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An uncertainty-based comprehensive decision support system for site-specific crop management

Published online by Cambridge University Press:  01 June 2017

V. Adamchuk ,
R. Lacroix ,
S. Shinde ,
N. Tremblay  and
H. Huang
V. Adamchuk*
Affiliation:
McGill University, Ste-Anne-de-Bellevue, QC, H9X 3V9, Canada
R. Lacroix
Affiliation:
Valacta, Ste-Anne-de-Bellevue, QC, H9X 3R4, Canada
S. Shinde
Affiliation:
McGill University, Ste-Anne-de-Bellevue, QC, H9X 3V9, Canada
N. Tremblay
Affiliation:
Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC, J3B 3E6, Canada
H. Huang
Affiliation:
McGill University, Ste-Anne-de-Bellevue, QC, H9X 3V9, Canada
*
† E-mail: viacheslav.adamchuk@mcgill.ca
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Abstract

This paper presents a framework for the implementation of a decision support system that considers spatial, temporal and managerial factors in assessing the potential impact of crop amendments on the cost of a given production scenario. The proposed system includes a database and a numeric simulation model. The database is linked to previously recorded crop responses for a given agricultural input under different conditions while the numeric simulation model determines the probability of different levels of profit for each decision option. This system then determines the optimal uniform rate of application of an amendment to maximize profits, or to define the range of such rates for a case of variable rate application. Uncertainty-based treatment of each model input allows for a balance between the potential results of under-application or over-application.

Keywords

Crop fertilizationdecision support systemsproduction functionprofitabilityuncertainty
Type
Information and Decision Support Systems
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 625 - 629
Copyright
© The Animal Consortium 2017. This is a work of the U.S. Government and is not subject to copyright protection in the United States 

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References

Adamchuck, VI 2013. Theoretical basis for sensor-based in-season nitrogen management. In: Precision Agriculture: Papers from the 9th European Conference on Precision Agriculture, ed. J. Stafford, Wageningen Academic Publishing, Wageningen, The Netherlands, pp. 403–410.CrossRefGoogle Scholar
Adamchuck, VI, Morgan, MT and Lowenberg-Deboer, J 2004. A model for agro-economic analysis of soil pH mapping. Precision Agriculture 5 (2), 111–129.Google Scholar
Anselin, L, Bongiovanni, R and 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 (3), 675–687.Google Scholar
Bachmaier, M 2012. Sources of inaccuracy when estimating economically optimum N fertilizer rates. Agricultural Sciences 3 (3), 331–338.Google Scholar
Bongiovanni, R and Lowenberg-DeBoer, J 2000. Economics of variable rate lime in Indiana. Precision Agriculture 2 (1), 55–70.CrossRefGoogle Scholar
Bullock, G and Bullock, DD 1994. Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: a comparison. Agronomy Journal 86, 191–195.Google Scholar
Griffin, T, Dobbins, C, Vyn, T, Florax, R and Lowenberg-DeBoer, J 2008. Spatial analysis of yield monitor data: Case studies of on-farm trials and farm management decision making. Precision Agriculture 9 (5), 269–283.Google Scholar
Holland, KH and Schepers, JS 2010. Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agronomy Journal 102 (5), 1415–1424.Google Scholar
McBratney, AB, Whelan, BM and Shatar, TM 1997. Variability and uncertainty in spatial, temporal and spatiotemporal crop-yield and related data. In: Novartis Foundation Symposium, Ciba Foundation, pp. 141–160.CrossRefGoogle Scholar
Rossiter, DG 2016. Technical note: Curve fitting with the R environment for statistical computing. http://www.css.cornell.edu/faculty/dgr2/teach/R/R_CurveFit.pdf (accessed on November 11, 2016).Google Scholar
Samborski, SM, Tremblay, N and Fallon, E 2009. Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agronomy Journal 101 (4), 800–816.Google Scholar
Tremblay, N, Bouroubi, YM, Bélec, C, Mullen, RW, Kitchen, NR and Thomason, WE 2012. Corn response to nitrogen is influenced by soil texture and weather. Agronomy Journal 104 (6), 1658–1671.Google Scholar
Tremblay, N, Bouroubi, YM, Panneton, B, Guillaume, S, Vigneault, P and Bélec, C 2010. Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features. Precision Agriculture 11 (6), 621–635.Google Scholar
Tumusiime, E, Brorsen, BW, Mosali, J, Johnson, J, Locke, J and Biermacher, JT. 2011. Determining optimal levels of nitrogen fertilizer using random parameter models. Journal of Agricultural and Applied Economics 43 (4), 541–552.Google Scholar