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

Environmental Nanoparticles Reach Human Fetal Brains

LILIAN OFELIA CALDERON GARCIDUEÑAS ANGEL AUGUSTO PEREZ CALATAYUD ANGELICA GONZALEZ MACIEL RAFAEL REYNOSO ROBLES Héctor Gabriel Silva Pereyra Andrea Ramos Morales RICARDO TORRES JARDON Candelario de Jesús Soberanes Cerino Raúl Carrillo Esper JESÚS CARLOS BRIONES GARDUÑO Yazmin del Socorro Conde Gutiérrez (2022, [Artículo])

"Anthropogenic ultrafine particulate matter (UFPM) and industrial and natural nanoparticles (NPs) are ubiquitous. Normal term, preeclamptic, and postconceptional weeks(PCW) 8–15 human placentas and brains from polluted Mexican cities were analyzed by TEM and energy-dispersive X-ray spectroscopy. We documented NPs in maternal erythrocytes, early syncytiotrophoblast, Hofbauer cells, and fetal endothelium (ECs). Fetal ECs exhibited caveolar NP activity and widespread erythroblast contact. Brain ECs displayed micropodial extensions reaching luminal NP-loaded erythroblasts. Neurons and primitive glia displayed nuclear, organelle, and cytoplasmic NPs in both singles and conglomerates. Nanoscale Fe, Ti, and Al alloys, Hg, Cu, Ca, Sn, and Si were detected in placentas and fetal brains. Preeclamptic fetal blood NP vesicles are prospective neonate UFPM exposure biomarkers. NPs are reaching brain tissues at the early developmental PCW 8–15 stage, and NPs in maternal and fetal placental tissue compartments strongly suggests the placental barrier is not limiting the access of environmental NPs. Erythroblasts are the main early NP carriers to fetal tissues. The passage of UFPM/NPs from mothers to fetuses is documented and fingerprinting placental single particle composition could be useful for postnatal risk assessments. Fetal brain combustion and industrial NPs raise medical concerns about prenatal and postnatal health, including neurological and neurodegenerative lifelong consequences."

Environmental medicine Placental impairment Neurodevelopmental disorders Fetal brains Erythroblasts Preeclampsia Nanoparticles NPs extracellular vesicles Petrochemical pollution Villahermosa Tabasco BIOLOGÍA Y QUÍMICA QUÍMICA BIOQUÍMICA BIOQUÍMICA

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

Assessing the Response of Nematode Communities to Climate Change-Driven Warming: A Microcosm Experiment

RUTH GINGOLD WERMUTH (2013, [Artículo])

Biodiversity has diminished over the past decades with climate change being among the main responsible factors. One consequence of climate change is the increase in sea surface temperature, which, together with long exposure periods in intertidal areas, may exceed the tolerance level of benthic organisms. Benthic communities may suffer structural changes due to the loss of species or functional groups, putting ecological services at risk. In sandy beaches, free-living marine nematodes usually are the most abundant and diverse group of intertidal meiofauna, playing an important role in the benthic food web. While apparently many functionally similar nematode species co-exist temporally and spatially, experimental results on selected bacterivore species suggest no functional overlap, but rather an idiosyncratic contribution to ecosystem functioning. However, we hypothesize that functional redundancy is more likely to observe when taking into account the entire diversity of natural assemblages. We conducted a microcosm experiment with two natural communities to assess their stress response to elevated temperature. The two communities differed in diversity (high [HD] vs. low [LD]) and environmental origin (harsh vs. moderate conditions). We assessed their stress resistance to the experimental treatment in terms of species and diversity changes, and their function in terms of abundance, biomass, and trophic diversity. According to the Insurance Hypothesis, we hypothesized that the HD community would cope better with the stressful treatment due to species functional overlap, whereas the LD community functioning would benefit from species better adapted to harsh conditions. Our results indicate no evidence of functional redundancy in the studied nematofaunal communities. The species loss was more prominent and size specific in the HD; large predators and omnivores were lost, which may have important consequences for the benthic food web. Yet, we found evidence for alternative diversity-ecosystem functioning relationships, such as the Rivets and the Idiosyncrasy Model. © 2013 Gingold et al.

aquaculture, article, bacterivore, benthos, biodiversity, biomass, climate, community dynamics, controlled study, ecosystem, environmental temperature, microcosm, nematode, nonhuman, population abundance, species diversity, species richness, taxonomy CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA