Spatial variability in commercial orange groves. Part 2: relating canopy geometry to soil attributes and historical yield

Abstract

Site-specific management strategies are usually dependant on the understanding of the underlying cause and effect relationships that occur at the within-field level. The assessment of canopy geometry of tree crops has been facilitated in recent years through the development of light detection and ranging sensors mounted on terrestrial platforms. The main objective of this study was to uncover the factors driving orange tree variability in commercial orange groves. Secondly, this study sought to investigate whether tree geometry information derived from a terrestrial sensing platform is useful information to guide management zones delineation in such groves. A database of soil physical attributes, elevation, historical yield and canopy geometry (canopy volume and height) was analysed in three commercial orange groves in São Paulo, Brazil. Canopy geometry and historical yield were correlated with soil attributes in two of the three groves evaluated; in these groves, the correlation coefficient between yield and soil/landscape information was often above 0.6, depending on the year. Zones of different tree sizes presented different historical yield and soil properties in all three groves. In conclusion, assessing canopy volume provides useful information to delineate management zones and guide enhanced site-specific management strategies.

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References

  1. Berk, P., Hocevar, M., Stajnko, D., & Belsak, A. (2016). Development of alternative plant protection product application techniques in orchards, based on measurement sensing systems: A review. Computers and Electronics in Agriculture, 124, 273–288. https://doi.org/10.1016/j.compag.2016.04.018.

    Article  Google Scholar 

  2. Cambardella, C. A., Moorman, T. B., Novak, L. M., Parkin, T. B., Karlen, D. L., Turco, R. F., et al. (1994). Field scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58(5), 1501–1511.

    Article  Google Scholar 

  3. Colaço, A.F., Molin, J.P. (2014). Comparação em larga escala entre fertilização variável e convencional na cultura da laranja (Large scale evaluation between variable and fixed rate fertilization in orange crop) in: Sociedade Brasileira de Engenharia Agrícola - SBEA (Ed.), Congresso Brasileiro de Agricultura de Precisão - 2014. São Pedro, Brazil.

  4. Colaço, A. F., & Molin, J. P. (2017). Variable rate fertilization in citrus: A long term study. Precision Agriculture, 18, 169–191. https://doi.org/10.1007/s11119-016-9454-9.

    Article  Google Scholar 

  5. Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018a). Application of light detection and ranging and ultrasonic sensors to high throughput phenotyping and precision horticulture: current status and challenges. Horticulture Research, 5(1), 35–46. https://doi.org/10.1038/s41438-018-0043-0.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018b). Spatial variability in commercial orange groves. Part 1: Canopy volume and height. Precision Agriculture. https://doi.org/10.1007/s11119-018-9612-3.

    Article  Google Scholar 

  7. Colaço, A. F., Rosa, H. J. A., & Molin, J. P. (2014). A model to analyze as-applied reports from variable rate applications. Precision Agriculture, 15, 304–320. https://doi.org/10.1007/s11119-014-9358-5.

    Article  Google Scholar 

  8. Colaço, A.F., Trevisan, R.G., Karp, F.H.S., Molin, J.P. (2015). Yield mapping methods for hand harvested crops, Stafford, J. V. (Ed.), Precision Agriculture` 15. Proceedings of the10th European Conference on Precision Agriculture. The Netherlands: Wageningen Academic Publishers, pp 225 – 232. https://doi.org/10.3920/978-90-8686-814-8_27.

  9. Colaço, A. F., Trevisan, R. G., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2017). A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling. Remote Sensing, 9, 763. https://doi.org/10.3390/rs9080763.

    Article  Google Scholar 

  10. Esau, T., Zaman, Q., Groulx, D., Corscadden, K., Chang, Y., Schumann, A., et al. (2016). Economic analysis for smart sprayer application in wild blueberry fields. Precision Agriculture, 17, 753–765. https://doi.org/10.1007/s11119-016-9447-8.

    Article  Google Scholar 

  11. Escolà, A., Martínez-Casasnovas, J. A., Rufat, J., Arnó, J., Arbonés, A., Francesc Sebé, F., et al. (2017). Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture, 18(1), 111–132. https://doi.org/10.1007/s11119-016-9474-5.

    Article  Google Scholar 

  12. Fridgen, J. J., Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Wiebold, W. J., & Fraisse, C. W. (2004). Management zone analyst (MZA): Software for subfield management zone delineation. Agronomy Journal, 96, 100–108.

    Article  Google Scholar 

  13. Leão, M. G. A., Marques, J., Jr., de Souza, Z. M., & Pereira, G. T. (2010). Variabilidade espacial da textura de um latossolo sob cultivo de citros (Spatial variability of texture of a Latosol under cultivation of citrus). Ciência e Agrotecnologia, 34(1), 121–131.

    Article  Google Scholar 

  14. Lee, K. H., & Ehsani, R. (2009). A laser scanner based measurement system for quantification of citrus tree geometric. Applied Engineering in Agriculture, 25, 777–788.

    Article  Google Scholar 

  15. Mann, K. K., Schumann, A. W., & Obreza, T. A. (2011). Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data. Precision Agriculture, 12, 457–472. https://doi.org/10.1007/s11119-010-9189-y.

    Article  Google Scholar 

  16. Minasny, B., McBratney, A. B.,Whelan, B. M. (2005). VESPER version 1.62. Australian Centre for Precision Agriculture, McMillan Building A05, the University of Sydney, NSW. Retrieved June 28, 2018, from http://sydney.edu.au/agriculture/pal/software/vesper.shtml.

  17. Molin, J. P., Colaço, A. F., Carlos, E. F., & Mattos, D., Jr. (2012). Yield mapping, soil fertility and tree gaps in an orange orchard. Revista Brasileira de Fruticultura, 34, 1256–1265.

    Article  Google Scholar 

  18. Molin, J. P., & Mascarin, L. S. (2007). Colheita de citros e obtenção de dados para mapeamento da produtividade (Characterization of harvest systems and development of yield mapping for citrus). Engenharia Agrícola, 27, 259–266.

    Article  Google Scholar 

  19. Nawar, S., Corstanje, R., Halcro, G., Mulla, D., & Mouazen, A. M. (2017). Chapter four—Delineation of soil management zones for variable-rate fertilization: A review. In Advances in agronomy (pp. 175–245). Cambridge, UK: Academic Press. https://doi.org/10.1016/bs.agron.2017.01.003.

  20. QGIS v2.10—QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2018. Retrieved June 28, 2018, from http://www.qgis.org.

  21. R v3.2.2 - R Core Team 2018. R: A language and environment for statistical computing. Software. R Foundation for Statistical Computing, Vienna, Austria. Retrieved August 5, 2018, from http://www.R-project.org.

  22. Rosell-Polo, J. R., Llorens, J., Sanz, R., Arnó, J., Ribes-Dasi, M., Masip, J., et al. (2009a). Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning. Agriculture and Forest Meteorology, 149, 1505–1515. https://doi.org/10.1016/j.agrformet.2009.04.008.

    Article  Google Scholar 

  23. Rosell-Polo, J. R., Sanz, R., Llorens, J., Arnó, J., Escolà, A., Ribes-Dasi, M., et al. (2009b). A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosystems Engineering, 102, 128–134. https://doi.org/10.1016/j.biosystemseng.2008.10.009.

    Article  Google Scholar 

  24. Schueller, J. K., Whitney, J. D., Wheaton, T. A., Miller, W. M., & Turner, A. E. (1999). Low-cost automatic yield mapping in hand-harvested citrus. Computers and Electronics in Agriculture, 23, 145–153.

    Article  Google Scholar 

  25. Schumann, A. W., Hostler, K. H., Buchanon, S., & Zaman, Q. U. (2006a). Relating citrus canopy size and yield to precision fertilization. Proceedings of Florida State Horticultural Society, 119, 148–154.

    Google Scholar 

  26. Schumann, A. W., Miller, W. M., Zaman, Q. U., Hostler, K. H., Buchanon, S., & Cugati, S. A. (2006b). Variable rate granular fertilization of citrus groves: Spreader performance with single-tree prescription zones. Applied Engineering in Agriculture, 22, 19–24.

    Article  Google Scholar 

  27. Siqueira, D. S., Marques, J., Jr., & Pereira, G. T. (2010). The use of landforms to predict the variability of soil and orange attributes. Geoderma, 155, 55–66.

    Article  Google Scholar 

  28. Spekken, M., Anselmi, A.A., Molin, J.P. 2013. A simple method for filtering spatial data, in: Stafford, J. V. (Ed.), Precision Agriculture` 13. Proceedings of the 9th European Conference on Precision Agriculture. The Netherlands: Wageningen Academic Publishers, pp 259–266. https://doi.org/10.3920/978-90-8686-778-3_30.

  29. Tumbo, S. D., Salyani, M., Whitney, J. D., Wheaton, T. A., & Miller, W. M. (2002a). Investigation of laser and ultrasonic ranging sensors for measurements of citrus canopy volume. Applied Engineering in Agriculture, 18, 367–372.

    Google Scholar 

  30. Tumbo, S. D., Whitney, J. D., Miller, W. M., & Wheaton, T. A. (2002b). Development and testing of a citrus yield monitor. Applied Engineering in Agriculture, 18, 399–403.

    Google Scholar 

  31. Whitney, J. D., Ling, Q., Miller, W. M., & Wheaton, T. A. (2001). A dgps yield monitoring system for florida citrus. Applied Engineering in Agriculture, 17, 115–119.

    Google Scholar 

  32. Whitney, J. D., Miller, W. M., Wheaton, T. A., Salyani, M., & Schueller, J. K. (1999). Precision farming applications in Florida citrus. Applied Engineering in Agriculture, 15, 399–403.

    Article  Google Scholar 

  33. Zaman, Q. U., & Schumann, A. W. (2006). Nutrient management zones for citrus based on variation in soil properties and tree performance. Precision Agriculture, 7, 45–63. https://doi.org/10.1007/s11119-005-6789-z.

    Article  Google Scholar 

  34. Zaman, Q. U., Schumann, A. W., & Hostler, K. H. (2006). Estimation of citrus fruit yield using ultrasonically-sensed tree size. Applied Engineering in Agriculture, 22, 39–44.

    Article  Google Scholar 

  35. Zaman, Q. U., Schumann, A. W., & Miller, W. M. (2005). Variable rate nitrogen application in Florida citrus based on ultrasonically-sensed tree size. Applied Engineering in Agriculture, 21, 331–336.

    Article  Google Scholar 

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Acknowledgements

We thank Citrosuco and Jacto companies for supporting this project, the São Paulo Research Foundation (FAPESP) for providing a scholarship to the first author (grant: 2013/18853-0) and the Coordination for the Improvement of Higher Education Personnel (CAPES), for funding the first author as an exchange visitor at the University of Lleida (Grant: bex_3751/15-5).

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Correspondence to André F. Colaço.

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Colaço, A.F., Molin, J.P., Rosell-Polo, J.R. et al. Spatial variability in commercial orange groves. Part 2: relating canopy geometry to soil attributes and historical yield. Precision Agric 20, 805–822 (2019). https://doi.org/10.1007/s11119-018-9615-0

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Keywords

  • Precision horticulture
  • Management zones
  • Mobile terrestrial laser scanner
  • LiDAR
  • Site-specific management
  • Orange groves