Spatial variability in commercial orange groves. Part 1: canopy volume and height
- 193 Downloads
Characterizing crop spatial variability is crucial for estimating the opportunities for site-specific management practices. In the context of tree crops, ranging sensor technology has been developed to assess tree canopy geometry and control real-time variable rate application of plant protection products and fertilizers. The objective of this study was to characterize the variability of canopy geometry attributes in commercial orange groves in Brazil and therefore estimate the potential impact of sensor-based site-specific management. Using a mobile terrestrial laser scanner, canopy volume and canopy height were measured in 0.25 m length transversal sections along the rows across five large scale commercial orange groves in São Paulo, Brazil. The coefficient of variation of canopy volume ranged from 30 to 40%. Canopy height was less variable, but closely related to canopy volume. Histograms of canopy volume and height were usually negatively skewed indicating regions of the groves with smaller plants and punctual plant resets. In scenarios where input application rates followed canopy volume variability, input savings were around 40% compared to constant rates based on the maximum canopy volume. Maps of canopy geometry derived from mobile terrestrial laser scanning revealed significant canopy spatial variability, suggesting that the groves would benefit from strategies based on management zones and other forms of site-specific management.
KeywordsPrecision horticulture Mobile terrestrial laser scanner LiDAR Variable rate technology Orange groves
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).
- Byers, R. E. (1987). Tree-row-volume spraying rate calculator for apples. HortScience, 22, 506–507.Google Scholar
- Byers, R. E., Lyons, C. G., Yoder, K. S., Horsburgh, R. L., Barden, J. A., & Donohue, S. J. (1984). Effect of apple tree size and canopy density on spray chemical deposit. HortScience, 19, 93–94.Google Scholar
- CloudCompare [GPL software] v2.6.1. (2018). Retrieved June 28, 2018 from, http://www.cloudcompare.org.
- Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). 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.CrossRefGoogle Scholar
- Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (in press). Spatial variability in commercial orange groves. Part 2: relating canopy geometry to soil attributes and historical yield. Precision Agriculture.Google Scholar
- Escolà, A., Martínez-Casasnovas, J. A., Rufat, J., Arnó, J., Arbonés, A., 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, 111–132. https://doi.org/10.1007/s11119-016-9474-5.CrossRefGoogle Scholar
- FAO. (2018). Food and Agriculture Organization, Faostat. Retrieved June 28, 2018, from http://faostat.fao.org/.
- Farias, P. R. S., Nociti, L. A. S., Barbosa, J. C., & Perecin, D. (2003). Agricultura de precisão: Mapeamento da produtividade em pomares cítricos usando geoestatística (Precision Agriculture: Mapping of yield in citrus groves using geostatistics). Revista Brasileira de Fruticultura, 25(2), 235–241.CrossRefGoogle Scholar
- Fisher, P. D., Abuzar, M., Rab, M. A., Best, F., & Chandra, S. (2009). Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers’ historical paddock yields and normalised difference vegetation index. Crop Pasture Science, 60, 844. https://doi.org/10.1071/CP08347.CrossRefGoogle Scholar
- Méndez, V., Rosell-Polo, J. R., Pascual, M., & Escolà, A. (2016). Multi-tree woody structure reconstruction from mobile terrestrial laser scanner point clouds based on a dual neighbourhood connectivity graph algorithm. Biosystems Engineering, 148, 34–47. https://doi.org/10.1016/j.biosystemseng.2016.04.013.CrossRefGoogle Scholar
- 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.
- Oliveira, P. C. G., Farias, P. R. S., Lima, H. V., Fernandes, A. R., Oliveira, F. A., & Pita, J. D. (2009). Variabilidade espacial de propriedades químicas do solo e da produtividade de citros na Amazônia Oriental (Spatial variability of soil chemical properties and yield of citrus orchards in eastern Amazonia). Engenharia Agrícola e Ambiental, 13(6), 708–715.CrossRefGoogle Scholar
- Pringle, M. J., McBratney, A. B., Whelan, B. M., & Taylor, J. A. (2003). A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data. Agricultural Systems, 76, 273–292. https://doi.org/10.1016/S0308-521X(02)00005-7.CrossRefGoogle Scholar
- QGIS v2.10—QGIS Development Team. (2018). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Retrieved June 28, 2018 http://www.qgis.org.
- Rosell-Polo, J. R., Llorens, J., Sanz, R., Arnó, J., Ribes-Dasi, M., Masip, J., et al. (2009). 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.CrossRefGoogle Scholar
- 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
- Solanelles, F., Escolà, A., Planas, S., Rosell-Polo, J. R., Camp, F., & Gràcia, F. (2006). An electronic control system for pesticide application proportional to the canopy width of tree crops. Biosystems Engineering, 95, 473–481. https://doi.org/10.1016/j.biosystemseng.2006.08.004.CrossRefGoogle Scholar
- Uribeetxebarria, A., Daniele, E., Escolà, A., Arnó, J., & Martínez-Casasnovas, J. A. (2018). Spatial variability in orchards after land transformation: Consequences for precision agriculture practices. Science of the Total Environment, 635, 343–352. https://doi.org/10.1016/j.scitotenv.2018.04.153.CrossRefGoogle Scholar