Spatial variability of soil properties under different land use in the Dang district of Nepal

Increased nutrient mining, soil erosion, and limited nutrient management has led to declines in soil quality and reduced productivity in many parts of Nepal. A study was conducted in the eastern part of the Dang district of Nepal in 2015 to assess the variability of selected soil properties of three different land use types (agricultural, agroforestry, and grassland) and to map their spatial distribution. A total of 120 soil samples were collected from 0–15 cm depth and analyzed for soil fertility parameters: pH, organic matter (OM), nitrogen (N), phosphorus (P), potassium (K), boron (B), and zinc (Zn). Results revealed that the average value of the soil pH significantly (P \u3c 0.05) varied from agroforestry to agricultural land use. Soil OM and N contents were in the medium range in all land use with minor variation, with the highest average OM and N found in grassland (2.87% and 0.14%), followed by agricultural land (2.64% and 0.13%), and agroforestry (2.45% and 0.12%). Soil P showed a significant variation between agroforest (18.99 kg ha−1) and grassland (8.49 kg ha−1). Soil K content was high in grassland (144.44 mg kg−1) and low in agricultural land (120.95 mg kg−1) but was not statistically significant. Micronutrient B was low (0.28–0.35 mg kg−1) and Zn was very low (0.14 mg kg−1). The interpolated soil maps thus generated may assist farmers in identifying the expected nutrient levels for their localities and encourage them to modify their management practices to improve productivity and lift income

Digital soil mapping in the Bara district of Nepal using kriging tool in ArcGIS

Digital soil mapping has been widely used to develop statistical models of the relationships between environmental variables and soil attributes. This study aimed at determining and mapping the spatial distribution of the variability in soil chemical properties of the agricultural floodplain lands of the Bara district in Nepal. The study was carried out in 23 Village Development Committees with 12,516 ha total area, in the southern part of the Bara district. A total of 109 surface soil samples (0 to 15 cm depth) were collected and analyzed for pH, organic matter (OM), nitrogen (N), phosphorus (P, expressed as P2O5), potassium (K, expressed as K2O), zinc (Zn), and boron (B) status. Descriptive statistics showed that most of the measured soil chemical variables (other than pH and P2O5) were skewed and nonnormally distributed and logarithmic transformation was then applied. A geostatistical tool, kriging, was used in ArcGIS to interpolate measured values for those variables and several digital map layers were developed based on each soil chemical property. Geostatistical interpolation identified a moderate spatial variability for pH, OM, N, P2O5, and a weak spatial variability for K2O, Zn, and B, depending upon the use of amendments, fertilizing methods, and tillage, along with the inherent characteristics of each variable. Exponential (pH, OM, N, and Zn), Spherical (K2O and B), and Gaussian (P2O5) models were fitted to the semivariograms of the soil variables. These maps allow farmers to assess existing farm soils, thus allowing them to make easier and more efficient management decisions and maintain the sustainability of productivity

Spatial variability of soil properties under different land use in the Dang district of Nepal

Increased nutrient mining, soil erosion, and limited nutrient management has led to declines in soil quality and reduced productivity in many parts of Nepal. A study was conducted in the eastern part of the Dang district of Nepal in 2015 to assess the variability of selected soil properties of three different land use types (agricultural, agroforestry, and grassland) and to map their spatial distribution. A total of 120 soil samples were collected from 0–15 cm depth and analyzed for soil fertility parameters: pH, organic matter (OM), nitrogen (N), phosphorus (P), potassium (K), boron (B), and zinc (Zn). Results revealed that the average value of the soil pH significantly (P \u3c 0.05) varied from agroforestry to agricultural land use. Soil OM and N contents were in the medium range in all land use with minor variation, with the highest average OM and N found in grassland (2.87% and 0.14%), followed by agricultural land (2.64% and 0.13%), and agroforestry (2.45% and 0.12%). Soil P showed a significant variation between agroforest (18.99 kg ha−1) and grassland (8.49 kg ha−1). Soil K content was high in grassland (144.44 mg kg−1) and low in agricultural land (120.95 mg kg−1) but was not statistically significant. Micronutrient B was low (0.28–0.35 mg kg−1) and Zn was very low (0.14 mg kg−1). The interpolated soil maps thus generated may assist farmers in identifying the expected nutrient levels for their localities and encourage them to modify their management practices to improve productivity and lift income

Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiments from text. The rapid increase in the volume of sentiment rich data on the web has resulted in an increased interaction among researchers regarding sentiment analysis and opinion (Kaushik & Mishra, 2014). However, limited research has been conducted considering location as another dimension along with the sentiment rich data. In this work, we analyze the sentiments of Geotweets, tweets containing latitude and longitude coordinates, and visualize the results in the form of a map in real time. We collect tweets from Twitter using its Streaming API, filtered by English language and location (bounding box). For those tweets which don’t have geographic coordinates, we geocode them using geocoder from GeoPy. Textblob, an open source library in python was used to calculate the sentiments of Geotweets. Map visualization was implemented using Leaflet. Plugins for clusters, heat maps and real-time have been used in this visualization. The visualization gives an insight of location sentiments

Spatial variability of soil properties under different land use in the Dang district of Nepal

Increased nutrient mining, soil erosion, and limited nutrient management has led to declines in soil quality and reduced productivity in many parts of Nepal. A study was conducted in the eastern part of the Dang district of Nepal in 2015 to assess the variability of selected soil properties of three different land use types (agricultural, agroforestry, and grassland) and to map their spatial distribution. A total of 120 soil samples were collected from 0–15 cm depth and analyzed for soil fertility parameters: pH, organic matter (OM), nitrogen (N), phosphorus (P), potassium (K), boron (B), and zinc (Zn). Results revealed that the average value of the soil pH significantly (P \u3c 0.05) varied from agroforestry to agricultural land use. Soil OM and N contents were in the medium range in all land use with minor variation, with the highest average OM and N found in grassland (2.87% and 0.14%), followed by agricultural land (2.64% and 0.13%), and agroforestry (2.45% and 0.12%). Soil P showed a significant variation between agroforest (18.99 kg ha−1) and grassland (8.49 kg ha−1). Soil K content was high in grassland (144.44 mg kg−1) and low in agricultural land (120.95 mg kg−1) but was not statistically significant. Micronutrient B was low (0.28–0.35 mg kg−1) and Zn was very low (0.14 mg kg−1). The interpolated soil maps thus generated may assist farmers in identifying the expected nutrient levels for their localities and encourage them to modify their management practices to improve productivity and lift income

Digital soil mapping in the Bara district of Nepal using kriging tool in ArcGIS.

Digital soil mapping has been widely used to develop statistical models of the relationships between environmental variables and soil attributes. This study aimed at determining and mapping the spatial distribution of the variability in soil chemical properties of the agricultural floodplain lands of the Bara district in Nepal. The study was carried out in 23 Village Development Committees with 12,516 ha total area, in the southern part of the Bara district. A total of 109 surface soil samples (0 to 15 cm depth) were collected and analyzed for pH, organic matter (OM), nitrogen (N), phosphorus (P, expressed as P2O5), potassium (K, expressed as K2O), zinc (Zn), and boron (B) status. Descriptive statistics showed that most of the measured soil chemical variables (other than pH and P2O5) were skewed and non-normally distributed and logarithmic transformation was then applied. A geostatistical tool, kriging, was used in ArcGIS to interpolate measured values for those variables and several digital map layers were developed based on each soil chemical property. Geostatistical interpolation identified a moderate spatial variability for pH, OM, N, P2O5, and a weak spatial variability for K2O, Zn, and B, depending upon the use of amendments, fertilizing methods, and tillage, along with the inherent characteristics of each variable. Exponential (pH, OM, N, and Zn), Spherical (K2O and B), and Gaussian (P2O5) models were fitted to the semivariograms of the soil variables. These maps allow farmers to assess existing farm soils, thus allowing them to make easier and more efficient management decisions and maintain the sustainability of productivity

Spatial variability of soil properties under different land use in the Dang district of Nepal

Increased nutrient mining, soil erosion, and limited nutrient management has led to declines in soil quality and reduced productivity in many parts of Nepal. A study was conducted in the eastern part of the Dang district of Nepal in 2015 to assess the variability of selected soil properties of three different land use types (agricultural, agroforestry, and grassland) and to map their spatial distribution. A total of 120 soil samples were collected from 0-15 cm depth and analyzed for soil fertility parameters: pH, organic matter (OM), nitrogen (N), phosphorus (P), potassium (K), boron (B), and zinc (Zn). Results revealed that the average value of the soil pH significantly (P −1) and grassland (8.49 kg ha−1). Soil K content was high in grassland (144.44 mg kg−1) and low in agricultural land (120.95 mg kg−1) but was not statistically significant. Micronutrient B was low (0.28-0.35 mg kg−1) and Zn was very low (0.14 mg kg−1). The interpolated soil maps thus generated may assist farmers in identifying the expected nutrient levels for their localities and encourage them to modify their management practices to improve productivity and lift income