Diversity, regeneration, and anthropogenic disturbance in major Indian Central Himalayan forest types: implications for conservation

Kaushal, Siddhartha; Siwach, Anshu; Baishya, Ratul

doi:10.1007/s10531-021-02203-w

Cited by 17 publications

(5 citation statements)

References 66 publications

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“…Detailed maps based on high-resolution sampling can act as stepping stones for many conservations policies at the national and international levels. Various attempts have been made to construct soil maps using digital and conventional methods at the global level (Arrouays et al, 2020; Grunwald, 2009; Hengl et al, 2014, 2015; Hengl, Leenaars, et al, 2017; Poggio et al, 2021) national-level (Purushothaman et al, 2022; Reddy et al, 2021) and at the local level (Arora et al, 2021; Dharumarajan et al, 2020, 2021, 2022; Kalambukattu et al, 2018; Kaushal et al, 2021; Santra et al, 2017; Srinivasan et al, 2022). However, most of the maps are at low resolution levels.…”

Section: Introductionmentioning

confidence: 99%

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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The availability of high throughput soil profile information is an important component in precision agriculture to perform efficient soil management for sustainable production. We collected 14 soil physiochemical features from Nagpur, Pune, and Haveri, representing target environments of safflower cultivation, and also from our experiment station at Delhi, at fine resolution and created graphical maps to depict the variability. Additionally, we evaluated the predictive ability of two statistical learning models, random forest (RF) and self-organizing maps (SOM) against multinomial regression models for correctly classifying the soil profile. Clustering was performed around the medoids produced from the dissimilarity matrices of these models using partitioning around medoids (PAM) model. The robustness, versatility, and predictive ability of models in correctly classifying the soil profile to clusters were then tested using cross-validation which was repeated 100 times. This study was performed using training data with proportionate size varying from 60 to 95%, and increasing the unit area of observation up to nine times (or decreasing the total number of observations up to a ninth). RF model was found to be the best performing with average prediction accuracy above 85% in all settings which reached close to 100% in some settings. The predictive ability of all the models was maintained even when only the most influencing six variables were used for classification. The optimal training population size for prediction was found to be 70 to 80%. Based on our study, it is recommended to i) collect fine resolution edaphic features from a marginal farm before crop season, ii) use RF or SOM model to identify the most influencing features distinguishing the soil samples iii) expand the area of sample collection, find values for the most influencing features, and use RF model to correctly predict the class to which the new set of the soil belongs to.

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Section: Introductionmentioning

confidence: 99%

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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“…Understanding these natural patterns and associated mechanisms provide valuable benchmarks for the ecosystem management, which aims to reduce the differences between natural and managed landscapes to ensure long-term conservation of ecosystem functions and, thereby, retain the socioeconomic benefits (Führer, 2000;Gauthier et al, 2009;Heckwolf et al, 2021). This is helpful knowing that global decline of mountain forests, e.g., tree dieback or biodiversity erosion, results a degradation of ecosystem services (Foley et al, 2007;Laigle et al, 2021;Kaushal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Tree dieback, woody plant diversity, and ecosystem driven by topography in semi-arid mountain forests: Implication for ecosystem management

Heydari

Cheraghi

Omidipour

et al. 2023

Journal of Environmental Management

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“…It is popularly known as "the poor man's timber," denoting its fashionability among poor populations as a cover for precious wood from trees [1]. Tere are more than 1,600 species of bamboo comprising 75-107 subfamilies growing across the world [2][3][4]. Most of them grow naturally in the tropical and subtropical regions nearly 4,000 m a.s.l.…”

Section: Introductionmentioning

confidence: 99%

Species-Site Suitability Assessment of Bamboo and Its Detailed Study in Different Agroecological Zones of Kenya

Lohani,

Sigu,

Oduor

et al. 2023

International Journal of Forestry Research

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The site suitability assessment of Bamboo in Kenya was studied for diverse agroecological zones (AEZs) comprising one indigenous and fifteen exotic bamboo species in nine different AEZs of Kenya. Three bamboo clumps from each species that were at least five years old were assessed to accurately capture data on growth performance and yield. Soil samples collected at 0–30 cm depth from different areas varied considerably. The proportion of soil varied across the bamboo planting sites (F(1,11) = 24.94; p < 0.001), soil pH in the planting sites varied significantly (F(1,11) = 13.92; p < 0.001), and soil bulk densities had different results (F(1,11) = 13.92; p < 0.001). The lowest bulk density (0.61 ± 0.01) was recorded in Kakamega (UM1), while the highest (1.63 ± 0.01) was reported in Gede. Data on characteristics of clumps and internode length and wall thickness integrated through GenStat statistical software using ANOVA entail a huge variation in growth performance for each bamboo species across planting sites. Morphological characters were also studied in detail. The mean height, diameter, and stocking density were comparatively lower even in elevated zones that were only 200 m below its natural range. This resulted in an extremely low yield, which is not viable for economic investment. The species is unsuitable for plantation establishment outside its natural range of 2300 m–3200 m above the sea level (a.s.l.). Plantation below this range will not dissipate but will not attain a healthy growth. The primary use will be restricted for ornamental and conservation purposes such as scaffolding, roof construction, and fabrication of basic household furniture.

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Diversity, regeneration, and anthropogenic disturbance in major Indian Central Himalayan forest types: implications for conservation

Cited by 17 publications

References 66 publications

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Tree dieback, woody plant diversity, and ecosystem driven by topography in semi-arid mountain forests: Implication for ecosystem management

Species-Site Suitability Assessment of Bamboo and Its Detailed Study in Different Agroecological Zones of Kenya

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