| dc.contributor.author |
Papadopoulos, A |
en |
| dc.contributor.author |
Kalivas, D |
en |
| dc.contributor.author |
Hatzichristos, T |
en |
| dc.date.accessioned |
2014-06-06T06:51:15Z |
|
| dc.date.available |
2014-06-06T06:51:15Z |
|
| dc.date.issued |
2011 |
en |
| dc.identifier.issn |
01681699 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.compag.2011.06.007 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5411 |
|
| dc.subject |
Cotton |
en |
| dc.subject |
Decision support |
en |
| dc.subject |
Fertilization |
en |
| dc.subject |
Fuzzy logic |
en |
| dc.subject |
Nitrogen |
en |
| dc.subject |
Site specific management |
en |
| dc.subject.other |
Agricultural conditions |
en |
| dc.subject.other |
Agricultural land |
en |
| dc.subject.other |
Decision supports |
en |
| dc.subject.other |
Design and application |
en |
| dc.subject.other |
Empirical approach |
en |
| dc.subject.other |
Farming practices |
en |
| dc.subject.other |
Fertilization |
en |
| dc.subject.other |
Fuzzy decision support system |
en |
| dc.subject.other |
Fuzzy logic methodology |
en |
| dc.subject.other |
Fuzzy theory |
en |
| dc.subject.other |
Geographical Information System |
en |
| dc.subject.other |
Knowledge base |
en |
| dc.subject.other |
Level structure |
en |
| dc.subject.other |
Nitrogen balance |
en |
| dc.subject.other |
Nitrogen fertilization |
en |
| dc.subject.other |
Point data |
en |
| dc.subject.other |
Reference sites |
en |
| dc.subject.other |
Site specific management |
en |
| dc.subject.other |
Site-specific |
en |
| dc.subject.other |
State variables |
en |
| dc.subject.other |
System use |
en |
| dc.subject.other |
Agriculture |
en |
| dc.subject.other |
Artificial intelligence |
en |
| dc.subject.other |
Cotton |
en |
| dc.subject.other |
Decision making |
en |
| dc.subject.other |
Decision support systems |
en |
| dc.subject.other |
Decision theory |
en |
| dc.subject.other |
Fuzzy systems |
en |
| dc.subject.other |
Geographic information systems |
en |
| dc.subject.other |
Knowledge based systems |
en |
| dc.subject.other |
Nitrogen |
en |
| dc.subject.other |
Nitrogen fertilizers |
en |
| dc.subject.other |
Sensitivity analysis |
en |
| dc.subject.other |
Uncertainty analysis |
en |
| dc.subject.other |
Fuzzy logic |
en |
| dc.subject.other |
agricultural land |
en |
| dc.subject.other |
agricultural practice |
en |
| dc.subject.other |
agrochemical |
en |
| dc.subject.other |
carbon |
en |
| dc.subject.other |
decadal variation |
en |
| dc.subject.other |
decision support system |
en |
| dc.subject.other |
empirical analysis |
en |
| dc.subject.other |
fuzzy mathematics |
en |
| dc.subject.other |
GIS |
en |
| dc.subject.other |
nitrogen |
en |
| dc.subject.other |
sensitivity analysis |
en |
| dc.subject.other |
Greece |
en |
| dc.subject.other |
Gossypium hirsutum |
en |
| dc.title |
Decision support system for nitrogen fertilization using fuzzy theory |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.compag.2011.06.007 |
en |
| heal.publicationDate |
2011 |
en |
| heal.abstract |
During the last three decades there has been great concern about the impact of agriculture on the environment and its resources. Conventional agriculture is based on whole field and mostly empirical approaches to defining and applying agrochemical inputs, which poses certain limitations regarding the management of existing variability in agricultural land. In this paper, the design and application of a fuzzy decision support system, concerning site specific nitrogen fertilization, is described. The system uses an easy but efficient way of solving the nitrogen equation under agricultural conditions and is based on knowledge elicitation and fuzzy logic methodologies. More specifically, the system is composed of two parts; a knowledge base and an analytical modular part which simulates nitrogen balance. The analytical part is built in a four level structure which consists of eleven fuzzy systems. The evaluation of the system presupposes the availability of 14 state variables that can be easily collected and refer to characteristics of the soil, weather and farming practices. The incorporated knowledge and the formulation of fuzzy rules were based on interviews with experts and on annotating scientific and technical bibliographic resources. A sensitivity analysis of the developed system was carried out in order to evaluate its robustness against errors or uncertainty in the state variables and further to assess and highlight the important variables. The application of the system using a set of point data, drawn from cotton fields in central Greece and stored in a Geographical Information System, is described in brief and the results show considerable variability in the recommended amount of nitrogen fertilizer among the reference sites. © 2011 Elsevier B.V. |
en |
| heal.journalName |
Computers and Electronics in Agriculture |
en |
| dc.identifier.issue |
2 |
en |
| dc.identifier.volume |
78 |
en |
| dc.identifier.doi |
10.1016/j.compag.2011.06.007 |
en |
| dc.identifier.spage |
130 |
en |
| dc.identifier.epage |
139 |
en |