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Water scarcity will be one of the main issues of the 21 st century, because of competing needs between civil, industrial, and agriculture use. While agriculture is the largest user of water, its share is bound to decrease as societies develop. Clearly, agriculture needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important although there is a long way into translating this at the farm/landscape level. As we move up from a cell/organ/plant scale to more integrated scales such as plot, field, farm system, and landscape, other factors need to be considered, including trade-offs, to possibly improve WUE. These include choices of crop variety/species, farm management, landscape design, infrastructure development, ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyze ways to address WUE at these different scales where metrics of analysis are defined and trade-offs considered. The equations in this perspective paper use similar metrics across scales for an easier connection and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes. International audience

French Polynesia atolls are spread on a vast 2300 by 1200 km Central Pacific Ocean area exposed to spatially and temporally dependent wave forcing. They also have a wide range of closed to open morphologies and several have been suitable to develop from black-lipped pearl oysters a substantial pearl farming activity in the past 30 years, representing nowadays the 2nd source of income for French Polynesia. Considering here only the component of lagoon renewal that is driven by waves, we investigate for 74 atolls different lagoon renewal metrics using 20 years of wave model data at 0.05° spatial resolution. Wavelet spectral analyses highlight that atolls, even in close vicinity, can be exposed to different and characteristic periodicities in wave-driven flows and water renewal. These characteristics are discussed in relation to pearl farming atolls, including atolls known to be efficient oyster spat producers, a critical activity for pearl farming sustainability.

The aim of this study was to assess, for the first time, the concentrations, sources, dry deposition and human health risks of polycyclic aromatic hydrocarbons (PAHs), aliphatic hydrocarbons (AHs), poly-chlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in total suspended particle (TSP) samples collected in Bizerte city, Tunisia (North Africa), during one year (March 2015eJanuary 2016). Concentrations of PAHs, AHs, PCBs and OCPs ranged 0.5e17.8 ng m À3 , 6.7e126.5 ng m À3 , 0.3e11 pg m À3 and 0.2e3.6 pg m À3 , respectively, with higher levels of all contaminants measured in winter. A combined analysis revealed AHs originating from both biogenic and petrogenic sources, while diesel vehicle emissions were identified as dominant sources for PAHs. PCB potential sources included electronic, iron, cement, lubricant factories located within or outside Bizerte city. The dominant OCP congeners were p,p 0-DDT and p,p 0-DDE, reflecting a current or past use in agriculture. Health risk assessment showed that the lifetime excess cancer risk from exposure to airborne BaP was negligible in Bizerte, except in winter, where a potential risk to the local population may occur. International audience

In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution (HRV) satellite data is proposed. Grain yields are then statistically estimated as a function of Leaf Area Index (LAI) during the maximum growth period between 25 March and 5 April. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV data. This study is based on the analysis of a rich database, which was acquired over a period of two years (2010&ndash 2013) at the Merguellil site in central Tunisia (North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images. The validation and calibration of this methodology is presented, on the basis of two subsets of observations derived from the experimental database. Finally, an inversion technique is applied to estimate the overall yield of the entire studied site. 2011, 2012&ndash

Mediterranean agricultural landscapes provide ecosystem services and disservices that are driven by land use pattern dynamics, the latter of which results from the crop spatiotemporal distribution. Farmland fragmentation is known to be a driver of crop management and farm performance. However, existing studies on farmland fragmentation have not addressed the impact of farmland fragmentation and the subsequent neighbouring interactions on decision-making about annual crop allocation. Therefore, the current study aims to explore how much farmland fragmentation can drive the decisions made by farmer about annual crop allocation by characterizing and quantifying the influences of both crop sequences and neighbouring crops at the field scale. We addressed this issue within the Lebna watershed (210 km² size, located on the Cap Bon Peninsula in Tunisia), which is typified by a hilly topography, rainfed mixed farming (cereals, fodder, legumes, spices) and livestock (cattle, sheep and goat), and strong farmland fragmentation. The experimental phases consisted of conducting 30 farmer interviews and collecting data regarding the field spatial distribution for a total number of 360 fields in 2015 and 355 fields in 2016, hereafter called farmer fields. We also recorded (1) crop types for the farmer fields in 2015 and 2016 and (2) land uses (including crop types) in the neighbouring pieces of land in 2016. Data analysis relied on differentiating the farmer fields between isolated and non-isolated fields. Isolation/non-isolation depended upon farmland fragmentation and field dispersion relative to the other farm fields and to the farmstead. Using univariate and linear discriminant analysis on both crop sequences and neighbouring crops, data analysis revealed a significant effect of farmland fragmentation on farmer decision-making regarding crop allocation. When fields are isolated (fragmented farmlands), farmers implement with some of their neighbours, collective rules of crop allocation that permit the management of common constraints, such as the lack of roads to access fields. The landscape subsequently depicts aggregates of fields with the same type of crop. When fields are non-isolated (aggregated farmlands close to the farmsteads), the allocation constraints are mainly related to the cropping systems, with a strong impact from the crop sequences. Overall, these results indicate that to improve our understanding of crop allocation drivers at the landscape level, it is not only sufficient to address rules and drivers at the field and farm scales but it is also necessary to account for the collective contexts in which farmers operate. International audience

Abstract. In semiarid areas, agricultural production is restricted by water availability; hence, efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the normalized difference vegetation index, NDVI) and water availability under water stress (through the surface temperature Tsurf), which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat flux LE) in the Kairouan plain (central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) model fed by low-resolution remote sensing data (Terra and Aqua MODIS). The work's goal was to assess the operational use of the SPARSE model and the accuracy of the modeled (i) sensible heat flux (H) and (ii) daily ET over a heterogeneous semiarid landscape with complex land cover (i.e., trees, winter cereals, summer vegetables). SPARSE was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass times. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 and 53.85 W m−2 for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS) H measurements along a path length of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scales (RMSE = 47.20 and 43.20 W m−2 for Terra and Aqua, respectively, and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modeled (SPARSE) and observed (XLAS) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

The present study highlights the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations at distant sites, based on soil texture similarity only, was tested. This extrapolation was assessed using SAR estimates and manual thetaprobe measurements of SM recorded at these distant sites. The results reveal a bias of approximately 0.63% and 3.04%, and an RMSE equal to 6.11% and 4.5%, for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months. Although the SM was measured on only seven different dates for the purposes of calibration, satisfactory results 30 were obtained as a result of the wide range of SM values recorded in these seven images. This led to good overall calibration of the soil parameters, thus demonstrating the considerable potential of Sentinel-1 images for daily soil moisture monitoring using simple models. [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS International audience