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Determinants of self-employment are studied using data from the transition to working life survey conducted by the International Labour Office (ILO) in 2012 in collaboration with Beninese Government. Although the preference for self-employment is evident among young people,up to $60 \%$, it hides many social struggles. From a multiple factor analysis of the self-employed,we find that self-employment is more a subordinated alternative to cope with unemployment,or underemployment. It is a subsistence economy, informal at $99 \%$, and essentially accountyoung people with little formal education, with a significant influence of household structureacross financial situation, and marital status herein. A Bayesian modeling supported by contingency tables confirms the impact of family structure : we have identified a notorious transfer ofcultural capital for activities related to agriculture and trade for example. Moreover, the poorperformance of the national economy and lack of adequate supervision of vocational trainingundervalued, have discouraged the choice of self-employment for young people whose age rangeis between 15 and 29 years. This is what reveals a generalized Tobit selection model based onWynand and Praag method, which also shows that education has a dual significance : firstlythrough the educational level of parents and the individual, and through the retrograde effect ofvocational training for parents, albeit parent influence are not confirmed with tested Zhelonkinrobustess. To guide self-employment to a prosperous economy, it is necessary to create a morefavorable environment for its development, particularly through an organization of the informalsector, taking into account each of the identified groups.

System identification and machine learning are two similar concepts independently used in automatic and computer science community. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. Machine learning algorithms build a mathematical model based on sample data, known as "training data" (clean or not), in order to make predictions or decisions without being explicitly programmed to do so. Except prediction accuracy, converging speed and stability are another two key factors to evaluate the training process, especially in the online learning scenario, and these properties have already been well studied in control theory. Therefore, this thesis will implement the interdisciplinary researches for following topic: 1) System identification and optimal control on macroeconomic data: We first modelize the China macroeconomic data on Vector Auto-Regression (VAR) model, then identify the cointegration relation between variables and use Vector Error Correction Model (VECM) to study the short-time fluctuations around the long-term equilibrium, Granger Causality is also studied with VECM. This work reveals the trend of China's economic growth transition: from export-oriented to consumption-oriented; Due to limitation of China economic data, we turn to use France macroeconomic data in the second study. We represent the model in state-space, put the model into a feedback control framework, the controller is designed by Linear-Quadratic Regulator (LQR). The system can apply the control law to bring the system to a desired state. We can also impose perturbations on outputs and constraints on inputs, which emulates the real-world situation of economic crisis. Economists can observe the recovery trajectory of economy, which gives meaningful implications for policy-making. 2) Using control theory to improve the online learning of deep neural network: We propose a performance-based learning rate algorithm: E (Exponential)/PD (Proportional Derivative) feedback control, which consider the Convolutional Neural Network (CNN) as plant, learning rate as control signal and loss value as error signal. Results show that E/PD outperforms the state-of-the-art in final accuracy, final loss and converging speed, and the result are also more stable. However, one observation from E/PD experiments is that learning rate decreases while loss continuously decreases. But loss decreases mean model approaches optimum, we should not decrease the learning rate. To prevent this, we propose an event-based E/PD. Results show that it improves E/PD in final accuracy, final loss and converging speed; Another observation from E/PD experiment is that online learning fixes a constant training epoch for each batch. Since E/PD converges fast, the significant improvement only comes from the beginning epochs. Therefore, we propose another event-based E/PD, which inspects the historical loss, when the progress of training is lower than a certain threshold, we turn to next batch. Results show that it can save up to 67% epochs on CIFAR-10 dataset without degrading much performance.3) Machine learning out of unreliable data: We propose a generic framework: Robust Anomaly Detector (RAD), The data selection part of RAD is a two-layer framework, where the first layer is used to filter out the suspicious data, and the second layer detects the anomaly patterns from the remaining data. We also derive three variations of RAD namely, voting, active learning and slim, which use additional information, e.g., opinions of conflicting classifiers and queries of oracles. We iteratively update the historical selected data to improve accumulated data quality. Results show that RAD can continuously improve model's performance under the presence of noise on labels. Three variations of RAD show they can all improve the original setting, and the RAD Active Learning performs almost as good as the case where there is no noise on labels.; L'identification de système et l'apprentissage automatique sont deux concepts similaires utilisés indépendamment dans la communauté automatique et informatique. L'identification des systèmes construit des modèles à partir de données mesurées. Les algorithmes d'apprentissage automatique construisent des modèles basés sur des données d'entraînement (propre ou non), afin de faire des prédictions sans être explicitement programmé pour le faire. Sauf la précision de prédiction, la vitesse de convergence et la stabilité sont deux autres facteurs clés pour évaluer le processus de l'apprentissage, en particulier dans le cas d'apprentissage en ligne, et ces propriétés ont déjà été bien étudiées en théorie du contrôle. Donc, cette thèse implémente des recherches suivantes : 1) Identification du système et contrôle optimal des données macroéconomiques : Nous modélisons d'abord les données macroéconomiques chinoises sur le modèle VAR (Vector Auto-Regression), puis identifions la relation de cointégration entre les variables et utilisons le Vector Error Correction Model (VECM) pour étudier le court terme fluctuations autour de l'équilibre à long terme, la causalité de Granger est également étudiée avec VECM. Ce travail révèle la tendance de la transition de la croissance économique de la Chine : de l'exportation vers la consommation ; La deuxième étude est avec des données de la France. On représente le modèle dans l'espace d'états, mettons le modèle dans un cadre de feedback-control, le contrôleur est conçu par un régulateur linéaire-quadratique (LQR). On peut également imposer des perturbations sur les sorties et des contraintes sur les entrées, ce qui simule la situation réelle de crise économique. 2) Utilisation de la théorie du contrôle pour améliorer l'apprentissage en ligne du réseau neuronal profond : Nous proposons un algorithme de taux d'apprentissage basé sur les performances : E (Exponential)/PD (Proportional Derivative) contrôle, qui considère le Convolutional Neural Network (CNN) comme une plante, taux d'apprentissage comme signal de commande et valeur de loss comme signal d'erreur. Le résultat montre que E/PD surpasse l'état de l'art en termes de précision finale, de loss finale et de vitesse de convergence, et le résultat est également plus stable. Cependant, une observation des expériences E/PD est que le taux d'apprentissage diminue tandis que la loss diminue continuellement. Mais la loss diminue, le modèle s’approche d’optimum, on ne devait pas diminuer le taux d'apprentissage. Pour éviter cela, nous proposons un event-based E/PD. Le résultat montre qu'il améliore E/PD en précision finale, loss finale et vitesse de convergence ; Une autre observation de l'expérience E/PD est que l'apprentissage en ligne fixe des époques constantes pour chaque batch. Puisque E/PD converge rapidement, l'amélioration significative ne vient que des époques initiales. Alors, nous proposons un autre event-based E/PD, qui inspecte la loss historique. Le résultat montre qu'il peut épargner jusqu'à 67% d'époques sur la donnée CIFAR-10 sans dégrader beaucoup les performances.3) Apprentissage automatique à partir de données non fiables : Nous proposons un cadre générique : Robust Anomaly Detector (RAD), la partie de sélection des données de RAD est un cadre à deux couches, où la première couche est utilisée pour filtrer les données suspectes, et la deuxième couche détecte les modèles d'anomalie à partir des données restantes. On dérive également trois variantes de RAD : voting, active learning et slim, qui utilisent des informations supplémentaires, par exemple, les opinions des classificateurs conflictuels et les requêtes d'oracles. Le résultat montre que RAD peut améliorer la performance du modèle en présence de bruit sur les étiquettes de données. Trois variations de RAD montrent qu'elles peuvent toutes améliorer le RAD original, et le RAD Active Learning fonctionne presque aussi bien que dans le cas où il n'y a pas de bruit sur les étiquettes.

System identification and machine learning are two similar concepts independently used in automatic and computer science community. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. Machine learning algorithms build a mathematical model based on sample data, known as "training data" (clean or not), in order to make predictions or decisions without being explicitly programmed to do so. Except prediction accuracy, converging speed and stability are another two key factors to evaluate the training process, especially in the online learning scenario, and these properties have already been well studied in control theory. Therefore, this thesis will implement the interdisciplinary researches for following topic: 1) System identification and optimal control on macroeconomic data: We first modelize the China macroeconomic data on Vector Auto-Regression (VAR) model, then identify the cointegration relation between variables and use Vector Error Correction Model (VECM) to study the short-time fluctuations around the long-term equilibrium, Granger Causality is also studied with VECM. This work reveals the trend of China's economic growth transition: from export-oriented to consumption-oriented; Due to limitation of China economic data, we turn to use France macroeconomic data in the second study. We represent the model in state-space, put the model into a feedback control framework, the controller is designed by Linear-Quadratic Regulator (LQR). The system can apply the control law to bring the system to a desired state. We can also impose perturbations on outputs and constraints on inputs, which emulates the real-world situation of economic crisis. Economists can observe the recovery trajectory of economy, which gives meaningful implications for policy-making. 2) Using control theory to improve the online learning of deep neural network: We propose a performance-based learning rate algorithm: E (Exponential)/PD (Proportional Derivative) feedback control, which consider the Convolutional Neural Network (CNN) as plant, learning rate as control signal and loss value as error signal. Results show that E/PD outperforms the state-of-the-art in final accuracy, final loss and converging speed, and the result are also more stable. However, one observation from E/PD experiments is that learning rate decreases while loss continuously decreases. But loss decreases mean model approaches optimum, we should not decrease the learning rate. To prevent this, we propose an event-based E/PD. Results show that it improves E/PD in final accuracy, final loss and converging speed; Another observation from E/PD experiment is that online learning fixes a constant training epoch for each batch. Since E/PD converges fast, the significant improvement only comes from the beginning epochs. Therefore, we propose another event-based E/PD, which inspects the historical loss, when the progress of training is lower than a certain threshold, we turn to next batch. Results show that it can save up to 67% epochs on CIFAR-10 dataset without degrading much performance. 3) Machine learning out of unreliable data: We propose a generic framework: Robust Anomaly Detector (RAD), The data selection part of RAD is a two-layer framework, where the first layer is used to filter out the suspicious data, and the second layer detects the anomaly patterns from the remaining data. We also derive three variations of RAD namely, voting, active learning and slim, which use additional information, e.g., opinions of conflicting classifiers and queries of oracles. We iteratively update the historical selected data to improve accumulated data quality. Results show that RAD can continuously improve model's performance under the presence of noise on labels. Three variations of RAD show they can all improve the original setting, and the RAD Active Learning performs almost as good as the case where there is no noise on labels., L'identification de système et l'apprentissage automatique sont deux concepts similaires utilisés indépendamment dans la communauté automatique et informatique. L'identification des systèmes construit des modèles à partir de données mesurées. Les algorithmes d'apprentissage automatique construisent des modèles basés sur des données d'entraînement (propre ou non), afin de faire des prédictions sans être explicitement programmé pour le faire. Sauf la précision de prédiction, la vitesse de convergence et la stabilité sont deux autres facteurs clés pour évaluer le processus de l'apprentissage, en particulier dans le cas d'apprentissage en ligne, et ces propriétés ont déjà été bien étudiées en théorie du contrôle. Donc, cette thèse implémente des recherches suivantes : 1) Identification du système et contrôle optimal des données macroéconomiques : Nous modélisons d'abord les données macroéconomiques chinoises sur le modèle VAR (Vector Auto-Regression), puis identifions la relation de cointégration entre les variables et utilisons le Vector Error Correction Model (VECM) pour étudier le court terme fluctuations autour de l'équilibre à long terme, la causalité de Granger est également étudiée avec VECM. Ce travail révèle la tendance de la transition de la croissance économique de la Chine : de l'exportation vers la consommation ; La deuxième étude est avec des données de la France. On représente le modèle dans l'espace d'états, mettons le modèle dans un cadre de feedback-control, le contrôleur est conçu par un régulateur linéaire-quadratique (LQR). On peut également imposer des perturbations sur les sorties et des contraintes sur les entrées, ce qui simule la situation réelle de crise économique. 2) Utilisation de la théorie du contrôle pour améliorer l'apprentissage en ligne du réseau neuronal profond : Nous proposons un algorithme de taux d'apprentissage basé sur les performances : E (Exponential)/PD (Proportional Derivative) contrôle, qui considère le Convolutional Neural Network (CNN) comme une plante, taux d'apprentissage comme signal de commande et valeur de loss comme signal d'erreur. Le résultat montre que E/PD surpasse l'état de l'art en termes de précision finale, de loss finale et de vitesse de convergence, et le résultat est également plus stable. Cependant, une observation des expériences E/PD est que le taux d'apprentissage diminue tandis que la loss diminue continuellement. Mais la loss diminue, le modèle s’approche d’optimum, on ne devait pas diminuer le taux d'apprentissage. Pour éviter cela, nous proposons un event-based E/PD. Le résultat montre qu'il améliore E/PD en précision finale, loss finale et vitesse de convergence ; Une autre observation de l'expérience E/PD est que l'apprentissage en ligne fixe des époques constantes pour chaque batch. Puisque E/PD converge rapidement, l'amélioration significative ne vient que des époques initiales. Alors, nous proposons un autre event-based E/PD, qui inspecte la loss historique. Le résultat montre qu'il peut épargner jusqu'à 67% d'époques sur la donnée CIFAR-10 sans dégrader beaucoup les performances.3) Apprentissage automatique à partir de données non fiables : Nous proposons un cadre générique : Robust Anomaly Detector (RAD), la partie de sélection des données de RAD est un cadre à deux couches, où la première couche est utilisée pour filtrer les données suspectes, et la deuxième couche détecte les modèles d'anomalie à partir des données restantes. On dérive également trois variantes de RAD : voting, active learning et slim, qui utilisent des informations supplémentaires, par exempe, les opinions des classificateurs conflictuels et les requêtes d'oracles. Le résultat montre que RAD peut améliorer la performance du modèle en présence de bruit sur les étiquettes de données. Trois variations de RAD montrent qu'elles peuvent toutes améliorer le RAD original, et le RAD Active Learning fonctionne presque aussi bien que dans le cas où il n'y a pas de bruit sur les étiquettes.

EnglishWhile many countries are pondering their responses to the economic crisis arising from the Covid-19 pandemic and as the question faces them of how to articulate these with strategies to fight climate change, what can energy--transition foresight bring to the table? Continuing the ‘Energy-¬Climate’ series of articles begun in these pages in March, Patrick Criqui and Henri Waisman offer an analysis of the various foresight tools developed over the last half-century to inform decisions in the area of energy transition. After going back over the history of the development of integrated energy--economy-environment modelling studies, which had their golden age in the period 1992-2014, they stress the limits this kind of foresight has run up against internationally, beginning with the Paris Agreements, which encouraged the reintroduction of the ‘political-economy’ dimension into transition scenarios. Since then, a new approach has consisted in combining scoping based on the comprehensive integrated models approach with national modelling, in order to reconcile international objectives of decarbonization with the socio-economic priorities and context specific to each state. The authors outline the guiding principles of foresight exercises in this area (which draw on the experience of the National Debate on ener¬gy transition in France) and present an example of implementation in the shape of the Deep Decarbonization Pathways Project. Lastly, they mention various lines of strategic thinking aiming towards carbon neutrality — a key point in these being a change in lifestyles — and the constraints inherent in having to gain society’s acceptance for such measures. This broad conspectus of the evolution of energy-transition foresight will be an essential tool for taking long term exigencies into account in the strategies currently being drawn up for overcoming the present crisis. francaisAlors que de nombreux pays reflechissent aux reponses a apporter a la crise economique issue de la pandemie de Covid-19 et que se pose la question de leur articulation avec les strategies de lutte contre le changement climatique, quel peut etre l’apport de la prospective des transitions energetiques ? Prolongeant la serie « Energie-climat » ouverte dans nos colonnes en mars dernier, Patrick Criqui et Henri Waisman proposent une analyse des differents outils de prospective developpes depuis une cinquantaine d’annees pour eclairer les decisions en matiere de transition energetique. Apres un rappel historique de l’evolution des travaux de modelisation integree energie-economie-environnement, dont l’âge d’or est la periode 1992-2014, ils soulignent les limites rencontrees par ce type de prospective, a l’echelle internationale, a compter des accords de Paris qui incitent a reintroduire la dimension « economie politique » dans les scenarios de transition. Depuis, une nouvelle approche consiste a combiner le cadrage de l’approche globale des modeles integres, avec les modelisations nationales, afin de concilier les objectifs internationaux de decarbonation avec les priorites socio-economiques et le contexte propres a chaque Etat. Les auteurs presentent les principes directeurs des exercices de prospective en la matiere (qui s’appuient sur l’experience du Debat national sur la transition energetique en France) et un exemple de mise en application : celui engage par le projet Deep Decarbonization Pathways. Enfin, ils mentionnent divers axes strategiques visant la neutralite carbone — dont un point clef consiste en un changement des modes de vie — et les contraintes inherentes pour que les mesures en ce sens soient acceptees par la societe. Ce panorama de l’evolution de la prospective des transitions energetiques sera un outil essentiel pour tenir compte des exigences de long terme dans les strategies de sortie de crise qui s’ecrivent actuellement.

International audience; Non-point source pollution is a cause of major concern within the European Union. This is reflected in increasing public and political focus on a more sustainable use of pesticides, as well as a reduction in diffuse pollution. Climate change will likely to lead to an even more intensive use of pesticides in the future, affecting agriculture in many ways. At the same time, the Water Framework Directive (WFD) and associated EU policies called for a “good” ecological and chemical status to be achieved for water bodies by the end of 2015, currently delayed to 2021–2027 due to a lack of efficiency in policies and timescale of resilience for hydrosystems, especially groundwater systems. Water managers need appropriate and user-friendly tools to design agro-environmental policies. These tools should help them to evaluate the potential impacts of mitigation measures on water resources, more clearly define protected areas, and more efficiently distribute financial incentives to farmers who agree to implement alternative practices. At present, a number of reports point out that water managers do not use appropriate information from monitoring or models to make decisions and set environmental action plans. In this paper, we propose an integrated and collaborative approach to analyzing changes in land use, farming systems, and practices and to assess their effects on agricultural pressure and pesticide transfers to waters. The integrated modeling of agricultural scenario (IMAS) framework draws on a range of data and expert knowledge available within areas where a pesticide action plan can be defined to restore the water quality, French “Grenelle law” catchment areas, French Water Development and Management Plan areas, etc. A so-called “reference scenario” represents the actual soil occupation and pesticide-spraying practices used in both conventional and organic farming. A number of alternative scenarios are then defined in cooperation with stakeholders, including socio-economic conditions for developing alternative agricultural systems or targeting mitigation measures. Our integrated assessment of these scenarios combines the calculation of spatialized environmental indicators with integrated bio-economic modeling. The latter is achieved by a combined use of Soil and Water Assessment Tool (SWAT) modeling with our own purpose-built land use generator module (Generator of Land Use version 2 (GenLU2)) and an economic model developed using General Algebraic Modeling System (GAMS) for cost-effectiveness assessment. This integrated approach is applied to two embedded catchment areas (total area of 360,000 ha) within the Charente river basin (SW France). Our results show that it is possible to differentiate scenarios based on their effectiveness, represented by either evolution of pressure (agro-environmental indicators) or transport into waters (pesticide concentrations). By analyzing the implementation costs borne by farmers, it is possible to identify the most cost-effective scenarios at sub-basin and other aggregated levels (WFD hydrological entities, sensitive areas). Relevant results and indicators are fed into a specifically designed database. Data warehousing is used to provide analyses and outputs at all thematic, temporal, or spatial aggregated levels, defined by the stakeholders (type of crops, herbicides, WFD areas, years), using Spatial On-Line Analytical Processing (SOLAP) tools. The aim of this approach is to allow public policy makers to make more informed and reasoned decisions when managing sensitive areas and/or implementing mitigation measures.

Dans un contexte de controverse sur la capacité des modèles agricoles à répondre conjointement aux enjeux alimentaires, environnementaux et de développement en Afrique, nous analysons les conditions de viabilité d’une agriculture à caractère biologique au Cameroun. La démarche mobilise une enquête par entretiens semi-directifs auprès des acteurs engagés dans les filières de production biologique et une mise en débat des connaissances générées lors d’ateliers participatifs multi-acteurs. Elle met en interaction les connaissances scientifiques, entrepreneuriales et techniques. Les résultats mettent en exergue trois types d’agriculture biologique : l’un certifié suivant les cahiers des charges internationaux ; le deuxième hybride, du fait de sa nature entrepreneuriale et sans certification ; et le troisième, « naturel sans certification », qui renvoie aux pratiques traditionnelles à faible usage d’intrants. En utilisant le cadre d’analyse de la théorie des transitions multi-niveaux, ces trois types permettent de définir des trajectoires possibles d’évolution de l’agriculture biologique au Cameroun., In a context of controversy over the capacity of agricultural models to respond jointly to food, environmental and development issues in Africa, we analyze the conditions of viability of organic farming in Cameroon. The approach mobilizes a survey through semi-structured interviews with stakeholders involved in the organic production sectors and a discussion of the knowledge generated during multi-stakeholder participatory workshops. The approach brings together scientific, entrepreneurial and technical knowledge. The results highlight three types of organic farming: one type certified according to international specifications; the second, hybrid because of its entrepreneurial nature and without certification; and the third, “natural without certification”, that refers to traditional practices, with low use of inputs. Using the framework of analysis of the multi-level transition theory, these three types make it possible to define possible trajectories for the evolution of organic agriculture in Cameroon.

Multiple constraints prevent smallholders from adopting fertilizers even with regional supply of agricultural inputs expanding and soils being weared-out. Using comprehensive farm-level data from the eastern Brazilian Amazon, we found that market proximity had a significant positive correlation with fertilizer adoption, even after controlling for liquidity, land tenure, education, experience and access to rural extension services. Nevertheless, few smallholders completely replaced nutrients from vegetation with fertilizers. Instead, we found that a hybrid system that combines nutrients from vegetation and fertilizers was approximately twice as common as exclusive fertilizer use. We suggest that the option for this diversified “nutrient portfolio” may result not only from a lack of capital or knowledge regarding return on fertilizer use, but also from the need to adapt to the economic constraints facing smallholders and minimize risk. Results indicate that a rural extension program aimed at supporting a rapid and complete replacement of ashes from vegetation by fertilizers could prove unsuccessful for Amazonian smallholders.

This paper investigates the effect of global prices in the Thai palm oil market. We provide comprehensive evidence of growth and volatility spillovers for the key crop, crude palm oil in Thailand over the period 1996 to 2015. The Dynamic Conditional Correlation model (DCC) and Granger Causality tests are used, to analyze price volatility effects among Thai and Malaysian crude palm oil and World crude oil prices. Our findings delivered several significant conclusions; that higher prices indicates a positive impact on their uncertainty and also increase a correlation variance among them. Moreover, the Malaysian crude palm oil and world crude oil returns have spillover effects on each of their uncertainty, while Malaysian crude palm oil return has a strong effect on the Thai crude palm oil price and vice versa. Therefore, the government and policy makers in Thailand should pay heed to the changes and fluctuations in those global prices.

This study assesses the economic value of supplemental irrigation as a rainfall variability adaptation strategy in a small catchment in south-western Burkina Faso. The bio-economic model built for the catchment maximises net cash income by optimally allocating land, labour, water and capital. Yields vary according to soil type, agricultural practice and the type of rainy season. We introduced farm ponds as an adaptation strategy – called supplemental irrigation – for grain crops during long dry spells in the rainy season. Simulation results show that supplemental irrigation can be cost effective and increase incomes, particularly during years with poor rainfall. However, in this catchment, gains from supplemental irrigation are limited because labour and capital are constraints on pond implementation.

Au cours des deux dernières décennies les pays émergents ont gagné d’importantes parts du marché mondial des produits agricoles et industriels. La meilleure performance sur la période 1995-2010 a été réalisée par la Chine, qui s’est imposée comme premier pays exportateur, devançant les États-Unis. La pression concurrentielle imposée par les pays émergents est particulièrement forte pour les produits technologiques, une spécialisation traditionnellement réservée aux pays industrialisés. Comment les pays membres de l’UE font-ils face à ces pressions et comment se situent leurs performances par rapport à celles d’autres grands pays industrialisés ? Afin de répondre à ces questions, on analyse la redistribution du marché mondial à un niveau fin par produits et par variétés de produits vendus à des prix différents. Une méthode économétrique originale nous permet de distinguer les variations de parts de marché dues à la pure compétitivité de celles dues à la composition par produits et par destinations des exportations. La bonne tenue des parts de marché de l’Union européenne à 27 (UE27) contraste en comparaison des pertes japonaises et américaines. À la différence des autres grands pays industrialisés, l’UE27 bénéficie d’un positionnement fort sur le haut de gamme et évite des pertes de parts de marché sur les produits technologiques. Dans le secteur agroalimentaire, malgré ses pertes de parts de marché en faveur des pays émergents, l’UE27 s’approprie la position de leader, longtemps détenue par les États-Unis, et une place dominante sur les exportations agroalimentaires haut de gamme. Ces performances s’expliquent par la capacité des pays membres de l’UE27 à concentrer leurs exportations sur les marchés les plus porteurs.

La problématique de ce numéro interroge les interactions entre les changements de modèles d'innovation, l'écologisation de la fonction de production et leurs conséquen ces sociales. Six processus d'innovation sont analysés respectivement dans les agricultures du Burkina Faso, Cameroun, Haïti, Madagascar Sénégal. Ces situations convergent en démontrant l'utilit é pour le développement de modèles d'inno vation collaboratifs qui adaptent les processus étudiés aux besoins locaux. Ces modèles impliquent plus d'engagement des politiques publiques d'innovation.

This article describes a hydrologic-economic optimization model for allocating available river flow between competing off- and in-stream demands, based on the marginal benefits (MBs) of sectoral water uses in a segment of the Teesta River in Bangladesh. Irrigation, capture fishery and navigation are the main direct water uses considered. The value of irrigation water was estimated using the residual imputation method. Losses in yield caused by lowered irrigation supply, resulting from reduced river flow, formed the basis for establishing the total and MB functions for off-stream river water use (irrigation). Total and MB functions for in-stream water use (capture fishery, navigation) were developed using field survey data of beneficiaries' income as a function of river flow. Analysis was enhanced by applying AQUARIUS, which allocates water between users to maximize consumer surplus based on MB functions. Model results show that in-stream uses could not compete with off-stream uses in the case of the Teesta, as substantial benefit was obtained from irrigation. Environmental flow to safeguard river health and in-stream use was considered to be a constraint in the optimization, which results in a sizeable reduction in irrigation benefit with a small increase in in-stream benefit. The necessary trade-offs between economic efficiency and environmental protection are depicted, providing insight into a justifiable water allocation strategy for the Teesta. (Resume d'auteur)

International audience; In mountainous areas, forest resources are spatially heterogeneous, difficult to quantify and harvest because of topographical constraints. In the framework of the PlanEter project, we implemented a case study with the objective to map the quantity and mobilisation cost of fuelwood in six communes (600 km 2 ), thanks to LiDAR remote sensing data, a GIS-based accessibility model and an economical model. The main challenge was to design an easily reproducible methodology for larger application, thanks to the use of available public data as inputs. Wood growing stock was mapped at 20 m resolution with models calibrated with national LiDAR and forest inventory data. The technical accessibility of forests to common machinery was mapped with the GIS-based model Sylvaccess using national digital terrain and landscape models. Local forest statistics and cover maps were used to derive the annual timber and fuelwood potential. The results are used at two different levels. For strategic planning, a simplified model of forest management is used to map the annual timber and fuelwood harvest and the corresponding costs, depending on the technical accessibility and forest structure. For operational planning, resource and accessibility information are used as inputs along with expert information, into the detailed model CalCouFor for cost calculation of harvesting, yarding, transport, storage and chipping operations. This case study shows that it is possible to bridge the gap between regional forest statistics and field level analysis through the use of existing datasets. Additional remote sensing information could provide information about annual forest increment and species which would improve the model of forest management, taking into account other socio-economic services provided by forests. It would then be possible to map the effects of various factors (energy price, labour costs, public subsidies) on forest biomass mobilisation.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM; International audience; While estimation methods for dynamic panel data and spatial econometric models are standard in economic literature, there has been a relatively recent development in methods which include spatial considerations in dynamic panel data models. This paper proposes two estimation strategies for spatial dynamic panel data models using the generalized method of moments (GMM). The first is to extend the moment restrictions of Arellano and Bond's estimator to a spatial autoregressive dynamic panel. The second allows for spatial dependence in the error process. The empirical application focuses on European regional growth over a 25-year period. We find empirical evidence of conditional convergence, which is significantly affected by spatial disparities.; Bien que les méthodes d'estimation pour les données de panels dynamiques, et les modèles économétriques spatiaux, soient des instruments standards dans les ouvrages d'économie, on a assisté à une évolution relativement récente des méthodes, qui comprend des considérations spatiales dans les modèles de panels dynamiques. La présente communication propose deux stratégies d'estimation concernant des modèles de données de panel dynamique spatiales faisant usage de la méthodes des moments généralisés(MMG). La première consiste à étendre les restrictions de moments de l'estimateur d'Arellano et Bond à un panel dynamique autorégressif spatial. La deuxième tient compte de la dépendance spatiale dans le processus des erreurs. L'application empirique se concentre sur l'expansion régionale en Europe au cours d'une période de 25 ans. Nous relevons des preuves empiriques de convergence conditionnelle, qui sont affectées de façon significative par des disparités spatiales.

Our study does not challenge the mainly cyclical nature of the net job losses observed in the U.S. in the 2007-2009 crisis. However , we show that the U.S. labor market’s responsiveness to post-crisis GDP changes is relatively similar to the pattern observed in the “ jobless recoveries ” after the 1990 and 2001 recessions. This econometric result is supported by the available data on flows into and out of unemployment, on part-time work, and on the number of hours worked. A breakdown by economic sector shows that the phenomenon is partly due to the decline in the share of manufacturing jobs in total employment. This leaves the service sector, which is less cyclical, as the main source of job creation during recoveries., Sans remettre en cause le caractère principalement cyclique des destructions nettes d’emplois observées lors de la crise de 2007-2009, cette étude montre que la réactivité du marché du travail américain à l’activité constatée depuis est relativement similaire à celle observée lors des « reprises sans emploi » (après les récessions de 1990 et 2001). Ce résultat économétrique est conforté par les données disponibles sur les flux d’entrée et sortie du chômage, l’emploi à temps partiel et le nombre d’heures travaillées. Une décomposition par secteurs permet d’expliquer en partie ce phénomène par la baisse de la part de l’emploi manufacturier dans l’emploi total, laissant le secteur des services, moins cyclique, comme principale source de création d’emplois en période de reprise., Rivaud Sophie, Grossmann-Wirth Vincent. Reprise américaine : quel contenu en emplois ? . In: Économie & prévision, n°195-196, 2010-4-5. pp. 165-171.

In Ghana, coconut developed extensively during the 1960s, 1970s and 1980s in the coastal strip of the "Western" region. Subsequently, many coconut plantations disappeared as they were progressively affected by lethal yellowing disease. The response of the planters was to stop investing in coconut, then, after an interval of a few years, to start diversifying by planting oil palm, cocoa and rubber. At the same time, villages located further inland, which were untouched by the disease, continued to produce coconut. Between these two areas, in the "heart" of the coconut production region around Jomoro, coconut plantations were not affected by the disease, but were aging and undergoing a decrease in yield. The result of these developments was the relocation of production centres, which is the subject of analysis in this paper. We used a model that was developed for other perennial crops in conditions of almost complete monoculture, particularly cocoa. The model revealed a pattern of regional cycles succeeding one another that are relatively independent of changes in coconut prices. The cycles are based on the exhaustion of natural resources as a source of income, and on the concurrence of the life cycle of the plantation and that of family life. The model was first successfully tested on the situation of coconut in Ghana before 2005-2006. In 2005-2006, a major economic upheaval occurred. The arrival of Nigerian buyers helped cause a 50% increase in the price of coconut. Prices continued to rise in 2007. From a strictly technical and economic point of view, given the prices offered by Nigerian buyers, the cultivation of hybrid coconut, which is less vulnerable than the local coconut, again appeared to be very profitable, with a risk factor that was acceptable in regions not affected by the disease. These economic conditions enabled us to check what the model would predict in the case of a rise in prices, which was that in coconut production areas, the response would be very limited, i.e. the opposite of what classical theory predicts. In fact, in areas of aging monocrops, a rise in prices no longer has much effect. The model explained the real state of affairs at the regional scale: the recession of the previously leading crop is almost inevitable. On the other hand, a favourable response to the rise in prices is possible in districts and provinces in Ghana (and in Côte d'Ivoire) that have not been affected by the disease, thus reinforcing the process of relocation of production centres. This structural relocation could mean a new future for coconut in Ghana in the context of expanding regional demand in Nigeria, Burkina Faso and Mali. Yet the end of research, the end of the production of planting material, the lack of low-interest credit, (a precondition for re-investment), and finally, the abandonment of coconut planters by public policies, has left thousands of families and particularly old people, trapped in a spiral of recession which the rise in the price of coconut will not break.

SUMMARYIn the context of market liberalization and in order to avoid trade distortions, European farmers risk experiencing a restriction of subsidies for market products. Moreover, policy makers cannot underestimate the public concerns about the non-productive functions of agriculture, such as environmental management. The current study illustrates different ways of modelling the trade-offs between environmental sustainability and economic viability for dairy farming systems in Reunion Island. Nitrogen (N) balance at the farm level is the result of complex interactions between pasture and animal management and between bio-technical and socio-economic management. Therefore, different multi-criteria models were tested using a common dynamic bio-economical model that integrated the different sub-systems of the farm and their interactions. Nitrogen excess mitigation in Reunion Island dairy farming systems was used as an illustration of a non-productive objective taken into account in the decision process modelling.The simulations highlighted the necessity to adapt the nitrogen mitigation objective to the technical level of local territories in order to adopt environmentally friendly practices, without jeopardizing the local dairy sector. Moreover, the models generated a different set of solutions that varied according to how non-production functions are integrated in farmers' decision-making processes. This constitutes a relevant basis for discussions between farmers and decision makers.

Working Paper N°16-02; In this paper, we use an econometric approach to investigate the impacts of potential changes incropping practices on the reduction in pesticide use implied by a taxation policy. We combineeconomic data, reflecting the relatively intensive cropping practices currently used in France, andexperimental agronomic data on a low-input technology to estimate micro-econometric models offarmers’ production and acreage choices. In a second step, these estimated models are used toconduct policy simulations. Our results show that a small tax on pesticide use could provideagricultural producers sufficient economic incentive to adopt low-input cropping practices andthereby lead to significant reductions in pesticide use, close to public short-term objectives.However, given the limited impacts of taxation once these practices have been adopted, otherpublic instruments or further improvement of low-input cropping systems should be considered toachieve more ambitious longer term public objectives.; Nous utilisons dans ce papier une approche économétrique pour analyser les impacts dechangements éventuels de pratiques culturales sur la réduction de l’utilisation de pesticides induitepar une politique de taxation. Nous combinons des données économiques, reflétant les pratiquesrelativement intensives actuellement utilisées en France, et des données agronomiquesexpérimentales sur une pratique à bas niveau d’intrants pour estimer des modèles microéconométriquesde choix de production et d’assolement d’agriculteurs. Ces modèles estimés sontutilisés dans une seconde étape pour conduire des simulations de politiques. Nos résultatsmontrent que qu’une faible taxe sur l’utilisation de pesticide pourrait suffisamment d’incitationéconomique aux producteurs pour adopter des pratiques à bas intrants et ainsi conduire à desréduction de l’utilisation de pesticides proches des objectifs politiques fixés à court terme.Toutefois, étant donné le faible impact d’une taxation une fois ces pratiques adoptées, d’autresinstruments de politique publique devront être envisagés pour atteindre des objectifs de long termeplus ambitieux.

In this paper, we use an econometric approach to investigate the impacts of potential changes in cropping practices on the reduction in pesticide use implied by a taxation policy. We combine economic data, reflecting the relatively intensive cropping practices currently used in France, and experimental agronomic data on a low-input technology to estimate micro-econometric models of farmers’ production and acreage choices. In a second step, these estimated models are used to conduct policy simulations. Our results show that a small tax on pesticide use could provide agricultural producers sufficient economic incentive to adopt low-input cropping practices and thereby lead to significant reductions in pesticide use, close to public short-term objectives. However, given the limited impacts of taxation once these practices have been adopted, other public instruments or further improvement of low-input cropping systems should be considered to achieve more ambitious longer term public objectives., Nous utilisons dans ce papier une approche économétrique pour analyser les impacts de changements éventuels de pratiques culturales sur la réduction de l’utilisation de pesticides induite par une politique de taxation. Nous combinons des données économiques, reflétant les pratiques relativement intensives actuellement utilisées en France, et des données agronomiques expérimentales sur une pratique à bas niveau d’intrants pour estimer des modèles microéconométriques de choix de production et d’assolement d’agriculteurs. Ces modèles estimés sont utilisés dans une seconde étape pour conduire des simulations de politiques. Nos résultats montrent que qu’une faible taxe sur l’utilisation de pesticide pourrait suffisamment d’incitation économique aux producteurs pour adopter des pratiques à bas intrants et ainsi conduire à des réduction de l’utilisation de pesticides proches des objectifs politiques fixés à court terme. Toutefois, étant donné le faible impact d’une taxation une fois ces pratiques adoptées, d’autres instruments de politique publique devront être envisagés pour atteindre des objectifs de long terme plus ambitieux.

Alternative agricultural systems, such as organic farming (OF), are promising options to sustain both agriculture productivity and environmental health. However, the adoption of OF by farmers is occurring more slowly than is advocated. A key factor limiting farmers is an inability to predict socio-economic consequences of converting to OF. To overcome this, we developed a novel method of integrated assessment of agricultural systems (IAAS) and applied it to scenarios of development of OF in the Camargue region, South of France. In collaboration with the local stakeholders, we characterized the agricultural systems at different spatial scales and defined scenario related to the future of agriculture and to OF. We then used agent-based modeling with farmers and bio-economic modeling with local stakeholders for scenario assessment. We examined the effects on the development of OF systems of key factors such as the ongoing reform in the European Common Agricultural Policy and the effects of regulations for decreased use of pesticides. The policy reform implied trends towards a diversification of crops and greater possibility for conversion to OF. Development of OF at the regional level led to improved environmental performance, but caused a decrease in profitability of the rice supply chains. In light of the observed trade-off between rice production and OF development, objectives and options towards more sustainable agricultural systems were discussed with farmers and local stakeholders. Stakeholders' assessment of the framework provided insights on the positive and specific aspects of the IAAS methodology requiring improvement. The complementarities of agent-based and bio-economic modeling provide stakeholders with a better-informed assessment of diverse scenarios, for the development of more sustainable agricultural systems.

Despite recent yet fragile economic growth, structural change in Africa has been slow and the continent is faced with the need for more inclusive and sustainable growth. This challenge implies rethinking development strategies and adopting multi-sectoral and place-based approaches.

International audience; L’objectif de cette pprésentation est d’étudier de l’impact du choix de la rente agricole sur les modèles économétriques d’usages des sols. Ont été ainsi comparé des modèles d’usage des sols avec trois différentes approximations de la rente agricole : (1) les revenus agricoles (2) les prix de la terre et (3) le prix-fictif (shadow-price) issu du modèle de programmation mathématique (AROPAj). A partir des données de Corine Land Cover (CLC), les auteurs ont estimé des modèles d’usage des sols à l’échelle d’une grille homogène (8 km x 8 km) pour les cinq usages : (1) agriculture, (2) prairie, (3) forêt, (4) urbain (5) autres usages. Les résultats montrent que les trois proxy de la rente agricole donnent des résultats similaires en termes de qualité de prédiction des modèles économétrique. Les résultats montrent également que l’introduction de l’auto-corrélation spatiale améliore significativement la qualité de la prédiction (meilleur R2 et RMSE plus faibles). Les modèles estimés seront utilisés pour répondre aux questions des arbitrages entre agriculture et forêt au niveau français dans un contexte de changement climatique.

The current trade negotiations' round has been called the development round in order to emphasize ambitious objectives in terms of poverty alleviation: halving the number of poor by 2015. However estimates from world economic models show little improvement for the Less Developed Countries. Moreover several models assumptions may overestimate gains and underestimate losses, especially in the poorest countries where numerous markets imperfections hold. It seems then necessary to avoid that poverty reduction be restricted to emerging countries that development policies and their funding in LDC be discussed simultaneously to trade negotiations. (Resume d'auteur)

Cet article analyse les raisons des faibles performances dans la lutte contre l'insécurité alimentaire et la pauvreté au Burkina Faso malgré les performances macroéconomiques enregistrées par le pays au cours des dernières décennies. Il analyse ensuite, à l'aide d'un modèle d'Équilibre Général Calculable, la capacité d'un investissement public dans l'agriculture à améliorer la situation actuelle des pauvres. Plusieurs éléments se conjuguent pour expliquer la persistance de la pauvreté et de l'insécurité alimentaire : la forte croissance démographique qui implique une faible croissance du PIB (Produit Intérieur Brut) par tête ; la faible contribution de l'agriculture à la croissance globale ; la persistance d'un chômage urbain ; et une répartition des fruits de la croissance peu favorable aux pauvres. L'investissement public dans l'agriculture, en augmentant la productivité dans les activités agricoles, permet des progrès en milieu rural et surtout en milieu urbain.

By adopting a semiparametric approach, the 'traditional' regional knowledge production function is developed in three complementary directions. First, the model is augmented with region-specific time trends to account for endogeneity due to selection on unobservables. Second, the nonparametric part of the model relaxes the standard assumptions of linearity and additivity regarding the effect of R&D and human capital. Finally, the assumption of homogeneity in the effects of R&D and human capital is also relaxed by explicitly accounting for the differences between developed and lagging regions. The analysis of the genesis of innovation in the regions of the European Union unveils nonlinearities, threshold effects, complex interactions and shadow effects that cannot be uncovered by standard parametric formulations.

A spatial hydroeconomic model was developed to analyze the competition between small private (SPIS) and large public (LPIS) irrigation systems for water control in tropical watersheds and applied to several water allocation policies in Kou watershed in Burkina Faso. Capital (cash and motorpumps) is the main constraining factor for SPIS expansion, and capital inflow accelerates SPIS development and reduces water flows for downstream LPIS users. As SPIS is more cost-effective and less water thirsty, LPIS needs to shift to less water-demanding and high-value crops or adopt more water-saving practices. Otherwise, only a sharp rice yield increase in LPIS can justify a reserved water quota for downstream users.

Our main objective is to analyze the effects of policy instruments that could provide agricultural producers economic incentives to the adoption of innovative cropping practices and thereby allow a reduction of pesticide use. To do so, we combine economic data, reflecting the intensive cropping practices currently used in France, and experimental agronomic data, on a low input technology, to estimate econometric models. These models are then used to conduct policy simulations. Our results show that without public incentives producers would not adopt the new technology. We also show that a tax on pesticides generates larger effects when these low input practices are available to farmers.

Le développement des biocarburants soutenu par la communauté internationale connait un essor depuis le début des années 2000 à l'échelle mondiale, pour réduire les émissions de gaz à effet de serre, répondre aux préoccupations énergétiques des pays industriels et diminuer la pauvreté énergétique dans les pays du Sud. Cette stratégie rencontre plusieurs controverses qui portent sur les impacts socio-économiques des biocarburants et les trajectoires technologiques les mieux adaptées aux besoins de développement des pays d'Afrique sub-saharienne. L'objectif de la thèse est de comprendre les déterminants des processus d'innovation technologique sur la production des biocarburants. La thèse pose pour cela deux hypothèses. La première hypothèse structurée par le croisement entre les théories de la transition et les analyses systémiques de l'innovation repose sur le déterminisme institutionnel des processus d'innovation qui structure l'émergence du secteur des biocarburants. La seconde hypothèse suppose que l'émergence et le développement du secteur bioénergétique résulte des conditions d'adoption micro-économiques et territoriales de la production des cultures énergétiques. Le test de ces hypothèses est réalisé en mobilisant un référentiel d'analyse en termes de système d'innovation dans le cas de la filière Jatropha au Burkina Faso. Les résultats soulignent que l'émergence des technologies biocarburants dans les pays d'Afrique subsaharienne est liée aux changements institutionnels induits par les crises énergétiques et environnementales. Ces changements institutionnels activent des ressources favorables à l'émergence et à la dissémination des technologies sur les biocarburants. Au Burkina Faso, ces changements sont impulsés par les politiques européennes et celles d'organisations sous régionales telles que l'UEMOA et la CEDEAO. Une modélisation économétrique permet de manière complémentaire de tester les conditions d'adoption micro-économique et d'implémentation des innovations technologiques sur les biocarburants. Les facteurs structurels de l'exploitation agricole (capital foncier, situation alimentaire, perception) ; la proximité des acteurs de la filière ; ainsi que les variables institutionnelles inhérentes au renforcement des capacités et compétences apparaissent structurants de ces processus d'adoption sur le Jatropha. Cette adoption dépend enfin des modèles technologiques qui permettent d'intégrer d'un point de vue territorial les phases de production de la matière première, transformation et utilisation dans un mécanisme de réponse aux besoins prioritaires du développement du Burkina Faso.

International audience; This paper is an original empirical attempt to explain the outcome of collective action in the domain of food safety. We examine conditions and institutions that influence pesticide residue levels in vegetables using econometric analysis on data gathered from 60 farmer organisations in Vietnam. Findings suggest that collective action affects safety in that it provides members with technical assistance and monitoring for pest management at the farming level. They confirm the U-shape hypothesis of the effect of group size on safety performance which derives from the trade-off that exists between economies of scale and free-riding. The contribution of public authorities and ecological conditions to food safety remains controversial, while market forces do not yet seem able to drive the production of safer vegetables.