Skip to main content

Spatial variation in yield and quality in a small apple orchard

Abstract

We describe the yield and quality of apples from a 0.8 ha apple orchard located in northern Greece over two growing seasons and consider the potential for site-specific management. The orchard has two apple cultivars: Red Chief (main cultivar) and Fuji (pollinator). Yield was measured by weighing all fruit harvested from groups of five adjacent trees and the position of the central tree was recorded by GPS. Apple quality at harvest was evaluated from samples of the two cultivars in both years for which fruit mass, flesh firmness, soluble solids content, juice pH and acidity of the juice were determined. The variation in tree flowering was also measured in the spring of the second season using a stereological sampling procedure. The results showed considerable variability in the number of tree flowers, yield and quality across the orchard for both cultivars. The number of flowers was strongly correlated with the final yield. These data could potentially be used to plan precise thinning and for early prediction of yield; the latter is important for marketing the fruit. Several quality characteristics, including fruit juice soluble solids content and acid content were negatively correlated with yield. The general patterns of spatial variation in several variables suggested that changes in topography and aspect had important effects on apple yield and quality.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  • Aggelopoulou, K. D., Pateras, D., Fountas, S., Gemtos, T. A., & Nanos, G. D. (2007). Soil spatial variability in small Greek apple orchards. In J. V. Stafford (Ed.), Precision agriculture ’07, Proceedings of the 6th European conference on precision agriculture (pp. 71–78). Wageningen: Wageningen Academic Publishers.

  • Best, S., Salazar, F., Bastías, R., & León, L. (2008). Crop load estimation model to optimize yield-quality ratio in apple orchards, Malus Domestica Borkh, Var. Royal Gala. Journal of Information Technology in Agriculture, 3, 11–18 (e-Journal at www.jitag.org).

    Google Scholar 

  • Best, S., & Zamora, I. (2008). Tecnologías aplicable en agricultura de precisión. Uso de Tecnología de Precisión en Evaluación, Diagnóstico y Solución de Problemas Productivos. Santiago, Chile: Fundacion para la Innovacion Agraria.

    Google Scholar 

  • Blankenship, S. M., Parker, M., & Unrath, C. R. (1997). Use of maturity indices for predicting poststorage firmness of Fuji apples. HortScience, 32, 909–910.

    Google Scholar 

  • Bramley, R. G. V. (2005). Understanding variability in winegrape production systems. 2. Within vineyard variation in quality over several vintages. Australian Journal of Grape and Wine Research, 11, 33–42.

    Article  Google Scholar 

  • Bramley, R. G. V., & Hamilton, R. P. (2003). Understanding variability in winegrape production systems. 1. Within vineyard variation in yield over several vintages. Australian Journal of Grape and Wine Research, 10, 32–45.

    Article  Google Scholar 

  • Cressie, N. (1993). Statistics for spatial data, revised version. New York: John Wiley & Sons.

    Google Scholar 

  • Delfiner, P. (1976). Linear estimation of nonstationary spatial phenomena. In M. Guarascio, M. David, & C. Hüijbregts (Eds.), Advanced geostatistics in the mining industry (pp. 49–68). Dordrecht: Reidel.

    Google Scholar 

  • Dobermann, A., & Ping, J. L. (2004). Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agronomy Journal, 96, 285–297.

    Article  Google Scholar 

  • Dris, R. (2002). Influence of orchard management on growth and production of fruits. In R. Dris, S. M. Jain, & I. A. Khan (Eds.), Environment and crop production (pp. 1–3). Enfield, NH: Science Publishers Inc.

    Google Scholar 

  • Fountas, S., Blackmore, S., Gemtos, T., & Markinos, A. (2004a). Trend yield maps in Greece and the UK. In M. Vlachopoulou, V. Manthou, L. Illiadis, S. Gertsis, & M. Salampasis (Eds.), HAICTA 2002: Proceedings of the 2nd international conference on information systems & innovative technologies in agriculture, food and environment (Vol. 2, pp. 309–319), Thessaloniki: Publishing Center T.E.I. of Thessaloniki.

  • Fountas, S., Ess, D., Sorensen, C. G., Hawkins, S., Pedersen, H. H., Blackmore, S., et al. (2004b). Farmer experience with Precision Agriculture in Denmark and US Eastern Corn Belt. Precision Agriculture, 5, 1–21.

    Google Scholar 

  • Gemtos, T., Fountas, S., Blackmore, S., & Griepentrog, H. W. (2002). Precision farming in Europe and the Greek potential. In A. Sideridis & C. Yialouris (Eds.), HAICTA 2002, Proceedings of the 1st Greek conference on information and communication technology in agriculture (pp. 45–55). Athens: Agricultural University of Athens.

  • Gemtos, T. A., Markinos, A., & Nassiou, T. (2005). Cotton lint quality spatial variability and correlation with soil properties and yield. In J. V. Stafford (Ed.), Precision agriculture ’05: Proceedings of the 5th European conference on precision agriculture (pp. 361–368). Wageningen: Wageningen Academic Publishers.

  • Geovariances. (2008). Structure identification in the intrinsic case. In Isatis technical reference, Release 8 (pp. 9 − 51). Avon, France: Geovariances. http://www.geovariances.com.

  • Godwin, R. J., Wood, G. A., Taylor, J. C., Knight, S. M., & Welsh, J. P. (2003). Precision farming of cereal crops: A review of a six year experiment to develop management guidelines. Biosystems Engineering, 84, 375–391.

    Article  Google Scholar 

  • Greer, D. H. (2005). Non-destructive chlorophyll fluorescence and colour measurements of ‘Braeburn’ and ‘Royal Gala’ apple (Malus domestica) fruit development throughout the growing season. New Zealand Journal of Crop and Horticultural Science, 33, 413–421.

    Article  Google Scholar 

  • Griffin, T. W., Lowenberg-DeBoer, J., Lambert, D. M., Peone, J., Payne, T., & Daberkow, S. G. (2004). Adoption, profitability, and making better use of precision farming data. Staff Paper #04-06. USA: Department of Agricultural Economics, Purdue University.

  • Hastings, A. (2004). Transients: the key to long-term ecological understanding? Trends in Ecology & Evolution, 19, 39–45.

    Article  Google Scholar 

  • Isagi, Y., Sugimura, K., Sumida, A., & Ito, H. (1997). How does masting happen and synchronize? Journal of Theoretical Biology, 187, 231–239.

    Article  Google Scholar 

  • Lakso, A. N., & Robinson, T. L. (1997). Principles of orchard systems management optimizing supply, demand and partitioning in apple trees. Acta Horticultura, 451, 405–415.

    Google Scholar 

  • López-Granados, F. L., Expósito, M. J., Álamo, S., & García-Torres, L. (2004). Leaf nutrient spatial variability and site-specific fertilization maps within olive (Olea europaea L.) orchards. European Journal of Agronomy, 21, 209–222.

    Article  Google Scholar 

  • Maletti, G. M., & Wulfsohn, D. (2006). Evaluation of variance models for fractionator sampling of trees. Journal of Microscopy, 222, 228–241.

    Article  CAS  PubMed  Google Scholar 

  • Mandallaz, D. (2000). Estimation of the spatial covariance in Universal kriging: Application to forest inventory. Environmental and Ecological Statistics, 7, 263–284.

    Article  Google Scholar 

  • Marcotte, D., & David, M. (1988). Trend surface analysis as a special case of IRF-k kriging. Mathematical Geology, 20, 821–824.

    Article  Google Scholar 

  • Markinos A., Gemtos T. A., Toulios L., Pateras D., Zerva G., & Papaeconomou, M. (2004). The influence of cotton variety in the calibration factor of a cotton yield monitor. In M. Vlachopoulou, V. Manthou, L. Illiadis, S. Gertsis, & M. Salampasis (Eds.), HAICTA 2002: Proceedings of the 2nd International conference on information systems & innovative technologies in agriculture, food and environment (Vol. 2, pp. 65–74). Thessaloniki: Publishing Center T.E.I. of Thessaloniki.

  • Marquina, P., Venturini, M. E., Oria, R., & Nequerela, A. I. (2004). Monitoring colour evolution during maturity in Fuji apples. Food Science and Technology International, 10, 315–321.

    Article  Google Scholar 

  • McGuire, R. G. (1992). Reporting of objective color measurements. HortScience, 27, 1254–1255.

    Google Scholar 

  • Pelletier, G., & Upadhyaya, S. K. (1999). Development of a tomato load/yield monitor. Computers and Electronics in Agriculture, 23, 103–107.

    Article  Google Scholar 

  • Pozdnyakova, L., Gimenez, D., & Oudemans, P. V. (2005). Spatial analysis of cranberry yield at three scales. Agronomy Journal, 97, 49–57.

    Article  Google Scholar 

  • Rao, C. (1971). Minimum variance quadratic unbiased estimation of variance components. Journal of Multivariate Analysis, 1, 445–456.

    Article  Google Scholar 

  • Roel, A., & Plant, R. E. (2004a). Factors underlying yield variability in two California rice fields. Agronomy Journal, 96, 1481–1494.

    Article  Google Scholar 

  • Roel, A., & Plant, R. E. (2004b). Spatiotemporal analysis of rice yield variability in two California fields. Agronomy Journal, 96, 77–90.

    Article  Google Scholar 

  • Sakai, K., Noguchi, Y., & Asada, S. (2008). Detecting chaos in a citrus orchard: Reconstruction of nonlinear dynamics from very short ecological time series. Chaos Solitons & Fractals, 38, 1274–1282.

    Article  Google Scholar 

  • Taylor, J. A. (2004). Digital terroirs and precision viticulture: Investigations into the application of information technology in Australian vineyards. Unpublished PhD dissertation, The University of Sydney, New South Wales, Australia.

  • Vasilakakis, M. (2004). General and specialized pomology. Thessaloniki: Gartaganis Publications.

    Google Scholar 

  • Wackernagel, H. (2003). Multivariate geostatistics (3rd ed.). Berlin: Springer-Verlag.

    Google Scholar 

  • Wulfsohn, D., Maletti, G. M., & Toldam-Andersen, T. B. (2006). Unbiased estimator of the number of flowers on a tree. Acta Horticulturae, 707, 245–252.

    Google Scholar 

  • Yanai, J., Choung, K. L., Kaho, T., Iida, M., Matsui, T., Umeda, M., et al. (2001). Geostatistical analysis of soil chemical properties and rice yield in a paddy field and application to the analysis of yield-determining factors. Soil Science and Plant Nutrition (Japan), 47, 291–301.

    CAS  Google Scholar 

  • Ye, X., Sakai, K., Manago, M., Asada, S., & Sasao, A. (2007). Prediction of citrus yield from airborne hyperspectral imagery. Precision Agriculture, 8, 111–125.

    Article  Google Scholar 

  • Zaman, Q., & Schuman, A. W. (2006). Nutrient management zones for citrus based on variation in soil properties and tree performance. Precision Agriculture, 7, 45–63.

    Article  Google Scholar 

  • Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36, 113–132.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to express our gratitude to the Rappos family and especially to Vagellis Rappos in Ptolemaida, Greece, for allowing us to apply our methods to their orchard and for their cooperation and assistance with field trials. We thank the editor and reviewers for valuable comments. This project was funded by the Greek Ministry of Education through the PYTHAGORAS II programme. DW acknowledges support from the SJVF (Danish Research Council) project “Applications of Stereology for Agricultural Systems”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. D. Aggelopoulou.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Aggelopoulou, K.D., Wulfsohn, D., Fountas, S. et al. Spatial variation in yield and quality in a small apple orchard. Precision Agric 11, 538–556 (2010). https://doi.org/10.1007/s11119-009-9146-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-009-9146-9

Keywords

  • Flowering
  • Fruit quality
  • Intrinsic random function-k (IRF-k) kriging
  • Malus domestica
  • Precision horticulture
  • Spatial variation
  • Stereology