Smallholder farmers’ willingness to pay for two-wheel tractor-based mechanisation services in Zambia and Zimbabwe

Hambulo Ngoma Paswel Marenya Adane Tufa Lovemore  Chipindu Md Abdul Matin Christian Thierfelder (2023, [Objeto de congreso])

Two-Wheel Tractors CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MECHANIZATION WHEELED TRACTORS WILLINGNESS TO PAY ANIMAL POWER

Facilitating mechanization for sustainable intensification using service provider models: lessons from the global south

Md Abdul Matin (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SUSTAINABLE INTENSIFICATION MECHANIZATION ENTERPRISES FARMERS

Efficacy of pre- and post-emergence herbicide combinations on weed control in no-till mechanically transplanted rice

PRATEEK MADHAB BHATTACHARYA Apurba Chowdhury Tapamay Dhar Md. Saiful Islam Alison Laing Mahesh Gathala (2022, [Artículo])

Mechanized Transplanted Rice Weed Biomass Weed Density Weed Control Efficiency CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HERBICIDES WEED CONTROL RICE ZERO TILLAGE MECHANIZATION

Synthesis of hollow carbon spheres by chemical activation of carbon nanoparticles for their use in electrochemical capacitor

Cesar Eduardo Sanchez Rodriguez EDUARDO TOVAR MARTINEZ MARISOL REYES REYES Luis Felipe Cházaro Ruiz ROMAN LOPEZ SANDOVAL (2022, [Artículo])

"Naphthalene combustion has been used to synthesize grams per hour of solid carbon spheres (CS). The carbon soot was activated by acid treatment consisting in a mixture of HNO3 and H2SO4 (1/3 v/v) to produce hollow carbon spheres (HCS). The effect of two concentrations of CSs (5 and 10 mg mL−1) in the acid mixture, on the physicochemical properties of the activated HCSs was studied. The HSCs were subjected to a thermal treatment to increase their graphitization to enhance their electrical conductivity. High-resolution transmission electron microscopy confirmed the formation of HCSs due to the acid treatment whereas FTIR spectra showed that the chemical activation produced functional groups on the carbon spheres surface and the heat treatment effect to remove some of them as well. A specific surface area of 300 m2 g−1 and a large density of micropores for the acid-treated CSs as well as the heat-treated CSs were estimated by analysis of N2 adsorption-desorption isotherms. A specific capacitance 70 F g−1 was calculated by cyclic voltammetry of the acid and thermally treated HCSs at 5 mV s−1, for both CS concentrations, indicating the possibility of synthesizing these HCSs using a simple method in large quantities for their use in electrochemical capacitors."

Physicochemical properties Carbon nanoparticles Chemical activation Electrochemical capacitor BIOLOGÍA Y QUÍMICA QUÍMICA QUÍMICA

Stability of FeVO4-II under Pressure: A First-Principles Study

PRICILA BETBIRAI ROMERO VAZQUEZ SINHUE LOPEZ MORENO Daniel Errandonea (2022, [Artículo])

"In this work, we report first-principles calculations to study FeVO4 in the CrVO4-type (phase II) structure under pressure. Total-energy calculations were performed in order to analyze the structural parameters, the electronic, elastic, mechanical, and vibrational properties of FeVO4-II up to 9.6 GPa for the first time. We found a good agreement in the structural parameters with the experimental results available in the literature. The electronic structure analysis was complemented with results obtained from the Laplacian of the charge density at the bond critical points within the Quantum Theory of Atoms in Molecules methodology. Our findings from the elastic, mechanic, and vibrational properties were correlated to determine the elastic and dynamic stability of FeVO4-II under pressure. Calculations suggest that beyond the maximum pressure covered by our study, this phase could undergo a phase transition to a wolframite-type structure, such as in CrVO4 and InVO4."

FeVO4 under pressure CrVO4-type structure First-principles Mechanical properties Vibrational properties Electronic properties CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA FÍSICA FÍSICA DEL ESTADO SÓLIDO CRISTALOGRAFÍA CRISTALOGRAFÍA

Assessment of resistance mechanisms to fall armyworm, Spodoptera frugiperda in tropical maize inbred lines

Anani Bruce Dan Makumbi Yoseph Beyene Prasanna Boddupalli Paul André Calatayud (2023, [Artículo])

Native Resistance Antixenosis Maize Inbred Lines CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PEST RESISTANCE ANTIBIOSIS DEFENCE MECHANISMS FALL ARMYWORMS MAIZE INBRED LINES

Tensile behavior of 3D printed polylactic acid (PLA) based composites reinforced with natural fiber

Eliana M Agaliotis BALTAZAR DAVID AKE CONCHA ALEJANDRO MAY PAT Juan Pablo Morales Arias Celina Bernal Alex Valadez González Pedro Jesús Herrera Franco Gwenaelle Proust JUAN FRANCISCO KOH DZUL José Gonzalo Carrillo Baeza Emmanuel Alejandro Flores Johnson (2022, [Artículo])

Natural fiber-reinforced composite (NFRC) filaments for 3D printing were fabricated using polylactic acid (PLA) reinforced with 1–5 wt% henequen flour comprising particles with sizes between 90–250 μm. The flour was obtained from natural henequen fibers. NFRCs and pristine PLA specimens were printed with a 0° raster angle for tension tests. The results showed that the NFRCs’ measured density, porosity, and degree of crystallinity increased with flour content. The tensile tests showed that the NFRC Young’s modulus was lower than that of the printed pristine PLA. For 1 wt% flour content, the NFRCs’ maximum stress and strain to failure were higher than those of the printed PLA, which was attributed to the henequen fibers acting as reinforcement and delaying crack growth. However, for 2 wt% and higher flour contents, the NFRCs’ maximum stress was lower than that of the printed PLA. Microscopic characterization after testing showed an increase in voids and defects, with the increase in flour content attributed to particle agglomeration. For 1 wt% flour content, the NFRCs were also printed with raster angles of ±45° and 90° for comparison; the highest tensile properties were obtained with a 0° raster angle. Finally, adding 3 wt% content of maleic anhydride to the NFRC with 1 wt% flour content slightly increased the maximum stress. The results presented herein warrant further research to fully understand the mechanical properties of printed NFRCs made of PLA reinforced with natural henequen fibers. © 2022 by the authors.

POLYLACTIC ACID (PLA) NATURAL FIBER HENEQUEN FIBER NATURAL FIBER REINFORCED COMPOSITE (NFRC) ADDITIVE MANUFACTURING 3D PRINTING MECHANICAL PROPERTY INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE MATERIALES PROPIEDADES DE LOS MATERIALES PROPIEDADES DE LOS MATERIALES

Transmission, localization, and infectivity of seedborne maize chlorotic mottle virus

Suresh L.M. Pierce Paul Margaret Redinbaugh (2023, [Artículo])

Maize Lethal Necrosis Transmission Mechanisms CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE VIROSES SEEDS INOCULATION DISEASE MANAGEMENT

Modeling the growth, yield and N dynamics of wheat for decoding the tillage and nitrogen nexus in 8-years long-term conservation agriculture based maize-wheat system

C.M. Parihar Dipaka Ranjan Sena Prakash Chand Ghasal Shankar Lal Jat Yashpal Singh Saharawat Mahesh Gathala Upendra Singh Hari Sankar Nayak (2024, [Artículo])

Context: Agricultural field experiments are costly and time-consuming, and their site-specific nature limits their ability to capture spatial and temporal variability. This hinders the transfer of crop management information across different locations, impeding effective agricultural decision-making. Further, accurate estimates of the benefits and risks of alternative crop and nutrient management options are crucial for effective decision-making in agriculture. Objective: The objective of this study was to utilize the Crop Environment Resource Synthesis CERES-Wheat model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based wheat system. The study aimed to calibrate the model using data from a field experiment conducted during the 2019-20-2020-21 growing seasons and evaluation it with independent data from the year 2021–22. Method: Crop simulation models, such as the Crop Environment Resource Synthesis CERES-Wheat (DSSAT v 4.8), may provide valuable insights into crop growth and nitrogen dynamics, enabling decision makers to understand and manage production risk more effectively. Therefore, the present study employed the CERES-Wheat (DSSAT v 4.8) model and calibrated it using field data, including plant phenological phases, leaf area index, aboveground biomass, and grain yield from the 2019-20-2020-21 growing seasons. An independent dataset from the year 2021–22 was used for model evaluation. The model was used to investigate the relationship between growing degree days (GDD), temperature, nitrate and ammonical concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on wheat yields. The experimental site is situated at ICAR-Indian Agricultural Research Institute (IARI), New Delhi, representing Indian Trans-Gangetic Plains Zone (28o 40’N latitude, 77o 11’E longitude and an altitude of 228 m above sea level). The treatments consist of four nitrogen management options, viz., N0 (zero nitrogen), N150 (150 kg N ha−1 through urea), GS (Green seeker based urea application) and USG (urea super granules @150 kg N ha−1) in two contrasting tillage systems, i.e., CA-based zero tillage (ZT) and conventional tillage (CT). Result: The outcomes exhibited favorable agreement between the model’s simulations and the observed data for crop phenology (With less than 2 days variation in 50% onset of flowering), grain and biomass yield (Root mean square error; RMSE 336 kg ha−1 and 649 kg ha−1, respectively), and leaf area index (LAI) (RMSE 0.28 & normalized RMSE; nRMSE 6.69%). The model effectively captured the nitrate-N (NO3−-N) dynamics in the soil profile, exhibiting a remarkable concordance with observed data, as evident from its low RMSE = 12.39 kg ha−1 and nRMSE = 13.69%. Moreover, as it successfully simulated the N balance in the production system, the nitrate leaching and ammonia volatilization pattern as described by the model are highly useful to understand these critical phenomena under both conventional tillage (CT) and CA-based Zero Tillage (ZT) treatments. Conclusion: The study concludes that the DSSAT-CERES-Wheat model has significant potential to assess the impacts of tillage and nitrogen management practices on crop growth, yield, and soil nitrogen dynamics in the western Indo-Gangetic Plains (IGP) region. By providing reliable forecasts within the growing season, this modeling approach can facilitate better planning and more efficient resource management. Future implications: The successful implementation of the DSSAT-CERES-Wheat model in this study highlights its applicability in assessing crop performance and soil dynamics. Future research should focus on expanding the model’s capabilities by reducing its sensitivity to initial soil nitrogen levels to refine its predictions further. Moreover, the model’s integration with decision support systems and real-time data can enhance its usefulness in aiding agricultural decision-making and supporting sustainable crop management practices.

Nitrogen Dynamics Mechanistic Crop Growth Models Crop Simulation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA NITROGEN CONSERVATION AGRICULTURE WHEAT MAIZE CROP GROWTH RATE SIMULATION MODELS

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