Recent researches on the effects of environmental degradation on food security suggest that a better understanding of the relationship between agricultural intensification and pollutant transfer is urgently required to support the implementation of sustainable agricultural policies, globally. Poor understanding of the hydrological behaviour of clay-rich soils in intensively managed agricultural regions is highlighted as an important problem. The study therefore evaluated precipitation-soil water chemistry relationships, soil variability and concentration-discharge relationships at the farm-scale based on datasets from the North Wyke Farm Platform between 2011 and 2013. The three main hypothesis were that (1) precipitation and soil water chemistry are significantly related (2) significant relationships exists between the distribution of soil physiochemical characteristics and the managments of the fields, and that (3) hydrological behaviour of fields underlain by certain dominant soils in the study area are different from that of other fields. The basis of this work was to elucidate links between sources of pollutants and water quality, further understanding of the effect that management of the soil may have upon the quality of the water and improve understanding of the pathways of pollutants within intensively managed landscapes. Precipitation chemistry of the study area was chemically different from that of the other regions in the United Kingdom, and was influenced by contributions from sea salts and terrestrial dusts. The soil chemistry was rich in organic matter which contributed significantly (r2>0.60; p<0.05) to the distribution of total carbon and total nitrogen in the fields. Mean total carbon and nitrogen stocks ranged 32.4 - 54.1 t C ha-1, and 4 - 6.2 t Na ha-1, respectively in the entire farm platform while runoff coefficient at four selected fields (Pecketsford, Burrows, Middle and Higher Wyke Moor, and Longlands East) varied between 0.1 and 0.28 in January and November, 2013. The study rejected the first and third hypotheses, and concluded that the study area is largely influenced by contributions from the surface runoff mechanisms. The study also noted that sodium and chloride ions were dominant in the precipitation chemistry, and therefore suggests their further investigation as conservative tracers in the soil and runoff chemistry.

The research describes an optimized waste-to-energy technology that utilizes agricultural residues for renewable energy, while reducing global methane emissions and maintaining food security. Laboratory-, pilot- and farm-scale anaerobic batch digesters were evaluated to enhance methane production from the anaerobic digestion of untreated rice straw in dry conditions using a novel co-digestion approach. An existing farm-scale biogas plant loaded with rice straw and piggery wastewater produced 295 MWh in a 422-day digestion cycle. The long acclimation period (approximately 200 days) and low biogas yield (181 LCH4/kgVS) could be enhanced by adding anaerobic sludge from the pulp and paper mill treatment process. In a laboratory setting, the addition of the sludge resulted in a specific methane yield of 335 LCH4/kgVS within 92 days. Hydrolysis of the straw was accelerated, and stable conditions were observed in terms of pH, alkalinity and nutrients. Similar improvements were demonstrated in pilot-scale digesters (1 m3) - a specific methane yield of 231 LCH4/kgVS was achieved in a 93-day digestion cycle with the sludge compared to 189 days without the sludge. Insufficient mixing within the pilot-scale system caused lower overall methane yields than those obtained in the laboratory-scale digesters. If sufficient mixing and mesophilic conditions are maintained within the farm-scale system, the co-digestion of rice straw with pig wastewater and paper mill sludge (wet weight ratio of 1:1.25:0.5) has the potential to reduce the retention time to three months (versus 422 days) and increase methane yields to over 300 LCH4/kgVS

Ce travail de thèse présente l'optimisation d'une technologie de valorisation énergétique qui utilise des résidus agricoles pour la production d'énergies renouvelables, tout en réduisant les émissions mondiales de méthane et en garantissant la sécurité alimentaire. Des digesteurs anaérobies à l'échelle laboratoire, pilote et industrielle ont été évalués afin d'améliorer la production de méthane à partir de la digestion anaérobie de la paille de riz non traitée dans des conditions sèches en utilisant une approche nouvelle de co-digestion. Une installation de production biogaz à l'échelle d'une ferme chargée de paille de riz et d'eaux usées produites par une porcherie génère 295 MWh dans un cycle de digestion 422 jours. La période d'acclimatation relativement longue (environ 200 jours) et le faible rendement en biogaz (181 LCH4/kg MVS) pourraient être améliorées en ajoutant des boues anaérobies issues d'un procédé de traitement d'effluents de l'industrie papetière. Au laboratoire, l'ajout de la boue conduit à un rendement de méthane spécifique de 335 LCH4/kgMVS dans les 92 jours. L'hydrolyse de la paille a été accélérée, et des conditions stables ont été observées en termes de pH, d'alcalinité et de nutriments. Des améliorations similaires ont été démontrés dans des digesteurs à l'échelle pilote (1 m3) - un rendement de méthane spécifique de 231 LCH4/kgMVS a été obtenu dans un cycle de digestion à 93 jours avec de la boue comparativement à 189 jours sans la boue. Un mélange insuffisant dans le système à l'échelle pilote a causé des rendements de production de méthane inférieurs à ceux obtenus dans les digesteurs l'échelle du laboratoire. Si les conditions mésophiles et de mélange suffisantes sont maintenues dans le système à l'échelle industrielle, la co-digestion de la paille de riz avec des eaux usées produites par une porcherie et des boues issues d'un procédé de traitement d'effluent de l'industrie papetière (rapport poids humide de 1:1.25:0.5) a le potentiel de réduire le temps de rétention à trois mois (contre 422 jours) et d'augmenter les rendements de production de méthane à plus de 300 LCH4/kgMVS / The research describes an optimized waste-to-energy technology that utilizes agricultural residues for renewable energy, while reducing global methane emissions and maintaining food security. Laboratory-, pilot- and farm-scale anaerobic batch digesters were evaluated to enhance methane production from the anaerobic digestion of untreated rice straw in dry conditions using a novel co-digestion approach. An existing farm-scale biogas plant loaded with rice straw and piggery wastewater produced 295 MWh in a 422-day digestion cycle. The long acclimation period (approximately 200 days) and low biogas yield (181 LCH4/kgVS) could be enhanced by adding anaerobic sludge from the pulp and paper mill treatment process. In a laboratory setting, the addition of the sludge resulted in a specific methane yield of 335 LCH4/kgVS within 92 days. Hydrolysis of the straw was accelerated, and stable conditions were observed in terms of pH, alkalinity and nutrients. Similar improvements were demonstrated in pilot-scale digesters (1 m3) – a specific methane yield of 231 LCH4/kgVS was achieved in a 93-day digestion cycle with the sludge compared to 189 days without the sludge. Insufficient mixing within the pilot-scale system caused lower overall methane yields than those obtained in the laboratory-scale digesters. If sufficient mixing and mesophilic conditions are maintained within the farm-scale system, the co-digestion of rice straw with pig wastewater and paper mill sludge (wet weight ratio of 1:1.25:0.5) has the potential to reduce the retention time to three months (versus 422 days) and increase methane yields to over 300 LCH4/kgVS

L'assolement' c'est à dire l'attribution des cultures sur les différentes parcelles de l'exploitation agricole' est un élément fondamental de la stratégie de l'agriculteur. Il provient d'un équilibre entre des contraintes et objectifs multiples alliant les dimensions sociale' économique' agronomique et environnementale (utilisation des ressources). Il reflète deux dimensions: une dimension stratégique (lié aux orientations de l'exploitation) et une dimension tactique plus lié à l'adaptation des attributions suite à des contextes changeant (climatique' prix). Afin représenter ces deux niveaux et ainsi structurer les enjeux relatifs aux choix d'assolement à l'échelle de l'exploitation nous proposons une approche basées sur : i) une analyse du processus décisionnel par le biais d'enquêtes en exploitation; ii) une modélisation dynamique aussi bien sur des processus biophysiques (modèles de culture) que sur des processus de gestion (modèle de décision) pour comprendre et simuler. / Evolutions of the institutional and environmental contexts are driving search for alternative cropping systems to reduce water use while maintaining high levels of productivity. This thesis is an account of the long tradition of research on cropping-plan choices at the farm level. It concerns the scope of modelling agricultural systems with an opening to economy. The objective of the research described in this thesis is to produce formalised knowledge on farmers’ croppingplan choices under uncertain environment (price and weather) by analysing and modelling their decision-making processes. Formalising and modelling decision-making processes is becoming a crucial point to develop decision support systems that go beyond limitations of formerly developed prescriptive approaches. This thesis contributes to the development of a formalised and integrated methodology to study and model complex decision-making process. This methodology enables to fill the gap between field surveys and decision-model implementation. The methodology is drawn upon a theoretical background of the decision-making' and consistently combined tools to respectively survey' analyse' model and implement coupled agent and biophysical models. In this thesis' I address the question of uncertainty in two directions. I first analyse the spatio-temporal dynamic of individual farmers’ decision-making process. Then I estimate farmers’ aversion to risk by comparing stated and revealed elicitation methods. On the basis of field survey results' I develop a decision model called CRASH. The approach to develop the model stresses on explicit formalisation of the decision-making process in its temporal and spatial dimensions' and representation of the domain knowledge through generic concepts that are close to ones used by decision-makers. The implementation of developed models is carried out on the RECORD platform as trail blazing project. The originality relies on the use of dynamic models on both the biophysical and management processes. This research opens new perspectives for developing farm specific decision support systems that are based on simulating farmers’ decisionmaking processes. The modelling and simulating the cropping-plan decision-making processes should enable to design with farmers cropping systems that re-conciliate the required adaptive capacities and needs to maintain cropping systems productivity.

This research develops a methodology for determining farm scale land managementunits (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution.The design and implementation of a two-stage methodology using a spatiallyweighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution.The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area.

No description available.