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

• Published in: Precision agriculture 2018-04, Vol.19 (2), p.365-378
• Main Authors: Herrero-Langreo, Ana, Tisseyre, Bruno, Roger, Jean Michel, Scholasch, Thibaut
• Format: Article
• Language: English
• Subjects: Agricultural management; Agricultural sciences; Agriculture; Agronomy; Atmospheric Sciences; Biomedical and Life Sciences; Calibration; Chemistry and Earth Sciences; Climatic conditions; Computer Science; Environmental Sciences; Life Sciences; Physics; Precision farming; Remote Sensing/Photogrammetry; Sampling; Sampling methods; Soil Science & Conservation; Standard error; Statistics for Engineering; Temporal variations; Test procedures; Variation; Viticulture
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-017-9523-8

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.