El impacto de la violencia en los flujos migratorios procedentes de Guatemala

Thania Berenice Hernández Alarcón (2021, [Tesis de maestría])

Con este trabajo buscamos entender cómo la violencia en el lugar de origen de una persona puede motivar su decisión de migrar. La aproximación teórica al fenómeno de la migración sugiere que la provisión relativa de bienes públicos es un factor en el análisis costo beneficio mediante el cual el agente evalúa la rentabilidad de la migración. Desde esta perspectiva, la búsqueda de beneficios pecuniarios, como un mayor salario, es compatible con la búsqueda de beneficios no pecuniarios, como mayores niveles de seguridad. Nos interesamos por evaluar lo anterior para el caso de Guatemala. Sirviéndonos de la variación trimestral de trecientos veintinueve municipios en una ventana de once años buscamos evidencia de que la tasa de migración de personas de origen guatemalteco que buscan empleo en México o Estados Unidos se correlaciona de manera positiva con la tasa de homicidios en sus municipios de origen. Nuestros resultados sugieren que esto se cumple y que aumentos en los niveles de violencia homicida pueden propiciar desplazamientos laborales.

Guatemala -- Emigration and immigration -- Effect of violence on -- Econometric models United States -- Emigration and immigration -- Econometric models. CIENCIAS SOCIALES CIENCIAS SOCIALES

Contrasting spatial patterns in active-fire and fire-suppressed mediterranean climate old-growth mixed conifer forests

Danny L. Fry  (2014, [Artículo])

In Mediterranean environments in western North America, historic fire regimes in frequent-fire conifer forests are highly variable both temporally and spatially. This complexity influenced forest structure and spatial patterns, but some of this diversity has been lost due to anthropogenic disruption of ecosystem processes, including fire. Information from reference forest sites can help management efforts to restore forests conditions that may be more resilient to future changes in disturbance regimes and climate. In this study, we characterize tree spatial patterns using four-ha stem maps from four old-growth, Jeffrey pine-mixed conifer forests, two with active-fire regimes in northwestern Mexico and two that experienced fire exclusion in the southern Sierra Nevada. Most of the trees were in patches, averaging six to 11 trees per patch at 0.007 to 0.014 ha-1, and occupied 27-46% of the study areas. Average canopy gap sizes (0.04 ha) covering 11-20% of the area were not significantly different among sites. The putative main effects of fire exclusion were higher densities of single trees in smaller size classes, larger proportion of trees (≥56%) in large patches (≥10 trees), and decreases in spatial complexity. While a homogenization of forest structure has been a typical result from fire exclusion, some similarities in patch, single tree, and gap attributes were maintained at these sites. These within-stand descriptions provide spatially relevant benchmarks from which to manage for structural heterogeneity in frequent-fire forest types.

article, climate, controlled study, ecosystem fire history, forest structure, geographic distribution, geographic mapping, land use, mathematical computing, mathematical model, Mexico, spatial analysis, taiga, United States, comparative study, conife CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

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.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

International Winter Wheat nurseries data: Facultative and Winter Wheat Observation Nurseries and International Winter Wheat yield trials for semi-arid and irrigated conditions

Mesut KESER fatih ozdemir Pietro Bartolini (2022, [Artículo])

Germplasm Exchange International Nurseries Multi-Locations CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WINTER WHEAT BREEDING GERMPLASM YIELDS DATA

CIMMYT Eastern Africa early- to intermediate maturity maize breeding pipelines (EA-PP1)

Yoseph Beyene Andrew Chavangi Manje Gowda Suresh L.M. Vijay Chaikam Anani Bruce Berhanu Tadesse Ertiro Walter Chivasa Aparna Das Juan Burgueño Jose Crossa Prasanna Boddupalli (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING LINES MARKET SEGMENTATION HYBRIDS GERMPLASM

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

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.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

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.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Highlight of CIMMYT East Africa maize product profile-3 (EA-PP3, highland)

Adefris Teklewold (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING MARKET SEGMENTATION HYBRIDS GENETIC GAIN INBRED LINES

Estimating lime requirements for tropical soils: Model comparison and development

Fernando Aramburu Merlos João Vasco Silva Frédéric Baudron Robert Hijmans (2023, [Artículo])

Acid tropical soils may become more productive when treated with agricultural lime, but optimal lime rates have yet to be determined in many tropical regions. In these regions, lime rates can be estimated with lime requirement models based on widely available soil data. We reviewed seven of these models and introduced a new model (LiTAS). We evaluated the models’ ability to predict the amount of lime needed to reach a target change in soil chemical properties with data from four soil incubation studies covering 31 soil types. Two foundational models, one targeting acidity saturation and the other targeting base saturation, were more accurate than the five models that were derived from them, while the LiTAS model was the most accurate. The models were used to estimate lime requirements for 303 African soil samples. We found large differences in the estimated lime rates depending on the target soil chemical property of the model. Therefore, an important first step in formulating liming recommendations is to clearly identify the soil property of interest and the target value that needs to be reached. While the LiTAS model can be useful for strategic research, more information on acidity-related problems other than aluminum toxicity is needed to comprehensively assess the benefits of liming.

Exchangeable Acidity Aluminum Saturation Calcium Carbonate Equivalent CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CHEMICOPHYSICAL PROPERTIES LIMES TROPICAL ZONES ACID SOILS ALUMINIUM BASE SATURATION CALCIUM CARBONATE

Local markets and food security. The case of the Milpera and Puuc regions in Yucatan

Ana Laura Bojórquez Carrillo Monserrat Vargas Jiménez Mireya Noemi Hernández Islas (2023, [Artículo, Artículo])

Food insecurity is a complex problem worldwide. A major part of this problem is the food supply. Local markets can represent a strategy for building social capital, as well as strategies for subsistence and sustainability of food value chains, contributing to food security and its effects. The objective of this research is to determine if the existence of a municipal market in the Milpera and Puuc regions of Yucatán favors the existence of food security, the consumption or the expense of natural foods. The population is located in 18 municipalities of Yucatán, Mexico. To carry out this study, a cross-sectional, non-experimental study, with a quantitative approach and correlational scope. The main techniques that were applied were descriptive statistics and contingency tables with respect to 6 hypotheses. This work shows that the existence of markets in the communities makes a significant difference because it positively impacts the food security of the inhabitants, since it allows them to have access to a wider variety of products and at the same time, favors the active dynamics of the economy of the community.

Local markets Food safety Local development Food sovereignty Rural areas Mercados locales Seguridad alimentaria Desarrollo local Soberanía alimentaria Zonas rurales CIENCIAS SOCIALES CIENCIAS SOCIALES