Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T07:19:54.006Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparing efficiency of different sampling schemes to estimate yield and quality parameters in fruit orchards

Published online by Cambridge University Press:  01 June 2017

J. Arnó ,
J.A. Martínez-Casasnovas ,
A. Uribeetxebarria ,
A. Escolà  and
J.R. Rosell-Polo
J. Arnó*
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
J.A. Martínez-Casasnovas
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Environmental and Soil Sciences, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
A. Uribeetxebarria
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
A. Escolà
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
J.R. Rosell-Polo
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
*
†E-mail: [email protected]
Get access

Abstract

Different sampling schemes were tested to estimate yield (kg/tree), fruit firmness (kg) and the refractometric index (°Baumé) in a peach orchard. In contrast to simple random sampling (SRS), the use of auxiliary information (NDVI and apparent electrical conductivity, ECa) allowed sampling points to be stratified according to two or three classes (strata) within the plot. Sampling schemes were compared in terms of accuracy and efficiency. Stratification of samples improved efficiency compared to SRS. However, yield and quality parameters may require different sampling strategies. While yield was better estimated using stratified samples based on the ECa, fruit quality (firmness and °Baumé) showed better results when stratifying by NDVI.

Keywords

samplingfruit growingefficiencyNDVIECa
Type
Precision Horticulture and Viticulture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 471 - 476
Copyright
© The Animal Consortium 2017 

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

Carrillo, E, Matese, A, Rousseau, J and Tisseyre, B 2016. Use of multi-spectral airborne imagery to improve yield sampling in viticulture. Precision Agriculture 17, 74–92.CrossRefGoogle Scholar
Corwin, DJ and Lesch, SM 2005. Apparent soil electrical conductivity in agriculture. Computers and Electronics in Agriculture 46, 11–43.CrossRefGoogle Scholar
Martínez-Casasnovas, JA, Agelet-Fernandez, J, Arno, J and Ramos, MC 2012. Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality. Spanish Journal of Agricultural Research 10 (2), 326–337.CrossRefGoogle Scholar
Meyers, JM and Vanden Heuvel, JE 2014. Research Note. Use of Normalized Difference Vegetation Index Images to Optimize Vineyard Sampling Protocols. American Journal of Enology and Viticulture 65 (2), 250–253.CrossRefGoogle Scholar
Miranda, C, Urretavizcaya, I, Santesteban, LG and Royo, JB 2015. Sampling stratification using aerial imagery to estimate fruit load and hail damage in nectarine trees. In: Precision Agriculture’15, Proceedings of the 10th European Conference on Precision Agriculture, edited by JV Stafford, Wageningen Academic Publishers, Wageningen, Netherlands, pp. 541–546.CrossRefGoogle Scholar
Monestiez, P, Audergon, JM and Habib, R 1990. Spatial dependences and sampling in a fruit tree: a geostatistical approach. Institut National de la Recherche Agronomique, Technical Report No. 163, 30 pp.Google Scholar
Moral, FJ, Terrón, JM and Marques da Silva, JR 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil & Tillage Research 106, 335–343.CrossRefGoogle Scholar
Peralta, NR and Costa, JL 2013. Delineation of management zones with soil apparent electrical conductivity to improve nutrient management. Computers and Electronics in Agriculture 99, 218–226.CrossRefGoogle Scholar
Soil Survey Staff 2014. Keys to Soil Taxonomy, 12th ed. USDA-Natural Resources Conservation Service: Washington, DC, USA.Google Scholar
Taylor, J, Tisseyre, B, Bramley, R and Reid, A 2005. A comparison of the spatial variability of vineyard yield in European and Australian production systems. In: Precision Agriculture’05, Proceedings of the 5th European Conference on Precision Agriculture, edited by JV Stafford, Wageningen Academic Publishers, Wageningen, Netherlands, pp. 907–914.Google Scholar
Webster, R and Lark, RM 2013. Field sampling for environmental science and management. Routledge, London and New York, 192 pp.CrossRefGoogle Scholar
Wulfsohn, D, Aravena Zamora, F, Potin Téllez, C, Zamora Lagos, I and García-Fiñana, M 2012. Multilevel systematic sampling to estimate total fruit number for yield forecasts. Precision Agriculture 13, 256–275.CrossRefGoogle Scholar