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| Author(s): |
Agda Loureiro Gonçalves Oliveira
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Agrícola |
| Defense date: | 2020-02-28 |
| Examining board members: |
Lucas Rios do Amaral;
Celia Regina Grego;
Alessandro Rosa
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| Advisor: | Lucas Rios do Amaral |
| Abstract | |
Maps used by precision agriculture to prescribe local application are based on sampling grid and interpolation. Interpolation by kriging presents some difficulties related to its usage since it is a complex method that considers several parameters for calculation (i.e. anisotropy, trend, nugget effect, partial sill, range) and, when they are not proper considered, it might jeopardize the quality of the predictions, reducing reliability of maps created by this method. Accordingly, this study aims to evaluate if considering directional effects in geostatistical modelling leads to expressive gain of quality of the maps used by precision agriculture. Thus, we studied the gain on predictive quality by treating anisotropy and tendency found in data, considering two sampling grids (1 sample/ ha and 1 sample/4ha), besides of calculating semivariogram by method of moments and method of restricted maximum likelihood. These parameters were tested in a simulated virtual field produced by the process of Gaussian Unconditional Simulation calculated by gstat package on R software. In addition, these effects were also tested in two experimental fields that presented trend and anisotropy on the dataset. The results obtained by the analyses were evaluated through comparison of predicted values by interpolation and observed values obtained in the virtual fields, and on experimental fields the predicted values were compared with validation sets. Our results showed that adding directional effects on semivariogram modelling contributes to increase the accuracy on geostatistical interpolation. Indeed, we found better results of determinist models than geostatistical models when none of the directional effects were regarded in modelling. However, the importance of directional effects also changed according the method of semivariogram modelling. As the restrict maximum likelihood method seemed to have a stronger relationship with anisotropy effect to achieve higher precision, the method of moments achieved better results with trend. Thus, the process of accounting for directional effects in the semivariogram modelling can provide higher prediction accuracy on the maps interpolated by kriging. In this way, is possible to obtain more reliable on maps used for Precision Agricultural practices (AU) | |
| FAPESP's process: | 18/25473-2 - Influence of geostatistical rigor on the quality of maps used in precision agriculture |
| Grantee: | Agda Loureiro Gonçalves Oliveira |
| Support Opportunities: | Scholarships in Brazil - Master |