As an alternative renewable source for bioenergy, forest biomass has recently drawn more attention from the U.S. government and the general public. Woody biomass policies have been adopted to encourage the new bioenergy industry. A variety of state policy incentives attempt to create a desirable legal climate and lure new firms, imposing two important questions regarding state government policies and the sustainable use of forest resources. This dissertation sheds some light on these questions. The first paper constructs a woody biomass policy index through scoring each statute and weighting different categories of policies from the vantage point of renewable energy investment. It analyzes the disparity in the strength of state government incentives in the woody biomass utilization. The second paper employs a conditional logit model (CLM) to explore the effects of woody biomass policies on the siting decisions of new bioenergy projects. In addition, significant state attributes influencing the births of new bioenergy firms are identified such as resource availability, business tax climate, delivered pulpwood price, and the average wage rate. The third paper uses the Sub-Regional Timber Supply (SRTS) model to examine the regional aggregate forest biomass feedstock potential in Tennessee and to predict the impacts of additional pulpwood demand on the regional roundwood market through 2030. The fourth paper includes the benefits of thinning and logging residues in a dynamic optimization model to analyze how bioenergy policies will impact forest stock, harvest levels, optimal rotation, and silvicultural effort. The results may have substantial implications regarding woody biomass policies, the creation of a new bioenergy industry, and sustainable forest resource management. A lucrative state woody biomass policy support and tax climate can attract new bioenergy businesses. States endowed with abundant forest resources may choose to provide strong tax incentives to spur the birth of new plants. However, overuse of forest biomass can impact roundwood markets and traditional wood processing industries. How government incentives will affect the sustainability of natural resources can be diverse. These findings offer constructive insights in the enactment and implementation of new woody biomass legislation.

Um dos maiores desafios envolvidos no projeto de grades computacionais é o escalonamento de processos, o qual consiste no mapeamento de processos sobre os computadores disponíveis, a fim de reduzir o tempo de execução de aplicações ou maximizar a utilização de recursos. A literatura na área de Sistemas Distribuídos trata, geralmente, esses dois objetivos separadamente, dando origem às abordagens de escalonamento orientado a aplicações e orientado a recursos, respectivamente. Mais recentemente, uma nova abordagem, denominada escalonamento orientado a sistemas, tem recebido destaque, buscando otimizar ambos objetivos simultaneamente. Seguindo essas abordagens, algoritmos heurísticos e de aproximação têm sido propostos. Os heurísticos buscam por soluções de maneira eficiente sem, contudo, apresentar garantias quanto à qualidade das soluções obtidas. Em contrapartida, os algoritmos de aproximação provêm tais garantias, contudo são mais difíceis de serem projetados, o que justifica o fato de haver apenas versões simplificadas desses algoritmos para cenários de escalonamento de processos. A falta de algoritmos de aproximação adequados para abordar o problema de escalonamento de processos e a necessidade de soluções que atendam o escalonamento orientado a sistemas motivaram esta tese de doutorado que apresenta a proposta do Min Heap-based Scheduling Algorithm (MHSA), um algoritmo de aproximação para o problema de escalonamento de processos orientado a sistemas. Esse algoritmo foi baseado em um modelo de otimização matemática proposto no contexto desta tese. Esse modelo considera os comportamentos de processos e recursos a fim de quantificar a qualidade de soluções de escalonamento. O funcionamento do MHSA envolve a construção de uma árvore min-heap, em que os nós representam computadores e as chaves de ordenação correspondem aos tempos de fila, i.e., ocupação dos computadores. Apesar de esse algoritmo primordialmente reduzir o tempo de execução (ou makespan) de aplicações, essa estrutura em árvore permite que qualquer computador que ocupe o nó raiz receba cargas, o que favorece a ocupação de recursos e, portanto, sua orientação a sistemas. Esse algoritmo tem complexidade assintótica de pior caso igual a O(\'log IND. 2 m\'), em que m corresponde ao número de computadores do sistema. Sua razão de aproximação foi estudada para ambientes distribuídos heterogêneos com e sem a presença de comunicação entre processos, o que permite conhecer, a priori, o nível mínimo de qualidade alcançado por suas soluções. Experimentos foram conduzidos para avaliar o algoritmo proposto e compará-lo a outras propostas. Os resultados confirmam que o MHSA reduz o tempo dispendido na obtenção de boas soluções de escalonamento / One of the most important challenges involved in the design of grid computing systems is process scheduling, which maps applications into the available computers in attempt to reduce the application execution time, or maximize resource utilization. The literature of Distributed Systems usually deals with these two objectives separately, supporting the application-centric and the resourcecentric scheduling, respectively. More recently, a third approach referred to as system-centric scheduling has emerged which attempts to optimize both objectives in conjunction. Heuristic-based and approximation-based algorithms have been proposed to address this third type of scheduling. Heuristics aim to find good solutions at acceptable time constraints, without guaranteeing solution quality. On the other hand, approximation-based algorithms provide optimal solution bounds, however they are more difficult to design what makes them available only to simple scenarios. The need for approximation-based algorithms to support system-centric scheduling has motivated this thesis which presents Min Heap-based Scheduling Algorithm (MHSA). This approximation algorithm is based on a mathematical optimization model, also proposed in this work, which considers process and resource behaviors to measure the quality of scheduling solutions. MHSA builds a min-heap data structure in which tree nodes represent computers and sorting keys correspond to queuing times, i.e., computer workloads. Besides this algorithm primarily reduces application execution times (also referred to as makespan), its data structure allows any computer assume the root node and, consequently, receive workloads, what favors resource utilization. This algorithm has the worst-case time complexity equals to O(\'log IND. 2 m\'), in which m represents the number of system computers. Its approximation ratio was analyzed to heterogeneous distributed systems considering bag-of-tasks and communication-intensive applications. Having this ratio, we know the minimum quality level provided by every scheduling solution. Experiments were performed to compare MHSA to others. Results confirm MHSA reduces the time spent to obtain good quality scheduling solutions

The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.

No description available.

Waste Load Allocation (WLA) in rivers refers to the determination of required pollutant fractional removal levels at a set of point sources of pollution to ensure that water quality standards are maintained throughout the system. Optimal waste load allocation implies that the selected pollution treatment vector not only maintains the water quality standards, but also results in the best value for the objective function defined for the management problem. Waste load allocation problems are characterized by uncertainties due to the randomness and imprecision. Uncertainty due to randomness arises mainly due to the random nature of the variables influencing the water quality. Uncertainty due to imprecision or fuzziness is associated with setting up the water quality standards and goals of the Pollution Control Agencies (PCA), and the dischargers (e.g., industries and municipal dischargers). Many decision problems in water resources applications are dominated by natural, extreme, rarely occurring, uncertain events. However usually such events will be absent or be rarely present in the historical records. Due to the scarcity of information of these uncertain events, a realistic decision-making becomes difficult. Furthermore, water resources planners often deal with imprecision, mostly due to imperfect knowledge and insufficient or inadequate data. Therefore missing data is very common in most water resources decision problems. Missing data introduces inaccuracy in analysis and evaluation. For instance, the sample mean of the available data can be an inaccurate estimate of the mean of the complete data. Use of sample statistics estimated from inadequate samples in WLA models would lead to incorrect decisions. Therefore there is a necessity to incorporate the uncertainty due to missing data also in WLA models in addition to the uncertainties due to randomness and imprecision. The uncertainty in the input parameters due to missing or inadequate data renders the input parameters (such as mean and variance) as interval grey parameters in water quality decision-making. In a Fuzzy Waste Load Allocation Model (FWLAM), randomness and imprecision both can be addressed simultaneously by using the concept of fuzzy risk of low water quality (Mujumdar and Sasikumar, 2002). In the present work, an attempt is made to also address uncertainty due to partial ignorance due to missing data or inadequate data in the samples of input variables in FWLAM, considering the fuzzy risk approach proposed by Mujumdar and Sasikumar (2002). To address the uncertainty due to missing data or inadequate data, the input parameters (such as mean and variance) are considered as interval grey numbers. The resulting output water quality indicator (such as DO) will also, consequently, be an interval grey number. The fuzzy risk will also be interval grey number when output water quality indicator is an interval grey number. A methodology is developed for the computation of grey fuzzy risk of low water quality, when the input variables are characterized by uncertainty due to partial ignorance resulting from missing or inadequate data in the samples of input variables. To achieve this, an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system to address uncertainties due to randomness, fuzziness and also due to missing data or inadequate data. Monte Carlo Simulation (MCS) incorporating a water quality simulation model is performed two times for each set of randomly generated input variables: once for obtaining the upper bound of DO and once for the lower bound of DO, by using appropriate upper or lower bounds of interval grey input variables. These two bounds of DO are used in the estimation of grey fuzzy risk by substituting the upper and lower values of fuzzy membership functions of low water quality. A backward finite difference scheme (Chapra, 1997) is used to solve the water quality simulation model. The goal of PCA is to minimize the bounds of grey fuzzy risk, whereas the goal of dischargers is to minimize the fractional removal levels. The two sets of goals are conflicting with each other. Fuzzy multiobjective optimization technique is used to formulate the multiobjective model to provide best compromise solutions. Probabilistic Global Search Lausanne (PGSL) method is used to solve the optimization problem. Finally the results of the model are compared with the results of risk minimization model (Ghosh and Mujumdar, 2006), when the methodology is applied to the case study of the Tunga-Bhadra river system in South India. The model is capable of determining a grey fuzzy risk with the corresponding bounds of DO, at each check point, rather than specifying a single value of fuzzy risk as done in a Fuzzy Waste Load Allocation Model (FWLAM). The IFWLAM developed is based on fuzzy multiobjective optimization problem with ‘max-min’ as the operator, which usually may not result in a unique solution and there exists a possibility of obtaining multiple solutions (Karmakar and Mujumdar, 2006b). Karmakar and Mujumdar (2006b) developed a two-phase Grey Fuzzy Waste Load Allocation Model (two-phase GFWLAM), to determine the widest range of interval-valued optimal decision variables, resulting in the same value of interval-valued optimal goal fulfillment level as obtained from GFWLAM (Karmakar and Mujumdar 2006a). Following Karmakar and Mujumdar (2006b), two optimization models are developed in this study to capture all the decision alternatives or multiple solutions: one to maximize and the other to minimize the summation of membership functions of the dischargers by keeping the maximum goal fulfillment level same as that obtained in IFWLAM to obtain a lower limit and an upper limit of fractional removal levels respectively. The aim of the two optimization models is to obtain a range of fractional removal levels for the dischargers such that the resultant grey fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision-making. The models are applied to the case study of Tunga-Bhadra river system. A range of upper and lower limits of fractional removal levels is obtained for each discharger; within this range, the discharger can select the fractional removal level so that the resulting grey fuzzy risk will also be within specified bounds. In IFWLAM, the membership functions are subjective, and lower and upper bounds are arbitrarily fixed. Karmakar and Mujumdar (2006a) developed a Grey Fuzzy Waste Load Allocation Model (GFWLAM), in which uncertainty in the values of membership parameters is quantified by treating them as interval grey numbers. Imprecise membership functions are assigned for the goals of PCA and dischargers. Following Karmakar and Mujumdar (2006a), a Grey Optimization Model with Grey Fuzzy Risk is developed in the present study to address the uncertainty in the memebership functions of IFWLAM. The goals of PCA and dischargers are considered as grey fuzzy goals with imprecise membership functions. Imprecise membership functions are assigned to the fuzzy set of low water quality and fuzzy set of low risk. The grey fuzzy risk approach is included to account for the uncertainty due to missing data or inadequate data in the samples of input variables as done in IFWLAM. Randomness and imprecision associated with various water quality influencing variables and parameters of the river system are considered through a Monte-Carlo simulation when input parameters (such as mean and variance) are interval grey numbers. The model application is demonstrated with the case study of Tunga-Bhadra river system in South India. Finally the results of the model are compared with the results of GFWLAM (Karmakar and Mujumdar, 2006a). For the case study of Tunga Bhadra River system, it is observed that the fractional removal levels are higher for Grey Optimization Model with Grey Fuzzy Risk compared to GFWLAM (Karmakar and Mujumdar, 2006a) and therefore the resulting risk values at each check point are reduced to a significant extent. The models give a set of flexible policies (range of fractional removal levels). Corresponding optimal values of goal fulfillment level and the grey fuzzy risk are all in terms of interval grey numbers. The IFWLAM and Grey Fuzzy Optimization Model with Grey Fuzzy Risk, developed in the study do not limit their application to any particular pollutant or water quality indicator in the river system. Given appropriate transfer functions for spatial distribution of the pollutants in water body, the models can be used for water quality management of any general river system.

The thesis contains the theoretical basis for study of possibilities of pricing information goods. The source areas are microeconomics, marketing, competitive advantage and economics of information goods. The model of market is created with constraints defined on the ground of theoretical basis. The thesis analyzes requirements that define the system that supports the choice of pricing strategy. It includes detailed design of the prototype of such system. The prototype is designed with robustness because of the future improvements. The design describes the prototype's input parameters and their transformation into useful outputs that cover basic characteristics of information goods. The designed prototype is implemented. The thesis includes demonstration of the prototype and possible directions for improvements that lead to validity of proposed model of market.

In the event of a radioactive release in an urban environment the consequent response mustbe swift and precise. As soon as first responders have correct information, they can make anaccurate risk assessment. However, if the position, release rate and time of the radioactiverelease is unknown it is hard to know how the pollutant will spread. This thesis aims to testa model which approximates these three unknowns using weather data (wind and rain) as wellas measurement data collected at sensors placed around an urban environment. An atmospheric dispersion model based on an existing Reynolds Averaged Navier-Stokes modelis set up in two geometries of different complexity to create forward mode synthetic depositiondata and adjoint mode concentration fields resulting from a fixed dry deposition velocity andscavenging effect for wet deposition. Variations of time- and space-dependent rainfall is simu-lated. The resulting data is used in an existing optimization model, where a parameter studyis conducted regarding regularization coefficients. This thesis shows that the optimization model accurately estimates position and its approximaterelease rate of a 2D geometry of radioactive releases using a logarithmic optimization approach,and fail to do so using a linear optimization approach. The logarithmic optimization model alsoapproximately estimates position and release rate in a 3D geometry. Regularization parametersshould be within the range of 0.1 and 1.2 depending on rain. More rain requires smallerparameters and will estimate a lower release rate. Time-dependent rainfall is shown to have amajor negative effect on simulation time.iii

High mountain regions including the Andean region are very sensitive to climate change. Farmers in the central Andes of Peru are increasingly being exposed to the impacts of climate variability. This transdisciplinary research uses field laboratories, combining the farming system and the sustainable livelihood approaches, to carry out social, ecological, and financial assessments so as to identify sustainable and resilient livelihood strategies for small-scale Andean farmers. The first research step studies and characterizes farm household systems, influenced by their biophysical and socioeconomic contexts, for which two vulnerability indices were elaborated. Focused on the climate variability, the five livelihood assets and the three IPCC’s vulnerability components, these indices show the highly sensitive conditions of most communities with poor health conditions, access to infrastructure and public services. Farmers’ capacity of response is often limited by the low on-farm diversity and lack of organization. Thereafter, sustainable livelihood strategies were identified. These include on-farm intensification and non-farm labor intensification for lowland and better-access communities. In the middle-access and highland communities, where temporary migration is a common coping strategy, sustainable scenarios include an increment in diversification strategies through agrobiodiversity and a larger share of tree-based production systems. Furthermore, research step II explores local strategies to cope with agricultural droughts and evaluates, by means of natural resource assessment methods, agroforestry systems as an alternative to reduce their negative effects. Mainly affected by the increasing variation in precipitation events, farmers identify off-farm and on-farm diversification as adaptive strategies against agricultural droughts that reduce the weather dependence and covariance between livelihood activities. Among the introduction of more resistant crop and pasture varieties, the incorporation of trees in their system is desired because of their positive influence in soil moisture and crop yields. Soil moisture in agroforestry systems with eucalyptus trees is 10-20% higher than in agricultural systems during the beginning of the wet season. Differences in the soil moisture during the end of the dry season and in the potato yield are not evident between these systems, although an area without sowing reduced the agricultural output in 13-17% in agroforestry systems. Research step III seeks to maximize the efficiency of resource allocation in farm household systems by developing a linear programming optimization model. This financial assessment underpinned the need of additional off-farm activities for resource-scarcer farmers. In addition, under interest rates below 15% the model includes tree-based production systems as part of the optimal solution. However, with increasing interest rates, a higher share of land is used to cover household’s basic needs and fewer resources are available for capital accumulation activities such as forestry. Variations introduced in the model show that pasture systems are more sensitive to changes in the production outputs, whereas variation in farm worker wages and tree prices affected less the optimal solutions, making farming systems less sensitive to these market changes. Finally, the incorporation of tree-based systems have proved to be a sustainable and resilient livelihood strategy against climate variability available for particular farm household systems of the study area. / Las zonas montañosas, incluyendo la región andina son muy sensibles al cambio climático. Los agricultores de los Andes centrales del Perú están cada vez más expuestos a los efectos de la variabilidad climática. Esta investigación transdisciplinaria utiliza laboratorios de campo (field laboratories), combinando los enfoques de sistemas agrícolas y de medios de vida sostenibles, para llevar a cabo evaluaciones sociales, ecológicas y financieras con el fin de identificar estrategias sostenibles y resilientes para los agricultores andinos de pequeña escala. La primera fase de la investigación caracteriza a los sistemas agrícolas familiares, influenciados por sus contextos biofísicos y socioeconómicos, para lo cual se elaboraron dos índices de vulnerabilidad centrados en la variabilidad del clima, los cinco activos de los medios de vida y los tres componentes de la vulnerabilidad del IPCC. Estos índices muestran las condiciones de alta sensibilidad de la mayoría de las comunidades, con malas condiciones de salud y poco acceso a la infraestructura y a los servicios públicos. La capacidad de respuesta de los agricultores es a menudo limitada por la baja diversidad en las actividades agrícolas y la falta de organización. Posteriormente se identificaron las estrategias de medios de vida sostenibles. Estas incluyen la intensificación en las actividades agrícolas y la intensificación del trabajo no agrícola en las comunidades de zonas bajas y con mejor acceso. En las comunidades con menor acceso y zonas altas la migración temporal es una estrategia de afrontamiento común. Los escenarios sostenibles en estas comunidades incluyen un incremento en las estrategias de diversificación p. ej. a través de un aumento de la biodiversidad agrícola y una mayor proporción de sistemas de producción asociados con árboles. Por otra parte, la segunda fase de la investigación explora las estrategias locales para hacer frente a las sequías agrícolas y evalúa, por medio de métodos de evaluación de recursos naturales, los sistemas agroforestales como alternativa para reducir sus efectos negativos. Afectados principalmente por el aumento en la variación de las precipitaciones, los pequeños agricultores identifican a la diversificación de actividades dentro y fuera de sus parcelas agrícolas como una estrategia de adaptación frente a las sequías agrícolas que reduce la dependencia climática y la covarianza entre las actividades de subsistencia. Dentro de la introducción de variedades de cultivos y pastos más resistentes, como parte de la solución, los agricultores desean la incorporación de árboles en su sistema debido a su influencia positiva en la humedad del suelo y en los rendimientos de los cultivos. La humedad del suelo en sistemas agroforestales con árboles de eucalipto es un 10-20% mayor que en los sistemas agrícolas durante el comienzo de la estación húmeda. Las diferencias en la humedad del suelo durante el final de la estación seca y en el rendimiento de los cultivos de papa no son evidentes entre estos dos sistemas. A pesar de esto, el espacio sin siembra dejado en los sistemas agroforestales redujo la producción agrícola en un 13-17%. La tercera fase de la investigación busca maximizar la eficiencia en la asignación de recursos en los sistemas agrícolas familiares mediante el desarrollo de un modelo de optimización de programación lineal. Esta evaluación financiera respalda la necesidad de actividades adicionales no-agrícolas para agricultores con recursos más escasos. Además, con tasas de interés por debajo del 15%, el modelo siempre incluye a los sistemas de producción forestales y/o agroforestales como parte de las soluciones óptimas. Sin embargo, con el aumento de las tasas de interés, una mayor proporción de tierra se utiliza para cubrir las necesidades básicas del hogar y menos recursos están disponibles para las actividades de acumulación de capital como la silvicultura. Las variaciones introducidas en el modelo muestran que los sistemas de pastoreo son más sensibles a los cambios en los condiciones de producción. Por otro lado, la variación en los salarios de los trabajadores agrícolas y en los precios de los árboles afectan en un menor grado las soluciones óptimas, proporcionando sistemas agrícolas menos sensibles a estos cambios en el mercado. Finalmente, la incorporación de árboles en los sistemas agrícolas ha demostrado ser una estrategia de vida sostenible y resiliente a la variabilidad climática disponible para determinados sistemas agrícolas familiares de la zona de estudio. / Hochgebirgsregionen einschließlich der Andenregion sind gegenüber dem Klimawandel sehr empfindlich. Die in den zentralen Anden von Peru lebenden Bauern sind mehr und mehr den Auswirkungen durch Klimaschwankungen ausgesetzt. Diese transdisziplinäre Forschung nutzt Feldlabore, die das System der landwirtschaftlichen Bewirtschaftung und Ansätze zur nachhaltigen Lebensunterhaltssicherung kombinieren, um soziale, ökologische und ökonomische Erhebungen durchzuführen, so dass nachhaltige Livelihood-Strategien für die Kleinbauern in den Anden aufgezeigt werden können. Der erste Forschungsschritt untersucht und charakterisiert die bäuerlichen Haushaltssysteme, die durch ihre biophysikalischen und sozioökonomischen Kontexte beeinflusst sind. Hierfür wurden zwei Vulnerabilitätsindizes herausgearbeitet, die Klimavariabilität und die fünf Güter des Sustainable Livelihood-Konzepts im Fokus haben, sowie die drei Vulnerabilitätskomponenten des Intergovernmental Panel on Climate Change (IPCC). Diese Indizes decken die hochgradige Sensitivität für die meisten Gemeinden auf, aufgrund des schlechten Gesundheitszustandes sowie dem Mangel an Infrastruktur und öffentlichen Dienstleistungen. Die Fähigkeit der Bauern damit umzugehen, ist zumeist begrenzt durch eine geringe Diversität und fehlende Organisation auf den Farmen. Anschließend werden nachhaltige Livelihood-Strategien aufgezeigt. Diese umfassen die Intensivierung der Arbeit in der Landwirtschaft und der Arbeitskraft außerhalb der Landwirtschaft für Gemeinden im Flachland sowie besser erreichbare Gemeinden. In Hochlandgemeinden und Gemeinden die schwer zugänglich sind, ist temporäre Migration eine geläufige Bewältigungsstrategie. Nachhaltige Szenarien in diesen Gemeinden beinhalten eine höhere Anzahl an Diversifizierungsstrategien wie die Steigerung von Agro-Biodiversität und dem Anteil an baumbasierten Produktionssystemen. Forschungsschritt II untersucht lokale Strategien, um die landwirtschaftliche Dürre zu bewältigen und bewertet – mit Hilfe von Naturressourcenbewertungsverfahren – Agroforstsysteme als eine Alternative, um die negativen Auswirkungen der Trockenzeiten zu verringern. Beeinträchtigt durch zunehmende Niederschlagsschwankungen, identifizieren Bauern die Diversifizierung von landwirtschaftlichen und nicht-landwirtschaftlichen Aktivitäten als Anpassungsstrategie bei landwirtschaftliche Dürre, wodurch die Abhängigkeit vom Wetter und die Kovarianz zwischen den Aktivitäten für den Lebensunterhalt reduziert werden kann. Neben der Einführung resistenterer Kultur- und Weidepflanzen, ist die Einbeziehung von Bäumen in das System wünschenswert, aufgrund ihres positiven Einflusses auf die Bodenfeuchte und Erträge. Die Bodenfeuchte in agroforstwirtschaftlichen Systemen mit Eukalyptusbäumen ist während der beginnenden Feuchtperiode 20% höher als in landwirtschaftlichen Systemen. Die Unterschiede der Bodenfeuchte am Ende der Trockenzeit und bezüglich des Kartoffelertrags sind zwischen diesen Systemen nicht markant, obwohl eine Fläche, auf der keine Saat ausgebracht wurde, den landwirtschaftlichen Ertrag in Agroforstsystemen um 13 bis 17% mindert. Forschungsschritt III versucht die Effizienz der Ressourcenzuordnung in Farmhaushaltssystemen zu maximieren, indem ein Optimierungsmodell mit Hilfe der linearen Programmierung entwickelt wird. Diese ökonomische Erhebung unterstreicht die Notwendigkeit zusätzlicher nichtlandwirtschaftlicher Aktivitäten für ressourcenärmere Bauern. Bei Zinsraten unter 15% umfasst das Model baumbasierte Produktionssysteme als einen Teil der optimalen Lösung. Mit steigenden Zinsraten wird jedoch eine größere Bodenfläche dazu verwendet, um die Grundbedürfnisse der Haushalte zu decken und es stehen weniger Ressourcen für Aktivitäten zur Kapitalanhäufung wie Forstwirtschaft zur Verfügung. Die in das Modell involvierten Variationen zeigen, dass Weidesysteme sensibler auf Veränderungen des Produktionsausstoßes reagieren. Schwankungen bei den Löhnen der Farmer und Veränderungen der Baumpreise beeinträchtigen hingegen die optimalen Lösungen weniger. Dadurch sind die landwirtschaftlichen Systeme gegenüber Marktschwankungen weniger anfällig. Abschließend erweist sich, dass – für bestimmte Farmhaushaltssysteme im Untersuchungsgebiet – die Einbeziehung baumbasierter Systeme als nachhaltige und resiliente Livelihood-Strategie angesichts von Klimaschwankungen nützlich ist. / Regiões altomontanas, incluindo os Andes são extremamente sensíveis aos impactos das mudanças climáticas. Pequenos agricultores da região central dos Andes Peruanos estão progressivamente sendo expostos aos impactos das variações climáticas. A presente investigação transdisciplinar utiliza “field laboratories”, combinando os enfoques de sistemas rurais e dos meios de subsistência sustentáveis, visando uma avaliação social, ecológica e financeira, com intuito de se identificar estratégias resilientes e sustentáveis para os pequenos agricultores Andinos. A primeira etapa do presente estudo investiga e caracteriza os sistemas rurais, influenciados por seus contextos biofísicos e socioeconômicos, para os quais foram elaborados dois índices de vulnerabilidade focados na variabilidade climática, nos recursos dos meios de vida (cinco capitais) e nos três componentes da vulnerabilidade do IPCC. Esses índices mostram as condições altamente sensíveis da maioria das comunidades, com más condições de saúde, acesso à infra-estrutura e serviços públicos. A capacidade de resposta dos pequenos agricultores é frequentemente limitada pela baixa diversificação de actividades na exploração agricola e falta de organização. Posteriormente, foram identificadas estratégias de subsitência sustentáveis. Estas incluem a intensificação tanto do trabalho rural, quanto do não-agrícola para as comunidades de terras baixas e mais acessíveis. Para as comunidades altomontanas e com menor acesso, a migração temporária é uma estratégia de enfrentamento comum. Cenários sustentáveis para essas comunidades incluem um incremento nas estratégias de diversificação p. ex. aumentando a agrobiodiversidade e a parcela dos sistemas de produção florestais. A segunda etapa da pesquisa explora estratégias locais para lidar com as secas agrícolas e investiga, por meio de métodos de avaliação de recursos naturais, sistemas agroflorestais como alternativa para reduzir os seus efeitos negativos. Afetado principalmente pelo aumento da variação da precipitação, os agricultores identificam a diversificação tanto no trabalho rural, quanto no não-agrícola, como estratégias adaptativas contra secas agrícolas que reduzam a dependência do clima e covariância entre atividades de subsitência. Entre a introdução de culturas e de pastagens de variedades mais resistentes, a incorporação de árvores em seu sistema é desejada por conta da sua influência positiva na umidade do solo e no rendimento das culturas. A umidade do solo em sistemas agroflorestais com árvores de eucalipto é de 10-20% maior do que em sistemas agrícolas durante o início da estação chuvosa. As diferenças na umidade do solo durante o final da estação seca e na produtividade da batata não são evidentes entre estes dois sistemas. Apesar disso, o espaço sem semeadura deixado em sistemas agroflorestais reduziu a produção agrícola em 13-17%. A terceira etapa da presente investigação visa maximizar a eficiência da alocação de recursos em sistemas agrícolas familiares por meio do desenvolvimento de um modelo de otimização de programação linear. Esta avaliação financeira sustenta a necessidade de atividades não-agrícolas adicionais para agricultores com recursos escassos. Ademais, sob taxas de juros abaixo de 15%, o modelo inclui sistemas de produção florestais como parte da solução ideal. Contudo, com o aumento das taxas de juros, uma parcela maior da propriedade é usada para garantir as necessidades básicas, e portanto, menos recursos do agregado familiar estão disponíveis para atividades de acumulação de capital, tais como a silvicultura. Variações introduzidas no modelo mostram que sistemas de pastagem são mais sensíveis a mudanças nas condições de produção. Ademais, variaçãoes nos salários dos trabalhadores agrícolas e nos preços de árvores afetam menos as soluções ótimas, tornando os sistemas agrícolas menos sensíveis a estas mudanças do mercado. Por fim, a incorporação de sistemas florestais provaram ser uma estratégia de subsistência sustentável e resiliente contra a variação climática para determinados sistemas de agricultura familiar da área de estudo.

High mountain regions including the Andean region are very sensitive to climate change. Farmers in the central Andes of Peru are increasingly being exposed to the impacts of climate variability. This transdisciplinary research uses field laboratories, combining the farming system and the sustainable livelihood approaches, to carry out social, ecological, and financial assessments so as to identify sustainable and resilient livelihood strategies for small-scale Andean farmers. The first research step studies and characterizes farm household systems, influenced by their biophysical and socioeconomic contexts, for which two vulnerability indices were elaborated. Focused on the climate variability, the five livelihood assets and the three IPCC’s vulnerability components, these indices show the highly sensitive conditions of most communities with poor health conditions, access to infrastructure and public services. Farmers’ capacity of response is often limited by the low on-farm diversity and lack of organization. Thereafter, sustainable livelihood strategies were identified. These include on-farm intensification and non-farm labor intensification for lowland and better-access communities. In the middle-access and highland communities, where temporary migration is a common coping strategy, sustainable scenarios include an increment in diversification strategies through agrobiodiversity and a larger share of tree-based production systems. Furthermore, research step II explores local strategies to cope with agricultural droughts and evaluates, by means of natural resource assessment methods, agroforestry systems as an alternative to reduce their negative effects. Mainly affected by the increasing variation in precipitation events, farmers identify off-farm and on-farm diversification as adaptive strategies against agricultural droughts that reduce the weather dependence and covariance between livelihood activities. Among the introduction of more resistant crop and pasture varieties, the incorporation of trees in their system is desired because of their positive influence in soil moisture and crop yields. Soil moisture in agroforestry systems with eucalyptus trees is 10-20% higher than in agricultural systems during the beginning of the wet season. Differences in the soil moisture during the end of the dry season and in the potato yield are not evident between these systems, although an area without sowing reduced the agricultural output in 13-17% in agroforestry systems. Research step III seeks to maximize the efficiency of resource allocation in farm household systems by developing a linear programming optimization model. This financial assessment underpinned the need of additional off-farm activities for resource-scarcer farmers. In addition, under interest rates below 15% the model includes tree-based production systems as part of the optimal solution. However, with increasing interest rates, a higher share of land is used to cover household’s basic needs and fewer resources are available for capital accumulation activities such as forestry. Variations introduced in the model show that pasture systems are more sensitive to changes in the production outputs, whereas variation in farm worker wages and tree prices affected less the optimal solutions, making farming systems less sensitive to these market changes. Finally, the incorporation of tree-based systems have proved to be a sustainable and resilient livelihood strategy against climate variability available for particular farm household systems of the study area.:1 Introduction - 1 - 1.1 Introduction and justification - 1 - 1.2 Objectives and thesis statements - 2 - 1.3 Outline - 3 - 1.4 Definition of terms - 5 - 1.4.1 Vulnerability - 5 - 1.4.2 Resilience - 7 - 1.4.3 Agroforestry systems - 8 - 1.4.4 Farming system approach - 9 - 1.4.5 Farm household system - 10 - 1.4.6 Sustainable livelihood approach - 10 - 2 Framework and study site - 14 - 2.1 Theoretical framework - 14 - 2.2 Methodological framework - 18 - 2.2.1 Field laboratories - 18 - 2.2.2 Methods - 19 - 2.2.3 Methodology applied in research step I: Vulnerability in Achamayo - 21 - 2.2.4 Methodology applied in research step II: Agroforestry systems and agricultural droughts - 29 - 2.2.5 Methodology applied in research step III: Modeling small farm production systems - 33 - 2.2.6 Selection of case studies - 34 - 2.3 Study area - 35 - 2.3.1 Soils and topography - 35 - 2.3.2 Weather - 37 - 2.3.3 Agro-ecological zones and vegetation - 38 - 2.3.4 Climate change - 40 - 2.3.5 Socioeconomic characteristics - 42 - 2.3.6 Population - 43 - 2.3.7 External determinants - 71 - 2.4 Case studies - 47 - 2.4.1 Lowland communities (L) - 49 - 2.4.2 Middle access communities (M) - 50 - 2.4.3 Highland communities (H) - 51 - 3 Vulnerability in Achamayo - 53 - 3.1 Results - 53 - 3.1.1 Sustainable Livelihood Vulnerability Index (S-LVI) - 53 - 3.1.2 IPCC Livelihood Vulnerability Index (LVI-IPCC) - 68 - 3.2 Discussion - 71 - 3.2.1 Climate variability and extreme events - 71 - 3.2.2 Human capital - 71 - 3.2.3 Social capital - 71 - 3.2.4 Natural capital - 71 - 3.2.5 Physical capital - 71 - 3.2.6 Financial capital - 71 - 3.2.7 Livelihood strategies following the S-LVI and LVI-IPCC indices - 86 - 3.3 Conclusion - 92 - 4 Agroforestry systems and agricultural droughts - 95 - 4.1 Results - 96 - 4.1.1 Farmers’ experience and perception on climate variability and agricultural droughts - 96 - 4.1.2 Agricultural droughts in the farm household systems - 97 - 4.1.3 Farming forestry systems and land-use decision-making - 102 - 4.1.4 Influence of trees in the soil moisture and yield - 104 - 4.2 Discussion - 110 - 4.2.1 Climate change and agricultural droughts - 110 - 4.2.2 Farm forestry systems and land-use decision-making - 115 - 4.2.3 Influence of trees in the soil moisture and yield - 117 - 4.3 Conclusion - 121 - 5 Modeling small farm production systems: optimization of resource allocation - 123 - 5.1 Methodology - 124 - 5.1.1 Optimization Model - 126 - 5.1.2 Plan of optimization - 128 - 5.1.3 Production systems - 131 - 5.1.4 Constraints - 134 - 5.2 Results - 138 - 5.2.1 Model - 138 - 5.2.2 Interest rates - 142 - 5.2.3 Sensitivity analyses - 146 - 5.3 Discussion - 151 - 5.3.1 Cash flows - 151 - 5.3.2 Model outcomes - 152 - 5.3.3 Interest rates - 155 - 5.3.4 Sensitivity analyses - 159 - 5.4 Conclusion - 169 - 6 Synthesis - 171 - 6.1 Lessons learned - 171 - 6.1.1 Research step I - 172 - 6.1.2 Research step II - 175 - 6.1.3 Research step III - 176 - 6.2 Conclusions & outlook - 179 - 6.2.1 General conclusions - 179 - 6.2.2 Outlook - 181 - References - 185 - Appendix - 199 - / Las zonas montañosas, incluyendo la región andina son muy sensibles al cambio climático. Los agricultores de los Andes centrales del Perú están cada vez más expuestos a los efectos de la variabilidad climática. Esta investigación transdisciplinaria utiliza laboratorios de campo (field laboratories), combinando los enfoques de sistemas agrícolas y de medios de vida sostenibles, para llevar a cabo evaluaciones sociales, ecológicas y financieras con el fin de identificar estrategias sostenibles y resilientes para los agricultores andinos de pequeña escala. La primera fase de la investigación caracteriza a los sistemas agrícolas familiares, influenciados por sus contextos biofísicos y socioeconómicos, para lo cual se elaboraron dos índices de vulnerabilidad centrados en la variabilidad del clima, los cinco activos de los medios de vida y los tres componentes de la vulnerabilidad del IPCC. Estos índices muestran las condiciones de alta sensibilidad de la mayoría de las comunidades, con malas condiciones de salud y poco acceso a la infraestructura y a los servicios públicos. La capacidad de respuesta de los agricultores es a menudo limitada por la baja diversidad en las actividades agrícolas y la falta de organización. Posteriormente se identificaron las estrategias de medios de vida sostenibles. Estas incluyen la intensificación en las actividades agrícolas y la intensificación del trabajo no agrícola en las comunidades de zonas bajas y con mejor acceso. En las comunidades con menor acceso y zonas altas la migración temporal es una estrategia de afrontamiento común. Los escenarios sostenibles en estas comunidades incluyen un incremento en las estrategias de diversificación p. ej. a través de un aumento de la biodiversidad agrícola y una mayor proporción de sistemas de producción asociados con árboles. Por otra parte, la segunda fase de la investigación explora las estrategias locales para hacer frente a las sequías agrícolas y evalúa, por medio de métodos de evaluación de recursos naturales, los sistemas agroforestales como alternativa para reducir sus efectos negativos. Afectados principalmente por el aumento en la variación de las precipitaciones, los pequeños agricultores identifican a la diversificación de actividades dentro y fuera de sus parcelas agrícolas como una estrategia de adaptación frente a las sequías agrícolas que reduce la dependencia climática y la covarianza entre las actividades de subsistencia. Dentro de la introducción de variedades de cultivos y pastos más resistentes, como parte de la solución, los agricultores desean la incorporación de árboles en su sistema debido a su influencia positiva en la humedad del suelo y en los rendimientos de los cultivos. La humedad del suelo en sistemas agroforestales con árboles de eucalipto es un 10-20% mayor que en los sistemas agrícolas durante el comienzo de la estación húmeda. Las diferencias en la humedad del suelo durante el final de la estación seca y en el rendimiento de los cultivos de papa no son evidentes entre estos dos sistemas. A pesar de esto, el espacio sin siembra dejado en los sistemas agroforestales redujo la producción agrícola en un 13-17%. La tercera fase de la investigación busca maximizar la eficiencia en la asignación de recursos en los sistemas agrícolas familiares mediante el desarrollo de un modelo de optimización de programación lineal. Esta evaluación financiera respalda la necesidad de actividades adicionales no-agrícolas para agricultores con recursos más escasos. Además, con tasas de interés por debajo del 15%, el modelo siempre incluye a los sistemas de producción forestales y/o agroforestales como parte de las soluciones óptimas. Sin embargo, con el aumento de las tasas de interés, una mayor proporción de tierra se utiliza para cubrir las necesidades básicas del hogar y menos recursos están disponibles para las actividades de acumulación de capital como la silvicultura. Las variaciones introducidas en el modelo muestran que los sistemas de pastoreo son más sensibles a los cambios en los condiciones de producción. Por otro lado, la variación en los salarios de los trabajadores agrícolas y en los precios de los árboles afectan en un menor grado las soluciones óptimas, proporcionando sistemas agrícolas menos sensibles a estos cambios en el mercado. Finalmente, la incorporación de árboles en los sistemas agrícolas ha demostrado ser una estrategia de vida sostenible y resiliente a la variabilidad climática disponible para determinados sistemas agrícolas familiares de la zona de estudio.:1 Introduction - 1 - 1.1 Introduction and justification - 1 - 1.2 Objectives and thesis statements - 2 - 1.3 Outline - 3 - 1.4 Definition of terms - 5 - 1.4.1 Vulnerability - 5 - 1.4.2 Resilience - 7 - 1.4.3 Agroforestry systems - 8 - 1.4.4 Farming system approach - 9 - 1.4.5 Farm household system - 10 - 1.4.6 Sustainable livelihood approach - 10 - 2 Framework and study site - 14 - 2.1 Theoretical framework - 14 - 2.2 Methodological framework - 18 - 2.2.1 Field laboratories - 18 - 2.2.2 Methods - 19 - 2.2.3 Methodology applied in research step I: Vulnerability in Achamayo - 21 - 2.2.4 Methodology applied in research step II: Agroforestry systems and agricultural droughts - 29 - 2.2.5 Methodology applied in research step III: Modeling small farm production systems - 33 - 2.2.6 Selection of case studies - 34 - 2.3 Study area - 35 - 2.3.1 Soils and topography - 35 - 2.3.2 Weather - 37 - 2.3.3 Agro-ecological zones and vegetation - 38 - 2.3.4 Climate change - 40 - 2.3.5 Socioeconomic characteristics - 42 - 2.3.6 Population - 43 - 2.3.7 External determinants - 71 - 2.4 Case studies - 47 - 2.4.1 Lowland communities (L) - 49 - 2.4.2 Middle access communities (M) - 50 - 2.4.3 Highland communities (H) - 51 - 3 Vulnerability in Achamayo - 53 - 3.1 Results - 53 - 3.1.1 Sustainable Livelihood Vulnerability Index (S-LVI) - 53 - 3.1.2 IPCC Livelihood Vulnerability Index (LVI-IPCC) - 68 - 3.2 Discussion - 71 - 3.2.1 Climate variability and extreme events - 71 - 3.2.2 Human capital - 71 - 3.2.3 Social capital - 71 - 3.2.4 Natural capital - 71 - 3.2.5 Physical capital - 71 - 3.2.6 Financial capital - 71 - 3.2.7 Livelihood strategies following the S-LVI and LVI-IPCC indices - 86 - 3.3 Conclusion - 92 - 4 Agroforestry systems and agricultural droughts - 95 - 4.1 Results - 96 - 4.1.1 Farmers’ experience and perception on climate variability and agricultural droughts - 96 - 4.1.2 Agricultural droughts in the farm household systems - 97 - 4.1.3 Farming forestry systems and land-use decision-making - 102 - 4.1.4 Influence of trees in the soil moisture and yield - 104 - 4.2 Discussion - 110 - 4.2.1 Climate change and agricultural droughts - 110 - 4.2.2 Farm forestry systems and land-use decision-making - 115 - 4.2.3 Influence of trees in the soil moisture and yield - 117 - 4.3 Conclusion - 121 - 5 Modeling small farm production systems: optimization of resource allocation - 123 - 5.1 Methodology - 124 - 5.1.1 Optimization Model - 126 - 5.1.2 Plan of optimization - 128 - 5.1.3 Production systems - 131 - 5.1.4 Constraints - 134 - 5.2 Results - 138 - 5.2.1 Model - 138 - 5.2.2 Interest rates - 142 - 5.2.3 Sensitivity analyses - 146 - 5.3 Discussion - 151 - 5.3.1 Cash flows - 151 - 5.3.2 Model outcomes - 152 - 5.3.3 Interest rates - 155 - 5.3.4 Sensitivity analyses - 159 - 5.4 Conclusion - 169 - 6 Synthesis - 171 - 6.1 Lessons learned - 171 - 6.1.1 Research step I - 172 - 6.1.2 Research step II - 175 - 6.1.3 Research step III - 176 - 6.2 Conclusions & outlook - 179 - 6.2.1 General conclusions - 179 - 6.2.2 Outlook - 181 - References - 185 - Appendix - 199 - / Hochgebirgsregionen einschließlich der Andenregion sind gegenüber dem Klimawandel sehr empfindlich. Die in den zentralen Anden von Peru lebenden Bauern sind mehr und mehr den Auswirkungen durch Klimaschwankungen ausgesetzt. Diese transdisziplinäre Forschung nutzt Feldlabore, die das System der landwirtschaftlichen Bewirtschaftung und Ansätze zur nachhaltigen Lebensunterhaltssicherung kombinieren, um soziale, ökologische und ökonomische Erhebungen durchzuführen, so dass nachhaltige Livelihood-Strategien für die Kleinbauern in den Anden aufgezeigt werden können. Der erste Forschungsschritt untersucht und charakterisiert die bäuerlichen Haushaltssysteme, die durch ihre biophysikalischen und sozioökonomischen Kontexte beeinflusst sind. Hierfür wurden zwei Vulnerabilitätsindizes herausgearbeitet, die Klimavariabilität und die fünf Güter des Sustainable Livelihood-Konzepts im Fokus haben, sowie die drei Vulnerabilitätskomponenten des Intergovernmental Panel on Climate Change (IPCC). Diese Indizes decken die hochgradige Sensitivität für die meisten Gemeinden auf, aufgrund des schlechten Gesundheitszustandes sowie dem Mangel an Infrastruktur und öffentlichen Dienstleistungen. Die Fähigkeit der Bauern damit umzugehen, ist zumeist begrenzt durch eine geringe Diversität und fehlende Organisation auf den Farmen. Anschließend werden nachhaltige Livelihood-Strategien aufgezeigt. Diese umfassen die Intensivierung der Arbeit in der Landwirtschaft und der Arbeitskraft außerhalb der Landwirtschaft für Gemeinden im Flachland sowie besser erreichbare Gemeinden. In Hochlandgemeinden und Gemeinden die schwer zugänglich sind, ist temporäre Migration eine geläufige Bewältigungsstrategie. Nachhaltige Szenarien in diesen Gemeinden beinhalten eine höhere Anzahl an Diversifizierungsstrategien wie die Steigerung von Agro-Biodiversität und dem Anteil an baumbasierten Produktionssystemen. Forschungsschritt II untersucht lokale Strategien, um die landwirtschaftliche Dürre zu bewältigen und bewertet – mit Hilfe von Naturressourcenbewertungsverfahren – Agroforstsysteme als eine Alternative, um die negativen Auswirkungen der Trockenzeiten zu verringern. Beeinträchtigt durch zunehmende Niederschlagsschwankungen, identifizieren Bauern die Diversifizierung von landwirtschaftlichen und nicht-landwirtschaftlichen Aktivitäten als Anpassungsstrategie bei landwirtschaftliche Dürre, wodurch die Abhängigkeit vom Wetter und die Kovarianz zwischen den Aktivitäten für den Lebensunterhalt reduziert werden kann. Neben der Einführung resistenterer Kultur- und Weidepflanzen, ist die Einbeziehung von Bäumen in das System wünschenswert, aufgrund ihres positiven Einflusses auf die Bodenfeuchte und Erträge. Die Bodenfeuchte in agroforstwirtschaftlichen Systemen mit Eukalyptusbäumen ist während der beginnenden Feuchtperiode 20% höher als in landwirtschaftlichen Systemen. Die Unterschiede der Bodenfeuchte am Ende der Trockenzeit und bezüglich des Kartoffelertrags sind zwischen diesen Systemen nicht markant, obwohl eine Fläche, auf der keine Saat ausgebracht wurde, den landwirtschaftlichen Ertrag in Agroforstsystemen um 13 bis 17% mindert. Forschungsschritt III versucht die Effizienz der Ressourcenzuordnung in Farmhaushaltssystemen zu maximieren, indem ein Optimierungsmodell mit Hilfe der linearen Programmierung entwickelt wird. Diese ökonomische Erhebung unterstreicht die Notwendigkeit zusätzlicher nichtlandwirtschaftlicher Aktivitäten für ressourcenärmere Bauern. Bei Zinsraten unter 15% umfasst das Model baumbasierte Produktionssysteme als einen Teil der optimalen Lösung. Mit steigenden Zinsraten wird jedoch eine größere Bodenfläche dazu verwendet, um die Grundbedürfnisse der Haushalte zu decken und es stehen weniger Ressourcen für Aktivitäten zur Kapitalanhäufung wie Forstwirtschaft zur Verfügung. Die in das Modell involvierten Variationen zeigen, dass Weidesysteme sensibler auf Veränderungen des Produktionsausstoßes reagieren. Schwankungen bei den Löhnen der Farmer und Veränderungen der Baumpreise beeinträchtigen hingegen die optimalen Lösungen weniger. Dadurch sind die landwirtschaftlichen Systeme gegenüber Marktschwankungen weniger anfällig. Abschließend erweist sich, dass – für bestimmte Farmhaushaltssysteme im Untersuchungsgebiet – die Einbeziehung baumbasierter Systeme als nachhaltige und resiliente Livelihood-Strategie angesichts von Klimaschwankungen nützlich ist.:1 Introduction - 1 - 1.1 Introduction and justification - 1 - 1.2 Objectives and thesis statements - 2 - 1.3 Outline - 3 - 1.4 Definition of terms - 5 - 1.4.1 Vulnerability - 5 - 1.4.2 Resilience - 7 - 1.4.3 Agroforestry systems - 8 - 1.4.4 Farming system approach - 9 - 1.4.5 Farm household system - 10 - 1.4.6 Sustainable livelihood approach - 10 - 2 Framework and study site - 14 - 2.1 Theoretical framework - 14 - 2.2 Methodological framework - 18 - 2.2.1 Field laboratories - 18 - 2.2.2 Methods - 19 - 2.2.3 Methodology applied in research step I: Vulnerability in Achamayo - 21 - 2.2.4 Methodology applied in research step II: Agroforestry systems and agricultural droughts - 29 - 2.2.5 Methodology applied in research step III: Modeling small farm production systems - 33 - 2.2.6 Selection of case studies - 34 - 2.3 Study area - 35 - 2.3.1 Soils and topography - 35 - 2.3.2 Weather - 37 - 2.3.3 Agro-ecological zones and vegetation - 38 - 2.3.4 Climate change - 40 - 2.3.5 Socioeconomic characteristics - 42 - 2.3.6 Population - 43 - 2.3.7 External determinants - 71 - 2.4 Case studies - 47 - 2.4.1 Lowland communities (L) - 49 - 2.4.2 Middle access communities (M) - 50 - 2.4.3 Highland communities (H) - 51 - 3 Vulnerability in Achamayo - 53 - 3.1 Results - 53 - 3.1.1 Sustainable Livelihood Vulnerability Index (S-LVI) - 53 - 3.1.2 IPCC Livelihood Vulnerability Index (LVI-IPCC) - 68 - 3.2 Discussion - 71 - 3.2.1 Climate variability and extreme events - 71 - 3.2.2 Human capital - 71 - 3.2.3 Social capital - 71 - 3.2.4 Natural capital - 71 - 3.2.5 Physical capital - 71 - 3.2.6 Financial capital - 71 - 3.2.7 Livelihood strategies following the S-LVI and LVI-IPCC indices - 86 - 3.3 Conclusion - 92 - 4 Agroforestry systems and agricultural droughts - 95 - 4.1 Results - 96 - 4.1.1 Farmers’ experience and perception on climate variability and agricultural droughts - 96 - 4.1.2 Agricultural droughts in the farm household systems - 97 - 4.1.3 Farming forestry systems and land-use decision-making - 102 - 4.1.4 Influence of trees in the soil moisture and yield - 104 - 4.2 Discussion - 110 - 4.2.1 Climate change and agricultural droughts - 110 - 4.2.2 Farm forestry systems and land-use decision-making - 115 - 4.2.3 Influence of trees in the soil moisture and yield - 117 - 4.3 Conclusion - 121 - 5 Modeling small farm production systems: optimization of resource allocation - 123 - 5.1 Methodology - 124 - 5.1.1 Optimization Model - 126 - 5.1.2 Plan of optimization - 128 - 5.1.3 Production systems - 131 - 5.1.4 Constraints - 134 - 5.2 Results - 138 - 5.2.1 Model - 138 - 5.2.2 Interest rates - 142 - 5.2.3 Sensitivity analyses - 146 - 5.3 Discussion - 151 - 5.3.1 Cash flows - 151 - 5.3.2 Model outcomes - 152 - 5.3.3 Interest rates - 155 - 5.3.4 Sensitivity analyses - 159 - 5.4 Conclusion - 169 - 6 Synthesis - 171 - 6.1 Lessons learned - 171 - 6.1.1 Research step I - 172 - 6.1.2 Research step II - 175 - 6.1.3 Research step III - 176 - 6.2 Conclusions & outlook - 179 - 6.2.1 General conclusions - 179 - 6.2.2 Outlook - 181 - References - 185 - Appendix - 199 - / Regiões altomontanas, incluindo os Andes são extremamente sensíveis aos impactos das mudanças climáticas. Pequenos agricultores da região central dos Andes Peruanos estão progressivamente sendo expostos aos impactos das variações climáticas. A presente investigação transdisciplinar utiliza “field laboratories”, combinando os enfoques de sistemas rurais e dos meios de subsistência sustentáveis, visando uma avaliação social, ecológica e financeira, com intuito de se identificar estratégias resilientes e sustentáveis para os pequenos agricultores Andinos. A primeira etapa do presente estudo investiga e caracteriza os sistemas rurais, influenciados por seus contextos biofísicos e socioeconômicos, para os quais foram elaborados dois índices de vulnerabilidade focados na variabilidade climática, nos recursos dos meios de vida (cinco capitais) e nos três componentes da vulnerabilidade do IPCC. Esses índices mostram as condições altamente sensíveis da maioria das comunidades, com más condições de saúde, acesso à infra-estrutura e serviços públicos. A capacidade de resposta dos pequenos agricultores é frequentemente limitada pela baixa diversificação de actividades na exploração agricola e falta de organização. Posteriormente, foram identificadas estratégias de subsitência sustentáveis. Estas incluem a intensificação tanto do trabalho rural, quanto do não-agrícola para as comunidades de terras baixas e mais acessíveis. Para as comunidades altomontanas e com menor acesso, a migração temporária é uma estratégia de enfrentamento comum. Cenários sustentáveis para essas comunidades incluem um incremento nas estratégias de diversificação p. ex. aumentando a agrobiodiversidade e a parcela dos sistemas de produção florestais. A segunda etapa da pesquisa explora estratégias locais para lidar com as secas agrícolas e investiga, por meio de métodos de avaliação de recursos naturais, sistemas agroflorestais como alternativa para reduzir os seus efeitos negativos. Afetado principalmente pelo aumento da variação da precipitação, os agricultores identificam a diversificação tanto no trabalho rural, quanto no não-agrícola, como estratégias adaptativas contra secas agrícolas que reduzam a dependência do clima e covariância entre atividades de subsitência. Entre a introdução de culturas e de pastagens de variedades mais resistentes, a incorporação de árvores em seu sistema é desejada por conta da sua influência positiva na umidade do solo e no rendimento das culturas. A umidade do solo em sistemas agroflorestais com árvores de eucalipto é de 10-20% maior do que em sistemas agrícolas durante o início da estação chuvosa. As diferenças na umidade do solo durante o final da estação seca e na produtividade da batata não são evidentes entre estes dois sistemas. Apesar disso, o espaço sem semeadura deixado em sistemas agroflorestais reduziu a produção agrícola em 13-17%. A terceira etapa da presente investigação visa maximizar a eficiência da alocação de recursos em sistemas agrícolas familiares por meio do desenvolvimento de um modelo de otimização de programação linear. Esta avaliação financeira sustenta a necessidade de atividades não-agrícolas adicionais para agricultores com recursos escassos. Ademais, sob taxas de juros abaixo de 15%, o modelo inclui sistemas de produção florestais como parte da solução ideal. Contudo, com o aumento das taxas de juros, uma parcela maior da propriedade é usada para garantir as necessidades básicas, e portanto, menos recursos do agregado familiar estão disponíveis para atividades de acumulação de capital, tais como a silvicultura. Variações introduzidas no modelo mostram que sistemas de pastagem são mais sensíveis a mudanças nas condições de produção. Ademais, variaçãoes nos salários dos trabalhadores agrícolas e nos preços de árvores afetam menos as soluções ótimas, tornando os sistemas agrícolas menos sensíveis a estas mudanças do mercado. Por fim, a incorporação de sistemas florestais provaram ser uma estratégia de subsistência sustentável e resiliente contra a variação climática para determinados sistemas de agricultura familiar da área de estudo.:1 Introduction - 1 - 1.1 Introduction and justification - 1 - 1.2 Objectives and thesis statements - 2 - 1.3 Outline - 3 - 1.4 Definition of terms - 5 - 1.4.1 Vulnerability - 5 - 1.4.2 Resilience - 7 - 1.4.3 Agroforestry systems - 8 - 1.4.4 Farming system approach - 9 - 1.4.5 Farm household system - 10 - 1.4.6 Sustainable livelihood approach - 10 - 2 Framework and study site - 14 - 2.1 Theoretical framework - 14 - 2.2 Methodological framework - 18 - 2.2.1 Field laboratories - 18 - 2.2.2 Methods - 19 - 2.2.3 Methodology applied in research step I: Vulnerability in Achamayo - 21 - 2.2.4 Methodology applied in research step II: Agroforestry systems and agricultural droughts - 29 - 2.2.5 Methodology applied in research step III: Modeling small farm production systems - 33 - 2.2.6 Selection of case studies - 34 - 2.3 Study area - 35 - 2.3.1 Soils and topography - 35 - 2.3.2 Weather - 37 - 2.3.3 Agro-ecological zones and vegetation - 38 - 2.3.4 Climate change - 40 - 2.3.5 Socioeconomic characteristics - 42 - 2.3.6 Population - 43 - 2.3.7 External determinants - 71 - 2.4 Case studies - 47 - 2.4.1 Lowland communities (L) - 49 - 2.4.2 Middle access communities (M) - 50 - 2.4.3 Highland communities (H) - 51 - 3 Vulnerability in Achamayo - 53 - 3.1 Results - 53 - 3.1.1 Sustainable Livelihood Vulnerability Index (S-LVI) - 53 - 3.1.2 IPCC Livelihood Vulnerability Index (LVI-IPCC) - 68 - 3.2 Discussion - 71 - 3.2.1 Climate variability and extreme events - 71 - 3.2.2 Human capital - 71 - 3.2.3 Social capital - 71 - 3.2.4 Natural capital - 71 - 3.2.5 Physical capital - 71 - 3.2.6 Financial capital - 71 - 3.2.7 Livelihood strategies following the S-LVI and LVI-IPCC indices - 86 - 3.3 Conclusion - 92 - 4 Agroforestry systems and agricultural droughts - 95 - 4.1 Results - 96 - 4.1.1 Farmers’ experience and perception on climate variability and agricultural droughts - 96 - 4.1.2 Agricultural droughts in the farm household systems - 97 - 4.1.3 Farming forestry systems and land-use decision-making - 102 - 4.1.4 Influence of trees in the soil moisture and yield - 104 - 4.2 Discussion - 110 - 4.2.1 Climate change and agricultural droughts - 110 - 4.2.2 Farm forestry systems and land-use decision-making - 115 - 4.2.3 Influence of trees in the soil moisture and yield - 117 - 4.3 Conclusion - 121 - 5 Modeling small farm production systems: optimization of resource allocation - 123 - 5.1 Methodology - 124 - 5.1.1 Optimization Model - 126 - 5.1.2 Plan of optimization - 128 - 5.1.3 Production systems - 131 - 5.1.4 Constraints - 134 - 5.2 Results - 138 - 5.2.1 Model - 138 - 5.2.2 Interest rates - 142 - 5.2.3 Sensitivity analyses - 146 - 5.3 Discussion - 151 - 5.3.1 Cash flows - 151 - 5.3.2 Model outcomes - 152 - 5.3.3 Interest rates - 155 - 5.3.4 Sensitivity analyses - 159 - 5.4 Conclusion - 169 - 6 Synthesis - 171 - 6.1 Lessons learned - 171 - 6.1.1 Research step I - 172 - 6.1.2 Research step II - 175 - 6.1.3 Research step III - 176 - 6.2 Conclusions & outlook - 179 - 6.2.1 General conclusions - 179 - 6.2.2 Outlook - 181 - References - 185 - Appendix - 199 -