Operación y evaluación de sistemas de riego presurizado

JUAN MANUEL GONZALEZ CAMACHO (2000, [Libro])

Tabla de contenido: 1. Descripción y selección de sistemas de riego presurizado – 2. Descripción de una bomba centrífuga – 3. Hidráulica básica de los sistemas de riego presurizado – 4. Diseño del sistema de riego – 5. Evaluación del sistema de riego.

1. Descripción y selección de sistemas de riego presurizado – 2. Descripción de una bomba centrífuga – 3. Hidráulica básica de los sistemas de riego presurizado – 4. Diseño del sistema de riego – 5. Evaluación del sistema de riego.

Riego presurizado Operación y mantenimiento CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Results from rapid-cycle recurrent genomic selection in spring bread wheat

Susanne Dreisigacker Paulino Pérez-Rodríguez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2023, [Artículo])

Genomic-Assisted Breeding Molecular Markers Pedigree Information Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS GENETIC MARKERS WHEAT BREEDING PROGRAMMES

Smallholder maize yield estimation using satellite data and machine learning in Ethiopia

Zhe Guo Jordan Chamberlin Liangzhi You (2023, [Artículo])

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Sentinel-2 Smallholder Agriculture Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING

Drought risk management in Mexico: progress and challenges

David Ortega Gaucin Mario Lopez Perez Felipe Ignacio Arreguín Cortés (2016, [Artículo])

Drought is one of the most complex natural phenomena, which affects the most people in the world. In Mexico, drought has been a recurrent and persistent problem throughout its history. In recent years, drought has affected large agricultural areas and rural communities, leading to severe imbalances in the regional and national economies, as occurred during the 2011–2012 drought, the most severe of the last 70 years. Therefore, in this paper an analysis of the measures that have recently been implemented to cope with drought in Mexico, which highlights the beginning of the transition from a reactive approach based on the crisis management towards a proactive approach aimed to risk management, with the implementation of the National Program Against Drought (PRONACOSE, for its acronym in Spanish) launched in 2013 is presented. So, in this paper, the components of this program are presented, along with a brief description of the Programs of Preventive and Mitigation Drought Measures (PMPMS, for its acronym in Spanish), which have been formulated as an integral part of PRONACOSE for each of the 26 basin councils in the country. Similarly, some of the main future challenges in drought manage¬ment and research needs identified during the formulation of the PMPMS are exposed. We concluded that there is no way to avoid a drought but there are ways to mitigate its impacts and reduce losses of those affected by the phenomenon. Drought risk can’t be completely eliminated, but preventive actions implemented in the future will be useful to mitigate its effects.

Sequías Prevención de desastres Gestión del riesgo Vulnerabilidad CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA

Comunicación global en el 68 mexicano: el caso de la prensa española.

JAVIER VIEIRA CID (2022, [Tesis de doctorado])

El propósito fundamental de esta tesis doctoral consiste en analizar diferentes aspectos de la prensa española en su empresa de noticiar lo ocurrido durante el movimiento estudiantil mexicana de 1968. Se pretende visibilizar no sólo su alcance en los medios españoles sino también advertir las diferentes estrategias comunicativas que se desarrollaron para cubrir la información que llegaba desde la capital mexicana. En definitiva, y atendiendo a las cuestiones coyunturales que regulaban la labor periodística de la España de 1968, trataremos de analizar la cobertura mediática que diferentes editoriales realizaron en su empresa de comunicar el 68 mexicano.

CIENCIAS SOCIALES Medios de comunicación Cobertura de prensa Movimiento estudiantil Prensa Estudiante universitario Matanza de Tlatelolco

Genome-wide association study and genomic prediction on plant architecture traits in sweet corn and waxy corn

XUECAI ZHANG Ao Zhang (2023, [Artículo])

Genome-Wide Association Study Genomic Prediction Ear Height Tassel Branch Number Waxy Corn CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS PLANT HEIGHT SWEET CORN WAXY MAIZE

Gender, rainfall endowment, and farmers’ heterogeneity in wheat trait preferences in Ethiopia

Hom Nath Gartaula Moti Jaleta (2024, [Artículo])

Wheat is a vital cereal crop for smallholders in Ethiopia. Despite over fifty years of research on wheat varietal development, consideration of gendered trait preferences in developing target product profiles for wheat breeding is limited. To address this gap, our study used sex-disaggregated survey data and historical rainfall trends from the major wheat-growing regions in Ethiopia. The findings indicated heterogeneity in trait preferences based on gender and rainfall endowment. Men respondents tended to prefer wheat traits with high straw yield and disease-resistance potential, while women showed a greater appreciation for wheat traits related to good taste and cooking quality. Farmers in high rainfall areas seemed to prioritize high straw yield and disease resistance traits, while those in low rainfall areas valued good adaptation traits more highly. Most of the correlation coefficients among the preferred traits were positive, indicating that farmers seek wheat varieties with traits that serve multiple purposes. Understanding men's and women's preferences and incorporating them in breeding and seed systems could contribute to the development of more targeted and effective wheat varieties that meet the diverse needs of men and women farmers in Ethiopia.

Trait Preferences Multivariate Probit Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT AGRONOMIC CHARACTERS GENDER RAINFALL PROBIT ANALYSIS

Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design

Junjie Fu XUECAI ZHANG (2023, [Artículo])

Genomic Prediction Prediction Model Genetic Effects Hybrid Performance CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE GENETICS HYBRIDS PERFORMANCE ASSESSMENT

Willingness to pay (WTP) for heat-tolerant maize hybrids in the Mid-Western Terai region of Nepal

Atul Kulkarni Keshab Babu Koirala Pervez Zaidi (2023, [Artículo])

Heat-Tolerant Maize Hybrid Preference Heterogeneity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HEAT TOLERANCE MAIZE HYBRIDS WILLINGNESS TO PAY CONTINGENT VALUATION

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