Articles
ASSESSING N FERTILISER REQUIREMENTS AND THE RELIABILITY OF DIFFERENT RECOMMENDATION SYSTEMS
Article number
700_2
Pages
35 – 48
Language
English
Abstract
This paper reviews the factors governing the N fertiliser requirements of vegetable crops and discusses how well these are accounted for in the different approaches currently used for making fertiliser recommendations within Europe.
Evidence is presented to show that methods based on look-up tables and measurements of Nmin can give reliable predictions under average soil and weather conditions, but are limited by their inability to adjust for variations in expected yield or N released by mineralization.
The greater flexibility of the N balance sheet method and (in particular) decision support systems (DSS) based on simulation models can improve reliability by allowing recommendations to be adjusted for local soil and weather conditions, albeit at the expense of increased complexity.
Despite the latter, there has been an excellent take up of the WELL_N DSS in the UK because it guides users through the wide range of options available.
However, even the most complex system cannot account for the effects of unexpected weather on N supply during growth.
Difficulties in interpreting N response data are discussed, and guidelines given for evaluating the performance of different recommendation methods using response experiments.
Opportunities for exploiting an intelligent DSS incorporating several different recommendation methods, which uses previous experience about a crop and/or site to automatically select the best N fertiliser recommendation are discussed.
Evidence is presented to show that methods based on look-up tables and measurements of Nmin can give reliable predictions under average soil and weather conditions, but are limited by their inability to adjust for variations in expected yield or N released by mineralization.
The greater flexibility of the N balance sheet method and (in particular) decision support systems (DSS) based on simulation models can improve reliability by allowing recommendations to be adjusted for local soil and weather conditions, albeit at the expense of increased complexity.
Despite the latter, there has been an excellent take up of the WELL_N DSS in the UK because it guides users through the wide range of options available.
However, even the most complex system cannot account for the effects of unexpected weather on N supply during growth.
Difficulties in interpreting N response data are discussed, and guidelines given for evaluating the performance of different recommendation methods using response experiments.
Opportunities for exploiting an intelligent DSS incorporating several different recommendation methods, which uses previous experience about a crop and/or site to automatically select the best N fertiliser recommendation are discussed.
Publication
Authors
L.G. Burns
Keywords
N recommendations, look-up tables, Nmin method, N balance sheet method, decision support systems, simulation models, N response curves
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