Pollen-based predictive modelling of wine production: application to an arid region

European Journal of Agronomy - Tập 73 - Trang 42-54 - 2016
Mário Cunha1,2, Helena Ribeiro1,3, Ilda Abreu1,3
1Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal
2Centro de Investigação em Ciências Geoespaciais, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal
3Instituto de Ciências da Terra (ICT), Pólo da Faculdade de Ciências da Universidade do Porto, Rua Campo Alegre, 4169-007 Porto, Portugal

Tài liệu tham khảo

Abid, A., 1991. Contribuition à l’etude de la pollinisation de l’olivier (Olea europea L.) et du clementier (Citrus reticulata B.). Utilization des données pollinique comme indice prévionnel des récoltes à l’ échelle locale et régionale, Université de Montepllier II, France. Agri4cast, 2015. Crop Yield Forecasting System, Available online at http://mars.jrc.ec.europa.eu/mars/About-us/AGRI4CAST (assessed 2.04.15), Joint Research Center, Institute for environment and sustainability (IES). Altman, 1971, Measurement in medicine: the analysis of method comparision studies, Statistician, 221, 850 Bellocchi, 2010, Validation of biophysical models: issues and methodologies. A review, Agron. Sustain. Dev., 30, 109, 10.1051/agro/2009001 Bellvert, 2014, Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle, Precis. Agric., 15, 361, 10.1007/s11119-013-9334-5 Belmonte, 1998, Comparison of pollen data obtained by Cour and modified Durham methods, Pollen Spores, 30, 257 Besselat, B., Cour, P., 1996. Early crop prediction. Summary and prospects for the use of a new tool based on pollen analysis of the atmosphere. In: Dalleman, J., Vossen, P. (Eds.), Agrometeorological Models: Theory and Applications in the Mars Project Official Publications of the European Communities, Italy, ISPRA, pp. 73–82. Bindi, 1996, Modelling the impact of future climate scenarios on yield and yield variability of grapevine, Clim. Res., 07, 213, 10.3354/cr007213 Blom, 2009, Trellis tension monitoring improves yield estimation in vineards, HortSci., 44, 678, 10.21273/HORTSCI.44.3.678 Breusch, 1979, A simple test for heteroscedasticity and random coefficient variation, Econometrica, 47, 1287, 10.2307/1911963 Carboneau, 1993, Ètude de la coulure et maîtrise de la production, Progrès Agricole et Viticole, 15, 331 Chaves, 1987, Photosynthesis and water relations in grapevines response to environmental factors, 279 Clingeleffer, P., Dunn, G., Krstic, M., Martin, S., 2001. Crop development, crop estimation and crop control to secure quality and production of major wine grape varieties: A national approach, Australian Grape and Wine Authority. Cola, 2014, Description and testing of a weather-based model for predicting phenology, canopy development and source-sink balance in Vitis vinifera L. cv. Barbera, Agric. For. Meteorol., 184, 117, 10.1016/j.agrformet.2013.09.008 Cour, 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, XVI, 103 Cour, 1980, Prèvisions de récoltes á partir du contenu pollinique de ĺatmosphere, C. R. Acad. Sci. Paris, 290, 1043 Cunha, 2012, Measuring the impact of temperature changes on the wine production in the Douro Region using the short time Fourier transform, Int. J. Biometeorol., 56, 357, 10.1007/s00484-011-0439-0 Cunha, 1999, Early estimate of wine prodction by means of airborne pollen: demarcated region of Douro, Ciência e Técnica Vitivinicola, 14, 45 Cunha, 2003, Early estimate of wine production using airborne pollen samples: application to northern Portugal, Polen, 13, 325 Cunha, 2003, Airborne pollen samples for early-season estimates of wine production in a Mediterranean Climate of Northern Portugal, Am. J. Enol. Vitic., 54, 189, 10.5344/ajev.2003.54.3.189 Cunha, 2010, Very early prediction of wine yield based on satellite data from VEGETATION, Int. J. Remote Sens., 31, 3125, 10.1080/01431160903154382 Cunha, 2015, A comparative study of vineyard phenology and pollen metrics extracted from airborne pollen time series, Aerobiologia, 31, 45, 10.1007/s10453-014-9345-3 CVRA, 2015. Comissão Vitivinícola Regional Alentejana, dados estatísticos sobre a produção de vinho no Alentejo, Available online at <http://www.vinhosdoalentejo.pt/> (assessed 5.01.15). de la Fuente, 2015, Comparison of different methods of grapevine yield prediction in the time window between fruitset and veraison, J. Int. Sci. Vigne Vin, 49, 27 Dunn, G., 2010. Yield Forecasting, Grape and wine research and development corporation. Ebadi, 1995, Effect of low temperature near flowering time on ovule development and pollen tube growth in the grapevine (Vitis vinifera L.), cvs Chardonnay and Shiraz, Aust. J. Grape Wine Res., 1, 11, 10.1111/j.1755-0238.1995.tb00072.x European Commission, 1997. Oliwin Project: agrometeorogical models for the estimation at harvest of olive and vine yield; regional and national level. Fernandez-Gonzalez, 2011, Prediction of grape production by grapevine cultivar Godello in north-west Spain, J. Agric. Sci., 149, 725, 10.1017/S0021859611000244 Fernandez-Gonzalez, 2011, Estimation of yield ‘Loureira’ variety with an aerobiological and phenological model, Grana, 50, 63, 10.1080/00173134.2011.561871 Finger, 2010, Revisiting the evaluation of robust regression techniques for crop yield data detrending, Am. J. Agric. Econ., 92, 205, 10.1093/ajae/aap021 Fraga, 2014, Climate factors driving wine production in the Portuguese Minho region, Agric. For. Meteorol., 185, 26, 10.1016/j.agrformet.2013.11.003 García-Mozo, 2014, Statistical approach to the analysis of olive long-term pollen season trends in southern Spain, Sci. Total Environ., 473–474, 103, 10.1016/j.scitotenv.2013.11.142 Guilpart, 2014, Grapevine bud fertility and number of berries per bunch are determined by water and nitrogen stress around flowering in the previous year, Eur. J. Agron., 54, 9, 10.1016/j.eja.2013.11.002 Gujarati, 1995 Huglin, 1998, Research de méthodes de prévision quantitative de la vendage, Bulletin de l’Organisation Internationale de la Vigne et du Vin, 58, 71 Hyndman, 2006, Another look at measures of forecast accuracy, Int. J. Forecast., 22, 679, 10.1016/j.ijforecast.2006.03.001 Jones, 2007 Jones, 2000, Climate influences on grapevine phenology, grape composition, and wine production and quality for Bordeaux, France, Am. J. Enol. Vitic., 51, 249, 10.5344/ajev.2000.51.3.249 Kennelly, 2005, Seasonal development of ontogenic resistance to downy mildew in grape berries and rachises, Phytopathology, 95, 1445, 10.1094/PHYTO-95-1445 Lagerstrom, 2015, Pollen image classification using the classifynder system: algorithm comparison and a case study on New Zealand honey, 207 Legates, 1999, Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233, 10.1029/1998WR900018 Lltejos, R., Bartroli, R., Esteban, A., Riera, S., 1993. Forecasting hazelnut (Corylus avellana L.). Crop production based on monitoring airborne pollen concentration. International symposium on fruit, nut and vegetable production engineering, Sapian, Valencia, pp. 18–25. Lobell, 2007, Historical effects of temperature and precipitation on California crop yields, Clim. Change, 81, 187, 10.1007/s10584-006-9141-3 May, 2004 Montgomery, 2012 Moriasi, 2007, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Trans. ASABE, 50, 885, 10.13031/2013.23153 Nash, 1970, River flow forecasting through conceptual models Part I —a discussion of principles, J. Hydrol., 10, 282, 10.1016/0022-1694(70)90255-6 Nuske, 2014, Automated visual yield estimation in vineyards, J. Field Rob., 31, 837, 10.1002/rob.21541 OIV, 2015. Organisation Internationale de la Vigne et du Vin—Statistiques, Available online at <http://www.oiv.int/> (assessed 15.01.15). Ortega, 1998, Statistic considerations relating to forecast of wine production by airborne pollen concentration, Vitic./Enol. Prof., 55, 5 Oteros, 2014, Better prediction of Mediterranean olive production using pollen-based models, Agron. Sustain. Dev., 34, 685 Palm, R. and Dagnelie, P., 1993. Tendance général et effects du climat dans la prévision des rendements agricoles des diferents pays de la C.E. Panigai, 1988, La prévision de récoltes en Champagne, Le vigneron Champanois, 6, 359 Pearson, 1990 Pereira, 2015, Modeling malt barley water use and evapotranspiration partitioning in two contrasting rainfall years. Assessing aquacrop and SIMDualKc models, Agric. Water Manag., 159, 239, 10.1016/j.agwat.2015.06.006 Perry, 2015, Outils de prédiction du rendement en champagne: Les capteurs à pollens, Le vigneron Champanois, 6, 32 Picard, 1984, Cross-validation of regression models, J. Am. Stat. Assoc., 79, 575, 10.1080/01621459.1984.10478083 Pinchon, O., 1983. Contribuition a l’étude du pollen et de la polinisation du pommier (Mallus pimula Miller) et previsions de recolte à partir de l’analyse du contenu pollinique de l’atmosphere., Ecole National Superior Agronomie de Montpellier, France. Qian, 2009, Statistical spring wheat yield forecasting for the Canadian prairie provinces, Agric. For. Meteorol., 149, 1022, 10.1016/j.agrformet.2008.12.006 Quiroga, 2009, A comparison of the climate risks of cereal, citrus, grapevine and olive production in Spain, Agric. Syst., 101, 91, 10.1016/j.agsy.2009.03.006 Reidsma, 2010, Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses, Eur. J. Agron., 32, 91, 10.1016/j.eja.2009.06.003 Reis, R., Lamelas, H., 1988. Statistical study of decade series of water balance and its components of potencial evapotranspiration calculated by Penman’s method. Vol. 36, Instituto Nacional de Meteorologia e Geofisica, Lisbon. Ribeiro, 2007, Definition of the main pollen season using a logistic model, Ann. Agric. Environ. Med., 14 Ribeiro, 2007, Improving early-season estimates of olive production using airborne pollen multi-sampling sites, Aerobiologia, 23, 71, 10.1007/s10453-007-9050-6 Ribeiro, 2009, A bioclimatic model for forecasting olive yield, J. Agric. Sci., 147, 647, 10.1017/S0021859609990256 Rittenour, 2012, Immunologic, spectrophotometric and nucleic acid based methods for the detection and quantification of airborne pollen, J. Immunol. Methods, 383, 47, 10.1016/j.jim.2012.01.012 Rojo, 2015, Effect of land uses and wind direction on the contribution of local sources to airborne pollen, Sci. Total Environ., 538, 672, 10.1016/j.scitotenv.2015.08.074 Sabbatini, P., Howell, G.S., 2012. Predicting harvest yield in juice and wine and wine grape vineyards. Santos, 2013, Ensemble projections for wine production in the Douro Valley of Portugal, Clim. Change, 117, 211, 10.1007/s10584-012-0538-x Scheifinger, 2013, Monitoring, modelling and forecasting of the pollen season, 71 Tedeschi, 2006, Assessment of the adequacy of mathematical models, Agric. Syst., 89, 225, 10.1016/j.agsy.2005.11.004 Thornthwaite, 1948, An approach toward a rational classification of climat, Geogr. Rev., 38, 55, 10.2307/210739 Urhausen, 2011, Climatic conditions and their impact on viticulture in the Upper Moselle region, Clim. Change, 109, 349, 10.1007/s10584-011-0059-z Valdes-Gomez, 2009, Modelling soil water content and grapevine growth and development with the STICS crop-soil model under two different water management strategies, J. Int. Sci. Vigne Vin, 43, 13 Vasconcelos, 2009, The flowering Process of Vitis vinifera: A Review, Am. J. Enol. Vitic., 60, 411, 10.5344/ajev.2009.60.4.411 Vossen, P., Rijks, D., 1995. Early crop yield assessment of the EU countries: The system implemented by the Join Research Centre. Ye, 2015, Performance of detrending models of crop yield risk assessment: evaluation on real and hypothetical yield data, Stoch. Environ. Res. Risk Assess., 29, 109, 10.1007/s00477-014-0871-x