Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-20T06:14:56.887Z Has data issue: false hasContentIssue false
  • English
  • Français

A bioclimatic model for forecasting olive yield

Published online by Cambridge University Press:  09 September 2009

H. RIBEIRO ,
M. CUNHA  and
I. ABREU
H. RIBEIRO
Affiliation:
Departamento de Botânica and Centro de Geologia: Grupo de Ambiente, Sociedade e Educação, Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre, S/N 4150 Porto, Portugal
M. CUNHA
Affiliation:
Secção Autónoma de Ciências Agrárias, Faculdade de Ciências, Universidade do Porto, Rua do Monte, 4485 661 Vairão, Portugal Centro de Investigação em Ciências Geo-espaciais, Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre, 687 4150 Porto, Portugal
I. ABREU*
Affiliation:
Departamento de Botânica and Centro de Geologia: Grupo de Ambiente, Sociedade e Educação, Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre, S/N 4150 Porto, Portugal
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The aim of the present study was to develop a hierarchical bioclimatic model for forecasting olive crop yields in the Alentejo region of south-eastern Portugal. The model was estimated for three different developmental stages: (1) at flowering, using only the regional pollen index (RPI); (2) at fruit growth using RPI and a plant water requirements index (PWRI) and (3) at fruit maturing using RPI plus a water requirements index plus a phytopathological index (PPI). Olive airborne pollen was sampled from 1999 to 2007, using a Cour trap installed in Reguengos de Monsaraz. The meteorological parameters used in the calculation of the post-flowering indices corresponded to data from a meteorological station located near the airborne sampling point. At the flowering stage, 0·66 of the regional olive yield can be explained by the RPI with an average deviation between observed and predicted production of 0·15 for the forecast model internal validation and of 0·19 for the cross-validation. The addition of the variable PWRI to the forecasting model explained an additional 0·26 of the variation, while the PPI explained an additional 0·05. The final bioclimatic model, with all the three variables tested, explained 0·97 of the regional olive fruit yield being the average deviation between observed and predicted production of 0·04 for the internal validation of the model and of 0·07 for the external validation. The hierarchical nature of this bioclimatic model, along three different development stages, enabled the prediction to be updated as the growing season progressed.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 147 , Issue 6 , December 2009 , pp. 647 - 656
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abid, A. (1984). Contribution à l'étude de la pollinisation de l'olivier (Olea europaea). Ph.D. thesis, Université de Montpellier II.Google Scholar
Allen, R. G., Pereira, L. S., Smith, M. & Raes, D. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Requirements. Irrigation and Drainage Paper No. 56. Rome: FAO.Google Scholar
Bacelar, E. A., Santos, L. D., Moutinho-Pereira, J. M., Gonçalves, B. C., Ferreira, H. F. & Correia, C. M. (2006). Immediate responses and adaptive strategies of three olive cultivars under contrasting water availability regimes: changes on structure and chemical composition of foliage and oxidative damage. Plant Science 10, 596–605.CrossRefGoogle Scholar
Beed, F. D., Paveley, N. D. & Sylvester-Bradley, R. (2007). Predictability of wheat growth and yield in light-limited conditions. Journal of Agricultural Science, Cambridge 145, 63–79.CrossRefGoogle Scholar
Cardoso, C., Bento, A. & Torres, L. (2006). Evolução do ataque da mosca-da-azeitona (Bactrocera oleae Gmelin) nas cultivares Cobrançosa, Madural e Verdeal Transmontana. Melhoramento 41, 124–130.Google Scholar
Chartzoulakis, K., Michelakis, N. & Tzompanakis, I. (1992). Effects of water amount and application date on yield and water utilization efficiency of Koroneiki olives under drip irrigation. Advances in Horticultural Science 6, 82–84.Google Scholar
Cour, P. (1974). Nouvelles technique de détection des flux et des retombées polliniques: étude de la sedimentation des pollens et des spores à la surface du sol. Pollen et Spores 16, 103–141.Google Scholar
Cristofolini, F. & Gottardini, E. (2000). Concentration of airborne pollen of Vitisvinifera L. and yield forecast: a case study at S. Michele all'Adige, Trento, Italy. Aerobiologia 16, 125–129.CrossRefGoogle Scholar
Cunha, M., Abreu, I., Pinto, P. & Castro, R. (2003). Airborne pollen samples for early-season estimates of wine production in a Mediterranean climate of Northern Portugal. American Journal of Enology and Viticulture 54, 189–194.CrossRefGoogle Scholar
Fornaciari, M., Orlandi, F. & Romano, B. (2005). Yield forecasting for Olive trees: a new approach in a historical series (Umbria, Central Italy). Agronomy Journal 97, 1537–1542.CrossRefGoogle Scholar
Galán, C., Vásquez, L., García-Mozo, H. & Domínguez, E. (2004). Forecasting olive (Olea europaea) crop yield based on pollen emission. Field Crops Research 86, 43–51.CrossRefGoogle Scholar
Galán, C., García-Mozo, H., Vázquez, L., Ruiz, L., Díaz De La Guardia, C. & Domínguez-Vilches, E. (2008). Modeling olive crop yield in Andalusia, Spain. Agronomy Journal 100, 98–104.CrossRefGoogle Scholar
Giorio, P., Sorrentino, G. & D'Andria, R. (1999). Stomatal behaviour, leaf water status and photosynthetic response in field-grown olive trees under water deficit. Environmental and Experimental Botany 42, 95–104.CrossRefGoogle Scholar
Goldhamer, D. A. (1999). Regulated deficit irrigation for California canning olives. Acta Horticulturae 474, 369–372.CrossRefGoogle Scholar
Gommes, R., Das, H., Mariani, L., Challinor, A., Tychon, B., Balaghi, R. & Dawod, M. A. A. (1981). Agrometeorological forecasting. In Guide to Agricultural Meteorological Practices, 2nd edn. Geneva, Switzerland: World Meteorological Organization.Google Scholar
González-Minero, F. J., Candau, P., Morales, J. & Tomas, C. (1998). Forecasting olive crop production based on ten consecutive years of monitoring airborne pollen in Andalusia (southern Spain). Agriculture, Ecosystems and Environment 69, 201–215.CrossRefGoogle Scholar
Grattan, S. R., Berenguer, M. J., Connell, J. H., Polito, V. S. & Vossen, P. M. (2006). Olive oil production as influenced by different quantities of applied water. Agricultural Water Management 85, 133–140.CrossRefGoogle Scholar
Jaggard, K. W., Qi, A. & Semenov, M. A. (2007). The impact of climate change on sugarbeet yield in the UK: 1976–2004. Journal of Agricultural Science, Cambridge 145, 367–375.CrossRefGoogle Scholar
Ji, B., Sun, Y., Yang, S. & Wan, J. (2007). Artificial neural networks for rice yield prediction in mountainous regions. Journal of Agricultural Science, Cambridge 145, 249–261.CrossRefGoogle Scholar
Ko, J., Piccinni, G., Guo, W. & Steglich, E. (2009). Parameterization of EPIC crop model for simulation of cotton growth in South Texas. Journal of Agricultural Science, Cambridge 147, 169–178.CrossRefGoogle Scholar
Lavee, S. & Wodner, M. (1991). Factors affecting the nature of oil accumulation in fruit of olive (Olea europaea L.) cultivars. Journal of Horticultural Science 66, 583–591.CrossRefGoogle Scholar
Lavee, S., Nashef, M., Wodner, M. & Harshemesh, H. (1990). The effect of complementary irrigation added to olive trees (Olea europaea L.) cv. Souri on fruit characteristics, yield and oil production. Advances in Horticultural Science 4, 135–138.Google Scholar
Lavee, S., Hanoch, E., Wodner, M. & Abramowitch, H. (2007). The effect of predetermined deficit irrigation on the performance of cv. Muhasan olives (Olea europaea L.) in the eastern coastal plain of Israel. Scientia Horticulturae 112, 156–163.CrossRefGoogle Scholar
Miranda, P., Coelho, F. E. S., Tomé, A. R. & Valente, M. A. (2001). 20th Century Portuguese climate and climate scenarios. In Climate Change in Portugal. Scenarios, Impacts and Adaptation Measures – SIAM. Executive Summary and Conclusions (Eds Santos, F. D., Forbes, K. & Moita, R.), pp. 23–84. Lisbon: Gradiva.Google Scholar
Moita, S. & Mendes, L. (2007). Spatial Variability of Percentiles of the Maximum and Minimum Temperatures in the Period 1961–90 of the Air. Lisbon, Portugal: Portuguese Institute of Meteorology.Google Scholar
Moriana, A., Orgaz, F., Pastor, M. & Fereres, E. (2003). Yield responses of a mature olive orchard to water deficits. Journal of the American Society of Horticultural Science 128, 425–431.CrossRefGoogle Scholar
Moriondo, M., Orlandini, S., De Nuntiis, P. & Mandrioli, P. (2001). Effect of agrometeorological parameters on the phenology of pollen emission and production of olive trees (Olea europea L.). Aerobiologia 7, 225–232.CrossRefGoogle Scholar
Muñoz, A. F., Silva, I. & Tormo, R. (2000). The relationships between Poaceae pollination levels and cereal yields. Aerobiologia 16, 281–286.CrossRefGoogle Scholar
Orgaz, F. & Fereres, E. (1999). Riego. In El Cultivo del Olivo (Eds Barranco, D., Fernandez-Escobar, R. & Rallo, L.), pp. 251–272. Madrid: Mundi Prensa.Google Scholar
Orgaz, F., Mateos, L. & Fereres, E. (1992). Season length and cultivar determine the optimum evapotranspiration deficit in cotton. Agronomy Journal 84, 700–706.CrossRefGoogle Scholar
Orlandi, F., Romano, B. & Fornaciari, M. (2005). Relationship between pollen emission and fruit production in olive (Olea europaea). Grana 44, 98–103.CrossRefGoogle Scholar
Patumi, M., D'Andria, R., Marsilio, V., Fontanazza, G., Morelli, G. & Lanza, B. (2002). Olive and olive oil quality after intensive monocone olive growing (Olea europaea L., cv. Kalamata) in different irrigation regimes. Food Chemistry 77, 27–34.CrossRefGoogle Scholar
Proietti, P. & Antognozzi, E. (1996). Effect of irrigation on fruit quality of table olives (Olea europaea), cultivar ‘Ascolana tenera’. New Zealand Journal of Crop and Horticultural Science 24, 175–181.CrossRefGoogle Scholar
Ribeiro, H., Cunha, M. & Abreu, I. (2007 a). Definition of the main pollen season using a logistic model. Annals of Agricultural and Environmental Medicine 14, 159–167.Google ScholarPubMed
Ribeiro, H., Cunha, M. & Abreu, I. (2007 b). Improving early-season estimates of olive production using airborne pollen multi-sampling sites. Aerobiologia 23, 71–78.CrossRefGoogle Scholar
Selles Van Sch, G., Ferreyra, R. E., Selles, I. M. & Lemus, G. S. (2006). Efecto de diferentes regímenes de riego sobre la carga frutal, tamaño de fruta y rendimento del olivo cv. sevillana. Agricultura Técnica 66, 48–56.CrossRefGoogle Scholar
Sepulcre-Cantó, G., Zarco-Tejada, P. J., Jiménez-Muñoz, J. C., Sobrino, J. A., Soriano, M. A., Fereres, E., Vega, V. & Pastor, M. (2007). Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER. Remote Sensing of Environment 107, 455–470.CrossRefGoogle Scholar
Soler, C. M. T., Maman, N., Zhang, X., Mason, S. C. & Hoogenboom, G. (2008). Determining optimum planting dates for pearl millet for two contrasting environments using a modelling approach. Journal of Agricultural Science, Cambridge 146, 445–459.CrossRefGoogle Scholar
SPSS (2007). SPSS for Windows, Release 16.0.1. Chicago: SPSS Inc.Google Scholar
Statistics Portugal (2007). Statistical Data. Lisbon, Portugal: Statistics Portugal. Available online at http://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_unid_territorial&menuBOUI=13707095&contexto=ut&selTab=tab3 (verified 12 June 2009).Google Scholar
Talhinhas, P., Martins, S., Ramos, P., Sreenivasaprasad, S., Neves-Martins, J. & Oliveira, H. (2006). Aspectos epidemiológicos da antracnose da oliveira (gafa da azeitona) e diversidade genética dos agentes causais (Colletotrichum acutatum e C. gloeosporioides). Melhoramento 41, 171–179.Google Scholar