Geostatistical estimation of surface soil carbon stock in Mt. Wakakusa grassland of Japan

Kamarudin, Khairun N.; Tomita, Mayu; Kondo, Keiko; Abe, Susumu S.

doi:10.1007/s11355-019-00370-1

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…As a result, the maps of OC and TN had a certain similarity to one another. As reported by Kamarudin et al (2019), the impact of the deer browsing on the vegetation was evident because vigorous growth and thus large biomass production of M. sinensis in the protection area were observed during the field survey and also visibly confirmed by Google Earth TM image, where a darker color in the protected area than in the surrounding non-protected area was visible (Fig. 1).…”

Section: Interpolation Maps Of the Examined Soil Properties At The supporting

confidence: 77%

“…Wakakusa grassland yielded a better estimation accuracy compared to the classical survey method, which relied on a few soil profile data with little consideration to soil variability (e.g., Matsuura et al 2012). Kamarudin et al (2019) uncovered that the classical approach, if calculated based on the soil survey data by Tamura et al (2001), resulted in 21% overestimation of the surface soil OC stock compared to the geostatistical method. On the other hand, in contrast to Av-P, the soil pH varied in the narrowest range (4.7 to 6.2) with the lowest variability (CV = 5.4%).…”

Section: Discussionmentioning

confidence: 95%

“…Wakakusa grassland. Another advantage of understanding soil variability (and spatial characteristics) has been reported by Kamarudin et al (2019); the geostatistical approach to estimate the surface soil (0-5 cm) OC stock in Mt. Wakakusa grassland yielded a better estimation accuracy compared to the classical survey method, which relied on a few soil profile data with little consideration to soil variability (e.g., Matsuura et al 2012).…”

Section: Discussionmentioning

confidence: 99%

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Spatial Variability and Geostatistical Mapping of Selected Soil Properties in Mt. Wakakusa Grassland of Japan

Kamarudin

Tomita

Kondo

et al. 2019

JARQ

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Mt. Wakakusa in Central Japan is a semi-natural grassland which has been maintained by repeated application of prescribed burnings of vegetation for more than 500 years. We assessed the spatial variability of selected soil properties and constructed their distribution maps using a geostatistical method to provide spatial information on soil resource for sustainable land management in this grassland. All the examined soil properties showed intermediate (coefficient of variation [CV] = 10%-90%) or high (CV > 90%) variability, except for the soil pH (CV < 10%), suggesting that the precision soil management approach is recommendable in the study site. The variogram analysis revealed that all of the soil properties, except for the electrical conductivity with a very weak spatial dependency (nugget-to-sill [N/S] ratio > 0.75), showed a very strong or moderately strong spatial dependency (N/S ratio ≤ 0.50), which might occur under the strong influence of intrinsic factors such as the inherent soil quality. The soil maps constructed by the ordinary kriging method helped in understanding the distribution patterns of the examined soil properties and identifying specific locations with signs of degradation and pollution. These spatial distribution patterns should be considered when developing a sustainable soil management strategy in Mt. Wakakusa grassland. Discipline: Agricultural EnvironmentAdditional key words: ordinary kriging, prescribed burning, semivariogram, spatial dependency

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Section: Interpolation Maps Of the Examined Soil Properties At The supporting

confidence: 77%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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Spatial Variability and Geostatistical Mapping of Selected Soil Properties in Mt. Wakakusa Grassland of Japan

Kamarudin

Tomita

Kondo

et al. 2019

JARQ

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“…[12] use a SW normality test with data of penetration resistance of soil in a region of Poland. [13] assess the distribution pattern of organic carbon in a region of central Japan. [13] assess distribution pattern of organic carbon in a region of central Japan.…”

Section: Introductionmentioning

confidence: 99%

Pruebas de Normalidad en Geoestadística. Un nuevo enfoque basado en la distancia de Mahalanobis

Giraldo

Porcu

2022

Cienc. En Desarro.

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En geoestadística, bajo estacionariedad, kriging simple (KS) es el mejor predictor lineal (MPL) y kriging ordinario (KO) es el mejor predictor lineal insesgado (MPLI). Cuando el proceso estocástico es Normal, KS no es solo un MPL sino un mejor predictor (MP), es decir que bajo la función de pe ́rdida cuadrática, éste coincide con la esperanza condicional del predictor dada la información. En este escenario, el predictor KO sirve como aproximación del MP. Por esta razón, en geoestadística aplicada, es importante probar el supuesto de normalidad. Dada una realización de un proceso espacial, KS será un predictor óptimo si el vector aleatorio subyacente sigue una distribución normal multivariada. Algunas pruebas de normalidad clásicas como Shapiro-Wilk (SW), Shapiro-Francia (SF), o Anderson-Darling (AD) son usadas para evaluar este supuesto. Estas asumen independencia y por ello no son apropiadas en geoestadística (y en general en estadística espacial). Por un lado, las observaciones en geoestadística son espacialmente correlacionadas. Por otro lado la optimalidad del kriging es fundamentada en normalidad multivariada (no en normalidad univariada). En este trabajo se presenta un estudio de simulación para mostrar por qué es inapropiado el uso de pruebas univaridas de normalidad con datos geoestadísticos. También, como solución al problema anterior, se propone una adaptación de la prueba de Mahalanobis al contexto geoestadístico para hacer de manera correcta el test de normalidad en este ambito.

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“…Most landscape-scale assessments of SOC have focused on grasslands (Wang et al, 2009;Kamarudin et al, 2019), agricultural landscapes (Lacoste et al, 2014;Piccini et al, 2014), and sandy land (Zuo et al, 2010;Li et al, 2018). Many factors affect the spatial variability of SOC, such as hydrothermal conditions (e.g., temperature and precipitation; Post et al, 1982;Wang S. et al, 2017;Wang et al, 2019), soil properties (e.g., soil type, texture andmoisture, Homann et al, 1995;Wang et al, 2002;Zinn et al, 2005), topographic factors (e.g., elevation, slope aspect, and slope; Tsui et al, 2004;Garten et al, 2006;Seibert et al, 2007;Wiesmeier et al, 2014), and vegetation conditions (e.g., vegetation types, cover, and productivity;Wang et al, 2000;Fang et al, 2012;Yang et al, 2014;Xin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Soil Organic Carbon and Total Nitrogen in Desert Steppes of China’s Hexi Corridor

Wang

Duan

et al. 2021

Front. Environ. Sci.

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Stock estimates are critical to quantifying carbon and nitrogen sequestration, quantifying greenhouse gas emissions, and understanding key biogeochemical processes (i.e., soil carbon and nutrient cycling). Many studies have assessed soil organic matter and nutrients in different ecosystems. However, the spatial distribution of carbon and nitrogen and the key influencing factors in arid desert steppe remain unclear. Here, we investigated the soil organic carbon (SOC) and soil total nitrogen (STN) to a depth of 100 cm at 126 sites in a desert steppe in northwestern China. SOC and STN contents decreased with increasing depth; the highest average SOC and STN contents were 12.70 and 0.65 g kg−1 in the surface 5 cm, and the lowest were from 80 to 100 cm (4.49 and 0.16 g kg−1, respectively). SOC density (SOCD) and STN density (STND) to a depth of 100 cm averaged 8.94 and 0.45 kg m−2, respectively. The top 1 m of the soils stored approximately 1,041 Tg SOC and 52 Tg STN in the study area. Geostatistical analysis showed strong and moderate spatial autocorrelation for SOCD in different soil layers, but the autocorrelation for STND gradually weakened with increasing depth. SOCD and STND decreased from southwest to northeast in the study area, along an elevation gradient. Both were significantly positively correlated with topographic variables, precipitation, and the normalized-difference vegetation index, but negatively correlated with temperature and aridity. More than 40% of the SOCD and STND spatial variation was explained by elevation, which was the dominant factor. The data and high-resolution maps from this study will support future soil carbon and nitrogen analyses.

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Geostatistical estimation of surface soil carbon stock in Mt. Wakakusa grassland of Japan

Cited by 8 publications

References 22 publications

Spatial Variability and Geostatistical Mapping of Selected Soil Properties in Mt. Wakakusa Grassland of Japan

Spatial Variability and Geostatistical Mapping of Selected Soil Properties in Mt. Wakakusa Grassland of Japan

Pruebas de Normalidad en Geoestadística. Un nuevo enfoque basado en la distancia de Mahalanobis

Spatial Variability of Soil Organic Carbon and Total Nitrogen in Desert Steppes of China’s Hexi Corridor

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