Figure 1.
Map showing the location of different land uses considered for the study.
Article considers the influence of additions of rare earth elements such as Sm, La, Ce, Nd, and Y on the structure and properties of hypereutectic high-chromium white cast iron of grade G-X300CrMo27-2. To obtain an increased content of carbides in the studied cast iron samples, the carbon content was 3.75–3.9 and 4.1–4.2 wt%. The amount of rare earth elements additives added to the melt is 0.2% by weight. Data were obtained on the effect of overheating and cooling rate in the crystallization interval on the effect of rare earth additives, the structure and properties of white cast iron castings are given. According to the results of the microprobe analysis, it was shown that, under the chosen crystallization conditions, Sm, La, and Ce can form solid solutions with primary and eutectic carbides (FeCr)7C3. La and Ce form solid solutions with austenite. Nd and Y do not dissolve in iron chromium phases. All listed rare earth elements form phosphides and oxyphosphides. Experimental data are presented on the effect of rare earth elements on the size of primary (FeCr)7C3 carbides and a hypothesis is proposed on the effect of rare earth elements on the crystallization process of hypereutectic chromium white cast irons. Experimental data are presented on the effect of REE additives on the microhardness of phases, hardness, strength, and resistance to abrasive wear of cast iron castings. It was found that the introduction of these additives into hypereutectic chromium white cast iron does not contribute to the modification of the structure and leads to an increase in the size of primary crystals, as well as a decrease in their mechanical properties. However, the addition of Y increases the abrasive wear resistance, but reduces the strength of castings made from such white cast iron.
Citation: Aleksander Panichkin, Alma Uskenbayeva, Aidar Kenzhegulov, Axaule Mamaeva, Akerke Imbarova, Balzhan Kshibekova, Zhassulan Alibekov, Didik Nurhadiyanto, Isti Yunita. Assessment of the effect of small additions of some rare earth elements on the structure and mechanical properties of castings from hypereutectic chromium white irons[J]. AIMS Materials Science, 2023, 10(3): 517-540. doi: 10.3934/matersci.2023029
| [1] | Binoy Kumar Barman, K. Srinivasa Rao, Kangkana Sonowal, Zohmingliani, N.S.R. Prasad, Uttam Kumar Sahoo . Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram, India. AIMS Geosciences, 2020, 6(4): 525-544. doi: 10.3934/geosci.2020030 |
| [2] | Yang Sheng, Dehua Sun, Weizhong Liu . Study on the spatial variation of sensitivity of soil nutrient system in Xinjiang, China. AIMS Geosciences, 2023, 9(4): 632-651. doi: 10.3934/geosci.2023034 |
| [3] | Marya McKee, Kristofor R. Brye, Lisa Wood . Soil carbon sequestration across a chronosequence of tallgrass prairie restorations in the Ozark Highlands region of northwest Arkansas. AIMS Geosciences, 2019, 5(1): 1-24. doi: 10.3934/geosci.2019.1.1 |
| [4] | Rosazlin Abdullah, Firuza Begham Mustafa, Subha Bhassu, Nur Aziaty Amirah Azhar, Benjamin Ezekiel Bwadi, Nur Syabeera Begum Nasir Ahmad, Aaronn Avit Ajeng . Evaluation of water and soil qualities for giant freshwater prawn farming site suitability by using the AHP and GIS approaches in Jelebu, Negeri Sembilan, Malaysia. AIMS Geosciences, 2021, 7(3): 507-528. doi: 10.3934/geosci.2021029 |
| [5] | Firoz Ahmad, Laxmi Goparaju . Soil and Water Conservation Prioritization Using Geospatial Technology – a Case Study of Part of Subarnarekha Basin, Jharkhand, India. AIMS Geosciences, 2017, 3(3): 375-395. doi: 10.3934/geosci.2017.3.375 |
| [6] | Watcharin Phoemphon, Bantita Terakulsatit . Assessment of groundwater potential zones and mapping using GIS/RS techniques and analytic hierarchy process: A case study on saline soil area, Nakhon Ratchasima, Thailand. AIMS Geosciences, 2023, 9(1): 49-67. doi: 10.3934/geosci.2023004 |
| [7] | Fernando Teixeira . Determining the relative importance of climate and soil properties affecting the scores of visual soil quality indicators with dominance analysis. AIMS Geosciences, 2024, 10(1): 107-125. doi: 10.3934/geosci.2024007 |
| [8] | Quentin Fiacre Togbévi, Luc Ollivier Sintondji . Hydrological response to land use and land cover changes in a tropical West African catchment (Couffo, Benin). AIMS Geosciences, 2021, 7(3): 338-354. doi: 10.3934/geosci.2021021 |
| [9] | Palavai Venkateswara Rao, Mangalampalli Subrahmanyam, Bakuru Anandagajapathi Raju . Groundwater exploration in hard rock terrains of East Godavari district, Andhra Pradesh, India using AHP and WIO analyses together with geoelectrical surveys. AIMS Geosciences, 2021, 7(2): 244-267. doi: 10.3934/geosci.2021015 |
| [10] | Andrew C. Stolte, Brady R. Cox . Feasibility of in-situ evaluation of soil void ratio in clean sands using high resolution measurements of Vp and Vs from DPCH testing. AIMS Geosciences, 2019, 5(4): 723-749. doi: 10.3934/geosci.2019.4.723 |
Article considers the influence of additions of rare earth elements such as Sm, La, Ce, Nd, and Y on the structure and properties of hypereutectic high-chromium white cast iron of grade G-X300CrMo27-2. To obtain an increased content of carbides in the studied cast iron samples, the carbon content was 3.75–3.9 and 4.1–4.2 wt%. The amount of rare earth elements additives added to the melt is 0.2% by weight. Data were obtained on the effect of overheating and cooling rate in the crystallization interval on the effect of rare earth additives, the structure and properties of white cast iron castings are given. According to the results of the microprobe analysis, it was shown that, under the chosen crystallization conditions, Sm, La, and Ce can form solid solutions with primary and eutectic carbides (FeCr)7C3. La and Ce form solid solutions with austenite. Nd and Y do not dissolve in iron chromium phases. All listed rare earth elements form phosphides and oxyphosphides. Experimental data are presented on the effect of rare earth elements on the size of primary (FeCr)7C3 carbides and a hypothesis is proposed on the effect of rare earth elements on the crystallization process of hypereutectic chromium white cast irons. Experimental data are presented on the effect of REE additives on the microhardness of phases, hardness, strength, and resistance to abrasive wear of cast iron castings. It was found that the introduction of these additives into hypereutectic chromium white cast iron does not contribute to the modification of the structure and leads to an increase in the size of primary crystals, as well as a decrease in their mechanical properties. However, the addition of Y increases the abrasive wear resistance, but reduces the strength of castings made from such white cast iron.
Soil contributes largely to the global carbon cycle because it comprises of an active carbon pool [1]. In the terrestrial ecosystem, soil is considered to be the largest sink of organic carbon storing more than three times carbon compared to the amount stored in the atmosphere and 3.8 times more than the amount stored in biotic pool [2]. Therefore, the substantial sequestration of carbon in soils can provide a significant opportunity to mitigate global warming [3]. Enhancing the capture and storage of atmospheric CO2 in different land use systems can be a successful approach to lower its concentration while also improving the quality of soil [4,5]. Soil profile in the top 1m stored 1500 Pg C soil organic carbon (SOC) globally, out of which Indian soil holds about 9 Pg C where the Himalayan zones account for about 33% of the total SOC reserves owing to thick forest vegetation [6]. SOC can either be increased or decreased depending on various factors such as soil type, climate, topography and soil management practices. However, SOC is greatly influenced by vegetation through organic matter input and therefore land use change is one of the most important factors which influences SOC stock build up. For example, it was reported that the conversion of farmland to apple orchard led to the decrease in the quality of soil owing to the reduced SOC stocks [7]. Soil carbon stock, following forest-pasture conversions, decreased to 51% in 20–30 years old pasture converted from wet tropical forest in Costa Rica, while SOC stock increased to 164% in a 33 years old leguminous pasture converted from native vegetation in Western Australia [8]. A meta-analysis reported that SOC stock decreased 13% and 42% when native forest converted to plantation and crop land respectively [9].
In natural ecosystem like forest and agroforestry, the soils are less disturbed due to less cultural operations and therefore may contain adequate nutrients and soil microorganisms when compared to agricultural lands [10,11,12]. Intensive management and cultural practices in agricultural lands increase the turnover rates of macro agammaegates and lead to destabilization of the labile soil organic matter compounds [13]. Study reported from Northeast India showed the highest SOC stock in dense forest (140.4 Tg) and the least in shifting cultivation (10.7 Tg) with a total SOC stock (339.82 Tg), irrespective of the land use system for an area of 10.10 million ha, wherein forest soils contributed more than 50% with great implications for SOC sequestration in the region [14]. Studies from northern Bangladesh reported highest SOC concentration in agroforestry system (1.063%) and least in fallow land (0.249%) [15], whereas a similar study from homegardens in Aizawl, Mizoram reported SOC stock of 258.43 t C ha−1 in 1 m soil depth [16]. Soil carbon sequestration proves to be a key indicator of soil health and crop efficiency [17,18], responsible for climate change mitigation and at the same time improving soil physical properties through moisture and nutrient retention [19]. However, the removal of biomass through deforestation and land use change can accelerate soil erosion resulting in significant loss of soil organic carbon from the surface soil [20,21]. The state of Mizoram reported a high percentage of forest cover (86.27% with respect to the total geographical area); however, forest cover has decreased considerably (by 531 km2 from 2015 to 2017) due to shifting cultivation, biotic pressure, illegal felling, conversion of forest lands for developmental activities and agriculture expansion [22]. Despite the great potential of forest to sequester soil organic carbon, studies on SOC stock in forest and various land use conversions are limited in Mizoram [23,24,25]. Estimating SOC stock in various land uses has become very essential because it will aid policy makers to work out techniques for managing land use systems sustainably as well as preventing extreme loss of SOC. Hence, the present study was undertaken with objectives to estimate SOC stock in different land uses and also to assess the relationship between SOC and land use types in Mizoram.
This study was conducted in the whole of Mizoram which is located between 21°58' N to 24°35' N, and 91°15' E to 93°29' E encompassing a total area of 21,081 km2 (Figure 1). The state is bounded internationally by Myanmar and Bangladesh on the southern part and domestically by Manipur, Assam and Tripura on the northern part. The climatic condition is mild with relatively cool summer 20 to 29 ºC but becomes warmer with temperature exceeding 30 ºC. In winter, the temperature varies between 7 to 22 ºC. The winter season is short and summer long with heavy rainfall from the south-west monsoon with an average annual rainfall of 2450 mm. The monsoon period starts from May lasting till September with slight rain in the cold season. A summary of the site characteristics including climate, vegetation and soil of the eight land uses studied are shown in Tables 1 and 2. The age of the different land uses were determined with the help of the landholders and villagers.
| Characteristics | Land use | |||||||
| SC | WRC | HG | Forest(Natural) | BP | GL | OPP | TP | |
| Elevation (m.a.s.l.) | 276–1658 | 39–1638 | 46–1682 | 562–2004 | 381–1532 | 792–1964 | 122–592 | 47–718 |
| Slope (Min–Max %) | 0–35 | 0–15 | 25–50 | > 50 | 25–50 | 25–50 | 25–50 | 15–25 |
| Annual temp (Min–Max ºC) | 17.3–24.76 ºC | 15.7–24.20 ºC | 15.7–23.83 ºC | 17.3–24.76 ºC | 15.7–24.20 ºC | 17.3–24.76 ºC | 18.85–24.20 ºC | 19.7–24.76 ºC |
| Annual rainfall (mm) | 2510–3155 | 2346–3067 | 2346–3155 | 2510–3155 | 2346–3155 | 2200–3067 | 2819–3067 | 2616–2819 |
| Dominant species | Musa accuminata colla Musa sylvestris | Oryza sativa | Parkia timoriana Mangifera indica Artocarpus spp. | Engelhardtia spicata Oroxylum indicum Helicia excelsia Castanopsis tribuloidess | Melocanna baccifera | Quercus spp. | Elaeis guineensis | Tectona grandis |
| SC—Shifting cultivation, WRC—Wet Rice Cultivation, HG—Homegardens, BP—Bamboo plantation, GL—Grassland, OPP—Oil palm plantation, TP—Teak plantation. | ||||||||
DownLoad:
CSV
| Land usetypes | Characteristics | ||||
| Soil pH | Soil textural class | Soil colour | Major soil types | Parent material | |
| SC | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Dystrochrepts, Humic Hapludults, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| WRC | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| HG | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Humic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| Forest(Natural) | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Dystrochrepts, Humic Hapludults, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| BF | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Humic Hapludults, Typic Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| GL | Acidic | Sandy loam | Red and yellow loamy | Humic Hapludults, Typic Hapludults, Typic Dystrochrepts, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| OPP | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| TP | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| SC—Shifting cultivation, WRC—Wet Rice Cultivation, HG—Homegardens, BP—Bamboo plantation, GL—Grassland, OPP—Oil palm plantation, TP—Teak plantation. | |||||
DownLoad:
CSV
In each land use, five sample plots of 20 × 20 m2 were randomly selected, their locations and altitude recorded by a GPS. Within each sample plot, soils were collected from four corners and in the centre of the square plot at three depth classes: 0–15, 15–30 and 30–45 cm respectively. The five sub samples in each plot were mixed thoroughly and a composite sample was obtained for each depth class. A total of 120 samples (8 land use × 5 plots × 3 depths) were collected for SOC estimation. Similarly, a total of 120 samples (8 land use × 5 plots × 3 depths) for soil bulk density (BD) measurements were collected with the help of a soil corer of known volume. In the laboratory, the composite soil samples were mixed thoroughly, air-dried, crushed and passed through 2 mm sieve and replicates were made to analyse soil organic carbon content through Walkley and Black method [26]. For each depth, three replicates from each composite sample were analysed. Soil bulk density was determined by dry weight method by oven drying the soils at 80 ºC for 24 hours and rocky fragments (>2 mm) were separated. Soil pH was measured using a pH meter and soil textural class was identified following ISSS soil mixture classification system.
Soil carbon stock for each site was estimated by multiplying with corresponding values of fine bulk density and SOC content. SOC stock was calculated following the formula given by Intergovernmental Panel on Climate Change (IPCC) [27].
| C storage=∑horizon=nhorizon=1(SOC×Bulk Density×Depth×(1−frag)×10)horizon | (1) |
where, C storage—representative C stock for the soil of interest Mg C ha−1. SOC—concentration of soil organic carbon in a given soil mass, g C kg−1. Bulk Density—soil mass per sample volume, g cm−3. Depth—horizon depth or thickness of soil layer, m. Frag—% volume of coarse fragments (stone and gravel)/100, dimensionless.
SOC stock change (Mg C ha−1) is estimated depending on the SOC stock changes between previous (CLU0) and present (CLUn) land use type [28]. The carbon stock of the previous land use type was set as the baseline for calculating the rate of change in SOC stock (Mg C ha−1 yr−1) after land use conversion. The following equation is used to calculate the rate of change (Rstock):
| Rstock=CLU0−CLUnAge of CLUn | (2) |
Descriptive statistics analysis was carried out using SPSS version 17.0. An analysis of variance (ANOVA) was done to evaluate if different land uses have significant SOC stock distribution and significant effect (P < 0.05) was determined with Tukey honest significant difference (HSD) post hoc multiple comparisons.
Bulk density (BD) of fine soil (<2 mm) in different land use types across varying depths ranged between 0.40 to 0.71 g cm−3 (Figure 2). On an average, bulk density of fine soil in 0–45 cm soil profile was lowest in shifting cultivation (0.42 g cm−3) and the highest in forest (0.68 g cm−3). The higher soil bulk density in homegardens and wet rice cultivation as compared to other land uses maybe due to the cultivation practices such as tillage which cause soil compaction. Many studies have indicated that with the increase in soil depth, bulk density also tends to increase [29,30]. Conversely, our studies revealed that except in wet rice cultivation, the other land uses did not indicate any particular trend of bulk density increasing with increase in depth. It was reported that bulk density higher than 1.6 g/cm3 is unfavourable for plant growth as it can restrict the penetration of plant roots in clay loam soil [31]. In regard to this, the soil bulk density in all the land uses studied was found to be clearly below the critical value denoting that there is no extreme soil compaction.
Amongst all the land uses studied, forest (natural) recorded the highest SOC concentration in all soil depths with 3.74, 2.70 and 1.79% in 0–15cm, 15–30 cm and 30–45 cm respectively. Bamboo forest recorded the least SOC concentration at 0–15 and 15–30 cm depth (1.28%, 1.10%) while grassland recorded the least (0.56%) at 30–45 cm depth (Table 3). SOC concentration decreased with increasing soil depth class in each of the land use types (Figure 3). On an average, forest soils have the highest SOC concentration compared to other land uses studied (Figure 4). The highest SOC concentration in the forest can be related to the presence of more vegetation generating more litter falls which are returned to the soils as organic matters. In the top surface soil (0–15 cm), maximum SOC stock was found in wet rice cultivation (26.36 Mg C ha−1) followed by forest (24.50 Mg C ha−1) and minimum in bamboo plantation (11.81 Mg C ha−1) (Table 4). Many studies have shown that paddy soils have the potential to hold a great amount of recalcitrant/stable carbon [32,33]. In continuous wet rice cultivation, the built up of organic matter is very high due to their submerged conditions because in such a state, the decomposition of organic matter and SOC mineralization is very slow due to the anaerobic condition leading to higher net carbon storage [34]. Therefore, this may be one of the reasons why SOC stock in the upper 0–15 cm is higher in wet rice cultivation than the other land uses. In soil depth of 15–30 cm and 30–45 cm, the maximum SOC stock was recorded in homegarden and the least in grassland (Table 4). Grasslands have the ability to store a considerable amount of soil carbon in the upper stratum of the soil, however, the lower strata accumulates very less carbon which may be due to the shallow rooting of the grasses and absence of deep rooting trees. Highest SOC concentration and SOC stock in the top soil and decreasing with increase in soil depth from the study is in harmony with similar studies carried out by other researchers [11,23,35]. Overall amongst the different land uses, the mean SOC stock in 0–45 cm soil profile was highest in forest (52.74 Mg C ha−1) followed by homegarden (50.85 Mg C ha−1), wet rice cultivation (46.21 Mg C ha−1), teak (44.66 Mg C ha−1), oil palm (36.73 Mg C ha−1), bamboo (29.83 Mg C ha−1), shifting cultivation (27.87 Mg C ha−1) and lowest in grassland (27.68 Mg C ha−1) as presented in Figure 5. This is in disagreement with other studies where considerably high amount of SOC stock in grassland (75.76 Mg C ha−1) than plantations (46.13 Mg C ha−1) [36]; and higher value of SOC stock in grassland (95.54 Mg C ha−1) than in agricultural land (75.70 Mg C ha−1) [11] were reported. The significantly lower SOC stock in grassland reported in our study as compared to the other land uses might be because of the absence of deep rooted trees and fewer canopy covers. The potential for soils to store atmospheric carbon is primarily affected by the balance between the rate at which fresh photosynthetic material i.e. roots and exudates, is deposited and the time required by these carbon inputs to get broken down through heterotrophic respiration [37]. Moreover, as root tissue is more recalcitrant to degradation and mineralization than top soil litter, root derived carbon has long residence time [38]. Greater accumulation of organic matter in soils under tree canopies than open grassland can reduce leaching. Also, due to the absence of canopy covers, the soils in grasslands are directly exposed to the solar radiation thereby increasing the rate of mineralization. Similar findings have been reported by several other researchers [39,40]. Shifting cultivation recorded significantly lower (P < 0.05) SOC stock as compared to wet rice cultivation, homegardens and forest. Soils in shifting cultivation are usually much depleted due to reduction in biomass, reduced nutrients because of the shortened fallow periods and thus resulting in reduced soil organic carbon [41]. Another reason might be due to the steep slope condition of the state combined with heavy rainfall which leads to incidence of more soil erosion consequently leading to loss of SOC of the surface soils. A comparatively lesser SOC stock value of 29.83 Mg C ha−1 in bamboo forest was observed from the study where other similar studies from Mizoram reported 46.04 Mg C ha−1 [25]. This may be due to several reasons such as location, age of the bamboo stands and density of the bamboo stands. It was also reported by other studies that bamboo leaves releases allelopathic compounds during decomposition [42,43] which may reduce the growth of seedlings leading to low species richness which in turn reduces the input of litter thereby affecting the soil carbon stock. A correlation analysis of SOC concentration showed positive significant relationship with SOC stock, soil moisture content, clay and sand at P < 0.001 level of significance. However, it correlated negatively with bulk density at P < 0.001 and silt at P < 0.05 level of significance respectively (Table 5). The positive relationship between SOC concentration and soil moisture content implies that SOC increases with increase in soil moisture content. This might be due to the microbial activity as soil moisture plays an important role in regulating the activity of soil microbes and determining the microbial population in forest floor [44]. Similar observations were reported by other studies too [45,46]. Furthermore, the significant positive relationship between SOC with clay and sand indicates the importance of fine soil particles for SOC storage for longer duration, especially clay minerals which protects against weathering and microbial degradation. Additionally, the relationship of SOC between different land use types is presented in Table 6. The significant positive relationship of SOC between different land use types may be due to similar land management practices such as in shifting cultivation and homegarden where the lands are regularly subjected to practices such as weeding and hoeing. Whereas, in case of shifting cultivation, grasslands and bamboo plantation, the lower input of litter due to less tree canopies might be one of the probable reasons. Forest exhibited a positive significant relationship only with teak plantation which may be attributed to the denser understory vegetation and less soil disturbances in both the land uses. Similarly, the significant negative relationship between oil palm with homegarden and bamboo; teak with grassland and oil palm can also be due to different management practices and types of inputs.
| Land use Types | Soil Depth class (cm) | ||
| 0–15 | 15–30 | 30–45 | |
| Shifting Cultivation | 2.32 ± 0.24 | 1.64 ± 0.17 | 1.09 ± 0.11 |
| Wet Rice Cultivation | 2.90 ± 0.05 | 1.28 ± 0.06 | 0.69 ± 0.05 |
| Homegarden | 2.04 ± 0.10 | 1.76 ± 0.08 | 1.30 ± 0.08 |
| Forest (Natural) | 3.74 ± 0.32 | 2.70 ± 0.30 | 1.79 ± 0.13 |
| Bamboo plantation | 1.28 ± 0.04 | 1.10 ± 0.04 | 0.90 ± 0.03 |
| Grassland | 2.20 ± 0.36 | 1.19 ± 0.12 | 0.56 ± 0.03 |
| Oil palm plantation | 1.87 ± 0.25 | 1.15 ± 0.04 | 0.96 ± 0.13 |
| Teak plantation | 2.11 ± 0.12 | 1.40 ± 0.19 | 0.90 ± 0.03 |
| ± standard error of mean. | |||
DownLoad:
CSV
| Land use Types | Soil Depth Class (cm) | ||
| 0–15 | 15–30 | 30–45 | |
| Shifting Cultivation | 13.14 ± 0.73 | 9.19 ± 0.63 | 5.55 ± 0.43 |
| Wet Rice Cultivation | 26.36 ± 0.86 | 12.61 ± 0.58 | 7.24 ± 0.47 |
| Homegarden | 19.95 ± 0.93 | 17.54 ± 0.61 | 13.36 ± 0.92 |
| Forest (Natural) | 24.50 ± 2.31 | 16.52 ± 1.91 | 11.71 ± 1.14 |
| Bamboo plantation | 11.81 ± 0.36 | 9.91 ± 0.34 | 8.11 ± 0.33 |
| Grassland | 16.09 ± 3.06 | 7.98 ± 1.12 | 3.60 ± 0.46 |
| Oil palm plantation | 17.29 ± 2.80 | 9.66 ± 0.42 | 9.76 ± 1.84 |
| Teak plantation | 20.57 ± 3.15 | 15.05 ± 3.37 | 9.03 ± 1.34 |
| ± standard error of mean. | |||
DownLoad:
CSV
| SOC | SMC | pH | BD | Clay | Silt | Sand | |
| SOC | 1 | 0.375** | 0.003 | −0.324** | 0.263** | −0.227* | 0.336** |
| SMC | 1 | 0.263** | −0.537** | 0.100 | −0.440** | 0.511** | |
| pH | 1 | −0.213* | −0.410** | −0.014 | 0.119 | ||
| BD | 1 | −0.207* | 0.304** | −0.250** | |||
| Clay | 1 | −0.689** | 0.151 | ||||
| Silt | 1 | −0.089 | |||||
| Sand | 1 | ||||||
|
* Correlation is significant at P < 0.05 level (two-tailed). ** Correlation is significant at P < 0.01 level (two-tailed). |
|||||||
DownLoad:
CSV
| SC | WRC | HG | Forest | Bamboo | Grassland | Oil palm | Teak | |
| SC | 1.00 | |||||||
| WRC | 0.356 | 1.00 | ||||||
| HG | 0.766** | 0.016 | 1.00 | |||||
| Forest | 0.048 | −0.493 | 0.328 | 1.00 | ||||
| Bamboo | 0.933** | 0.259 | 0.862** | 0.041 | 1.00 | |||
| Grassland | 0.894** | 0.125 | 0.885** | 0.195 | 0.967** | 1.00 | ||
| Oil palm | −0.664 | 0.524 | −0.906** | −0.975 | −0.891** | 0.973** | 1.00 | |
| Teak | 0.634 | −0.491 | 0.899** | 0.996** | 0.904** | −0.977** | −0.971** | 1.00 |
| ** Correlation is significant at P < 0.01 level (two-tailed). SC—shifting cultivation, WRC—wet rice cultivation, HG—homegarden. | ||||||||
DownLoad:
CSV
Meanwhile the estimated loss of SOC stock following conversion of forest to different land uses is presented in Figure 5. Result indicated a maximum loss of SOC stock when forest was converted to shifting cultivation (−5.74 Mg C ha−1 yr−1) followed by oil palm plantation (−2.29 Mg C ha−1 yr−1), bamboo plantation (−1.56 Mg C ha−1 yr−1) and the least in homegardens (−0.14 Mg C ha−1 yr−1). This indicates that emphasis should be given on proper management of shifting cultivation with respect to its intensity of practice and adoptability. The loss in SOC stock when forest is converted to other land use systems was also reported by other studies [8,9]. However, our research did not take into consideration of many other factors which are responsible for the gain or loss of SOC stock such as climate, altitude, soil types, physical, chemical, microbiological, biochemical properties of soil, etc. For instance, the plants community and productivity can be altered by the variations in climate along an altitudinal gradient which ultimately leads to the increase or decrease in the amount and the rate of soil organic matter (SOM) mineralization [47,48,49]. The decrease in temperature with increasing altitude has been proven to reduce the turnover rate of SOC in forest soils and thereby enhancing SOC stabilization and storage [50]. Furthermore, soil microbes play a huge role in the storage and stabilization of soil organic carbon as soil microbes can accelerate the rate of SOM mineralization. Studies reported that the higher microbial biomass in grassland comparing to arable land increased the mineralization of SOM [51]. Also, the increase or decrease in SOC stock may have been affected by indirect factors such as mycorrhizal colonization and soil agammaegate size [52,53] Therefore, it is of utmost importance to consider these parameters while estimating SOC stock change in any land use system.
This study has shown that land use system is one of the major factors affecting soil organic carbon stock. It supports the existing knowledge of forest soils holding the maximum carbon stock. Our findings also indicated that a substantial amount of organic carbon can be stored in wet rice cultivation but shifting cultivation, which is a more dominant form of agriculture in Mizoram, resulted in a greater loss of SOC stocks due to their nature of practice and topography. Based on the results, SOC stocks in other land uses were low compared to forest and this indicate the presence of a good potential to sequester carbon in the soils of these land uses in the study area. Therefore, a detailed study of different land uses in Mizoram, and identification of its appropriate management practices aimed to increasing inputs and reduce soil organic carbon losses need to be conducted in the study area. Soil carbon sequestration will eventually minimize effects of climate change.
The authors would like to thank the Funding Agency: Department of Science and Technology, New Delhi under AICP- North-East CO2 Sequestration Research Program of Ministry of Science and Technology, Government of India (DST/1S-STAC/CO2-SR-227/14(G)-AICP-AFOLU-IV). The authors would also like to thank the editors and anonymous reviewers for their critical and valuable suggestions on this paper.
All authors declare no conflicts of interest in this paper
| [1] |
Panichkin AV, Korotenko RY, Kenzhegulov АК, et al. (2022) Porosity and non-metallic inclusions in cast iron produced with a high proportion of scrap. Complex Use Miner Resour 323: 68–76. https://doi.org/10.31643/2022/6445.42 doi: 10.31643/2022/6445.42
|
| [2] |
Tabrett CP, Sare IR, Ghomashchi MR (1996) Microstructure-property relationships in high chromium white iron alloys. Int Mater Rev 41: 59–82. https://doi.org/10.1179/imr.1996.41.2.59 doi: 10.1179/imr.1996.41.2.59
|
| [3] |
Llewellyn RJ, Yick SK, Dolman KF (2004) Scouring erosion resistance of metallic materials used in slurry pump service. Wear 256: 592–599. https://doi.org/10.1016/j.wear.2003.10.002 doi: 10.1016/j.wear.2003.10.002
|
| [4] |
Karantzalis E, Lekatou A, Mavros H (2009) Microstructure and properties of high chromium cast irons: Effect of heat treatments and alloying additions. Int J Cast Met Res 22: 448–456. https://doi.org/10.1179/174313309X436637 doi: 10.1179/174313309X436637
|
| [5] |
Lu B, Luo J, Chiovelli S (2006) Corrosion and wear resistance of chrome white irons—A correlation to their composition and microstructure. Metall Mater Tran A 37: 3029–3038. https://doi.org/10.1007/s11661-006-0184-x doi: 10.1007/s11661-006-0184-x
|
| [6] |
Semushkina L, Abdykirova G, Mukhanova A, et al. (2022) Improving the copper-molybdenum ores flotation technology using a combined collecting agent. Minerals 12: 1416. https://doi.org/10.3390/min12111416 doi: 10.3390/min12111416
|
| [7] |
Koizhanova AK, Berkinbayeva AN, Magomedov DR, et al. (2022) Study of the technology for gold recovery from gravity-flotation concentrate from ore beneficiation with the use of oxidizing reagents. J Inst Eng India Ser D 103: 663–672. https://link.springer.com/article/10.1007/s40033-022-00366-6 doi: 10.1007/s40033-022-00366-6
|
| [8] |
Semushkina L, Abdykirova G, Turysbekov D, et al. (2021) On the possibility to process copper-molybdenum ore using a combined flotation reagent. Complex Use Miner Resour 4: 57–64. https://doi.org/10.31643/2021/6445.41 doi: 10.31643/2021/6445.41
|
| [9] |
Toktar G, Magomedov DR, Kоizhanova AK, et al. (2023) Extraction of gold from low-sulfide gold-bearing ores by beneficiating method using a pressure generator for pulp microaeration. Complex Use Miner Resour 325: 62–71. https://doi.org/10.31643/2023/6445.19 doi: 10.31643/2023/6445.19
|
| [10] |
Ngqase M, Pan X (2020) An overview on types of white cast irons and high chromium white cast irons. J Phys Conf Ser 1495: 012023. https://doi.org/10.1088/1742-6596/1495/1/012023 doi: 10.1088/1742-6596/1495/1/012023
|
| [11] |
Uskenbayeva AM, Panichkin AV, Mamaeva AA, et al. (2022) Trends in improving the properties of wear-resistant chromium cast irons. EJSU 144: 17–23 (in Russian). https://doi.org/10.51301/ejsu.2022.i1.03 doi: 10.51301/ejsu.2022.i1.03
|
| [12] | Gavrilyuk VP, Tikhonovich VI, Shalevskaya IA, et al. (2010) Abrasion-Resistant High-Chromium Cast Irons, Lugansk: Knowledge, 141 (in Russian). Available from: https://foundry.kpi.ua/wp-content/uploads/2020/05/gavrylyuk-vp-abrazyvostojkye-v%D1%8Bsokohromyst%D1%8Be-chugun%D1%8B.pdf. |
| [13] | Garber ME (2010) Wear-Resistant White Cast Irons: Properties, Structure, Technology, Operation, Moscow: Mashinostroenie, 280 (in Russian). Available from: https://foundry.kpi.ua/wp-content/uploads/2020/05/garber-me-yznosostojkye-bel%D1%8Be-chugun%D1%8B-svojstva-struktura-tehnologyya-%D1%8Dkspluataczyya.pdf. |
| [14] | Uskenbayeva AM, Shamel'khanova NA, Volochko AT (2016) Spectral researches of carbonic nanostructures used as cast iron modifiers. Complex Use Miner Resour 1: 61–65. |
| [15] |
Dojka M, Stawarz M (2020) Bifilm defects in Ti-inoculated chromium white cast iron. Materials 13: 3124. https://doi.org/10.3390/ma13143124 doi: 10.3390/ma13143124
|
| [16] |
Dojka M, Kondracki M, Studnicki A, et al. (2018) Crystallization process of high chromium cast iron with the addition of Ti and Sr. Arch Foundry Eng 18: 564. https://doi.org/10.24425/122503 doi: 10.24425/122503
|
| [17] |
Dojka M, Dojka R, Stawarz M, et al. (2019) Influence of Ti and REE on primary crystallization and wear resistance of chromium cast iron. J Mater Eng Perform 28: 4002–4011. https://doi.org/10.1007/s11665-019-04088-x doi: 10.1007/s11665-019-04088-x
|
| [18] |
Guo E, Wang L, Wang L, et al. (2009) Effects of RE, V, Ti and B composite modification on the microstructure and properties of high chromium cast iron containing 3% molybdenum. Rare Metals 28: 606–611. https://doi.org/10.1007/s12598-009-0116-1 doi: 10.1007/s12598-009-0116-1
|
| [19] |
Ibrahim MM, El-Hadad S, Mourad M (2021) Influence of niobium content on the mechanical properties and abrasion wear resistance of heat-treated high-chromium cast iron. Int J Met 15: 500–509. https://doi.org/10.1007/s40962-020-00474-7 doi: 10.1007/s40962-020-00474-7
|
| [20] |
Sánchez A, Bedolla-Jacuinde A, Guerra FV et al. (2020) Vanadium additions to a high-Cr white iron and its effects on the abrasive wear behavior. MRS Adv 5: 3077–3089. https://doi.org/10.1557/adv.2020.414 doi: 10.1557/adv.2020.414
|
| [21] |
Radulovic M, Fiset M, Peev K (1994) Effect of rare earth elements on microstructure and properties of high chromium white iron. Mater Sci Technol 10: 1057–1062. http://dx.doi.org/10.1179/mst.1994.10.12.1057 doi: 10.1179/mst.1994.10.12.1057
|
| [22] |
Aubakirov D, Issagulov A, Kvon S, et al. (2022) Modifying effect of a new boron-barium ferroalloy on the wear resistance of low-chromium cast iron. Metals 12: 1153. https://doi.org/10.3390/met12071153 doi: 10.3390/met12071153
|
| [23] |
Uskenbaeva AM, Volochko AT, Shamel'khanova NA, et al. (2016) Effect of nanocarbon additions on graphitization and tribological properties of gray cast iron. Metallurgist 60: 191–197. https://doi.org/10.1007/s11015-016-0272-0 doi: 10.1007/s11015-016-0272-0
|
| [24] |
Uskenbayeva AM, Volochko AT, Shamel'khanova NA, et al. (2017) The study of the role of fullerene black additive during the modification of ductile cast iron. MSF 891: 235–241. https://doi.org/10.4028/www.scientific.net/msf.891.235 doi: 10.4028/www.scientific.net/MSF.891.235
|
| [25] |
Zhi Х, Xing J, Fu H, et al. (2008) Effect of fluctuation and modification on microstructure and impact toughness of 20 wt% Cr hypereutectic white cast iron. Mater Werkst 39: 391–393. https://doi.org/10.1002/mawe.200700219 doi: 10.1002/mawe.200700219
|
| [26] |
Wu X, Xing J, Fu H, et al. (2007) Effect of titanium on the morphology of primary M7C3 carbides in hypereutectic high chromium white iron. Mater Sci Eng A-Struct 457: 180–185. https://doi.org/10.1016/j.msea.2006.12.006 doi: 10.1016/j.msea.2006.12.006
|
| [27] |
Chung RJ, Tang X, Li DY, et al. (2009) Effects of titanium addition on microstructure and wear resistance of hypereutectic high chromium cast iron Fe–25wt.%Cr–4wt.%C. Wear 267: 356–361. https://doi.org/10.1016/j.wear.2008.12.061 doi: 10.1016/j.wear.2008.12.061
|
| [28] |
Chung RJ, Tang X, Li DY, et al. (2013) Microstructure refinement of hypereutectic high Cr cast irons using hard carbide-forming elements for improved wear resistance. Wear 301: 695–706. https://doi.org/10.1016/j.wear.2013.01.079 doi: 10.1016/j.wear.2013.01.079
|
| [29] |
Ibrahim MM, El-Hadad S, Mourad M (2018) Enhancement of wear resistance and impact toughness of as cast hypoeutectic high chromium cast iron using niobium. Int J Cast Met Res 31: 72–79. https://doi.org/10.1080/13640461.2017.1366144 doi: 10.1080/13640461.2017.1366144
|
| [30] |
Bedolla-Jacuinde A, Aguilar SL, Hernández B (2005) Eutectic modification in a low-chromium white cast iron by a mixture of titanium, rare earths, and bismuth: I. Effect on microstructure. J Mater Eng Perform 14: 149–157. https://doi.org/10.1361/10599490523300 doi: 10.1361/10599490523300
|
| [31] |
Zhi X, Xing J, Fu H, et al. (2008) Effect of niobium on the as-cast microstructure of hypereutectic high chromium cast iron. Mater Lett 62: 857–860. https://doi.org/10.1016/j.matlet.2007.06.084 doi: 10.1016/j.matlet.2007.06.084
|
| [32] |
Li P, Yang Y, Shen D, et al. (2020) Mechanical behavior and microstructure of hypereutectic high chromium cast iron: the combined effects of tungsten, manganese and molybdenum additions. J Mater Resear Techn 9: 5735–5748. https://doi.org/10.1016/j.jmrt.2020.03.098 doi: 10.1016/j.jmrt.2020.03.098
|
| [33] |
Mampuru LA, Maruma MG, Moema JS (2016) Grain refinement of 25 wt% high-chromium white cast iron by addition of vanadium. J S Afr Inst Min Metall 116: 969–972. http://dx.doi.org/10.17159/2411-9717/2016/v116n10a12. doi: 10.17159/2411-9717/2016/v116n10a12
|
| [34] |
Sánchez A, Bedolla-Jacuinde A, Guerra FV, et al. (2020) Vanadium additions to a high-Cr ehite iron and its effects on the abrasive wear behavior. MRS Adv 5: 3077–3089. https://doi.org/10.1557/adv.2020.414 doi: 10.1557/adv.2020.414
|
| [35] |
Zhi X, Liu J, Xing J, et al. (2014) Effect of cerium modification on microstructure and properties of hypereutectic high chromium cast iron. Mater Scien Eng A-Struct 603: 98–103. https://doi.org/10.1016/j.msea.2014.02.080 doi: 10.1016/j.msea.2014.02.080
|
| [36] |
Zhi X, Han Y, Lui J (2015) Effect of aluminum on the primary carbides of a hypereutectic high chromium cast iron. Materialwiss Werkst 46: 33–39. https://doi.org/10.1002/mawe.201400258 doi: 10.1002/mawe.201400258
|
| [37] |
Mikhailov G, Makrovets L, Smirnov L (2015) Thermodynamic modeling of the reaction of lanthanum with components of iron-based melts. Steel Transl 45: 913–918. https://doi.org/10.3103/S0967091215120086 doi: 10.3103/S0967091215120086
|
| [38] |
Zhi Х, Xing J, Fu H, et al. (2009) Effect of fluctuation, modification and surface chill on structure of 20%Cr hypereutectic white cast iron. Mater Sci Tech 25: 56–60. https://doi.org/10.1179/174328407X245139 doi: 10.1179/174328407X245139
|
| [39] |
Guo Q, Fu H, Guo X, et al. (2022) Microstructure and properties of modified as-cast hypereutectic high chromium cast iron. Materialwiss Werkst 53: 208–219 https://doi.org/10.1002/mawe.202100183 doi: 10.1002/mawe.202100183
|
| [40] | Kolokoltsev VM, Shevchenko AV (2011) Improving the properties of castings from cast irons for special purposes by refining and modifying their melts. Vestnik 1: 23–28 (in Russian). |
| [41] | Goldstein YE, Mizin VG (1986) Modification and Microalloying of Cast Iron and Steel, Moscow: Metallurgy, 416 (in Russian). Available from: https://www.studmed.ru/goldshteyn-yae-mizin-vg-modificirovanie-i-mikrolegirovanie-chuguna-i-stali_d9183b4982a.html. |
| [42] |
Marukovich EI, Stetsenko VY, Stetsenko AV (2022) On deoxidation and modification of carbon steel. Litiyo i Metallurgiya 4: 24–28 (in Russian). https://doi.org/10.21122/1683-6065-2022-4-24-28 doi: 10.21122/1683-6065-2022-4-24-28
|
| [43] |
Ri K, Dzyuba GS, Ri EKh, et al. (2015) Structure and properties control of chromium white cast iron by their modifying. Izvestiya Ferrous Metallurgy 58: 412–416 (in Russian). https://doi.org/10.15825/0368-0797-2015-6-412-416 doi: 10.17073/0368-0797-2015-6-412-416
|
| [44] |
Lv H, Zhou R, Li L, et al. (2018) Effect of electric current pulse on microstructure and corrosion resistance of hypereutectic high chromium cast iron. Materials 11: 2220. https://doi.org/10.3390/ma11112220 doi: 10.3390/ma11112220
|
| [45] |
Chen L, Stahl JE, Zhao W, et al. (2018) Assessment on abrasiveness of high chromium cast iron material on the wear performance of PCBN cutting tools in dry machining. J Mater Process Technol 255: 110–120. https://doi.org/10.1016/j.jmatprotec.2017.11.054 doi: 10.1016/j.jmatprotec.2017.11.054
|
| [46] |
Abd El-Aziz K, Zohdy K, Saber D, et al. (2015) Wear and corrosion behavior of high-Cr white cast iron alloys in different corrosive media. J Bio Tribo Corros 1: 25. https://doi.org/10.1007/s40735-015-0026-8 doi: 10.1007/s40735-015-0026-8
|
| [47] |
Pinho KF, Boher C, Scandian C (2013) Effect of molybdenum and chromium contents on sliding wear of high-chromium white cast iron at high temperature. Lubr Sci 25: 153–162. https://doi.org/10.1002/ls.1171 doi: 10.1002/ls.1171
|
| [48] | Yang QX, Liao B, Liu JH, et al. (1998) Effect of rare earth elements on carbide morphology and phase transformation dynamics of high Ni-Cr alloy cast iron. J Rare Earth 16: 36–40. |
| [49] |
Zhi X, Liu J, Xing J, et al. (2014) Effect of cerium modification on microstructure and properties of hypereutectic high chromium cast iron. Mater Sci Eng A-Struct 603: 98–103. https://doi.org/10.1016/j.msea.2014.02.080 doi: 10.1016/j.msea.2014.02.080
|
| [50] |
Shi Z, Shao W, Rao L, et al. (2020) Effects of Ce doping on mechanical properties of M7C3 carbides in hypereutectic Fe–Cr–C hardfacing alloy. J Alloys Compd 850: 156656. https://doi.org/10.1016/j.jallcom.2020.156656 doi: 10.1016/j.jallcom.2020.156656
|
| [51] |
Shi Z, Shao W, Rao L, et al. (2021) Effects of Y dopant on mechanical properties and electronic structures of M7C3 carbide in Fe-Cr-C hardfacing coating. Appl Surf Sci 538: 148108. https://doi.org/10.1016/j.apsusc.2020.148108 doi: 10.1016/j.apsusc.2020.148108
|
| 1. | Guruprasad M. Hugar, Effect of soil organic carbon on unsaturated earth properties, 2020, 3, 2523-8922, 267, 10.1007/s42398-020-00113-1 | |
| 2. | Ovung Etsoshan Yinga, Kewat Sanjay Kumar, Manpoong Chowlani, Shri Kant Tripathi, Vinod Prasad Khanduri, Sudhir Kumar Singh, Influence of land-use pattern on soil quality in a steeply sloped tropical mountainous region, India., 2020, 0365-0340, 1, 10.1080/03650340.2020.1858478 | |
| 3. | Angélica M Gómez, Adriana Parra, Tamlin M Pavelsky, Erika Wise, Juan Camilo Villegas, Ana Meijide, Ecohydrological impacts of oil palm expansion: a systematic review, 2023, 18, 1748-9326, 033005, 10.1088/1748-9326/acbc38 | |
| 4. | Noppol Arunrat, Sukanya Sereenonchai, Ryusuke Hatano, Effects of fire on soil organic carbon, soil total nitrogen, and soil properties under rotational shifting cultivation in northern Thailand, 2022, 302, 03014797, 113978, 10.1016/j.jenvman.2021.113978 | |
| 5. | Sujit Das, Abhijit Nama, Sourabh Deb, Uttam Kumar Sahoo, Soil quality, carbon stock and climate change mitigation potential of Dipterocarp natural and planted forests of Tripura, Northeast India, 2022, 2229-4473, 10.1007/s42535-022-00515-y | |
| 6. | Anup Maharjan, Peter M. Groffman, Charles J. Vörösmarty, Maria Tzortziou, Xiaojing Tang, Pamela A. Green, Sources of terrestrial nitrogen and phosphorus mobilization in South and South East Asian coastal ecosystems, 2022, 4, 25894714, 12, 10.1016/j.wsee.2021.12.002 | |
| 7. | Munesh Kumar, Amit Kumar, Tarun Kumar Thakur, Uttam Kumar Sahoo, Rahul Kumar, Bobbymoore Konsam, Rajiv Pandey, Soil organic carbon estimation along an altitudinal gradient of chir pine forests in the Garhwal Himalaya, India: A field inventory to remote sensing approach, 2022, 33, 1085-3278, 3387, 10.1002/ldr.4393 | |
| 8. | Tingting Ren, Jiahui Liao, Long Jin, Manuel Delgado-Baquerizo, Honghua Ruan, Application of biogas-slurry and biochar improves soil multifunctionality in a poplar plantation during afforestation processes, 2023, 0032-079X, 10.1007/s11104-023-05968-x | |
| 9. | Temesgen Addise, Bobe Bedadi, Alemayehu Regassa, Lemma Wogi, Samuel Feyissa, Fedor Lisetskii, Spatial Variability of Soil Organic Carbon Stock in Gurje Subwatershed, Hadiya Zone, Southern Ethiopia, 2022, 2022, 1687-7675, 1, 10.1155/2022/5274482 | |
| 10. | Omosalewa Odebiri, Onisimo Mutanga, John Odindi, Rowan Naicker, Modelling soil organic carbon stock distribution across different land-uses in South Africa: A remote sensing and deep learning approach, 2022, 188, 09242716, 351, 10.1016/j.isprsjprs.2022.04.026 | |
| 11. | Amit Kumar, Munesh Kumar, Rajiv Pandey, Yu ZhiGuo, Marina Cabral-Pinto, Forest soil nutrient stocks along altitudinal range of Uttarakhand Himalayas: An aid to Nature Based Climate Solutions, 2021, 207, 03418162, 105667, 10.1016/j.catena.2021.105667 | |
| 12. | Tolamariam Chimdessa, Forest carbon stock variation with altitude in bolale natural forest, Western Ethiopia, 2023, 45, 23519894, e02537, 10.1016/j.gecco.2023.e02537 | |
| 13. | Imanuel Lawmchullova, Ch. Udaya Bhaskara Rao, Estimation of siltation in Tuirial dam: a spatio-temporal analysis using GIS technique and bathymetry survey, 2024, 9, 2662-5571, 81, 10.1007/s43217-023-00158-2 | |
| 14. | Omosalewa Odebiri, Onisimo Mutanga, John Odindi, Rob Slotow, Paramu Mafongoya, Romano Lottering, Rowan Naicker, Trylee Nyasha Matongera, Mthembeni Mngadi, Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes, 2024, 243, 03418162, 108216, 10.1016/j.catena.2024.108216 | |
| 15. | Manmohan Kaith, Pushpa Tirkey, D. R. Bhardwaj, Jatin Kumar, Jai Kumar, Carbon Sequestration Potential of Forest Plantation Soils in Eastern Plateau and Hill Region of India: a Promising Approach Toward Climate Change Mitigation, 2023, 234, 0049-6979, 10.1007/s11270-023-06364-y | |
| 16. | Partha Deb Roy, Roomesh Kumar Jena, Tarik Mitran, Pravash Chandra Moharana, Nirmalendu Basak, Bholanath Saha, Do soil horizons and land-uses screen different sets of soil quality indicators in the hilly region of northeast India?, 2024, 83, 1866-6280, 10.1007/s12665-024-11797-7 | |
| 17. | Burhan U. Choudhury, Priyabatra Santra, Naseeb Singh, Poulamee Chakraborty, Development of land-use-specific pedotransfer functions for predicting bulk density of acidic topsoil in eastern Himalayas (India), 2023, 34, 23520094, e00671, 10.1016/j.geodrs.2023.e00671 | |
| 18. | Zebene Tadesse, Melkamu Abere, Belayneh Azene, Pan Kaiwen, Yigardu Mulatu, Meta Francis, Lowland bamboo (Oxytenanthera abyssinica) deforestation and subsequent cultivation effects on soil physico-chemical properties in northwestern Ethiopia, 2023, 4, 27731391, 100038, 10.1016/j.bamboo.2023.100038 | |
| 19. | 2024, Chapter 18, 978-981-97-2099-6, 311, 10.1007/978-981-97-2100-9_18 | |
| 20. | Jitendra Ahirwal, Uttam Thangjam, Uttam Kumar Sahoo, 2023, Chapter 12, 978-981-99-3302-0, 217, 10.1007/978-981-99-3303-7_12 | |
| 21. | Navamallika Gogoi, Moharana Choudhury, Mohd Sayeed Ul Hasan, Bishwajit Changmai, Debajit Baruah, Palas Samanta, Soil organic carbon pool in diverse land utilization patterns in North-East India: an implication for carbon sequestration, 2024, 1573-2975, 10.1007/s10668-024-05037-y | |
| 22. | Kavya Jeevan, Gunasekharan Shilpa, Kannankodantavida Manjusha, Anbazhagi Muthukumar, Muthukumar Muthuchamy, Comparison of Carbon Stock Potential of Different ‘Trees Outside Forest’ Systems of Palakkad District, Kerala: A Step Towards Climate Change Mitigation, 2024, 1085-3278, 10.1002/ldr.5400 | |
| 23. | Bipul Das Chowdhury, Alekhya Sarkar, Niladri Paul, Bimal Debnath, A comparative physico-chemical study of rhizosphere soils of healthy and infected agarwood trees in Tripura, Northeast India, 2024, 2229-4473, 10.1007/s42535-024-01123-8 | |
| 24. | Sanjay Kumar Ray, Dibyendu Chatterjee, Saikat Ranjan Das, Sanjib Ray, Basant Kumar Kandpal, Vinay Kumar Mishra, 2024, Chapter 10, 978-3-031-70387-4, 123, 10.1007/978-3-031-70388-1_10 | |
| 25. | Avijit Mistri, Michael von Hauff, 2025, Chapter 7, 978-3-031-81131-9, 127, 10.1007/978-3-031-81132-6_7 | |
| 26. | Rubina Debbarma, Kanika Tripura, Sujit Das, Sourabh Deb, Crop diversity, soil quality and traditional management practices in a young aged shifting cultivation land of Tripura, Northeast India, 2025, 2229-4473, 10.1007/s42535-025-01213-1 |
Figures(17) / Tables(1)
Aleksander Panichkin, Alma Uskenbayeva, Aidar Kenzhegulov, Axaule Mamaeva, Akerke Imbarova, Balzhan Kshibekova, Zhassulan Alibekov, Didik Nurhadiyanto, Isti Yunita. Assessment of the effect of small additions of some rare earth elements on the structure and mechanical properties of castings from hypereutectic chromium white irons[J]. AIMS Materials Science, 2023, 10(3): 517-540. doi: 10.3934/matersci.2023029
| Characteristics | Land use | |||||||
| SC | WRC | HG | Forest(Natural) | BP | GL | OPP | TP | |
| Elevation (m.a.s.l.) | 276–1658 | 39–1638 | 46–1682 | 562–2004 | 381–1532 | 792–1964 | 122–592 | 47–718 |
| Slope (Min–Max %) | 0–35 | 0–15 | 25–50 | > 50 | 25–50 | 25–50 | 25–50 | 15–25 |
| Annual temp (Min–Max ºC) | 17.3–24.76 ºC | 15.7–24.20 ºC | 15.7–23.83 ºC | 17.3–24.76 ºC | 15.7–24.20 ºC | 17.3–24.76 ºC | 18.85–24.20 ºC | 19.7–24.76 ºC |
| Annual rainfall (mm) | 2510–3155 | 2346–3067 | 2346–3155 | 2510–3155 | 2346–3155 | 2200–3067 | 2819–3067 | 2616–2819 |
| Dominant species | Musa accuminata colla Musa sylvestris | Oryza sativa | Parkia timoriana Mangifera indica Artocarpus spp. | Engelhardtia spicata Oroxylum indicum Helicia excelsia Castanopsis tribuloidess | Melocanna baccifera | Quercus spp. | Elaeis guineensis | Tectona grandis |
| SC—Shifting cultivation, WRC—Wet Rice Cultivation, HG—Homegardens, BP—Bamboo plantation, GL—Grassland, OPP—Oil palm plantation, TP—Teak plantation. | ||||||||
DownLoad:
CSV
| Land usetypes | Characteristics | ||||
| Soil pH | Soil textural class | Soil colour | Major soil types | Parent material | |
| SC | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Dystrochrepts, Humic Hapludults, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| WRC | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| HG | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Humic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| Forest(Natural) | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Dystrochrepts, Humic Hapludults, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| BF | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Humic Hapludults, Typic Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| GL | Acidic | Sandy loam | Red and yellow loamy | Humic Hapludults, Typic Hapludults, Typic Dystrochrepts, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| OPP | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| TP | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| SC—Shifting cultivation, WRC—Wet Rice Cultivation, HG—Homegardens, BP—Bamboo plantation, GL—Grassland, OPP—Oil palm plantation, TP—Teak plantation. | |||||
DownLoad:
CSV
| Land use Types | Soil Depth class (cm) | ||
| 0–15 | 15–30 | 30–45 | |
| Shifting Cultivation | 2.32 ± 0.24 | 1.64 ± 0.17 | 1.09 ± 0.11 |
| Wet Rice Cultivation | 2.90 ± 0.05 | 1.28 ± 0.06 | 0.69 ± 0.05 |
| Homegarden | 2.04 ± 0.10 | 1.76 ± 0.08 | 1.30 ± 0.08 |
| Forest (Natural) | 3.74 ± 0.32 | 2.70 ± 0.30 | 1.79 ± 0.13 |
| Bamboo plantation | 1.28 ± 0.04 | 1.10 ± 0.04 | 0.90 ± 0.03 |
| Grassland | 2.20 ± 0.36 | 1.19 ± 0.12 | 0.56 ± 0.03 |
| Oil palm plantation | 1.87 ± 0.25 | 1.15 ± 0.04 | 0.96 ± 0.13 |
| Teak plantation | 2.11 ± 0.12 | 1.40 ± 0.19 | 0.90 ± 0.03 |
| ± standard error of mean. | |||
DownLoad:
CSV
| Land use Types | Soil Depth Class (cm) | ||
| 0–15 | 15–30 | 30–45 | |
| Shifting Cultivation | 13.14 ± 0.73 | 9.19 ± 0.63 | 5.55 ± 0.43 |
| Wet Rice Cultivation | 26.36 ± 0.86 | 12.61 ± 0.58 | 7.24 ± 0.47 |
| Homegarden | 19.95 ± 0.93 | 17.54 ± 0.61 | 13.36 ± 0.92 |
| Forest (Natural) | 24.50 ± 2.31 | 16.52 ± 1.91 | 11.71 ± 1.14 |
| Bamboo plantation | 11.81 ± 0.36 | 9.91 ± 0.34 | 8.11 ± 0.33 |
| Grassland | 16.09 ± 3.06 | 7.98 ± 1.12 | 3.60 ± 0.46 |
| Oil palm plantation | 17.29 ± 2.80 | 9.66 ± 0.42 | 9.76 ± 1.84 |
| Teak plantation | 20.57 ± 3.15 | 15.05 ± 3.37 | 9.03 ± 1.34 |
| ± standard error of mean. | |||
DownLoad:
CSV
| SOC | SMC | pH | BD | Clay | Silt | Sand | |
| SOC | 1 | 0.375** | 0.003 | −0.324** | 0.263** | −0.227* | 0.336** |
| SMC | 1 | 0.263** | −0.537** | 0.100 | −0.440** | 0.511** | |
| pH | 1 | −0.213* | −0.410** | −0.014 | 0.119 | ||
| BD | 1 | −0.207* | 0.304** | −0.250** | |||
| Clay | 1 | −0.689** | 0.151 | ||||
| Silt | 1 | −0.089 | |||||
| Sand | 1 | ||||||
|
* Correlation is significant at P < 0.05 level (two-tailed). ** Correlation is significant at P < 0.01 level (two-tailed). |
|||||||
DownLoad:
CSV
| SC | WRC | HG | Forest | Bamboo | Grassland | Oil palm | Teak | |
| SC | 1.00 | |||||||
| WRC | 0.356 | 1.00 | ||||||
| HG | 0.766** | 0.016 | 1.00 | |||||
| Forest | 0.048 | −0.493 | 0.328 | 1.00 | ||||
| Bamboo | 0.933** | 0.259 | 0.862** | 0.041 | 1.00 | |||
| Grassland | 0.894** | 0.125 | 0.885** | 0.195 | 0.967** | 1.00 | ||
| Oil palm | −0.664 | 0.524 | −0.906** | −0.975 | −0.891** | 0.973** | 1.00 | |
| Teak | 0.634 | −0.491 | 0.899** | 0.996** | 0.904** | −0.977** | −0.971** | 1.00 |
| ** Correlation is significant at P < 0.01 level (two-tailed). SC—shifting cultivation, WRC—wet rice cultivation, HG—homegarden. | ||||||||
DownLoad:
CSV
| Characteristics | Land use | |||||||
| SC | WRC | HG | Forest(Natural) | BP | GL | OPP | TP | |
| Elevation (m.a.s.l.) | 276–1658 | 39–1638 | 46–1682 | 562–2004 | 381–1532 | 792–1964 | 122–592 | 47–718 |
| Slope (Min–Max %) | 0–35 | 0–15 | 25–50 | > 50 | 25–50 | 25–50 | 25–50 | 15–25 |
| Annual temp (Min–Max ºC) | 17.3–24.76 ºC | 15.7–24.20 ºC | 15.7–23.83 ºC | 17.3–24.76 ºC | 15.7–24.20 ºC | 17.3–24.76 ºC | 18.85–24.20 ºC | 19.7–24.76 ºC |
| Annual rainfall (mm) | 2510–3155 | 2346–3067 | 2346–3155 | 2510–3155 | 2346–3155 | 2200–3067 | 2819–3067 | 2616–2819 |
| Dominant species | Musa accuminata colla Musa sylvestris | Oryza sativa | Parkia timoriana Mangifera indica Artocarpus spp. | Engelhardtia spicata Oroxylum indicum Helicia excelsia Castanopsis tribuloidess | Melocanna baccifera | Quercus spp. | Elaeis guineensis | Tectona grandis |
| SC—Shifting cultivation, WRC—Wet Rice Cultivation, HG—Homegardens, BP—Bamboo plantation, GL—Grassland, OPP—Oil palm plantation, TP—Teak plantation. | ||||||||
| Land usetypes | Characteristics | ||||
| Soil pH | Soil textural class | Soil colour | Major soil types | Parent material | |
| SC | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Dystrochrepts, Humic Hapludults, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| WRC | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| HG | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Humic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| Forest(Natural) | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Typic Dystrochrepts, Humic Hapludults, Typic Hapludults | Ferruginous sandstone, shale, alluvial and colluvial materials |
| BF | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts, Humic Hapludults, Typic Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| GL | Acidic | Sandy loam | Red and yellow loamy | Humic Hapludults, Typic Hapludults, Typic Dystrochrepts, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| OPP | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| TP | Acidic | Sandy loam | Red and yellow loamy | Typic udorthents, Umbric-Dystrochrepts | Ferruginous sandstone, shale, alluvial and colluvial materials |
| SC—Shifting cultivation, WRC—Wet Rice Cultivation, HG—Homegardens, BP—Bamboo plantation, GL—Grassland, OPP—Oil palm plantation, TP—Teak plantation. | |||||
| Land use Types | Soil Depth class (cm) | ||
| 0–15 | 15–30 | 30–45 | |
| Shifting Cultivation | 2.32 ± 0.24 | 1.64 ± 0.17 | 1.09 ± 0.11 |
| Wet Rice Cultivation | 2.90 ± 0.05 | 1.28 ± 0.06 | 0.69 ± 0.05 |
| Homegarden | 2.04 ± 0.10 | 1.76 ± 0.08 | 1.30 ± 0.08 |
| Forest (Natural) | 3.74 ± 0.32 | 2.70 ± 0.30 | 1.79 ± 0.13 |
| Bamboo plantation | 1.28 ± 0.04 | 1.10 ± 0.04 | 0.90 ± 0.03 |
| Grassland | 2.20 ± 0.36 | 1.19 ± 0.12 | 0.56 ± 0.03 |
| Oil palm plantation | 1.87 ± 0.25 | 1.15 ± 0.04 | 0.96 ± 0.13 |
| Teak plantation | 2.11 ± 0.12 | 1.40 ± 0.19 | 0.90 ± 0.03 |
| ± standard error of mean. | |||
| Land use Types | Soil Depth Class (cm) | ||
| 0–15 | 15–30 | 30–45 | |
| Shifting Cultivation | 13.14 ± 0.73 | 9.19 ± 0.63 | 5.55 ± 0.43 |
| Wet Rice Cultivation | 26.36 ± 0.86 | 12.61 ± 0.58 | 7.24 ± 0.47 |
| Homegarden | 19.95 ± 0.93 | 17.54 ± 0.61 | 13.36 ± 0.92 |
| Forest (Natural) | 24.50 ± 2.31 | 16.52 ± 1.91 | 11.71 ± 1.14 |
| Bamboo plantation | 11.81 ± 0.36 | 9.91 ± 0.34 | 8.11 ± 0.33 |
| Grassland | 16.09 ± 3.06 | 7.98 ± 1.12 | 3.60 ± 0.46 |
| Oil palm plantation | 17.29 ± 2.80 | 9.66 ± 0.42 | 9.76 ± 1.84 |
| Teak plantation | 20.57 ± 3.15 | 15.05 ± 3.37 | 9.03 ± 1.34 |
| ± standard error of mean. | |||
| SOC | SMC | pH | BD | Clay | Silt | Sand | |
| SOC | 1 | 0.375** | 0.003 | −0.324** | 0.263** | −0.227* | 0.336** |
| SMC | 1 | 0.263** | −0.537** | 0.100 | −0.440** | 0.511** | |
| pH | 1 | −0.213* | −0.410** | −0.014 | 0.119 | ||
| BD | 1 | −0.207* | 0.304** | −0.250** | |||
| Clay | 1 | −0.689** | 0.151 | ||||
| Silt | 1 | −0.089 | |||||
| Sand | 1 | ||||||
|
* Correlation is significant at P < 0.05 level (two-tailed). ** Correlation is significant at P < 0.01 level (two-tailed). |
|||||||
| SC | WRC | HG | Forest | Bamboo | Grassland | Oil palm | Teak | |
| SC | 1.00 | |||||||
| WRC | 0.356 | 1.00 | ||||||
| HG | 0.766** | 0.016 | 1.00 | |||||
| Forest | 0.048 | −0.493 | 0.328 | 1.00 | ||||
| Bamboo | 0.933** | 0.259 | 0.862** | 0.041 | 1.00 | |||
| Grassland | 0.894** | 0.125 | 0.885** | 0.195 | 0.967** | 1.00 | ||
| Oil palm | −0.664 | 0.524 | −0.906** | −0.975 | −0.891** | 0.973** | 1.00 | |
| Teak | 0.634 | −0.491 | 0.899** | 0.996** | 0.904** | −0.977** | −0.971** | 1.00 |
| ** Correlation is significant at P < 0.01 level (two-tailed). SC—shifting cultivation, WRC—wet rice cultivation, HG—homegarden. | ||||||||
