Knock-Down of Both eIF4E1 and eIF4E2 Genes Confers Broad-Spectrum Resistance against Potyviruses in Tomato

Background: The eukaryotic translation initiation factor eIF4E plays a key role in plant-potyvirus interactions. eIF4E belongs to a small multigenic family and three genes, eIF4E1, eIF4E2 and eIF(iso)4E, have been identified in tomato. It has been demonstrated that eIF4E-mediated natural recessive resistances against potyviruses result from non-synonymous mutations in an eIF4E protein, which impair its direct interaction with the potyviral protein VPg. In tomato, the role of eIF4E proteins in potyvirus resistance is still unclear because natural or induced mutations in eIF4E1 confer only a narrow resistance spectrum against potyviruses. This contrasts with the broad spectrum resistance identified in the natural diversity of tomato. These results suggest that more than one eIF4E protein form is involved in the observed broad spectrum resistance Methodology/Principal Findings: To gain insight into the respective contribution of each eIF4E protein in tomato-potyvirus interactions, two tomato lines silenced for both eIF4E1 and eIF4E2 (RNAi-4E) and two lines silenced for eIF(iso)4E (RNAi-iso4E) were obtained and characterized. RNAi-4E lines are slightly impaired in their growth and fertility, whereas no obvious growth defects were observed in RNAi-iso4E lines. The F1 hybrid between RNAi-4E and RNAi-iso4E lines presented a pronounced semi-dwarf phenotype. Interestingly, the RNAi-4E lines silenced for both eIF4E1 and eIF4E2 showed broad spectrum resistance to potyviruses while the RNAi-iso4E lines were fully susceptible to potyviruses. Yeast two-hybrid interaction assays between the three eIF4E proteins and a set of viral VPgs identified two types of VPgs: those that interacted only with eIF4E1 and those that interacted with either eIF4E1 or with eIF4E2 Conclusion/Significance: These experiments provide evidence for the involvement of both eIF4E1 and eIF4E2 in broad spectrum resistance of tomato against potyviruses and suggest a role for eIF4E2 in tomato-potyvirus interactions

Characterisation of grapevine canopy leaf area and inter-row management using Sentinel-2 time series

Accurate data on crop canopy are among the prerequisites for hydrological modelling, environmental assessment, and irrigation management. In this regard, our study concentrated on an in-depth analysis of optical satellite data of Sentinel-2 (S2) time series of the leaf area index (LAI) to characterise canopy development and inter-row management of grapevine fields. Field visits were conducted in the Ouveze-Ventoux area, South Eastern France, for two years (2021 and 2022) to monitor phenology, canopy development, and inter-row management of eleven selected grapevine fields. Regarding the S2-LAI data, the annual dynamic of a typical grapevine canopy leaf area was similar to a double logistic curve. Therefore, an analytic model was adopted to represent the grapevine canopy contribution to the S2-LAI. Part of the parameters of the analytic model were calibrated from the actual grapevine canopy dynamics timing observation from the field visits, while the others were inferred at the field level from the S2-LAI time series. The background signal was generated by directly subtracting the simulated canopy from the S2 LAI time series. Rainfall data were examined to see the possible explanations behind variations in the inter-row grass development. From the background signals, we could group the inter-row management into three classes: grassed, partially grassed, and tilled, which corroborated our findings on the field. To consider the possibility of avoiding field visits, the model was recalibrated on a grapevine field with a clear canopy signal and applied to two fields with different inter-row management. The result showed slight differences among the inter-row signals, which did not prevent the identification of inter-row management, thus indicating that field visits might not be mandatory

Monitoring of flood irrigation for the characterization of irrigation practices of grassland fields in the Crau region (South of France)

International audienc

Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region

International audienceIn the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level

Delineation of Orchard, Vineyard, and Olive Trees Based on Phenology Metrics Derived from Time Series of Sentinel-2

This study aimed to propose an accurate and cost-effective analytical approach for the delineation of fruit trees in orchards, vineyards, and olive groves in Southern France, considering two locations. A classification based on phenology metrics (PM) derived from the Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegetation indices to delineate orchard and vineyard classes. The generated PM were introduced into a random forest (RF) algorithm for classification. The method was tested on different vegetation indices, with the best results obtained with the leaf area index. To delineate the olive class, the temporal features of the green chlorophyll vegetation index were found to be the most appropriate. Obtained overall accuracies ranged from 89–96% and a Kappa of 0.86–0.95 (2016–2021), respectively. These accuracies are much better than applying the RF algorithm to the LAI time series, which led to a Kappa ranging between 0.3 and 0.52 and demonstrates the interest in using phenological traits rather than the raw time series of the remote sensing data. The method can be well reproduced from one year to another. This is an interesting feature to reduce the burden of collecting ground-truth information. If the method is generic, it needs to be calibrated in given areas as soon as a phenology shift is expected

Assessment of agricultural practices from Sentinel 1 & 2 images applied on rice fields to get a farm typology in the camargue region

International audienceIn the global change context, an efficient management of the available resources has become one of the most important topic particularly for sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue, in the Southeastern France, which play a crucial role to control the soil salinity and whose the surfaces significantly decreased these last years from 20 000 ha in 2010 to around 14000 ha in 2014. The arrival of the new satellites Sentinel makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to: 1) accurately assess the surfaces of the main crops, rice, wheat and grassland in 2016 and 2017 from classifications based on multispectral data, (2) map some agricultural practices (sowing and harvest residue burning), and (3) elaborate a farm typology for the Camargue region based on variables computed from remote sensing data to better understand the farmer strategies

STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

International audienceThe assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts

Comparison of two modeling approaches to simulate rice production in the Camargue region using Sentinel 2 data

International audienceThe assessment of yield variability at the territory scale is often difficult due to the lack of knowledge on various factors involved, e.g., the agricultural practices of farmers and phenological calendars. Remote sensing can help to provide precise and timely information on crops particularly with the unprecedented amount of free Sentinel data within the Copernicus programme. This study focuses on the evaluation of the contribution of the new Sentinel 2 data to provide phenological information on rice cropping systems in the Camargue region in the South-Eastern France. Dense time series of data acquired at high spatial resolution (10m) were analyzed for 2016 and 2017 and were used to map rice, compute Leaf Area Index. Various methods combining remote sensing information were compared with two different crop models (STICS and SAFY) to assess the yields. The performances are discussed according to surveys made with farmers

A new tool for gene tagging in lettuce (Lactuca sativa): the Tnt1 retrotransposon from tobacco

International audienc

Successful Gene Tagging in Lettuce Using the Tnt1 Retrotransposon from Tobacco

The tobacco (Nicotiana tabacum) element Tnt1 is one of the few identified active retrotransposons in plants. These elements possess unique properties that make them ideal genetic tools for gene tagging. Here, we demonstrate the feasibility of gene tagging using the retrotransposon Tnt1 in lettuce (Lactuca sativa), which is the largest genome tested for retrotransposon mutagenesis so far. Of 10 different transgenic bushes carrying a complete Tnt1 containing T-DNA, eight contained multiple transposed copies of Tnt1. The number of transposed copies of the element per plant was particularly high, the smallest number being 28. Tnt1 transposition in lettuce can be induced by a very simple in vitro culture protocol. Tnt1 insertions were stable in the progeny of the primary transformants and could be segregated genetically. Characterization of the sequences flanking some insertion sites revealed that Tnt1 often inserted into genes. The progeny of some primary transformants showed phenotypic alterations due to recessive mutations. One of these mutations was due to Tnt1 insertion in the gibberellin 3β-hydroxylase gene. Taken together, these results indicate that Tnt1 is a powerful tool for insertion mutagenesis especially in plants with a large genome