NDLI: Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features

Content Provider	Springer Nature Link
Author	Tremblay, N. Bouroubi, M. Y. Panneton, B. Guillaume, S. Vigneault, P. Bélec, C.
Copyright Year	2010
Abstract	A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha$^{−1}$ conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (EC$_{a}$), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m$^{2}$ resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low EC$_{a}$, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving EC$_{a}$, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha$^{−1}$ compared to the recommended uniform rate of 170 kg N ha$^{−1}$, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.
Starting Page	621
Ending Page	635
Page Count	15
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	11
Issue Number	6
e-ISSN	15731618
Language	English
Publisher	Springer US
Publisher Date	2010-08-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Precision farming Variable-rate technology Fuzzy inference systems Topography Apparent electrical conductivity (EC$_{a}$) Nitrogen sufficiency index (NSI) Meteorology/Climatology Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Remote Sensing/Photogrammetry Soil Science & Conservation Agriculture
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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