CCAFS Outcome Synthesis Report:

Mathieu Ouédraogo John Recha Maren Radeny Paresh Shirsath Peter Läderach Osana Bonilla-Findji (2021, [Documento de trabajo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE-SMART AGRICULTURE INVESTMENT FARMING SYSTEMS CLIMATE CHANGE

Maize seed aid and seed systems development: Opportunities for synergies in Uganda

Jason Donovan Rachel Voss Pieter Rutsaert (2024, [Artículo])

In the name of food security, governments and NGOs purchase large volumes of maize seed in non-relief situations to provide at reduced or no cost to producers. At the same time, efforts to build formal maize seed systems have been frustrated by slow turnover rates – the dominance of older seed products in the market over newer, higher performing ones. Under certain conditions, governments and NGO seed aid purchases can support formal seed systems development in three ways: i) support increased producer awareness of new products, ii) support local private seed industry development, and iii) advance equity goals by targeting aid to the most vulnerable of producers who lack the capacity to purchase seeds. This study explores the objectives and activities of seed aid programmes in Uganda and their interactions with the maize seed sector. We draw insights from interviews with representatives of seed companies, NGOs and government agencies, as well as focus group discussions with producers. The findings indicated that seed aid programme objectives are largely disconnected from broader seed systems development goals. There is little evidence of public-private collaboration in design of these programmes. Better designed programs have the potential to align with varietal turnover objectives, commercial sector development and targeting of underserved markets could promote equity and ‘crowd in’ demand.

Seed Business Varietal Turnover Seed Aid CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SEED SYSTEMS SOCIAL INCLUSION MAIZE

Core partners kick-off meeting─Ukama Ustawi (UU): Diversification for Resilient Agrifood systems in East and Southern Africa (ESA)

Evan Girvetz Christian Thierfelder Iddo Dror (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE FOOD SYSTEMS DIVERSIFICATION RESILIENCE

MLN disease diagnostics, MLN disease-free seed production and MLN disease management

Suresh L.M. (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DISEASES DISEASE MANAGEMENT SEED PRODUCTION MAIZE NECROSIS YIELD LOSSES ECONOMIC IMPACT SURVEILLANCE SYSTEMS TRAINING

Redesigning crop varieties to win the race between climate change and food security

Kevin Pixley Jill Cairns Santiago Lopez-Ridaura Chris Ojiewo Baloua Nébié Godfrey Asea Biswanath Das Benoit Joseph Batieno Clare Mukankusi Sarah Hearne Kanwarpal Dhugga Sieglinde Snapp Ernesto Adair Zepeda Villarreal (2023, [Artículo])

Crop Breeding Expert Survey CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE CROPPING SYSTEMS FOOD SECURITY CROPS

Review of Nationally Determined Contributions (NCD) of Kenya from the perspective of food systems

Tek Sapkota (2023, [Documento de trabajo])

Agriculture is one of the fundamental pillars of the 2022–2027 Bottom-up Economic Transformation Plan of the Government of Kenya for tackling complex domestic and global challenges. Kenya's food system is crucial for climate change mitigation and adaptation. Kenya has prioritized aspects of agriculture, food, and land use as critical sectors for reducing emissions towards achieving Vision 2030's transformation to a low-carbon, climate-resilient development pathway. Kenya's updated NDC, as well as supporting mitigation and adaptation technical analysis reports and other policy documents, has identified an ambitious set of agroecological transformative measures to promote climate-smart agriculture, regenerative approaches, and nature-positive solutions. Kenya is committed to implementing and updating its National Climate Change Action Plans (NCCAPs) to present and achieve the greenhouse gas (GHG) emission reduction targets and resilience outcomes that it has identified.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE GREENHOUSE GAS EMISSIONS FOOD SYSTEMS LAND USE CHANGE AGRICULTURE POLICIES DATA ANALYSIS FOOD WASTES

Challenging the climate change effects on agriculture: need business unusual

ML JAT (2021, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE FOOD SYSTEMS CONSERVATION AGRICULTURE SUSTAINABILITY INNOVATION

Control de sistemas usando aprendizaje de máquina

Systems control using machine learning

Jesús Martín Miguel Martínez (2023, [Tesis de maestría])

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.

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

Can I speak to the manager? The gender dynamics of decision-making in Kenyan maize plots

Rachel Voss Zachary Gitonga Jason Donovan Mariana Garcia-Medina Pauline Muindi (2023, [Artículo])

Gender and social inclusion efforts in agricultural development are focused on making uptake of agricultural technologies more equitable. Yet research looking at how gender relations influence technology uptake often assumes that men and women within a household make farm management decisions as individuals. Relatively little is understood about the dynamics of agricultural decision-making within dual-adult households where individuals’ management choices are likely influenced by others in the household. This study used vignettes to examine decision-making related to maize plot management in 698 dual-adult households in rural Kenya. The results indicated a high degree of joint management of maize plots (55%), although some management decisions—notably those related to purchased inputs—were slightly more likely to be controlled by men, while other decisions—including those related to hiring of labor and maize end uses—were more likely to be made by women. The prevalence of joint decision-making underscores the importance of ensuring that both men’s and women’s priorities and needs are reflected in design and marketing of interventions to support maize production, including those related to seed systems, farmer capacity building, and input delivery.

Intrahousehold Jointness CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENDER HOUSEHOLDS MAIZE SEED SYSTEMS DECISION MAKING

Editorial: Conservation agriculture: knowledge frontiers around the world

Stéphane Cordeau ML JAT Cameron Pittelkow Christian Thierfelder (2023, [Artículo])

No-Tillage Direct Seeding Cover Crops Crop Diversification CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COVER PLANTS DIVERSIFICATION CROPPING SYSTEMS DIRECT SOWING ZERO TILLAGE