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La relación entre el crimen y ciclo económico en México: un enfoque espacial
Ricardo Masahiro Solis Ichien (2021)
En este trabajo se analiza la influencia del ciclo económico sobre los niveles de crimen en México desde el marco conceptual de la teoría de Cantor y Land (1985), los cuales distinguen el efecto de oportunidad y motivación criminal. Se presenta el análisis para el caso mexicano y se evalúa empíricamente el modelo de Cantor y Land para las 32 entidades federativas con datos trimestrales del 2010 a 2019. Utilizando un modelo econométrico que incorpora la interacción espacial en la tasa de crímenes y en el término de error, así como su rezago temporal, al que se le denomina como modelo espacial autorregresivo con errores autorregresivos de orden 1 (SARAR (1,1)) dinámico, se evalúa el modelo para siete tipos de crímenes: homicidios dolosos, secuestros, extorsiones, robo a negocios, robo a casa-habitación, robo de vehículo y robo a transeúnte. Los resultados muestran que se verifica la teoría de Cantor y Land (1985) para cuatro de los siete tipos de delitos. Los robos analizados se caracterizan como robos de necesidad y robos de especialización. Se encuentra evidencia del comportamiento espacial de la actividad criminal donde algunos delitos tienen un efecto regional y otros se concentran.
Master thesis
Crime -- Effect of business cycles on -- Mexico -- Econometric models. Violence -- Effect of business cycles on -- Mexico -- Econometric models. CIENCIAS SOCIALES CIENCIAS SOCIALES
Alonso Darío Pizarro Lagunas (2021)
La irrupción de COVID-19 en el escenario mundial no sólo se convirtió en una crisis de salud pública sino una crisis económica que tuvo efectos heterogéneos en muchos países en el mundo. En particular, muchas economías experimentaron una fuerte contracción en su producto interno bruto debido en gran parte a las medidas de distanciamiento social y restricción de actividades no esenciales que los países adoptaron para proteger la salud de sus habitantes. Esto afectó al sector eléctrico, ya que muchas industrias disminuyeron su consumo energético. En México, las pequeñas y medianas empresas representan una parte importante del consumo eléctrico de la industria. En este sentido, tomando una muestra representativa de pequeños y medianos establecimientos de la Zona Metropolitana de Aguascalientes estudiamos el comportamiento del consumo eléctrico de estas empresas notando que hubo caídas significativas del consumo eléctrico cuando irrumpió la pandemia en marzo de 2020 en la ZMA. Además, notamos que la caída en consumo eléctrico fue más pronunciada para establecimientos en el sector de servicios que establecimientos en el sector de comercios durante la pandemia. Además, se concluye que las disminuciones en la jornada laboral jugaron un papel importante en esta contracción, mientras que las medidas como digitalización y reducción de empleados no estuvieron relacionadas con la variación de consumo eléctrico en estos pequeños y medianos establecimientos.
Master thesis
Small business -- Energy consumption -- Effect of COVID-19 Pandemic, 2020- on -- Aguascalientes (Mexico) -- Econometric models. COVID-19 Pandemic, 2020- -- Aguascalientes (Mexico) -- Economic aspects. CIENCIAS SOCIALES CIENCIAS SOCIALES
Kindie Tesfaye Dereje Ademe Enyew Adgo (2023)
This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.
Article
Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS
Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022)
Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to
the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.
Article
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding
Maize seed aid and seed systems development: Opportunities for synergies in Uganda
Jason Donovan Rachel Voss Pieter Rutsaert (2024)
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.
Article
Seed Business Varietal Turnover Seed Aid CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SEED SYSTEMS SOCIAL INCLUSION MAIZE
Martin van Ittersum (2023)
Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.
Article
Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY
Hari Sankar Nayak C.M. Parihar Shankar Lal Jat ML JAT Ahmed Abdallah (2022)
Article
Non-Linear Growth Model Nitrogen Remobilization Right Placement Precision Nitrogen Management CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GROWTH MODELS NITROGEN NUTRIENT MANAGEMENT
Marlee Labroo Jeffrey Endelman Dorcus Gemenet Christian Werner Robert Gaynor GIOVANNY COVARRUBIAS-PAZARAN (2023)
Article
Reciprocal Recurrent Selection Clonal Diploids CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DIPLOIDY BREEDING HETEROSIS STOCHASTIC MODELS
Roberto Fritsche-Neto Marlee Labroo (2024)
Article
Genomic Prediction Reciprocal Recurrent Selection Heterotic Pools CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA STOCHASTIC MODELS RICE HYBRIDS GENETIC IMPROVEMENT GENETIC GAIN BREEDING PROGRAMMES
Timothy Joseph Krupnik Jeroen Groot (2024)
We investigated alternative cropping and feeding options for large (>10 cows), medium (5–10 cows) and small (≤4 cows) mixed crop – livestock farm types, to enhance economic and environmental performance in Jhenaidha and Meherpur districts – locations with increasing dairy production – in south western Bangladesh. Following focus group discussions with farmers on constraints and opportunities, we collected baseline data from one representative farm from each farm size class per district (six in total) to parameterize the whole-farm model FarmDESIGN. The six modelled farms were subjected to Pareto-based multi-objective (differential evolution algorithm) optimization to generate alternative dairy farm and fodder configurations. The objectives were to maximize farm profit, soil organic matter balance, and feed self-reliance, in addition to minimizing feed costs and soil nitrogen losses as indicators of sustainability. The cropped areas of the six baseline farms ranged from 0.6 to 4.0 ha and milk production per cow was between 1,640 and 3,560 kg year−1. Feed self-reliance was low (17%–57%) and soil N losses were high (74–342 kg ha−1 year−1). Subsequent trade-off analysis showed that increasing profit and soil organic matter balance was associated with higher risks of N losses. However, we found opportunities to improve economic and environmental performance simultaneously. Feed self-reliance could be increased by intensifying cropping and substituting fallow periods with appropriate fodder crops. For the farm type with the largest opportunity space and room to manoeuvre, we identified four strategies. Three strategies could be economically and environmentally benign, showing different opportunities for farm development with locally available resources.
Article
Ruminant Feed Pareto-Based Optimization Farm Bioeconomic Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUMINANT FEEDING BIOECONOMIC MODELS MIXED CROPPING FARMS LIVESTOCK