Abstract. 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Chilean Soil Organic Carbon database (CHLSOC), comprising 13 612 data points compiled from numerous sources, including unpublished and difficult-to-access data.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 6×10-5 % 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. The database is freely available to registered users at https://doi.org/10.17605/OSF.IO/NMYS3 (Pfeiffer et al., 2019b) under the Creative Commons Attribution 4.0 International Public License.
Abstract. 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 This dataset is the product of the compilation from numerous sources including unpublished and difficult to access data, allowing to fill numerous spatial gaps where no SOC estimates were publicly available before.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. Dataset available at https://www.doi.org/10.17605/OSF.IO/NMYS3 (Pfeiffer et al., 2019b).
Abstract. Spatial soil databases can help model complex phenomena in which soils are decisive, for example, evaluating agricultural potential or estimating carbon storage capacity. The Soil Information System for Latin America and the Caribbean, SISLAC, is a regional initiative promoted by the FAO's South American Soil Partnership to contribute to the sustainable management of soil. SISLAC includes data coming from 49,084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. However, some problems hinder its usages, such as the quality of the data and its high dimensionality. The objective of this research is twofold. First, to evaluate the quality of SISLAC and its data values and generate a new, improved version that meets the minimum quality requirements to be used by different interests or practical applications. Second, to demonstrate the potential of improved soil profile databases to generate more accurate information on soil properties, by conducting a case study to estimate the spatial variability of the percentage of soil organic carbon using 192 profiles in a 1473 km2 region located in the department of Valle del Cauca, Colombia. The findings show that 15 percent of the existing soil profiles had an inaccurate description of the diagnostic horizons. Further correction of an 4.5 additional percent of existing inconsistencies improved overall data quality. The improved database consists of 41,691 profiles and is available for public use at https://doi.org/10.5281/zenodo.6540710 (Díaz-Guadarrama, S. & Guevara, M., 2022). The updated profiles were segmented using algorithms for quantitative pedology to estimate the spatial variability. We generated segments one centimeter thick along with each soil profile data, then the values of these segments were adjusted using a spline-type function to enhance vertical continuity and reliability. Vertical variability was estimated up to 150 cm in-depth, while ordinary kriging predicts horizontal variability at three depth intervals, 0 to 5, 5 to 15, and 15 to 30 cm, at 250 m-spatial resolution, following the standards of the GlobalSoilMap project. Finally, the leave-one-out cross-validation provides information for evaluating the kriging model performance, obtaining values for the RMSE index between 1.77 % and 1.79 % and the R2 index greater than 0.5. The results show the usability of SISLAC database to generate spatial information on soil properties and suggest further efforts to collect a more significant amount of data to guide sustainable soil management.
The tests were conducted in the municipality of Carmen de Viboral, 40 minutes from the city of Medellin (6º05'09"N and 75º20'19"W, 2.150 m a.s.l.). Three greenhouses with the same dimensions were used, with different channel heights of 2, 2.5, and 3 m, respectively. Temperature and relative humidity measurements were taken every 30 minutes for 3 years, and crop production was assessed. The aim of the study was to evaluate the effect of the height of the greenhouse on climatic conditions generated on a chives crop (Allium schoenoprasum). A multiple linear regression, colinearity analysis, and analysis of heteroscedasticity was performed to determine climatic variations caused by the difference in height between the greenhouses, and to determine differences in production levels. For statistical analysis, the SPSS software was used. The results indicate that, under the studied conditions, the greenhouse height directly affects the internal weather condition, reduction of 1 m in the minimum height of channel (from 3 to 2 m) resulted in an increase of minimum, average and maximum temperature by 0.37°C, 1.42°C and 3.56 °C, respectively, and consequently the chives crop yields, produced a 4.78% higher fresh weight of chives.
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