Analyzing antifragility among smallholder farmers in Bihar, India: An assessment of farmers' vulnerability and the strengths of positive deviants

Roos Adelhart Toorop Santiago Lopez-Ridaura ML JAT Deepak Bijarniya Jeroen Groot (2023)

Farmers around the world are increasingly vulnerable: climate variability is identified as the primary stressor, but unfavorable biophysical circumstances and disturbances in the socioeconomic domain (labor dynamics and price volatility) also affect farm management and production. To deal with these disturbances, adaptations are recognized as essential. Antifragility acknowledges that adaptations and volatility are inherent characteristics of complex systems and abandons the idea of returning to the pre-disturbance system state. Instead, antifragility recognizes that disturbances can trigger reorganization, enabling selection and removal of weaker system features and allowing the system to evolve toward a better state. In this study, we assessed the vulnerability of different types of smallholder farms in Bihar, India, and explored the scope for more antifragile farming systems that can 'bounce back better' after disturbances. Accumulation of stocks, creation of optionality (i.e., having multiple options for innovation) and strengthening of farmer autonomy were identified as criteria for antifragility. We had focus group discussions with in total 92 farmers and found that most expressed themselves to be vulnerable: they experienced challenges but had limited adaptive capacity to change their situation. They mostly made short-term decisions to cope with or mitigate urgent challenges but did not engage in strategic planning driven by longer-term objectives. Instead, they waited for governmental support to improve their livelihoods. Despite being confronted with similar challenges, four positive deviant farmers showed to be more antifragile: their diverse farming systems were abundant in stocks and optionality, and the farmers were distinguished in terms of their autonomy, competence, and connectedness to peers, the community, and markets. To support antifragility among regular farmers, adaptations at policy level may be required, for example, by shifting from a top-down toward a bottom-up adaptation and innovation regime where initiative and cooperation are encouraged. With a more autonomous orientation, farmers' intrinsic motivation is expected to increase, enabling transitions at the farm level. In this way, connected systems can be developed which are socioeconomically and biophysically adaptive. When practices, knowledge, and skills are continuously developed, an antifragile system with ample stocks and optionality may evolve over time.

Article

Autonomy Adaptive Capacity Smallholder Farmers CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA POLICIES SMALLHOLDERS AGRICULTURAL PRACTICES

Distance learning for farmers: Experience during the pandemic

Andrea Gardeazabal (2023)

In response to the COVID-19 pandemic's disruption of farmer training—a crucial component for enhancing the resilience and livelihoods of smallholder farmers—CIMMYT innovated educational solutions to sustain capacity building in agri-food systems. Addressing the challenges of limited mobile device access, poor internet connectivity, and digital illiteracy, CIMMYT implemented two pilot projects in Mexico. These projects facilitated distance learning for adult farmers in rural areas, employing both internet-based and non-internet methods. The non-internet approach utilized traditional media like print, while the internet-based approach leveraged WhatsApp for educational content delivery. Building on these experiences, CIMMYT expanded its offerings by creating micro -courses delivered through WhatsApp, hosted on the Co-LAB's new Learning Network platform, specifically targeting farmers. This paper delves into the various strategies, methods, and techniques adopted, documenting the learning outcomes, results, and key conclusions drawn from these innovative training initiatives.

Working paper

Distance Learning Digital Inclusion Innovative Training CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DISTANCE EDUCATION CAPACITY DEVELOPMENT METHODS COMMUNICATION TECHNOLOGY

Chapter 11. Genomic insights on global journeys of adaptive wheat genes that brought us to modern wheat

deepmala sehgal Laura Dixon Diego Pequeno Jose Crossa Alison Bentley Susanne Dreisigacker (2024)

Book part

Hexaploid Wheat Adaptive Genes Novel Genomic Regions Gene-Based Modeling Process-Based Modeling Global Food Security CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HEXAPLOIDY WHEAT QUANTITATIVE TRAIT LOCI MODELLING FOOD SECURITY

The water crisis in the south-central region of the Chihuahua State and the 1997 UN Convention

Jorge Arturo Salas Plata Mendoza Thelma J. Garcia (2022)

The present writing focuses on the water crisis in the south-central part of Chihuahua State in the year 2020. Recent literature points to the drought, excess demand for the vital liquid and overpopulation of this region, among other issues, as the causes of the emergency. This paper argues that the reasons mentioned above are not causes, but effects of an economic policy of capital valorization and accumulation, which go far beyond the carrying capacity of the ecosystems and their capacity to regulate the polluting processes. The obsolescence of the water treaties between Mexico and the US make it necessary to consider other alternatives such as the 1997 UN Convention on water.

Article

Artículo

Chihuahua water crisis hydro-agricultural crisis carrying capacity expansive growth 1997 UN Convention Ecological Economics crisis del agua crisis hidroagrícola capacidad de carga crecimiento expansivo Convención de la ONU de 1997 Economía Ecológica CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA

Antioxidant profile of hot and sweet pepper cultivars by two extraction methods

MERCEDES GEORGINA RAMIREZ ARAGON ENRIQUE TROYO DIEGUEZ Pablo Preciado Rangel Victoria Jared Borroel García Miguel García-Carrillo JOSE LUIS GARCIA HERNANDEZ (2022)

"Chili peppers are among the most important vegetables in the world. The demand of this fruit reveals a noticeable rapid increasing, which importance is mainly due to its nutraceutical composition. These fruits are rich in capsaicinoids, phenolic compounds, carotenoids, and others, including vitamins. In this study, a comparative evaluation between two extraction methods of bioactive compounds of fourteen chili pepper cultivars was performed. Two extraction methods for antioxidants, the time-solvent and the ultrasound were evaluated. The design of the experiment was completely randomized with three repetitions where variables evaluated were total phenolic compounds, flavonoids content, antioxidant capacity and capsaicin. Results showed that the phenolic compounds oscillated between 48.7 - 634.1 mg GAE/100 g dry weight (DW), the flavonoids content varied from 1 - 97 mg QE/100 g DW, the antioxidant activity from 65 - 348 µmol Trolox/g DW and the capsaicin content oscillated from 0.3 - 922 mg/100 g DW. The extraction method with higher values of bioactive compounds for each of the chili pepper types was the ultrasound for all the measured variables."

Article

Capsicum annuum, phenolics, flavonoids, capsaicin, ultrasound, antioxidant capacity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CIENCIAS AGRARIAS AGRONOMÍA PRODUCCIÓN DE CULTIVOS PRODUCCIÓN DE CULTIVOS

Control de sistemas usando aprendizaje de máquina

Systems control using machine learning

Jesús Martín Miguel Martínez (2023)

El aprendizaje por refuerzo es un paradigma del aprendizaje de máquina con un amplio desarrollo y una creciente demanda en aplicaciones que involucran toma de decisiones y control. Es un paradigma que permite el diseño de controladores que no dependen directamente del modelo que describe la dinámica del sistema. Esto es importante ya que en aplicaciones reales es frecuente que no se disponga de dichos modelos de manera precisa. Esta tesis tiene como objetivo implementar un controlador óptimo en tiempo discreto libre de modelo. La metodología elegida se basa en algoritmos de aprendizaje por refuerzo, enfocados en sistemas con espacios de estado y acción continuos a través de modelos discretos. Se utiliza el concepto de función de valor (Q-función y función V ) y la ecuación de Bellman para resolver el problema del regulador cuadrático lineal para un sistema mecánico masa-resorte-amortiguador, en casos donde se tiene conocimiento parcial y desconocimiento total del modelo. Para ambos casos las funciones de valor son definidas explícitamente por la estructura de un aproximador paramétrico, donde el vector de pesos del aproximador es sintonizado a través de un proceso iterativo de estimación de parámetros. Cuando se tiene conocimiento parcial de la dinámica se usa el método de aprendizaje por diferencias temporales en un entrenamiento episódico, que utiliza el esquema de mínimos cuadrados con mínimos cuadrados recursivos en la sintonización del crítico y descenso del gradiente en la sintonización del actor, el mejor resultado para este esquema es usando el algoritmo de iteración de valor para la solución de la ecuación de Bellman, con un resultado significativo en términos de precisión en comparación a los valores óptimos (función DLQR). Cuando se tiene desconocimiento de la dinámica se usa el algoritmo Q-learning en entrenamiento continuo, con el esquema de mínimos cuadrados con mínimos cuadrados recursivos y el esquema de mínimos cuadrados con descenso del gradiente. Ambos esquemas usan el algoritmo de iteración de política para la solución de la ecuación de Bellman, y se obtienen resultados de aproximadamente 0.001 en la medición del error cuadrático medio. Se realiza una prueba de adaptabilidad considerando variaciones que puedan suceder en los parámetros de la planta, siendo el esquema de mínimos cuadrados con mínimos cuadrados recursivos el que tiene los mejores resultados, reduciendo significativamente ...

Reinforcement learning is a machine learning paradigm with extensive development and growing demand in decision-making and control applications. This technique allows the design of controllers that do not directly depend on the model describing the system dynamics. It is useful in real-world applications, where accurate models are often unavailable. The objective of this work is to implement a modelfree discrete-time optimal controller. Through discrete models, we implemented reinforcement learning algorithms focused on systems with continuous state and action spaces. The concepts of value-function, Q-function, V -function, and the Bellman equation are employed to solve the linear quadratic regulator problem for a mass-spring-damper system in a partially known and utterly unknown model. For both cases, the value functions are explicitly defined by a parametric approximator’s structure, where the weight vector is tuned through an iterative parameter estimation process. When partial knowledge of the dynamics is available, the temporal difference learning method is used under episodic training, utilizing the least squares with a recursive least squares scheme for tuning the critic and gradient descent for the actor´s tuning. The best result for this scheme is achieved using the value iteration algorithm for solving the Bellman equation, yielding significant improvements in approximating the optimal values (DLQR function). When the dynamics are entirely unknown, the Q-learning algorithm is employed in continuous training, employing the least squares with recursive least squares and the gradient descent schemes. Both schemes use the policy iteration algorithm to solve the Bellman equation, and the system’s response using the obtained values was compared to the one using the theoretical optimal values, yielding approximately zero mean squared error between them. An adaptability test is conducted considering variations that may occur in plant parameters, with the least squares with recursive least squares scheme yielding the best results, significantly reducing the number of iterations required for convergence to optimal values.

Master thesis

aprendizaje por refuerzo, control óptimo, control adaptativo, sistemas mecánicos, libre de modelo, dinámica totalmente desconocida, aproximación paramétrica, Q-learning, iteración de política reinforcement learning, optimal control, adaptive control, mechanical systems, modelfree, utterly unknown dynamics, parametric approximation, Q-learning, policy iteration INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES INTELIGENCIA ARTIFICIAL INTELIGENCIA ARTIFICIAL