Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes

Actualización del manejo de metástasis encefálicas

rain metastases represent a critical stage of oncological disease and its frequency is increasing over the recent years. The treatment of brain metastases has moved from a conservative approach to an active management that should be individualized for each patient: in case of single brain metastasis, surgery or radiosurgery should be considered as first option of treatment; in case of multiple lesions, whole-brain radiotherapy is the standard of care. The aim of this review is to present general aspects including new approaches in management of patients with brain metastases

CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

A critical aspect of predicting soil organic carbon (SOC) concentrations is the lack of available soil information; where information on soil characteristics is available, it is usually focused on regions of high agricultural interest. To date, in Chile, a large proportion of the SOC data have been collected in areas of intensive agricultural or forestry use; however, vast areas beyond these forms of land use have few or no soil data available. Here we present a new SOC database for the country, which is the result of an unprecedented national effort under the framework of the Global Soil Partnership. This partnership has helped build the largest database of SOC to date in Chile, named the Chilean Soil Organic Carbon database (CHLSOC), comprising 13 612 data points compiled from numerous sources, including unpublished and difficult-to-access data. The database will allow users to fill spatial gaps where no SOC estimates were publicly available previously. Presented values of SOC range from 6 x 10(-5) % to 83.3 %, reflecting the variety of ecosystems that exist in Chile. The database has the potential to inform and test current models that predict SOC stocks and dynamics at larger spatial scales, thus enabling benefits from the richness of geochemical, topographic and climatic variability in Chile.Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11160372 Convenio CONAF-UDeC 2015 Perturbaciones Araucaria ERANet-LAC joint program ELAC2014/DCC-0092 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1161492 Global Soil Partnership - Food and Agriculture Organization of the United Nations (FAO) South America Soil Partnership - Food and Agriculture Organization of the United Nations (FAO

CHLSOC: The Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

One of the critical aspects in modelling soil organic carbon (SOC) predictions is the lack of access to soil information which is usually concentrated in regions of high agricultural interest. In Chile, most soil and SOC data to date is highly concentrated in 25 % of the territory that has intensive agricultural or forestry use. Vast areas beyond those forms of land use have few or no soil data available. Here, we present a new database of SOC for the country, which is the result of an unprecedented national effort under the frame of the Global Soil Partnership that help to build the largest database on SOC to date in Chile named “CHLSOC" comprising 13,612 data points. This dataset is the product of the compilation from numerous sources including unpublished and difficult to access data, allowing to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC range from 6×10−5 to 83.3 percent, reflecting the variety of ecosystems that exists in Chile. Profiting from the richness of geochemical, topographic and climatic variability in Chile, the dataset has the potential to inform and test models trying to predict SOC stocks and dynamics at larger spatial scales.ISSN:1866-359