scholar 

  • Singels, A. 2007. A new approach to implementing computer-based decision support for sugarcane farmers and extension staff. The case of My Canesim. Proceedings of the International Society of Sugar Cane Technologists 26:211–219.

    Google Scholar 

  • Singels, A., and R.A. Donaldson. 2000. A simple model of unstressed sugarcane canopy development. Proceedings of the South African Sugar Technologists Association, Vol. 74, pp. 151–154.

    Google Scholar 

  • Singels, A., and C.N. Bezuidenhout. 2002. A new method of simulating dry matter partitioning in the Canegro sugarcane model. Field Crops Research 78:151–164.

    Article  Google Scholar 

  • Sparovek, G., and E. Schnug. 2001. Soil tillage and precision agriculture: A theoretical case study for soil erosion control in Brazilian sugar cane production. Soil and Tillage Research 61:47–54.

    Article  Google Scholar 

  • Srinivasan, A. 2006. Precision Agriculture: An Overview. In A. Srinivasan (ed.), Handbook of Precision Agriculture: Principles and Applications, pp. 3–18. The Haworth Press Inc, Binghamton, New York.

    Google Scholar 

  • Stafford, J.V. 2006. The role of technology in the emergence and current status of precision agriculture. In A. Srinivasan (ed.), Handbook of Precision Agriculture: Principles and Applications, pp. 19–56. The Haworth Press Inc, Bringham, New York.

    Google Scholar 

  • Star, S., and J. Griesemer. 1989. Institutional ecology, translations, and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology 1907–1939. Social Studies of Sciences 19:387–420.

    Article  Google Scholar 

  • Stöckle, C.O., M. Donatelli, and R. Nelson. 2003. CropSyst, a cropping systems simulation model. European Journal of Agronomy 18:289–307.

    Article  Google Scholar 

  • Tangwongkit, R., V. Salokhe, and H. Jayasuriya. 2006. Development of a tractor mounted real-time, variable rate herbicide applicator for sugarcane planting. Agricultural Engineering International: the CIGR Ejournal 8:1–11.

    Google Scholar 

  • Thorburn, P.J., M.E. Probert, and F.A. Robertson. 2001. Modelling decomposition of sugar cane surface residues with APSIM-Residue. Field Crops Research 70:223–232.

    Article  Google Scholar 

  • Tukey, J.W. 1977. Exploratory data analysis. Addison-Wesley Reading, Massachusetts.

    Google Scholar 

  • Ulbricht, K.A., and W.D. Heckendorff. 1998. Satellite images for recognition of landscape and landuse changes. ISPRS Journal of Photogrammetry and Remote Sensing 53:235–243.

    Article  Google Scholar 

  • Van den Berg, M., P.A. Burrough, and P.M. Driessen. 2000. Uncertainties in the appraisal of water availability and consequences for simulated sugarcane yield potentials in Sao Paulo State, Brazil. Agriculture, Ecosystems & Environment 81:43–55.

    Article  Google Scholar 

  • Whelan, B.M., and A.B. McBratney. 2000. The “null hypothesis” of precision agriculture management. Precision Agriculture 2:265–279.

    Article  Google Scholar 

  • Wong, M., and S. Asseng. 2006. Determining the causes of spatial and temporal variability of wheat yields at sub-field scale using a new method of upscaling a crop model. Plant and Soil 283:203–215.

    Article  CAS  Google Scholar 

  • Wood, A.W., L.P. Dibella, R.M. Pace, and B.L. Schroeder. 2005. Optimising season length to increase industry profitability in the Herbert River District, Queensland, Australia. Proceedings of the South African Sugar Technologists Association, Vol. 79, pp. 443–446.

    Google Scholar 

  • Xin, J., Z. Yu, L. van Leeuwen, and P.M. Driessen. 2002. Mapping crop key phenological stages in the North China Plain using NOAA time series images. International Journal of Applied Earth Observation and Geoinformation 4:109–117.

    Article  Google Scholar 

  • Zaizhi, Z. 2000. Landscape changes in a rural area in China. Landscape and Urban Planning 47:33–38.

    Article  Google Scholar 

  • Zhao, D.H., J.L. Li, and J.G. Qi. 2005. Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage. Computers and Electronics in Agriculture 48:155–169.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Australian Sugar Research and Development Corporation for funding this research.

Author information

Authors and Affiliations

  1. School of Engineering and Physical Sciences, James Cook University, 4811, Townsville, Queensland, Australia

    Daniel Zamykal

  2. School of Maths, Physics and IT, James Cook University, 4811, Townsville, Queensland, Australia

    Yvette L. Everingham

Authors
  1. Daniel Zamykal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yvette L. Everingham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Zamykal .

Editor information

Editors and Affiliations

  1. INRA-CMSE-PME, rue Sully 17, Dijon CX, 21065, France

    Eric Lichtfouse

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Zamykal, D., Everingham, Y.L. (2009). Sugarcane and Precision Agriculture: Quantifying Variability Is Only Half the Story – A Review. In: Lichtfouse, E. (eds) Climate Change, Intercropping, Pest Control and Beneficial Microorganisms. Sustainable Agriculture Reviews, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2716-0_9

Download citation

Publish with us

Policies and ethics

Search

Navigation