Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers’ historical paddock yields and normalised difference vegetation index
P. D. Fisher A D , M. Abuzar B , M. A. Rab A , F. Best C and S. Chandra AA Department of Primary Industries, 255 Ferguson Road, Tatura, Vic. 3616, Australia.
B Department of Primary Industries, 32 Lincoln Square North, Carlton, Vic. 3053, Australia.
C Birchip Cropping Group, 73 Cumming Ave, Birchip, Vic. 3483, Australia.
D Corresponding author. Email: peter.fisher@dpi.vic.gov.au
Crop and Pasture Science 60(9) 844-858 https://doi.org/10.1071/CP08347
Submitted: 9 October 2008 Accepted: 29 July 2009 Published: 8 September 2009
Abstract
Despite considerable interest by Australian farmers in precision agriculture (PA), its uptake has been low. Analysis of the possible financial benefits of alternative management options that are based on the underlying patterns of observed spatial and temporal yield variability in a paddock could increase farmer confidence in adopting PA.
The cost and difficulty in collecting harvester yield maps have meant that spatial yield data are generally not available in Australia. This study proposes a simple, economical and easy to use approach to generate simulated yield maps by using paddock-specific relationships between satellite normalised difference vegetation index (NDVI) and the farmer’s average paddock yield records. The concept behind the approach is illustrated using a limited dataset. For each of 12 paddocks in a property where a farmer’s paddock-level yield data were available for 3–5 years, the paddock-level yields showed a close to linear relationship with paddock-level NDVI across seasons. This estimated linear relationship for each paddock was used to simulate mean yields for the paddock at the subpaddock level at which NDVI data were available. For one paddock of 167 ha, for which 4 years of harvester yield data and 6 years of NDVI data were available, the map of simulated mean yield was compared with the map of harvester mean yield. The difference between the two maps, expressed as percentage deviation from the observed mean yield, was <20% for 63% of the paddock and <40% for 78% of the paddock area. For 3 seasons when there were both harvester yield data and NDVI data, the individual season simulated yields were within 30% of the observed yields for over 70% of the paddock area in 2 of the seasons, which is comparable with spatial crop modelling results reported elsewhere. For the third season, simulated yields were within 30% of the observed yield in only 22% of the paddock, but poor seasonal conditions meant that 40% of the paddock yielded <100 kg/ha. To illustrate the type of financial analysis of alternative management options that could be undertaken using the simulated yield data, a simple economic analysis comparing uniform v. variable rate nitrogen fertiliser is reported. This indicated that the benefits of using variable rate technology varied considerably between paddocks, depending on the degree of spatial yield variability. The proposed simulated yield mapping requires greater validation with larger datasets and a wider range of sites, but potentially offers growers and land managers a rapid and cost-effective tool for the initial estimation of subpaddock yield variability. Such maps could provide growers with the information necessary to carry out on-farm testing of the potential benefits of using variable applications of agronomic inputs, and to evaluate the financial benefits of greater investment in PA technology.
Additional keywords: yield map, variable rate technology, remote sensing, yield prediction, spatial variability, management zone.
Acknowledgments
This research was supported by funding from the Grains Research and Development Corporation through its Precision Agriculture Initiative (SIP09), and the Victorian Department of Primary Industries. The authors would like to thank members of the Birchip Cropping Group, Colin Aumann, Grant Boyle, Tony Fay, Janine Fitzpatrick, Ian Maling, Nick O’Halloran, Cressida Savige, and Cherie Reilly for their support and contributions to this paper. We would particularly like to thank Ian and Warrick McCelland for their support and enthusiasm throughout this project, and without whose generosity in time, equipment, and access to paddocks this project would not have been possible.
Anwar MR,
O’Leary GJ,
Rab MA,
Fisher PD, Armstrong RD
(2009) Advances in precision agriculture in south-eastern Australia. V. Effect of seasonal conditions on wheat and barley yield response to applied nitrogen across management zones. Crop & Pasture Science 60, 901–911.
(accessed 27 July 2008).
Labus MP,
Nielsen GA,
Lawrence RL, Engel R
(2002) Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote Sensing 23, 4169–4180.
| Crossref | GoogleScholarGoogle Scholar |
Lamb DW
(2000) The use of qualitative airborne multispectral imaging for managing agricultural crops—a case study in south-eastern Australia. Australian Journal of Experimental Agriculture 40, 725–738.
| Crossref | GoogleScholarGoogle Scholar |
MacDonald RB, Hall FG
(1980) Global crop forecasting. Science 208, 670–679.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Morgan CLS,
Norman JM, Lowery B
(2003) Estimating plant-available water across a field with inverse yield model. Soil Science Society of America Journal 67, 620–629.
|
CAS |
Rab MA,
Fisher PD,
Armstrong RD,
Abuzar M,
Robinson NJ, Chandra S
(2009) Advances in precision agriculture in south-eastern Australia. IV. Spatial variability in plant-available water capacity of soil and its relationship with yield in site-specific management zones. Crop & Pasture Science 60, 885–900.
Raun WR,
Johnson GV,
Stone ML,
Solie JB,
Thomson WE, Lukins EV
(1999) In-season prediction of yield potential in winter wheat. Better Crops 83, 24–25.
Robertson M,
Isbister B,
Maling I,
Oliver Y,
Wong M,
Adams M,
Bowden B, Tozer P
(2007) Opportunities and constraints for managing within-field spatial variability in Western Australian grain production. Field Crops Research 104, 60–67.
| Crossref | GoogleScholarGoogle Scholar |
Robinson NJ,
Rampant PC,
Callinan LAP,
Rab MA, Fisher PD
(2009) Advances in precision agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data. Crop & Pasture Science 60, 859–869.
Roy PS, Ravan SA
(1996) Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. Journal of Biosciences 21, 535–561.
| Crossref | GoogleScholarGoogle Scholar |
Rudorff BFT, Batista GT
(1990) Spectral response of wheat and its relationship to agronomic variables in the tropical region. Remote Sensing of Environment 31, 53–63.
| Crossref | GoogleScholarGoogle Scholar |
Scotford IM, Miller PCH
(2005) Applications of spectral reflectance techniques in Northern European cereal production: a review. Biosystems Engineering 90, 235–250.
Serrano L,
Filella I, Penuelas J
(2000) Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 40, 723–731.
Shanahan JF,
Doerge TA,
Johnson JJ, Vigil MF
(2004) Feasibility of site-specific management of corn hybrids and plant densities in the great plains. Precision Agriculture 5, 207–225.
| Crossref | GoogleScholarGoogle Scholar |
Shanahan JF,
Schepersa JS,
Francisa DD,
Varvela GE,
Wilhelma WW,
Tringea JM,
Schlemmera MR, Majorb DJ
(2001) Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal 93, 583–589.
Smith RCG,
Adams J,
Stephens DJ, Hick PT
(1995) Forecasting wheat yield in a Mediterranean-type environment from the NOAA Satellite. Australian Journal of Agricultural Research 46, 113–125.
| Crossref | GoogleScholarGoogle Scholar |
Weissteiner CJ, Kühbauch W
(2005) Regional yield forecasts of malting barley (Hordeum vulgare L.) by NOAA-AVHRR remote sensing data and ancillary data. Journal of Agronomy & Crop Science 191, 308–320.
| Crossref | GoogleScholarGoogle Scholar |
Whelan BM, McBratney AB
(2000) The ‘null hypothesis’ of precision agriculture management. Precision Agriculture 2, 265–279.
| Crossref | GoogleScholarGoogle Scholar |
