Skip to main content

Advertisement

Log in

Test of sampling methods to optimize the calibration of vine water status spatial models

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Plant water status is one of the main factors affecting yield and quality in viticulture. Nevertheless, it is generally difficult to characterize it with enough precision for management purposes. In addition to its temporal variation, related to climate conditions, it has been shown that it is also spatially variable within the vineyard. In practical terms, this makes traditional reference measurements both too costly and time consuming to be affordable. In contrast, it has been shown that spatial variation of plant water status can be inferred from more accessible information, such as plant vigour in Mediterranean conditions. The main practical limitation for this approach is that the relationship between vigour measurements and plant water status is specific for each block and needs to be explicitly calibrated. Furthermore, a high number of measurements are usually required for this calibration. The objective of this work was to propose and test sampling methods to optimize the calibration of a specific spatial model of vine water status using the minimum number of measurements. Two model-based sampling methods commonly used in non-spatial modelling, Kennard and Stone (K&S) and Surface Response (SR) were considered, tested and discussed. Satisfactory results were obtained with both methods: with a sample size of 9 calibration sites, both sampling methods gave similar errors to the reference model (Root Mean Standard Error of Prediction, RMSEP = 0.1 MPa), which was calibrated with 49 sites. Taking into consideration the advantages and limitations of each method, K&S is considered to be better adapted for the case study presented. The proposed sampling approach could be extended to other spatial models used in precision agriculture in which ancillary variables can be used to explain most of the spatial variation for any agronomic information of interest.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  • Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., & Ojeda, H. (2008). The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precision Agriculture, 9(5), 285–302.

    Article  Google Scholar 

  • Acevedo-Opazo, C., Tisseyre, B., Ojeda, H., & Guillaume, S. (2010). Spatial extrapolation of the vine (Vitis vinifera L.) water status: a first step towards a spatial prediction model. Irrigation Science, 28(2), 143–155.

    Article  Google Scholar 

  • Bongiovanni, R., & Lafayette, W. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359–387.

    Article  Google Scholar 

  • Box, G. E. P., & Draper, N. R. (1986). Empirical Model-Building and Response Surface. New York, USA: Wiley.

    Google Scholar 

  • Bramley, R. G. V., Ouzman, J., & Thornton, C. (2011). Selective harvesting is a feasible and profitable strategy even when grape and wine production is geared towards large fermentation volumes. Australian Journal of Grape and Wine Research, 17(3), 298–305.

    Article  CAS  Google Scholar 

  • Corwin, D. L., & Lesch, S. M. (2005). Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46(1–3), 11–43.

    Article  Google Scholar 

  • Coulouma, G., Tisseyre, B., & Lagacherie, P. (2010). Is a systematic two dimensional EMI soil survey always relevant for vineyard production management? A test on two pedologically contrasting Mediterranean vineyards. In R. A. V. Rossel, A. B. McBratney, & B. Minasny (Eds.), Proximal soil sensing. Progress in soil science series. Heidelburg, Germany: Springer.

    Google Scholar 

  • Craven, B. D., & Islam, S. M. (2011). Ordinary least-squares regression. In L. Moutinho & G. D. Hutcheson (Eds.), The SAGE dictionary of quantitative management research (pp. 224–228). Bangalore, India: SAGE Publications.

    Google Scholar 

  • Dutta, R. (2013). Monitoring green leaf tea quality parameters of different TV clones grown in northeast India using satellite data. Food Chemistry, 139(1–4), 689–694.

    Article  CAS  PubMed  Google Scholar 

  • Girona, J., Mata, M., Del Campo, J., Arbonés, A., Bartra, E., & Marsal, J. (2006). The use of midday leaf water potential for scheduling deficit irrigation in vineyards. Irrigation Science, 24(2), 115–127.

    Article  Google Scholar 

  • Heiniger, R. W., McBride, R. G., & Clay, D. E. (2003). Using soil electrical conductivity to improve nutrient management. Agronomy Journal, 95(3), 508–519.

    Article  CAS  Google Scholar 

  • Hengl, T., Rossiter, D. G., & Stein, A. (2003). Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Australian Journal of Soil Research, 41(8), 1403.

    Article  Google Scholar 

  • Johnson, L. F. (2003). Temporal stability of the NDVI-LAI relationship in a Napa Valley vineyard. Australian Journal of Grape and Wine Research, 9(2), 96–101.

    Article  Google Scholar 

  • Kazmierski, M., Glemas, P., Rousseau, J., & Tisseyre, B. (2011). Temporal stability of within-field patterns of NDVI in non irrigated mediterranean vineyards. Journal International Des Sciences De La Vigne Et Du Vin, 45(2), 61–73.

    Google Scholar 

  • Kennard, R., & Stone, L. (1969). Computer Aided Design of Experiments. Technometric, 11(1), 137–148.

    Article  Google Scholar 

  • Kyveryga, P. M., Blackmer, T. M., & Pearson, R. (2011). Normalization of uncalibrated late-season digital aerial imagery for evaluating corn nitrogen status. Precision Agriculture, 13(1), 2–16.

    Article  Google Scholar 

  • Lesch, S. M. (2005). Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties. Computers and Electronics in Agriculture, 46(1–3), 153–179.

    Article  Google Scholar 

  • Lesch, S., Strauss, D. J., & Rhoades, J. D. (1995). Spatial prediction of soil salinity using electromagnetic induction techniques: 2. An efficient spatial sampling algorithm suitable for multiple linear regression model identification and estimation. Water Resources Research, 31(2), 387.

    Article  Google Scholar 

  • Murisier, F., & Zufferey, V. (1997). Rapport feuille-fruit de la vigne et qualite du raisin. (Effect of the balance leave-fruit in vine plants and grape quality.). Revue Suisse de Viticulture, Arboriculture, Horticulture, 29, 355–362.

    Google Scholar 

  • Ojeda, H., Carrillo, N., Deis, L., Tisseyre, B., Heywang, M., & Carbonneau, A. (2005). Precision viti- culture and water status II: Quantitative and qualitative performance of different within field zones, defined from water potential mapping. In H. R. Schultz (Ed.), Proceedings of 14th GESCO congress (pp. 741–748). Geisenheim, Germany: Groupe d’Etudes des Systemes de Conduite de la Vigne

  • Onoyama, H., Ryu, C., Suguri, M., & Iida, M. (2015). Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model. Precision Agriculture, 16(5), 558–570.

    Article  Google Scholar 

  • Samani Majd, A. M., Bleiweiss, M. P., DuBois, D., & Shukla, M. K. (2013). Estimation of the fractional canopy cover of pecan orchards using Landsat 5 satellite data, aerial imagery, and orchard floor photographs. International Journal of Remote Sensing, 34(16), 5937–5952.

    Article  Google Scholar 

  • Santesteban, L. G., Miranda, C., & Royo, J. B. (2011). Regulated deficit irrigation effects on growth, yield, grape quality and individual anthocyanin composition in Vitis vinifera L. cv. “Tempranillo”. Agricultural Water Management, 98(7), 1171–1179.

    Article  Google Scholar 

  • Santesteban, L. G., & Royo, J. B. (2006). Water status, leaf area and fruit load influence on berry weight and sugar accumulation of cv. `Tempranillo’ under semiarid conditions. Scientia Horticulturae, 109(1), 60–65.

    Article  CAS  Google Scholar 

  • Scholander, P. F., Bradstreet, E. D., Hemmingsen, E. A., & Hammel, H. T. (1965). Sap pressure in vascular plants. Science, 148(3668), 339–346.

    Article  CAS  PubMed  Google Scholar 

  • Sibille, I., Ojéda, H., Prieto, J., Maldonado, S., Ladapère, J. N., & Carbonneau, A. (2007). Rapport entre les trois modalités de la chambre à pression (potentiel hydrique foliaire de base, foliaire à midi et «de tige» à midi) en fonction de la réponse de quatre cépages dans le sud de la France, applications pour le contrôle de l’irrigation. (Relation between the values of three pressure chamber modalities (midday leaf, midday stem and predawn water potential) of 4 grapevine cultivars in drought situation of the southern of France. Applications for the irrigation control). In Proceedings of 15th GESCO congress (pp. 685–695). Porec, Croatia: Institut za poljoprivredu i turizam.

  • Taylor, J. A., Acevedo-Opazo, C., Ojeda, H., & Tisseyre, B. (2010). Identification and significance of sources of spatial variation in grapevine water status. Australian Journal of Grape and Wine Research, 16(1), 218–226.

    Article  Google Scholar 

  • Tisseyre, B., Mazzoni, C., & Fonta, H. (2008). Whithin-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), 27–39.

    Google Scholar 

  • Wulfsohn D. (2010). Sampling Techniques for Plants and Soil. Landbauforschung Völkenrode, (Special Issue 340), 3–30.

  • Zarrouk, O., Francisco, R., Pinto-Marijuan, M., Brossa, R., Santos, R. R., Pinheiro, C., et al. (2012). Impact of irrigation regime on berry development and flavonoids composition in Aragonez (Syn. Tempranillo) grapevine. Agricultural Water Management, 114, 18–29.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ana Herrero-Langreo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Herrero-Langreo, A., Tisseyre, B., Roger, J.M. et al. Test of sampling methods to optimize the calibration of vine water status spatial models. Precision Agric 19, 365–378 (2018). https://doi.org/10.1007/s11119-017-9523-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-017-9523-8

Keywords

Navigation