Skip to main content Accessibility help
×
Home

Using ancillary yield data to improve sampling and grape yield estimation of the current season

  • M. Araya-Alman (a1) (a2), C. Acevedo-Opazo (a1), S. Guillaume (a2), H. Valdés-Gómez (a3), N. Verdugo-Vásquez (a1) (a2), Y. Moreno (a4) and B. Tisseyre (a2)...

Abstract

This paper proposes a methodology aiming at using historical yield data to improve yield sampling and yield estimation. The sampling method is based on a collaboration between historical data (at least three years) and yield measurements of the year performed on some sites within the field. It assumes a temporal stability of within field yield spatial patterns over the years. The first factor of a principal component analysis (PCA) is used to summarize the stable temporal patterns of within field yield data and it represents a large part of the variability of the different years assuming yield temporal stability and a high positive correlation between this factor and the yield. This main factor is then used to choose the best sites to sample (target sampling). Yield measurements are then used to calibrate a model that relates yield values to coordinates on the first factor of the PCA. This sampling method was tested on three vine fields (Vitis vinifera L.) in Chile and France with different varieties (Chardonnay, Cabernet Sauvignon and Syrah). For each of these fields, yield data of several years were available at the within field level. After temporal stability of yield patterns was verified for almost all the fields, the proposed sampling method was applied. Results were compared to those of a classical random sampling method showing that the use of historical yield data allows sampling sites selection to be optimized. Errors in yield estimations were reduced by more than 10% in all the cases, except when yield stable patterns are affected by specific events, i.e. early frost occurring on Chardonnay field.

Copyright

Corresponding author

E-mail: miaraya@utalca.cl

References

Hide All
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 (1), 7492.
Efron, B 1979. Computers and the theory of statistics: thinking the unthinkable. SIAM review 21 (4), 460480.
Taylor, JA, Tisseyre, B, Bramley, RGV 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 5th ECPA, Uppsala, Sweden, June 8–11. JV Stafford (ed). Wageningen Academic Publishers pp. 907–914.
Taylor, JA, Sánchez, L, Sams, B, Haggerty, L, Jakubowski, R, Djafour, S and Bates, TR 2016. Evaluation of a commercial grape yield monitor for use mid-season and at-harvest. OENO One 50 (2), 5763.
Tisseyre, B, Mazzoni, C and Fonta, H 2008. Within-field temporal stability of some parameters in viticulture: Potential toward a site specific management. Journal International des Sciences de la Vigne et du Vin 42 (1), 2739.

Keywords

Using ancillary yield data to improve sampling and grape yield estimation of the current season

  • M. Araya-Alman (a1) (a2), C. Acevedo-Opazo (a1), S. Guillaume (a2), H. Valdés-Gómez (a3), N. Verdugo-Vásquez (a1) (a2), Y. Moreno (a4) and B. Tisseyre (a2)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.