Coffee productivity mapping from mathematical models for prediction of harvest


  • Hélio Gallo Rocha IFSULDEMINAS
  • Adriano Bortolotti da Silva UNIFENAS
  • Denismar Alves Nogueira UNIFAL
  • José Messias Miranda UNIFENAS
  • José Ricardo Mantovani UNIFENAS


Forecast harvest, geostatistics, kriging


Correctly estimate coffee harvests assist public and private sectors in decision making in various areas of planning and avoid speculation with commodity that negatively affect the industry. The present work aimed to evaluate the use of geostatistics applied to harvest estimate two models using parameters such as phenological indices in the culture of coffee (Coffea arabica L.). The experiment was carried out in an area of one hectare cultivated with Red Catuai IAC-144, 5 years-old plants. 50 points of data were collected within this area. Data collection for the estimation models and obtaining the actual production occurred respectively in the months of March and May 2013. Then, the analysis of the residues was done between the observed (PO) and the estimate models, proposed by: Fahl et al. (2005) (M1) and Miranda, Reinato and Silva (2014) (M2). The minimum ordinary squares method was used estimate the theoretical semi variation. After being selected and validated, the model became the plot map of estimated by ordinary kriging. Considering the assumptions this research was conducted, it can be affirmed that all attributes presented spatial dependency, allowing distinction between areas of high and low variability observed in kriging maps. Using descriptive statistical analysis and geo-statistics, it was possible to verify that M2 mathematical model presented more accurate estimates than M1, thus being the best choice for estimating coffee productivity harvest conducted by coffee producers and companies that trade this commodity in future markets.



How to Cite

ROCHA, H. G.; SILVA, A. B. da; NOGUEIRA, D. A.; MIRANDA, J. M.; MANTOVANI, J. R. Coffee productivity mapping from mathematical models for prediction of harvest. Coffee Science - ISSN 1984-3909, [S. l.], v. 11, n. 1, p. 108–116, 2016. Disponível em: Acesso em: 21 apr. 2024.