How to define the size of a sampling unit to map high resolution spatial data?

International audienceThe development and the release of sensors able to provide data with a high spatial resolution (> 4000 points.ha-1 for some of them) in agriculture raises new questions as to how to represent the information. This study proposes adaptation and application of a theoretical framework to high resolution data in agriculture to explain how changes in sampling unit (SU) affects: i) the total variance of the data, ii) the nugget effect (C0) as well as iii) the proportion of variance corresponding to spatially correlated component (C1). The theoretical approach has been validated on simulated data with known characteristics and on real data sets (total soluble solids of grapes just before harvest). Application of this methodology demonstrated that for the type of data under consideration, increasing the SU resulted in an expected decrease in C0 and C1 simultaneously and in an independent decrease in C0. Our study demonstrates that it is not possible to find a SU that maximizes C1 while minimizing C0. It also demonstrates that for a given increase in the SU, the decrease in C0 depends only on the sample support (the size of the SU). The choice of an optimal SU can therefore be based on the resolution of the available information and the C0 value of the raw data

Développement d'un appareil permettant de prédire la maturité du raisin par spectroscopie proche infra-rouge(PIR)

La qualité optimale des vendanges dépend étroitement du choix de la date des vendanges. Différents critères de qualité du raisin sont donc suivis au cours de sa maturation. La spectroscopie visible proche infrarouge (Vis-NIR) permet d'analyser rapidement la composition des produits de manière non-destructive. Cet article présente le développement d'un capteur basé sur l'utilisation de la spectroscopie Vis-NIR destiné au suivi de maturité, le Spectron. Il permet de connaître le taux de sucre, l'acidité totale, la teneur en eau et la concentration en anthocyanes du raisin, qui sont directement mesurés sur la parcelle. Il a été mis au point par la société Pellenc S.A.en partenariat avec l'équipe de l'UMR-ITAP du Cemagref de Montpellier

Investigating the harmonization of highly noisy heterogeneous datasets hand-collected over the same study domain

International audienceThe objective of this paper is to propose an approach to harmonise noisy spatial data acquired by different operators using (low-cost) hand-held sensors over the same spatial domain. In such cases, datasets need to be harmonised first to be comparable before decision making. This work proposes a methodology to address this issue in the case of nested and noisy spatial data. First, it proposes the implementation of a non-parametric test of Kolmogorov-Smirnov to determine if harmonisation is needed. Then, it proposes an aspatial harmonization method based on standardization. The method was applied on grape sugar content datasets collected by 2 hand-held spectrometers. Results showed that harmonizing a less confident dataset is interesting solely if the size of the trusted one is too small

How to define the optimal grid size to map high resolution spatial data ?

International audienceThe development and the release of sensors capable of providing data with high spatial resolution (> 4 000 points ha'1) in agriculture raises new questions as to how to represent this spatial information. The objective of this study was to propose a methodology to help define the optimal grid size to map high resolution data in agriculture. The geostatistical method finds the grid size which maximizes the sum of two components: (i) the proportion of nugget variance that is removed, and (ii) the proportion of sill variance that remains in the data. The optimum grid size was found to be dependent on the resolution of the available information and the spatial structure of the raw data. Experiments on simulated datasets with varying data resolution (from 500 to 2 000 pts.ha'1) and spatial structure (range of variogram between 10 and 45 m) showed that the proposed methodology was able to define varying optimal grid sizes (from 5 to 12 m). The proposed geostatistical approach was then applied on a real dataset of total soluble solids/sugar content of table grape so that the optimal mapping grid size could be found. Once it was defined, two interpolation methods: simple averaging over blocks and block kriging, were applied to mapping the data. Results show that both methods help depict the within-field variability in the data. While the averaging procedure is easier to automate, the block kriging approach provides users with a level of uncertainty in the aggregated data. Both mapping approaches significantly impacted the within-field spatial structure: (i) the small-scale variations were ten times lower than in the raw data, and (ii) the signal-to-noise ratio of the aggregated data with the optimal grid was twice as high as that of the raw data. As the proposed geostatistical methodology is a first attempt to define the optimal grid size to map high resolution spatial data, areas for future development applications are also proposed. © 2018 Springer Science+Business Media, LLC, part of Springer Natur

Biological effects of mechanically and chemically dispersed oil on the Icelandic scallop (Chlamys islandica)

This study aimed to simulate conditions in which dispersant (Dasic NS) might be used to combat an oil spill in coastal sub-Arctic water of limited depth and water exchange in order to produce input data for Net Environmental Benefit Analysis (NEBA) of Arctic and sub-Arctic coastal areas. Concentration dependent differences in acute responses and long-term effects of a 48 h acute exposure to dispersed oil, with and without the application of a chemical dispersant, were assessed on the Arctic filter feeding bivalve Chlamys islandica. Icelandic scallops were exposed for 48 h to a range of spiked concentrations of mechanically and chemically dispersed oil. Short-term effects were assessed in terms of lysosomal membrane stability, superoxide dismutase, catalase, gluthatione S-transferases, glutathione peroxidases, glutathione reductase, glutathione, total oxyradical scavenging capacity, lipid peroxidation and peroxisomal proliferation. Post-exposure survival, growth and reproductive investment were followed for 2 months to evaluate any long-term consequence. Generally, similar effects were observed in scallops exposed to mechanically and chemically dispersed oil. Limited short-term effects were observed after 48 h, suggesting that a different timing would be required for measuring the possible onset of such effects. There was a concentration dependent increase in cumulative post-exposure mortality, but long-term effects on gonadosomatic index, somatic growth/condition factor did not differ among treatments

Face to face with distance education

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