Adoption factors and structural characteristics of irrigated olive grove agroforestry systems in Central Tunisia

International audienceIrrigated olive grove agroforestry systems in the Southern Mediterranean have rarely been studied. In the context of increased interest for agroecological approaches, this study questions why and how farmers undertake such associations, in the case of smallholder irrigated agriculture in Tunisia. The objectives were to characterize the physical structure of existing olive-summer vegetable associations and describe the rationales of farmers implementing them. Quantitative and qualitative approaches were used to collect data from 132 olive groves and 31 semi-directive interviews in the Merguellil plain, Central Tunisia. Dual crop input-intensive agroforestry systems were predominant, although agroforestry structures with a high species' diversity also existed. Adoption reasons and implementation of agroforestry systems varied. The latter were often perceived as an economically viable solution in a context of difficult access to productive resources. Particularly, limited and fragmented access to land or water was a strong driver of adoption, inducing contractual arrangements between farmers to share resources. Farmers implemented agroforestry systems mainly to maximize income, reduce production costs or reduce risks through a crop diversification strategy. Most characterized agroforestry olive-summer vegetable associations may fail to meet the principles of agroecology. Being already adopted by farmers, they may however serve as a base to conceive improved cropping systems

Tiny Machine Learning Zoo for Long-Term Compensation of Pressure Sensor Drifts

Pressure sensors embodied in very tiny packages are deployed in a wide range of advanced applications. Examples of applications range from industrial to altitude location services. They are also becoming increasingly pervasive in many other application fields, ranging from industrial to military to consumer. However, the inexpensive manufacturing technology of these sensors is strongly affected by environmental stresses, which ultimately affect their measurement accuracy in the form of variations in gain, hysteresis, and nonlinear responses. Thermal stresses are the main source of sensor behavior deviation. They are particularly insidious because even a few minutes of high temperature exposure can cause measurement drift for many days in the sensor responses. Therefore, conventional calibration techniques are challenged in their adequacy to achieve high accuracy and over the entire deployment life of the sensor. To manage this, several costly and time-consuming calibration procedures have to be performed. Machine learning (ML) techniques are known, supported by the universal approximation theorem, to provide effective data-driven solutions to the above problems. In this context, this paper addresses two case studies, corresponding to post-soldering thermal stresses and exposure to moderately high temperatures, for which two separate datasets have been built and 53 different tiny ML models (collected into a zoo) have been devised and compared. The ML zoo has been constructed with models such as artificial neural networks (ANN), random forest (RFR), and support vector regressors (SVR), able to predict the error introduced by the thermal drift and to compensate for the drift of the measurements. The models in the zoo also satisfy the memory, computational, and accuracy constraints associated with their deployment on resource-constrained embedded devices to be integrated at the edge. Quantitative results achieved by the zoo are reported and discussed, as well as their deployability on tiny micro-controllers. These results reveal the suitability of a tiny ML zoo for the long-term compensation of MEMS pressure sensors affected by drift in their measurements

Variations in Physicochemical Properties and Bioconversion Efficiency of Ulva lactuca Polysaccharides After Different Biomass Pretreatment Techniques

Green macroalgae are an abundant and undervalued biomass with a specific cell wall structure. In this context, different pretreatments, namely ethanol organosolv (Org), alkaline, liquid hot water (LHW), and ionic liquid (IL) pretreatments, were applied to the green macroalgae Ulva lactuca biomass and then evaluated. Their effects on chemical composition, biomass crystallinity, enzymatic digestibility, and theoretical ethanol potential were studied. The chemical composition analysis showed that the Org and LHW pretreatments allowed the highest glucan recovery (80.8 ± 3.6 and 62.9 ± 4.4 g/100 g DM, respectively) with ulvan (80.0 and 99.1%) and hemicellulose (55.0 and 42.3%) removal. These findings were in agreement with both thermogravimetric analysis and scanning electron microscopy results that confirm significant structural changes of the pretreated biomasses. It was found that the employed pretreatments did not significantly affect the cellulose crystallinity; however, they both increased the whole crystallinity and the enzymatic digestibility. This later reached 97.5% in the case of LHW pretreatment. Our results showed high efficiency saccharification of Ulva lactuca biomass that will constitute the key step of the implementation of a biorefinery process

A simple, fast and inexpensive method to assess salt stress tolerance of aerial plant part: Investigations in the mandarin group

International audienceFor grafted plants, salt stress tolerance of the aerial plant part is poorly documented. Thus, we developed a simple, fast and inexpensive method to identify tolerant genotypes. Twigs of 14 mandarin accessions that we previously analyzed as seedlings were cut in solution to prevent embolism and were then evaluated in salt stress condition for a week. Physiological parameters such as gas exchanges, leaf Cl- and Na+, as well as the presence of H2O2 and the activity of enzymes involved in ROS synthesis and detoxification processes were analyzed. One accession known to be tolerant as rootstock was shown to be sensitive with limited Cl- translocation from the solution to the shoot while sensitive accessions when grown as seedlings presented limited wilting symptoms and accumulated large leaf Cl- content. A model is proposed to explain the different strategies of the plant to cope with high toxic ion content. This method allows separation of the root compartment, where ion exclusion mechanisms may exist and have an impact on the salt stress tolerance of the whole plant. (C) 2015 Elsevier GmbH. All rights reserved

Diversity in the trifoliate orange taxon reveals two main genetic groups marked by specific morphological traits and water deficit tolerance properties

International audienceTrifoliate orange (Poncirus trifoliata (L.) Raf.) is a very useful taxon for the citrus industry since this rootstock is immune to the Citrus Tristeza virus and confers cold tolerance. Numerous trifoliate orange varieties exist but little is known regarding their behavioural variability when subjected to abiotic constraints. The diversity of 74 P. trifoliata accessions maintained in the INRA-CIRAD Citrus Germplasm Collection was investigated using simple sequence repeat markers. Two major genetic groups were clearly identified as a few homonyms, intergroup or intra-group hybrids and doubled-chromosome tetraploid forms. The Group 1 phenotype was characterized by larger flowers and leaves and smaller seeds than Group 2. Tetraploid accessions showed larger leaves and heavier seeds than all other diploid accessions, regardless of genetic classification. Eight genotypes belonging to both genetic groups, as well as two hybrids between the two groups, were selected to investigate their water deficit tolerance. Stress was applied by withdrawing irrigation for 4 weeks. Physiological parameters such as leaf stomatal conductance, quantum yield of photosystem II electron transport, soil water potential, leaf osmotic potential and transpiration rate were estimated. Some varieties, such as Rubidoux 0101033, were clearly more tolerant to water deficit than others, such as Pomeroy 0101040 and Pomeroy 0110081. Interestingly, accessions that had the highest soil water potential and were the least affected by stress belonged to genetic Group 2. Conversely, trifoliate oranges of genetic Group 1 were the least tolerant