Fine root biomass and soil properties following the conversion of miombo woodlands to shifting cultivation lands

Magalhães, Tarquinio Mateus; Mamugy, Faruk

doi:10.1016/j.catena.2020.104693

Cited by 9 publications

(5 citation statements)

References 79 publications

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“…A homogenized subsample of the soil (disturbed soil sample) was collected at each soil depth and kept in an airtight polythene bag. The soil pits were cleared vertically and two soil cores (undisturbed soil samples) taken horizontally, at two vertical positions along the soil pit, one at each half of each soil depth, using a 100 cm 3 volume corer (height: 51 mm, inner diameter: 50 mm) (Magalhães, 2017; Magalhães & Mamugy, 2020).…”

Section: Methodsmentioning

confidence: 99%

Miombo conversion to monoculture tree plantations: Changes in soil properties

2021

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Monoculture plantations replacing native woodlands are increasingly common globally. In Mozambique, vast miombo* woodlands have been replaced by single‐species plantations. Studies on the effect of replacing native woodlands by single‐species tree plantations on soil properties are abundant; however, those focused specifically on the replacement of miombo and considering a longer time since replacement and covering different growth stages are scarce. This article aimed at assessing the effects of the conversion of miombo to monoculture on soil properties and studying the effect of species planted, plantation age, and rotation on soil capacity to store carbon (C) and nitrogen (N). Plantations established in soils formerly covered by miombo were compared to the remaining original miombo with regard to soil properties. The effect of species planted, plantation age, and rotation on soil properties was examined using the three‐way ANOVA, followed by Tukey's test. Miombo conversion to plantations led to an increase of up to 66% of soil organic C (SOC) and 77% of total N (STN) and a decrease in soil bulk density (BD) of up to 16%. The replacement of the first‐ by second‐rotation plantations led to a decrease of up to 38% in SOC and STN and an increase of up to 32% in BD. BD decreased with plantation age; SOC and STN stocks tended to increase: 60 years after the replacement of miombo by plantations, SOC and STN increased by up to 88%. Changes in soil properties due to the replacement of miombo by monoculture were mainly driven by the species planted. * a sparse open deciduous woodland characteristic of dry parts of eastern Africa

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Section: Methodsmentioning

confidence: 99%

Miombo conversion to monoculture tree plantations: Changes in soil properties

2021

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“…All values of soil chemical properties associated with all land use (Table 5) were found within the normal range. Land use history helps to explain soil degradation due to land use change [16]. However, degradation of soil chemical properties may take more time to manifest than changes in land use and vegetation cover.…”

Section: Soil Chemical Propertiesmentioning

confidence: 99%

“…The majority of studies have been undertaken in intact miombo woodlands only by assessing their vegetation structures, yet most miombo woodlands are affected by human disturbance [12], leading to changes in their vegetation structure and soil properties, especially soil organic matter [9] [13] [14]. In addition, considering the land uses of specific sites, the species diversity and soil chemical properties will always be different [15] [16].…”

Section: Introductionmentioning

confidence: 99%

Tree Species Diversity and Edaphic Factors Associated with Different Land Uses in Tropical Forest Ecosystems, Tanzania

Bulenga,

Maliondo,

Katani

et al. 2023

OJE

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It has been reported that changes to miombo woodland ecosystems through conversion to other land uses alter tree species diversity and soil properties. The aim was to assess whether the Important Value Index (IVI), Shannon-Wiener diversity index (H'), and soil chemical properties differ between land uses in the Kibutuka miombo woodland ecosystem. IVI and H' were used to indicate tree species dominance and diversity. Statistical analyses were performed in R software. IVI of Brachystegia was significantly (p < 0.05) higher in the intact forest than in the combined land uses, while tree species of the genera Combretum, Milletia, and Diplorhynchus had significantly (p < 0.05) higher IVI in combined land uses than in the intact forest. The intact forest had significantly (p < 0.05) higher diversity than the degraded and agricultural lands. The intact forest had significantly (p < 0.05) higher soil Ca 2+ , K + , and Na + than combined land uses. Soil C, N, and P were significantly (p < 0.05) higher in intact forests than in the degraded forest. Degradation seen at a landscape scale for vegetation parameters, but not for soil parameters, indicates that the land use change taking place in the Kibutuka miombo woodland ecosystem is recent and the degradation seen in vegetation is still not reflected in the soil properties.

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“…Cropland STN content is second only to the northern mountainous areas, which may be due to the fertilization of farmland, which causes some nitrogen elements to infiltrate into the soil [15,61]. In addition, a large amount of agricultural waste, residue, and feces will be produced during the agricultural production process, and these materials will degrade into organic matter and release nitrogen elements, further increasing the STN content [62]. In terms of the prediction results, the spatial distribution of STN based on the XGBoost method shows that the STN content ranges from 0 to 2.01 g•kg −1 .…”

Section: Spatial Distribution Of Stn Contentmentioning

confidence: 99%

Comparison of Machine Learning Methods for Predicting Soil Total Nitrogen Content Using Landsat-8, Sentinel-1, and Sentinel-2 Images

et al. 2023

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Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling.

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Fine root biomass and soil properties following the conversion of miombo woodlands to shifting cultivation lands

Cited by 9 publications

References 79 publications

Miombo conversion to monoculture tree plantations: Changes in soil properties

Miombo conversion to monoculture tree plantations: Changes in soil properties

Tree Species Diversity and Edaphic Factors Associated with Different Land Uses in Tropical Forest Ecosystems, Tanzania

Comparison of Machine Learning Methods for Predicting Soil Total Nitrogen Content Using Landsat-8, Sentinel-1, and Sentinel-2 Images

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