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Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

Degradation issues correlated to microstructural changes are the main obstacles to solid oxide fuel cell and electrolyser applications, making their identification and understanding fundamental steps. Coupling experimental activities with modelling, this work analyses the state-of-the-art Ni-YSZ (Yttria-Stabilized Zirconia)/YSZ/CGO (Cerium Gadolinium Oxide)/LSCF (Lanthanum Strontium Cobalt Ferrite)-CGO-based cell after 1000 h of galvanostatic electrolysis operation at fixed temperature and high steam composition in the inlet gas. Following a multiscale approach, the system behaviour is characterized through electrochemical impedance spectra and polarization curves as well as studying microstructure evolution, with a focus on Ni-cermet functional layer in view of Ni instability detected as the main degradation cause. A comparison with a cell consisting of the same initial geometrical structure and materials but aged in fuel cell mode allows to highlight the influence of operating mode and parameters on Ni-YSZ microstructure. Ni particle size and phase fraction variations experimentally observed on the electrode surface are correlated to water content and applied polarization simulated local values. Ni uneven distribution at the electrolyte interface and particle coarsening, above all, lead to an increase in polarization loss under electrolysis and fuel cell mode, respectively, since both penalise the charge transfer reaction and migration.

Heat stress is a global issue constraining pig productivity, and it is likely to intensify under future climate change. Technological advances in earth observation have made tools available that enable identification and mapping livestock species that are at risk of exposure to heat stress due to climate change. Here, we present a methodology to map the current and likely future heat stress risk in pigs using R software by combining the effects of temperature and relative humidity. We applied the method to growing-finishing pigs in Uganda. We mapped monthly heat stress risk and quantified the number of pigs exposed to heat stress using 18 global circulation models and projected impacts in the 2050s. Results show that more than 800 000 pigs in Uganda will be affected by heat stress in the future. The results can feed into evidence-based policy, planning and targeted resource allocation in the livestock sector.

Les systèmes agricoles sont intimement liés au changement climatique : ils sont d'une part affectés par la dérive de son état moyen et de sa variabilité, et sont d'autre part des contributeurs nets à l'évolution du climat par l'extension de leur surface et l'intensité de leur gestion. L'évolution du système climat-agriculture repose sur nombreux mécanismes qui s'étendent sur une large gamme d'échelles temporelles et spatiales, et sont interdépendants. Pour réduire l'incertitude associée à l'évolution de ce système et guider les choix collectifs pertinents, il est nécessaire d'intégrer ces processus sur cette gamme d'échelle temporelle et spatiale. Dans cette thèse, je me suis intéressé aux échelles spatiales allant de la plus petite unité décisionnelle des systèmes de production agricole (l'exploitation) à celle de la décision publique concernant ces interactions entre climat et agriculture (échelle nationale à supranationale). Je me suis d'autre part intéressé au court terme (quelques années) et à l'Europe. J'ai poursuivi le développement d'un outil de modélisation reposant sur le couplage du modèle d'offre agricole Européenne (AROPAj) et du modèle générique de culture (STICS), qui permet de prendre en compte les mécanismes adéquats à l'échelle du système de production, et leurs facteurs d'hétérogénéité à l'échelle de l'Europe. Cet outil m'a permis de mettre en valeur le rôle important à l'échelle Européenne des mécanismes de court-terme dans la réponse de l'offre agricole au changement climatique. En particulier, la prise en compte des adaptations de court terme des systèmes de production agricole modifie la vision habituellement retenue des impacts du changement climatique en Europe. J'ai de plus développé des méthodes alliant agronomie et statistique pour explorer l'hétérogénéité du comportement des principales cultures Européenne sous changement climatique entre régions, espèces et scénarios de changement climatique. Enfin, j'ai pu mettre en valeur la faible interaction en première analyse entre adaptation au changement climatique et réponse à la mise en place d'une politique réduction des émissions de gaz à effet de serre au niveau des systèmes de production agricole. Agriculture, and the climate system are closely linked: agricultural systems are driven by changes in mean climate and its variability, while their expansion and intensification contribute to the anthropogenic perturbation of the climate system. The evolution of the climate-agriculture system relies on numerous processes, which extend over a wide large range of temporal and spatial scale, and are intertwined. It is necessary to integrate these processes across scales in order to both reduce the uncertainty that overshadow the evolution of the system, and help clever decision making. In this work, I focused on that integration goal in the specific case of Europe for short-term time scales in a future horizon. I focused on typical spatial scales of decision making: from the smallest decision unit in agriculture (farm scale) to the one of policy making regarding agriculture-climate interactions (Europe). I continued the development of a modelling framework relying on the coupling of a microeconomic model of European agricultural supply (AROPAj) to a generic crop model (STICS), which account for adequate processes at farm scale, and for the factors that drive the heterogeneity in their net effects over Europe. This tool allowed me to highlight the specific role of farm-scale adaptations in the response of European agricultural supply to climate change. In particular, accounting for these processes alters the usual picture of climate change impacts over Europe. I further developed analytical methods building on agronomic and statistic knowledge to explore the heterogeneity in the response of major European crops, among geographical locations, species, and climate change scenarios. Finally, first results showed that at the farm scale, little interaction is expected between the adaptation to climate change and the implementation of a greenhouse gas mitigation policy targeting the agricultural sector.

A membrane assisted process for green hydrogen production from a bioethanol derived feedstock is here developed and evaluated, starting from the conventional Steam Methane Reforming (SMR) process. Such a process is suitable for centralized hydrogen production, and is here analyzed for a large-scale H2 production unit with the capacity of 40.000 Nm3/h. The basic Steam Ethanol Reforming (SER) process scheme is modified in a membrane assisted process by integrating the Pd-membrane separation steps in the most suitable reaction steps. The membrane assisted process, configured in three alternative architectures (Open architecture, Membrane Reactor and Hybrid architecture) was evaluated in terms of efficiencies and hydrogen yields, obtaining a clear indication of improved process performance. The alternative membrane assisted process architectures are compared to the basic SER process and to the benchmark SMR process fed by natural gas, for an overall comparative assessment of the efficiency and specific CO2 emissions and for an economic analysis based on the operating expenditures.

Finding a numerical method to model solute transport in porous media with high heterogeneity is crucial, especially when chemical reactions are involved. The phase space formulation termed the multi-advective water mixing approach (MAWMA) was proposed to address this issue. The water parcel method (WP) may be obtained by discretizing MAWMA in space, time, and velocity. WP needs two transition matrices of velocity to reproduce advection (Markovian in space) and mixing (Markovian in time), separately. The matrices express the transition probability of water instead of individual solute concentration. This entails a change in concept, since the entire transport phenomenon is defined by the water phase. Concentration is reduced to a chemical attribute. The water transition matrix is obtained and is demonstrated to be constant in time. Moreover, the WP method is compared with the classic random walk method (RW) in a high heterogeneous domain. Results show that the WP adequately reproduces advection and dispersion, but overestimates mixing because mixing is a sub-velocity phase process. The WP method must, therefore, be extended to take into account incomplete mixing within velocity classes.

This paper discloses the research carried out at MCAT (INCAR-CSIC) in the last years, which has been aimed to develop a new microwave induced process (MIP) for the conversion of biosolids into syngas. The different organic substrates that can be processed, the operational conditions that lead to a maximum production of syngas and the characteristics of the syngas as well as the by-products obtained are discussed. Particular emphasis is placed on the partial recycling of the solid fraction and its role as microwave susceptor and as a catalyst of some of the gasification reactions taking place during the MIP of the organic residues. Additionally, some insights on the energy costs of the process are also given. Peer reviewed