Pay-offs to zone management in a variable climate: an example of nitrogen fertiliser on wheat
L. E. Brennan A D , M. J. Robertson B , N. P. Dalgliesh C and S. Brown AA CSIRO Sustainable Ecosystems, 306 Carmody Rd, St Lucia, Qld 4067, Australia.
B CSIRO Sustainable Ecosystems, Private Bag 5 PO, Wembley, WA 6913, Australia.
C CSIRO Sustainable Ecosystems, PO Box 102, Toowoomba, Qld 4350, Australia.
D Corresponding author. Email: Lisa.Brennan@csiro.au
Australian Journal of Agricultural Research 58(11) 1046-1058 https://doi.org/10.1071/AR06257
Submitted: 7 August 2006 Accepted: 20 June 2007 Published: 26 November 2007
Abstract
Temporal variability affects the profitability of zone management of nitrogen, particularly in the north-eastern grain-growing region of Australia. This paper presents a framework for systematically investigating the effect of the interaction between spatial and temporal variability on economic performance, their relative importance, and the value of spatially variable nitrogen management to a farmer with and without knowledge about the coming season. The paper also addresses the degree to which economic performance is influenced by the relative sizes of management zones for fertiliser inputs, prices, and the shape of the biophysical response to fertiliser in each zone. The analysis was based on a single field exhibiting spatial variability. Scenario analysis of seasonally and/or spatially adjusted nitrogen management strategies was based on response functions generated by the cropping systems model APSIM. The analysis shows that seasonal and spatial interactions in nitrogen management are significant issues for decision makers. In this case, knowledge of the coming season is worth more than knowledge of spatial variability, but knowledge of both creates the greatest value. The functional relationship between yields and fertiliser levels for a given crop also determines the economic value of variable-rate nitrogen. A field may exhibit yield variability but this does not automatically present a case for spatially variable nitrogen management. If economic optima of different payoff curves are aligned then returns to zone management will be limited, despite significant differences in yield between different zones.
Additional keywords: precision agriculture, economics, temporal variability, simulation.
Acknowledgments
We acknowledge the valuable support provided by collaborating grain grower, Michael Smith. Research funding from the Grains Research and Development Corporation and the Rural Industries Research and Development Corporation is also acknowledged.
Anderson JR
(1975) One more or less cheer for optimality. Journal of the Australian Institute of Agricultural Science 41, 195–197.
Anselin L,
Bongiovani R, Lowenberg-DeBoer J
(2004) A spatial econometric approach to the economics of site-specific nitrogen management in corn production. American Journal of Agricultural Economics 86, 675–687.
| Crossref | GoogleScholarGoogle Scholar |
Booltink HWG,
van Alphen BJ,
Batchelor WD,
Paz JO,
Stoorvogel JJ, Vargas R
(2001) Tools for optimising management of spatially-variable fields. Agricultural Systems 70, 445–476.
| Crossref | GoogleScholarGoogle Scholar |
Bullock D,
Lowenberg-DeBoer J, Swinton S
(2002) Adding value to spatially managed inputs by understanding site-specific yield response. Agricultural Economics 27, 233–245.
| Crossref | GoogleScholarGoogle Scholar |
Bullock DS, Bullock DG
(2000) From agronomic research to farm management guidelines: a primer on the economics of information and precision technology. Precision Agriculture 2, 71–101.
| Crossref | GoogleScholarGoogle Scholar |
Daberkow SG, McBride WD
(2003) Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precision Agriculture 4, 163–177.
| Crossref | GoogleScholarGoogle Scholar |
Doughton JA,
Vallis I, Saffigna PG
(1993) Nitrogen fixation in chickpea. 1. Influence of prior cropping or fallow, nitrogen fertilizer and tillage. Australian Journal of Agricultural Research 44, 1403–1413.
| Crossref | GoogleScholarGoogle Scholar |
Fawcett RG
(1977) A cone penetrometer for estimating available soil water. Australian Journal of Experimental Agriculture and Animal Husbandry 17, 842–848.
| Crossref | GoogleScholarGoogle Scholar |
Godwin RJ,
Wood GA,
Taylor JC,
Knight SM, Welsh JP
(2003) Precision farming of cereal crops: a review of a six year experiment to development management guidelines. Biosystems Engineering 84, 375–391.
| Crossref | GoogleScholarGoogle Scholar |
Hammer GL,
Holzworth DP, Stone R
(1996) The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability. Australian Journal of Agricultural Research 47, 717–737.
| Crossref | GoogleScholarGoogle Scholar |
Hayman PT, Alston CL
(1999) A survey of farmer practices and attitudes to nitrogen management in the northern New South Wales grains belt. Australian Journal of Experimental Agriculture 39, 51–63.
| Crossref | GoogleScholarGoogle Scholar |
Isik M, Khanna M
(2003) Stochastic technology, risk preferences, and adoption of site-specific technologies. American Journal of Agricultural Economics 85, 305–317.
| Crossref | GoogleScholarGoogle Scholar |
Keating BA,
Carberry PS,
Hammer GL,
Probert ME, Robertson MJ , et al.
(2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.
| Crossref | GoogleScholarGoogle Scholar |
Kingwell R, Pannell D
(2005) Economic trends and drivers affecting the wheatbelt of Western Australia to 2030. Australian Journal of Agricultural Research 56, 553–561.
| Crossref | GoogleScholarGoogle Scholar |
McCown RL,
Hammer GL,
Hargreaves JNG,
Holzworth DP, Freebairn DM
(1996) APSIM: A novel software system for model development, model testing, and simulation in agricultural systems research. Agricultural Systems 50, 255–271.
| Crossref | GoogleScholarGoogle Scholar |
Probert ME,
Dimes JP,
Keating BA,
Dalal RC, Strong WM
(1997) APSIM’s water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural Systems 56, 1–28.
| Crossref | GoogleScholarGoogle Scholar |
Probert ME,
Keating BA,
Thompson JP, Parton WJ
(1995) Modelling water, nitrogen and crop yield for a long-term fallow management experiment. Australian Journal of Experimental Agriculture 35, 941–950.
| Crossref | GoogleScholarGoogle Scholar |
Ritchie JT
(1981) Soil water availability. Plant and Soil 58, 327–338.
| Crossref | GoogleScholarGoogle Scholar |
Stafford JV
(2000) Implementing precision agriculture in the 21st Century. Journal of Agricultural Engineering Research 76, 267–275.
| Crossref | GoogleScholarGoogle Scholar |
Strong WM,
Harbison J,
Nielsen RGH,
Hall BD, Best EK
(1986) Nitrogen availability in a Darling Downs soil following cereal, oilseed and grain legume crops. 1. Soil nitrogen accumulation. Australian Journal of Experimental Agriculture 26, 347–351.
| Crossref | GoogleScholarGoogle Scholar |
Whelan BM, McBratney AB
(2000) The ‘null hypothesis’ of precision agriculture management. Precision Agriculture 2, 265–279.
| Crossref | GoogleScholarGoogle Scholar |
Zhang N,
Wang M, Wang N
(2002) Precision agriculture: a worldwide overview. Computers and Electronics in Agriculture 36, 113–132.
| Crossref | GoogleScholarGoogle Scholar |