The contribution of strong and weak ties to resilience: The case of small-scale maize farming systems in Mexico

Tania Carolina Camacho Villa Ernesto Adair Zepeda Villarreal Julio Díaz-José Roberto Rendon-Medel Bram Govaerts (2023, [Artículo])

Social Network Analysis Farm Typologies Social Ties Strong Ties CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INNOVATION NETWORKS PERSISTENCE SOCIAL NETWORK ANALYSIS MAIZE FARMING SYSTEMS

Modeling spatiotemporal patterns of land use/land cover change in Central Malawi using a neural network model

Leah Mungai Joseph Messina Leo Zulu Jiaguo Qi Sieglinde Snapp (2022, [Artículo])

Multilayer Perceptrons CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE LAND USE POPULATION SATELLITE IMAGERY TEXTURE LAND COVER NEURAL NETWORKS REMOTE SENSING

Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])

Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES

Chapter 9. Genome-informed discovery of genes and framework of functional genes in wheat

awais rasheed Rudi Appels (2024, [Capítulo de libro])

Wheat Genomics KASP Markers Gene Discovery Functional Markers Gene Networks CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENOMICS SINGLE NUCLEOTIDE POLYMORPHISMS FUNCTIONAL GENOMICS

Monitoreo con drones en gráficas con viento dinámico

Jovanni Manuel López Elisea (2024, [Tesis de maestría])

108 páginas. Maestría en Optimización.

Dada una gráfica completa no dirigida, se desea recorrer un subconjunto de sus aristas usando una flotilla de drones. Los drones tienen baterías limitadas que pueden recargarse al regresar a la base y, en principio, el tiempo para recorrer una arista está en función de la distancia entre sus vértices. Sin embargo, ante la presencia de viento el tiempo de recorrer una arista puede depender del sentido en el que se haga. La dificultad del problema aumenta si además la intensidad del viento puede variar de un instante a otro. En esta tesis se aborda el problema anteriormente descrito para el caso particular en el que los vértices son puntos en el plano, el impacto del viento en los tiempos de recorrido de las aristas está relativamente acotado y el subconjunto de las aristas a recorrer inducen un árbol que abarca todos los vértices excepto la base de los drones. Dado que los drones operan simultáneamente y pueden recorrer distintas partes de la gráfica de manera independiente, se desea minimizar el tiempo que emplea el dron con el recorrido más tardado. Esta tesis presenta un modelo matemático para resolver el problema de manera exacta, así como tres heurísticas diferentes para obtener buenas soluciones factibles. La primera de estas heurísticas transforma una solución sin viento y sin batería en una solución con viento y batería. La segunda heurística es un algoritmo glotón sin comunicación entre los drones y la última heurística también es un algoritmo glotón, pero con comunicación entre los drones. Aunque el problema abordado resulta ser lo suficientemente difícil como para que su resolución exacta sea inviable en la práctica, las heurísticas diseñadas son fáciles de implementar y obtuvieron resultados razonables en un tiempo corto de cómputo.

Drone aircraft--Control systems. Drone aircraft--Mathematical models. Mathematical optimization. Heuristic programming. Dynamical systems. Graph theory. Micro vehículos aéreos. Optimización matemática. Programación heurística. Teoría de grafos. TL589.4 CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS INVESTIGACIÓN OPERATIVA DISTRIBUCIÓN Y TRANSPORTE

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

Difusión de cursos que la Fundación Carlos Slim ofrece en aprende.org

Cesar Petroli (2021, [Poster de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TRAINING AGRICULTURAL TRAINING SOCIAL NETWORKS TRAINING COURSES SUSTAINABLE AGRICULTURE

E. African spring wheat breeding pipeline and Network (CIMMYT-KALRO)

sridhar bhavani (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT BREEDING RESEARCH NETWORKS

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Sorghum value chain analysis in semi-arid Zimbabwe

Abbyssinia Mushunje Munyaradzi Junia Mutenje Charles Pfukwa (2019, [Artículo])

Small Scale Farmers Extension Networks CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRO-INDUSTRIAL SECTOR MARKETING MARGINS SORGHUM VALUE CHAINS