Managing the Hydra in integration: developing an integrated assessment tool for agricultural systems

Keywords: modelling, bio-economic, farm, simulation, ontology, knowledge management, Europe, agricultural management, database, scenario Ex-ante assessment through science-based methods can provide insight into the impacts of potential policy measures or innovations to manage complex problems (e.g. environmental pollution, climate change, or farmers’ welfare). Integrated Assessment and Modelling (IAM) is a method that supports ex-ante assessment through modelling and modelling tools. One type of IAM links models focusing on particular processes on a specific scale into model chains covering multiple scales and disciplines. To achieve an operational model chain for IAM, methodological, semantic and technical integration is required of models, data sources, indicators and scenarios. In this thesis, methodological, semantic and technical integration focuses on two case studies. The first case study is on integration within bio-economic farm models covering two hierarchical systems levels involving a small team of scientists. The second case refers to modelling European agricultural systems. In this case, the integration covers five hierarchical systems levels and different types of models were linked by a large team of about hundred scientists. In the context of these two case studies, many different integration topics and challenges have been addressed: a review of the state-of-the-art in bio-economic farm models, a generic method to define alternative agricultural activities, development of a generic bio-economic farm model, development of an integrated database for agricultural systems, linking different agricultural models and a shared definition of scenarios across disciplines, models and scales. Ultimately, elaborating the methodological, semantic and technical integration greatly contributed to the development of an integrated assessment tool for European agricultural systems. This integrated assessment tool can be used across disciplines and for multi-scale analysis, and allows the assessment of many different policy and technology changes. </p

Coping with uncertainty: perspectives on sustainability of smallholder agriculture in Sub-Saharan Africa

Agricultural sustainability of smallholder farms in the tropics has rarely been examined in an integrated manner by addressing simultaneously ecological, social and economic dimensions and exploring its spatial and temporal characteristics. In this submission I have prepared a Context Statement (Part I) that provides a background to my submitted body of works on assessment of agricultural sustainability of smallholder farms in Kenya. In the Context Statement I have positioned myself and my body of works and its impacts, critiqued my methodologies and reflected on my epistemology, brought out overarching messages on sustainability of smallholder farms and examined my research journey and contributions to academic knowledge and to professional practice. I position my public works within an inclusive ontological realism and epistemological pluralism that informed my use of mixed-methods research. I used (i) decision support systems and models (NUTMON, MonQI and QUEFTS), (ii) participatory learning and interdisciplinary research methodologies (on-farm comparative participatory research, PTD, Farmer Field Schools), and (iii) qualitative perceptions of farmers and researchers to investigate sustainability of smallholder farms. The smallholder farms in the low-to-medium agricultural potential areas were moving in the direction of unsustainability with performance of major indictors related to soil quality, crop productivity and socio-economics below threshold values. This was in direct contrast to the situation in high agricultural potential areas. The collaborative and interdisciplinary research partnerships within which this body of works was prepared was productive with co-authored papers standing at 98.5% of the total number of papers and the average number of citations per paper by other researchers was 5. My research and the body of works presented together with this context statement created a positive impact on farmers’ attitudes, beliefs and behavior regarding sustainability of their farms. Smallholders adopted good agricultural practices and “new” technologies and improved their livelihoods. My reflections on the submitted body of works have further shown that it contributed to knowledge and practice through bridging knowledge gaps on sustainability of organic farming systems, developing new methodologies or adapting current ones to give new meaning in the areas of participatory technology development, communication between “hard sciences” and “soft sciences” on soil quality, farmer learning for sustainability on integrated nutrient management and smallholder tea production, and in the use of decision support systems and models to assess sustainability of smallholder agriculture in an integrated manner. In the Context Statement I have also reflected on my research journey and painted a picture of the impacts of this doctoral pathway on my research practice and future direction. This doctoral pathway provided the opportunity to blend an academic research doctoral model with my professional research practice resulting in a submission equivalent to PhD by thesis. Through it I have re-discovered myself as a research scientist, a flexible autonomous learner, framed my research experiences as forms of personal, professional and academic growth and created linkages with my career interests and opportunities for improving frontiers of my research practice in the future

Race, Nature, and Accumulation: A Decolonial World-Ecological Analysis of Indian Land Grabbing in the Gambella Province of Ethiopia

This dissertation situates the post-crisis phenomenon of large-scale agricultural land acquisition, otherwise known as the global land grab, within the longue duree of the capitalist world-ecology. It does so by advancing a theoretical and historical framework, which I call the decolonial world-ecological agrarian question, that clarifies the key role played by the co-production of race and nature in provisioning the ecological surplus of cheap food that has historically secured the emergence and reproduction of capitalist development. This framework specifically foregrounds the racialized denial of indigenous human presence as the necessary condition of possibility for the reduction of the colonial frontier to a state of unused nature. While the racialized denial of the reproductive conditions of the colonial frontiers fertile soils ultimately exhausts the latters surplus provisioning capacity, the longue duree of the capitalist world-ecology has been marked by successive attempts to overcome such exhaustion by forging, through technologies of racialization, new frontiers of unused externalized natures. The key premise of this dissertation is that, in light of the food price crisis indexing the exhaustion of the accumulation capacity of the neoliberal epoch of the capitalist world-ecology, the global land grab constitutes another such attempted moment of re-securing the cheap food premise through racialized frontier appropriation. This dissertation highlights the distinctive South-South dimensions of the contemporary global land grab by taking as its empirical site of investigation the case of Indian land grabbing in the Gambella province of Ethiopia. The central argument advanced here is that, within the neoliberal crisis conjuncture, the hegemonic resolution of the agrarian question in the core national space of India calls forth, through the practice of global primitive accumulation, the racialized construction of frontiers of unused nature in an emergent African zone of appropriation. Specifically, the cheap food imperative of Indian capitalist development constructs the fertile soils and abundant waters of Gambella as unused natures hitherto wasted by the primitive practices of the indigenous Anywaa people. Indian state and capital thus simultaneously appropriate and erase the indigenous practice and knowledge which has been historically integral to the socio-ecological foundation of Gambellas natural abundance

MIRANA: a socio-ecological model for assessing sustainability of community-based regulations

The Malagasy local communities managing forest resources have difficulties in assessing the impacts of the management plans they decide upon. To help them, we have designed an integrated model with the ecological processes, the various regulations (zoning, quota, etc..) and the resulting inhabitants behavior in order to explore the impacts of scenarios. The model MIRANA has been designed using the MIMOSA framework in which one must design a conceptual model using ontologies, annotate the conceptual model with the necessary processes, and design a concrete model from which to generate the simulation model. In MIRANA, the conceptual model is made of the set of ontologies describing the actors of the system (households, communities, etc.), the objects they are acting on (lands, animal and vegetal species, etc.), the actions carried out by the actors on the objects (hunting, cultivation, etc.) and the regulations on the actions. The actors are provided with needs (food, money, etc.) or objectives (conservation, production, etc.) and planning mechanisms. The objects are provided with spontaneous processes (fertility dynamics, growth of biomass, etc.). This paper is focused on the representation and use of a multiplicity of normative structures for the regulation of the interactions with the environmen

Knowledge description for the suitability requirements of different geographical regions for growing wine

The production of wine has progressed on every main continent. The knowledge modeling can support the sharing of expertise, methods and good practice concerning international grape vine growing and wine production while maintaining a high level of quality. Our research focuses specifically on the development of a support system for knowledge formalization. We describe some procedural rules to represent experienced knowledge in the viticulture domain and plant pathology. We use a graphical software for rules management. The visual representation is a step toward the improvements of interaction between Artificial Intelligence methods and domain experts to make interpretable learning models for concrete decisions. This implementation enables us to make valuable visual reasoning to search whether the Chinese regions are capable of receiving a production of French vineyards. In particular, one outcome is that two Chinese regions appear more favorable and consistent for the development of wine from the Bordeaux region

An IoT architecture for decision support system in precision livestock

Sustainable animal production is a primary goal of technological development in the livestock industry. However, it is crucial to master the livestock environment due to the susceptibility of animals to variables such as temperature and humidity, which can cause illness, production losses, and discomfort. Thus, livestock production systems require monitoring, reasoning, and mitigating unwanted conditions with automated actions. The principal contribution of this study is the introduction of a self-adaptive architecture named e-Livestock to handle animal production decisions. Two case studies were conducted involving a system derived from the e-Livestock architecture, encompassing a Compost Barn production system - an environment and technology where bovine milk production occurs. The outcomes demonstrate the effectiveness of e-Livestock in three key aspects: (i) abstraction of disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and their incorporation into a single architecture specific to the livestock domain, (ii) support for the reuse and derivation of an adaptive self-architecture to support the engineering of a decision support system for the livestock subdomain, and (iii) support for empirical studies in a real smart farm to facilitate future technology transfer to the industry. Therefore, our research’s main contribution is developing an architecture combining machine learning techniques and ontology to support more complex decisions when considering a large volume of data generated on farms. The results revealed that the e-Livestock architecture could support monitoring, reasoning, forecasting, and automated actions in a milk production/Compost Barn environment.Na indústria pecuária, a produção animal sustentável é o principal objetivo do desenvolvimento tecnológico. Porém, é fundamental manter boas condições no ambiente devido à suscetibilidade dos animais a variáveis como temperatura e umidade, que podem causar doenças, perdas de produção e desconforto. Assim, os sistemas de produção pecuária requerem monitoramento, controle e mitigação das condições indesejadas através de ações automatizadas. A principal contribuição deste estudo é a introdução de uma arquitetura auto-adaptativa denominada e-Livestock para apoiar as decisões relacionadas à produção animal. Foram conduzidos dois estudos de caso, envolvendo a arquitetura e-Livestock, que foi utilizada no sistema de produção Compost Barn - ambiente e tecnologia onde ocorre a produção de gado leiteiro. Os resultados demonstraram a utilidade do e-Livestock para avaliar três aspectos principais: (i) abstração de tecnologias disruptivas baseadas em Internet das Coisas (IoT) e Inteligência Artificial, e sua incorporação em uma arquitetura única, específica para o domínio da pecuária, (ii) suporte para a reutilização e derivação de uma arquitetura auto-adaptativa para apoiar o desenvolvimento de uma aplicação de apoio à decisão para o subdomínio da pecuária e (iii) suporte para estudos empíricos em uma fazenda inteligente real para facilitar a transferência de tecnologia para a indústria. Portanto, a principal contribuição dessa pesquisa é o desenvolvimento de uma arquitetura combinando técnicas de machine learning e ontologia para apoiar decisões mais complexas ao considerar um grande volume de dados gerados nas fazendas. Os resultados revelaram que a arquitetura e-Livestock pode apoiar monitoramento, controle, previsão e ações automatizadas em um ambiente de produção de leite/Compost Barn.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Towards a systemic research methodology in agriculture: Rethinking the role of values in science

The recent drastic development of agriculture, together with the growing societal interest in agricultural practices and their consequences, pose a challenge to agricultural science. There is a need for rethinking the general methodology of agricultural research. This paper takes some steps towards developing a systemic research methodology that can meet this challenge – a general self-reflexive methodology that forms a basis for doing holistic or (with a better term) wholeness-oriented research and provides appropriate criteria of scientific quality. From a philosophy of research perspective, science is seen as an interactive learning process with both a cognitive and a social communicative aspect. This means, first of all, that science plays a role in the world that it studies. A science that influences its own subject area, such as agricultural science, is named a systemic science. From this perspective, there is a need to reconsider the role of values in science. Science is not objective in the sense of being value-free. Values play, and ought to play, an important role in science – not only in form of constitutive values such as the norms of good science, but also in the form of contextual values that enter into the very process of science. This goes against the traditional criterion of objectivity. Therefore, reflexive objectivity is suggested as a new criterion for doing good science, along with the criterion of relevance. Reflexive objectivity implies that the communication of science must include the cognitive context, which comprises the societal, intentional, and observational context. In accordance with this, the learning process of systemic research is shown as a self-reflexive cycle that incorporates both an involved actor stance and a detached observer stance. The observer stance forms the basis for scientific communication. To this point, a unitary view of science as a learning process is employed. A second important perspective for a systemic research methodology is the relation between the actual, different, and often quite separate kinds of science. Cross-disciplinary research is hampered by the idea that reductive science is more objective, and hence more scientific, than the less reductive sciences of complex subject areas – and by the opposite idea that reductive science is necessarily reductionistic. Taking reflexive objectivity as a demarcator of good science, an inclusive framework of science can be established. The framework does not take the established division between natural, social and human science as a primary distinction of science. The major distinction is made between the empirical and normative aspects of science, corresponding to two key cognitive interests. Two general methodological dimensions, the degree of reduction of the research world and the degree of involvement in the research world, are shown to span this framework. The framework can form a basis for transdisciplinary work by way of showing the relation between more and less reductive kinds of science and between more detached and more involved kinds of science and exposing the abilities and limitations attendant on these methodological differences