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Carrillo E.,Montpellier SupAgro | Matese A.,CNR Institute for Biometeorology | Rousseau J.,Institute Cooperatif du Vin | Tisseyre B.,Montpellier SupAgro
Precision Agriculture | Year: 2016

The wine industry needs to know the yield of each vine field precisely to optimize quality management and limit the costs of harvest operations. Yield estimation is usually based on random vine sampling. The resulting estimations are often not precise enough because of the high variability within vineyard fields. The aim of the work was to study the relevance of using NDVI-based sampling strategies to improve estimation of mean field yield. The study was conducted in nine non-irrigated vine fields located in southern France. For each field, NDVI was derived from multi-spectral airborne images. The variables which define the yield: [berry weight at harvest (BWh), bunch number per vine (BuN) and berry number per bunch (BN)] were measured on a regular grid. This data-base allowed for five different sampling schemes to be tested. These sampling methods were mainly based on a stratification of NDVI values, they differed in the way as to whether NDVI was used as ancillary information to design a sampling strategy for BuN, BN, BW or for all yield variables together. Results showed a significant linear relationship between NDVI and BW, indicating the interest of using NDVI information to optimize sampling for this parameter. However this result is mitigated by the low incidence of BW in the yield variance (4 %) within the field. Other yield components, BuN and BN explain a higher percentage of yield variance (60 and 11 % respectively) but did not show any clear relationship with NDVI. A large difference was observed between fields, which justifies testing the optimized sampling methods on all of them and for all yield variables. On average, sampling methods based on NDVI systematically improved vine field yield estimates by at least 5–7 % compared to the random method. Depending on the fields, error improvement ranged from −2 to 15 %. Based on these results, the practical recommendation is to consider a two-step sampling method where BuN is randomly sampled and BW is sampled according to the NDVI values. © 2015, Springer Science+Business Media New York. Source

Lopez-Lozano R.,French National Institute for Agricultural Research | Baret F.,French National Institute for Agricultural Research | Poilve H.,Infoterra France | Tisseyre B.,Montpellier SupAgro | And 2 more authors.
Acta Horticulturae | Year: 2011

The effect of canopy architecture in the estimation of biophysical parameters from remote sensing data in orchards is studied through the particular case of vineyard canopies. Two different approximations (1D and 3D models) are tested to estimate Leaf Area Index (LAI) from canopy reflectance in a total of 31 points within commercial field plots. The results shows that 1D models are not pertinent to describe canopy reflectance in row structured canopies, while including a 3D description of canopy architecture provided satisfactory results (RMSE=0.19). A simulation study was also carried out to evaluate the contribution of observational parameters in the accuracy of LAI and the fraction of absorbed photosynthetically active radiation (fIPAR) estimations from canopy reflectance. The results highlight the effect of rows orientation in the uncertainties in the estimation of both parameters. When sun illuminates perpendicular to rows, the sensitivity of canopy reflectance to fIPAR and LAI is higher, thus providing more accurate estimations than in the case of parallel illumination. Source

Noble J.,Lallemand S.A.S. | Noble J.,Institute Cooperatif du Vin | Sanchez I.,French National Institute for Agricultural Research | Sanchez I.,Montpellier SupAgro | And 2 more authors.
Microbial Cell Factories | Year: 2015

Background: Wine yeasts can produce undesirable sulfur compounds during alcoholic fermentation, such as SO2 and H2S, in variable amounts depending mostly on the yeast strain but also on the conditions. However, although sulfur metabolism has been widely studied, some of the genetic determinants of differences in sulfite and/or sulfide production between wine yeast strains remain to be identified. In this study, we used an integrated approach to decipher the genetic determinants of variation in the production of undesirable sulfur compounds. Results: We examined the kinetics of SO2 production by two parental strains, one high and one low sulfite producer. These strains displayed similar production profiles but only the high-sulfite producer strain continued to produce SO2 in the stationary phase. Transcriptomic analysis revealed that the low-sulfite producer strain overexpressed genes of the sulfur assimilation pathway, which is the mark of a lower flux through the pathway consistent with a lower intracellular concentration in cysteine. A QTL mapping strategy then enabled us to identify MET2 and SKP2 as the genes responsible for these phenotypic differences between strains and we identified new variants of these genes in the low-sulfite producer strain. MET2 influences the availability of a metabolic intermediate, O-acetylhomoserine, whereas SKP2 affects the activity of a key enzyme of the sulfur assimilation branch of the pathway, the APS kinase, encoded by MET14. Furthermore, these genes also affected the production of propanol and acetaldehyde. These pleiotropic effects are probably linked to the influence of these genes on interconnected pathways and to the chemical reactivity of sulfite with other metabolites. Conclusions: This study provides new insight into the regulation of sulfur metabolism in wine yeasts and identifies variants of MET2 and SKP2 genes, that control the activity of both branches of the sulfur amino acid synthesis pathway and modulate sulfite/sulfide production and other related phenotypes. These results provide novel targets for the improvement of wine yeast strains. © 2015 Noble et al.; licensee BioMed Central. Source

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