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
M. R. Anwar A , G. J. O’Leary A C , M. A. Rab B , P. D. Fisher B and R. D. Armstrong AA Department of Primary Industries, PB 260, Horsham, Vic. 3400, Australia.
B Department of Primary Industries, PB 1 Tatura, Vic. 3616, Australia.
C Corresponding author. Email: garry.o’leary@dpi.vic.gov.au
Crop and Pasture Science 60(9) 901-911 https://doi.org/10.1071/CP08351
Submitted: 9 October 2008 Accepted: 24 July 2009 Published: 8 September 2009
Abstract
Spatial variability in grain yield across a paddock often indicates spatial variation in soil properties, especially in regions like the Victorian Mallee. We combined 2 years of field data and 119 years of simulation experiments (APSIM-Wheat and APSIM-Barley crop models) to simulate crop yield at various levels of N application in 4 different management zones to explore the robustness of the zones previously determined for an experimental site at Birchip. The crop models explained 96% and 67% of the observed variability in wheat and barley grain yields, with a root mean square error (RMSE) of 310 kg/ha and 230 kg/ha, respectively. The model produced consistent responses to the observed data from the field experiment in 2004 and 2005 where a high and stable yielding zone produced the highest dry matter as well as grain yield, while a low and variable zone recorded the lowest grain yield. However, from the long-term (119 years) simulation, the highest median wheat yield value was obtained on the low variable zone (2911 kg/ha) with high N fertiliser application, while the lowest was obtained on the high variable zone (851 kg/ha). Similarly, the highest barley yields (1880–3350 kg/ha) occurred on the low variable zone using the long-term simulation. In 10–20% of years the highest yield occurred in the high-yielding zones, with the variable and stable zones changing rank with interactive behaviour only under early-sown conditions. Our analyses highlight the problem of using a limited range of seasons of different weather conditions in agronomy to make strategic conclusions as the long-term simulation did not confirm the original yield zone determination. The challenge ahead is to predict in advance the seasons where application of N fertiliser will be beneficial.
Additional keyword: simulation.
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 are grateful to Colin Aumann, Tony Fay, Ashley Waite (DPI Victoria), Bobby Liston, and Cherie Rielly (from BCG) for providing technical and logistic support. We especially thank Janine Fitzpatrick (DPI) for the processing of the yield component samples. We are especially grateful to Ian and Warrick McClelland for allowing access to their paddock and providing assistance throughout the conduct of the field studies. An anonymous referee made helpful suggestions on an earlier draft.
Anwar MR,
Rodriguez D,
Liu DL,
Power S, O’Leary GJ
(2008a) Quality and potential utility of ENSO-based forecasts of spring rainfall and wheat yield in south-eastern Australia. Australian Journal of Agricultural Research 59, 112–126.
| Crossref | GoogleScholarGoogle Scholar |
(verified June 2006).
Nuttall JG,
Armstrong RD, Connor DJ
(2003a) Evaluating physicochemical constraints of Calcarosols on wheat yield in the Victorian southern Mallee. Australian Journal of Agricultural Research 54, 487–497.
| Crossref | GoogleScholarGoogle Scholar |
(verified 27 October 2008).
Rab MA,
Fisher PD,
Armstrong R,
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.
Robinson NJ,
Rampant PC,
Callinan APL,
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.
Rodriguez D,
Nuttall J,
Sadras VO,
van Rees H, Armstrong R
(2006) Impact of subsoil constraints on wheat yield and gross margin on fine-textured soils of the southern Victorian Mallee. Australian Journal of Agricultural Research 57, 355–365.
| Crossref | GoogleScholarGoogle Scholar |
Sadler EJ,
Sudduth KA, Jones JW
(2007) Separating spatial and temporal sources of variation for model testing in precision agriculture. Precision Agriculture 8, 297–310.
| Crossref | GoogleScholarGoogle Scholar |
Sadras V,
Baldock J,
Roget D, Rodriguez D
(2003) Measuring and modelling yield and water budget components of wheat crops in coarse-textured soils with chemical constraints. Field Crops Research 84, 241–260.
| Crossref | GoogleScholarGoogle Scholar |
van Herwaarden AF,
Angus JF,
Richards RA, Farquhar GD
(1998b) ‘Haying-off’, the negative grain yield response of dryland wheat to nitrogen fertiliser. II. Carbohydrate and protein dynamics. Australian Journal of Agricultural Research 49, 1083–1093.
| Crossref | GoogleScholarGoogle Scholar |
van Herwaarden AF,
Farquhar GD,
Angus JF,
Richards RA, Howe GN
(1998a) ‘Haying-off’’, the negative grain yield response of dryland wheat to nitrogen fertiliser. 1. Biomass, grain yield, and water-use. Australian Journal of Agricultural Research 49, 1067–1081.
| Crossref | GoogleScholarGoogle Scholar |