Deeper topsoils enhance ecosystem productivity and climate resilience in arid regions, but not in humid regions.

Understanding the controlling mechanisms of soil properties on ecosystem productivity is essential for sustaining productivity and increasing resilience under a changing climate. Here we investigate the control of topsoil depth (e.g., A horizons) on long-term ecosystem productivity. We used nationwide observations (n = 2401) of topsoil depth and multiple scaled datasets of gross primary productivity (GPP) for five ecosystems (cropland, forest, grassland, pasture, shrubland) over 36 years (1986-2021) across the conterminous USA. The relationship between topsoil depth and GPP is primarily associated with water availability, which is particularly significant in arid regions under grassland, shrubland, and cropland (r = .37, .32, .15, respectively, p < .0001). For every 10 cm increase in topsoil depth, the GPP increased by 114 to 128 g C m-2 year-1 in arid regions (r = .33 and .45, p < .0001). Paired comparison of relatively shallow and deep topsoils while holding other variables (climate, vegetation, parent material, soil type) constant showed that the positive control of topsoil depth on GPP occurred primarily in cropland (0.73, confidence interval of 0.57-0.84) and shrubland (0.75, confidence interval of 0.40-0.94). The GPP difference between deep and shallow topsoils was small and not statistically significant. Despite the positive control of topsoil depth on productivity in arid regions, its contribution (coefficients: .09-.33) was similar to that of heat (coefficients: .06-.39) but less than that of water (coefficients: .07-.87). The resilience of ecosystem productivity to climate extremes varied in different ecosystems and climatic regions. Deeper topsoils increased stability and decreased the variability of GPP under climate extremes in most ecosystems, especially in shrubland and grassland. The conservation of topsoil in arid regions and improvements of soil depth representation and moisture-retention mechanisms are critical for carbon-sequestration ecosystem services under a changing climate. These findings and relationships should also be included in Earth system models.

Land use and climate change effects on soil organic carbon in North and Northeast China.

Soil is recognized as the largest carbon reservoir in the terrestrial ecosystem. Soil organic carbon (SOC) is vulnerable to changes in land use and climate. For a better understanding of the SOC dynamics and its driving factors, we collected data of the 1980s and 2000s in the North and Northeast China and conducted the digital soil mapping for spatial variation of SOC for the respective period. In the 1980s, 585 soils were sampled and the area was resampled in 2003 and 2004 (1062 samples) in a 30-km grid. The main land use in the area was cropland, forest and grassland. The random forest was used to predict the SOC concentration and its temporal change using land use, terrain factors, vegetation index, vis-NIR spectra and climate factors as predictors. The average SOC concentration in 1985 was 10.0 g kg-1 compared to 12.5 g kg-1 in 2004. The SOC variation was similar over the two periods, and levels increased from south to north. The estimated SOC stock was 1.68 Pg in 1985 and 1.66 Pg in 2004, but the SOC changes were different under different land uses. Over the twenty-year period, average temperatures increased and large areas of forests and grassland were converted to cropland. SOC under cropland was increased by 0.094 Pg (+9%) whereas 0.089 Pg SOC was lost under forests (-25%) and 0.037 Pg in the soils under grassland (-25%). It is concluded that land use is the main drivers for SOC changes in this area while climate change had different contributions in different regions. SOC loss was remarkable under the land use conversion while cropland has considerable potential to sequester SOC.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

GIS-based multi-criteria analysis for Arabica coffee expansion in Rwanda.

The Government of Rwanda is implementing policies to increase the area of Arabica coffee production. Information on the suitable areas for sustainably growing Arabica coffee is still scarce. This study aimed to analyze suitable areas for Arabica coffee production. We analyzed the spatial distribution of actual and potential production zones for Arabica coffee, their productivity levels and predicted potential yields. We used a geographic information system (GIS) for a weighted overlay analysis to assess the major production zones of Arabica coffee and their qualitative productivity indices. Actual coffee yields were measured in the field and were used to assess potential productivity zones and yields using ordinary kriging with ArcGIS software. The production of coffee covers about 32 000 ha, or 2.3% of all cultivated land in the country. The major zones of production are the Kivu Lake Borders, Central Plateau, Eastern Plateau, and Mayaga agro-ecological zones, where coffee is mainly cultivated on moderate slopes. In the highlands, coffee is grown on steep slopes that can exceed 55%. About 21% percent of the country has a moderate yield potential, ranging between 1.0 and 1.6 t coffee ha-1, and 70% has a low yield potential (<1.0 t coffee ha-1). Only 9% of the country has a high yield potential of 1.6-2.4 t coffee ha-1. Those areas are found near Lake Kivu where the dominant soil Orders are Inceptisols and Ultisols. Moderate yield potential is found in the Birunga (volcano), Congo-Nile watershed Divide, Impala and Imbo zones. Low-yield regions (<1 t ha-1) occur in the eastern semi-dry lowlands, Central Plateau, Eastern Plateau, Buberuka Highlands, and Mayaga zones. The weighted overlay analysis and ordinary kriging indicated a large spatial variability of potential productivity indices. Increasing the area and productivity of coffee in Rwanda thus has considerable potential.

Digital mapping of soil organic carbon contents and stocks in Denmark.

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0-5, 5-15, 15-30, 30-60 and 60-100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg(-1) was reported for 0-5 cm soil, whereas there was on average 2.2 g SOC kg(-1) at 60-100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg(-1) was found at 60-100 cm soil depth. Average SOC stock for 0-30 cm was 72 t ha(-1) and in the top 1 m there was 120 t SOC ha(-1). In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Avaliação de cinco algoritmos de árvores de decisão e três tipos de modelos digitais de elevação para mapeamento digital de solos a nível semidetalhado na Bacia do Lageado Grande, RS, Brasil / Evaluation of five algorithms of decision trees and three digital elevation models for digital soil mapping at semidetail level at the Lageado Grande Watershed, RS, Brazil

O mapeamento digital de solos (MDS) tem como base a geração de sistemas de informações que permitem estabelecer relações matemáticas entre variáveis ambientais e solos e, dessa forma, predizer a distribuição espacial das classes ou propriedades dos solos. Dentre as abordagens mais utilizadas, as árvores de decisão têm se destacado por apresentar bons resultados no MDS. Por outro lado, dada a disponibilidade de novas fontes de informação sobre a elevação, torna-se necessário o teste e avaliação de modelos digitais de elevação (MDE) quanto ao seu uso para o MDS. Este estudo testa cinco algoritmos de árvores de decisão (Simple Chart, Random Tree, REP Tree, BF Tree e J48) e três MDE (Aster GDEM, SRTM e SRTM V3) para o MDS a nível semidetalhado, em situações em que o principal fator diferenciador entre os tipos de solo é o relevo. O uso do MDE Aster GDEM e árvore de decisão com algoritmo J48, Simple Tree e BF Tree foram os que produziram modelos de árvore de decisão capazes de produzir mapas de solo com maior similaridade ao mapa de referência.

Digital soil mapping (DSM) has been shown to be feasible to use in soil survey. Although several methods have been exploited, there is a lack in defining methodologies for doing DSM. This study tests five decision trees algorithms that have been identified as suitable (Simple Chart, Random Tree, REP Tree, BF Tree, and J48) and three digital elevation models (AsterGDEM, SRTM and SRTM V3) for DSM at semidetailed level in situations where the main differentiating factor between soil types is the relief. The use of MDE Aster GDEM and decision three algorithms J48, Simple Tree e BF Tree produced decision tree models capable of produce soil maps with larger accuracy related to reference soil maps.

Citations and the h index of soil researchers and journals in the Web of Science, Scopus, and Google Scholar.

Citation metrics and h indices differ using different bibliometric databases. We compiled the number of publications, number of citations, h index and year since the first publication from 340 soil researchers from all over the world. On average, Google Scholar has the highest h index, number of publications and citations per researcher, and the Web of Science the lowest. The number of papers in Google Scholar is on average 2.3 times higher and the number of citations is 1.9 times higher compared to the data in the Web of Science. Scopus metrics are slightly higher than that of the Web of Science. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus is on average 1.1 times larger than Web of Science. Over time, the metrics increase in all three databases but fastest in Google Scholar. The h index of an individual soil scientist is about 0.7 times the number of years since his/her first publication. There is a large difference between the number of citations, number of publications and the h index using the three databases. From this analysis it can be concluded that the choice of the database affects widely-used citation and evaluation metrics but that bibliometric transfer functions exist to relate the metrics from these three databases. We also investigated the relationship between journal's impact factor and Google Scholar's h5-index. The h5-index is a better measure of a journal's citation than the 2 or 5 year window impact factor.