Application of spheroidal agglomerates of γ-Al2O3 in the fluoride removal from aqueous medium

Aplicación de aglomerados esferoidales de γ-Al2O3 en la remoción de fluoruro de medio acuoso

RAFAEL ROMERO TOLEDO VICTOR RUIZ SANTOYO ULISES ZURITA LUNA GUSTAVO RANGEL PORRAS MERCED MARTINEZ ROSALES (2019, [Artículo])

En el presente estudio se investigó un adsorbente aglomerado esferoidal de γ-Al2O3 obtenido a partir de pseudoboehmita para la eliminación eficaz de fluoruro de un medio acuoso. Las propiedades superficiales se caracterizaron por diversas técnicas: XRD, fisisorción de N2, FE-SEM/EDS, RMN 27Al, adsorción de piridina por FT-IR, PZy tamaño de partícula. Se llevaron a cabo experimentos en lotes y se compararon con una alúmina activada comercial (AA). El proceso se llevó a cabo a pH 5, 7 y 9, a 25 y 35 ºC. Los resultados experimentales indicaron que los aglomerados esferoidales de γ-Al2O3 eliminan hasta 15 mg/g, con una capacidad de adsorción mayor que AA de 13 mg/g, a pH 5, estudiados a 25 y 35 ºC. El proceso de adsorción de F-en γ-Al2O3 y AA siguió la cinética de pseudo-primer orden y la isoterma de Langmuir. Los resultados muestran un adsorbente eficaz para la eliminación de F-.

A spheroidal agglomerate γ-Al2O3 adsorbent obtained from pseudoboehmite for effective removal of fluoride from aqueous medium was investigated in the present study. The surface properties were characterized by several techniques: XRD, physisorption of N2, FE-SEM/EDS, 27Al NMR, FT-IR Pyridine adsorption, PZ and particle size. Batch experiments were conducted and they were compared with a commercial activated alumina (AA). The process was carried out at pH 5, 7, and 9, then at 25 and 35 ºC. Batch experimental results indicated that the spheroidal agglomerates of γ-Al2O3 remove up to 15 mg/g with a higher adsorption capacity than AA of 13 mg/g, at pH 5, studied at 25 and 35 °C. The F−adsorption processes in γ-Al2O3 and AA followed the pseudo-first-order kinetics and the Langmuir isotherm. The results showed an adsorbent effective for removal of F−.

BIOLOGÍA Y QUÍMICA Fluoride γ-Al2O3 Spheroidal agglomerates Adsorbent Water Fluoruro γ-Al2O3 Aglomerados esferoidales Adsorbentes Agua

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

Programa regional hidrológico forestal para la región I península de Baja California

Maria Dolores Olvera Salgado HECTOR GREGORIO CORTES TORRES (2007, [Documento de trabajo])

Tabla de contenido: Planeación en material forestal – Objetivos, metas y estrategias en la Gerencia Región Conafor – Relación agua-bosque – Diagnóstico forestal – Necesidades de recuperación forestal – Planeación estratégica de inversiones para la conservación y recuperación forestal – Impactos esperados de la recuperación forestal sobre los principales recursos naturales – Participación social y planeación regional en materia forestal – Fuentes de financiamiento para implementar el PRHF en la región I península de Baja California – Otros componentes del programa regional hidrológico forestal – Conclusiones y recomendaciones – Bibliografía.

Planeación en material forestal – Objetivos, metas y estrategias en la Gerencia Región Conafor – Relación agua-bosque – Diagnóstico forestal – Necesidades de recuperación forestal – Planeación estratégica de inversiones para la conservación y recuperación forestal – Impactos esperados de la recuperación forestal sobre los principales recursos naturales – Participación social y planeación regional en materia forestal – Fuentes de financiamiento para implementar el PRHF en la región I península de Baja California – Otros componentes del programa regional hidrológico forestal – Conclusiones y recomendaciones – Bibliografía.

Bosques Planificación ambiental Erosión hídrica Informes de proyectos Baja California CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

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

QTL mapping and genomic prediction of resistance to wheat head blight caused by Fusarium verticillioides

XUECAI ZHANG Yong Zhang (2022, [Artículo])

Fusarium Head Blight Resistance Fusarium verticillioides QTL Mapping Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FUSARIUM QUANTITATIVE TRAIT LOCI MAPPING TRITICUM AESTIVUM

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

Phenotypic assessment of genetic gain from selection for improved drought tolerance in semi-tropical maize populations

Terence Molnar Somak Dutta Thanda Dhliwayo Samuel Trachsel Michael Lee (2023, [Artículo])

Drought Tolerant Population Topcross Water Deficit CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DROUGHT TOLERANCE WATER MAIZE CLIMATE CHANGE GENETIC GAIN

Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat

Admas Alemu Abebe Pawan Singh Aakash Chawade (2023, [Artículo])

Septoria Tritici Blotch Wheat Breeding Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOME-WIDE ASSOCIATION STUDIES MYCOSPHAERELLA GRAMINICOLA DISEASE RESISTANCE WHEAT PLANT GROWTH

Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection

Nick Fradgley Alison Bentley Keith Gardner Stéphanie M. Swarbreck (2023, [Artículo])

Sustainable Food Systems Genomic Prediction Genome-Wide Association Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MARKER-ASSISTED SELECTION VARIETIES FOOD SYSTEMS QUALITY

Programa regional hidrológico forestal para la región II noroeste

HECTOR GREGORIO CORTES TORRES Maria Dolores Olvera Salgado (2007, [Documento de trabajo])

El documento integra la información del sector disponible en la región y servirá de apoyo especialmente para la planeación de acciones encaminadas a la conservación y restauración del recurso forestal y sus asociados (agua, suelo, fauna y flora silvestre).

Bosques Planificación ambiental Erosión hídrica Sonora Chihuahua Informes de proyectos CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA