Geospatial distribution of soil organic carbon and soil pH within the cocoa agroecological zones of Ghana

Quaye, Amos Kojo; Doe, Eric Kofi; Attua, Emmanuel Morgan; Yiran, Gerald A. B.; Arthur, A. E.; Dogbatse, Jerome Agbesi; Konlan, S.; Nkroma, Yaw Dankwa; Addo, Divine

doi:10.1016/j.geoderma.2020.114921

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…Ordinary co‐kriging (COK) is a multivariate extension of OK, which makes full use of auxiliary variables to improve spatial estimation (McBratney et al, 2000; Quaye et al, 2021; Xia et al, 2019). In COK, a cross‐variogram is calculated to extract additional correlated information between target variable and auxiliary variables, to produce a more accurate estimation map.…”

Section: Methodsmentioning

confidence: 99%

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Hu,

Ni,

Xie

et al. 2023

Land Degrad Dev

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Soil organic matter (SOM) plays a critical role in terrestrial ecosystem functioning and is closely related to many global issues like soil fertility, soil health and climate regulation. Therefore, obtaining accurate information on the spatial distribution of SOM and its potential controlling factors is of global interest. However, this remains a great challenge since SOM is affected by numerous natural and anthropogenic factors and usually showed strong heterogeneity. In this study, we collected a total of 16,580 surface soil (0–20 cm) samples from the farmland throughout Jiangxi Province. And the Random Forest (RF), Cubist and gradient‐boosted models were compared and used to define the factor which is most associated with SOM. Then the ordinary kriging (OK) and machine learning‐ordinary co‐kriging (ML‐COK) were used to map SOM. We found that on average, 30.86 g kg−1 SOM was present in farmland soil of Jiangxi Province. Anthropogenic activities strongly affected SOM level, with five of the top 10 most important factors are anthropogenic related. The straw return amount was proved to have the largest importance (31.46%) for modelling SOM and a significant (p < 0.001) positive relationship between SOM content and the amount of straw returned to farmland was detected. Additionally, returning straw improved crop production. Soil derived from the Quaternary Subred Sand has the highest SOM content (37.82 g kg−1). Crop rotation also improved SOM content and the rice‐bean rotation system has the highest SOM content (34.27 g kg−1). With the best performance, the RF algorithm (R2 = 0.49, RMSE = 6.77 g kg−1) was selected to identify the primary control of SOM and integrated with COK, which we termed as ML‐COK, to map the SOM in the farmland of Jiangxi Province. ML‐COK outperformed OK method for mapping the SOM in farmland of Jiangxi Province with R2 of 0.351 and Lin's concordance correlation coefficient of 0.549. Farmland distributed in the central part of the province had high SOM content. In contrast, farmland in the north, south and east parts had relatively low SOM. Our study offers new insight for mapping soil properties, identifying potential factors driving variation in SOM, and also provides valuable information for making more reasonable and environmentally friendly farmland management measures.

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Section: Methodsmentioning

confidence: 99%

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Hu,

Ni,

Xie

et al. 2023

Land Degrad Dev

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“…Generally, SOC content increases with precipitation and with optimal levels in humid and cold climates and decreases with soil pH; beyond that, SOC storage links to biophysical factors and management practices [72]. A study in Ghana by Quaye et al [73] revealed unsuitably low SOC in strongly acidic soils in the western African Savannah woodlands. In the Miombo Woodlands of our study area, which are Savannah woodlands, low soil pH might have negatively affected SOC as well.…”

Section: Drivers Of Diversity At Plot Levelmentioning

confidence: 99%

Impacts of Diffuse Land-Use on Plant Diversity Patterns in the Miombo Woodlands of Western Zambia

Sichone

Oldeland²,

Phiri

et al. 2023

Diversity

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Land use is known to influence the diversity of vascular plants in the Miombo woodlands. However, little is known about the interaction between soil and land use in herbaceous and woody species. We compared the diversity of vascular plants at the plot level (20 m × 50 m) and site level for three sites in the Miombo woodlands of western Zambia subject to different levels of intensity classes of diffuse land use (e.g., livestock herbivory and selective timber harvesting). For each of the sites, twenty plots were randomly selected for assessment of species composition of vascular plant species, indicators of land-use intensity, and soil chemistry per plot. We hypothesized that the site with the lowest human impact would have the highest richness and diversity of woody and herbaceous species. At the site level, we found that richness and diversity of woody species were unaffected by land-use intensity, whereas herbaceous species richness was higher for the protected site (28 species on average per 1000 m2) than the two other sites (23 and 21 species on average per 1000 m2). At the plot level, herbaceous species richness was positively associated with woodcutting and soil pH. We interpret the positive effect of woodcutting on herbaceous species richness as the effect of lower competition by the woody component for resources such as water, nutrients, and light. With regard to the absence of any effect of land-use intensity on the richness of woody species, we conclude that in our study areas selective timber harvesting may be at a sustainable level and might even have a positive effect on the diversity of the herbaceous layer.

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Climatic spatial dynamics in the state of Pernambuco through geostatistical modeling

de Lucas,

de Sousa,

da Silva

et al. 2023

Theor Appl Climatol

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Geospatial distribution of soil organic carbon and soil pH within the cocoa agroecological zones of Ghana

Cited by 11 publications

References 51 publications

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

Impacts of Diffuse Land-Use on Plant Diversity Patterns in the Miombo Woodlands of Western Zambia

Climatic spatial dynamics in the state of Pernambuco through geostatistical modeling

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