scholarly journals Study on Spatial Distribution Characters of Rubber Yield and Soil Nutrients in Guangba Farm of Hainai Province

2019 ◽  
pp. 283-291
Author(s):  
Bei Cui ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Qimin Cao
Keyword(s):  
Spatial Distribution ◽  
Soil Nutrients

scholarly journals Effect of Soil Orders and Land Uses on Available Soil Nutrients in Meghalaya, India

2020 ◽  
pp. 62-71
Author(s):  
Chandan Goswami ◽  
Naorem Janaki Singh ◽  
Bijoy Krishna Handique
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Soil Nutrients ◽  
Land Management ◽  
Mean Value ◽  
Soil Samples ◽  
Land Uses ◽  
Sustainable Land Management ◽  
P2o5 Content ◽  
Zn Content

Understanding of spatial distribution of available soil nutrients is important for sustainable land management. An attempt has been made to assess the spatial distribution of available soil nutrients under different soil orders and land uses of RiBhoi, Meghalaya, India using geo-statistical techniques. Seven Land Use Land Cover (LULC) classes were selected from LULC map on 1:50,000 scale prepared by National Remote Sensing Centre (NRSC) viz. Abandoned Jhum (AJ), Current Jhum (CJ), Deciduous Forest (DF), Double Crop (DC), Evergreen Forest (EF), Kharif Crop (KC) and Wastelands (WL). Again, three soil orders were identified by National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) in RiBhoi district of Meghalaya, India viz. Alfisols, Inceptisols and Ultisols. 105 soil samples were collected, 5 replicated soil samples from 21 strata derived from 7 LULC and 3 soil orders. Soil samples were analyzed for available nitrogen (N), available phosphorus (P2O5), available potassium (K2O) and available zinc (Zn) using standard procedures. One way ANOVA was carried out using IBM SPSS Statistics 20.0 software. Significance levels were tested at p≤0.05. N content varied from low (215.50 kg/ha) to medium (414.30 kg/ha) with mean value of 291.50 kg/ha. On the other hand, P2O5 content varied from low (19.90 kg/ha) to high (68.30 kg/ha) with mean value of 43.52 kg/ha. Similarly, K2O content varied from low (112.09 kg/ha) to high (567.84 kg/ha) with mean value of 273.68 kg/ha. Again, Zn also varied from low (0.26 ppm) to high (1.46 ppm) with mean value of 0.64 ppm. In Alfisols, N was found to be higher in EF, AJ & CJ than DF, DC, KC and WL. KC has been found to have lower N than all other LULC classes. Higher P2O5 has been found under EF over KC and WL. AJ has been found to have higher K2O than all other LULC classes. K2O has also been found to be higher in CJ over DC, KC and WL. DF and EF have been found to have higher K2O than KC and WL. Zn has been found to be higher in EF over CJ, DC and WL. In Inceptisols, higher amount of N was observed under EF over all other LULC classes. Higher N has also been found under CJ over DF, DC, KC and WL. P2O5 content was found to be higher under DF over all other LULC classes. Higher P2O5 content was also found under AJ, CJ and DC than KC and WL. Higher amount of K2O has been found under AJ over all other LULC. K2O content of soil under DF was also higher than CJ, EF, KC and WL. Zn has been found to be higher under EF over all other LULC classes. Zn content under CJ has also been found to be higher than AJ, DF, KC and WL. In Ultsols, higher amount of N has been found under EF compared to all other LULC classes. Lowest N content was found under KC. P2O5 content was found to be higher under EF, DF and AJ over all other LULC. K2O content has been found to be higher under CJ in comparison to all other LULC classes. K2O content of EF and DF were also found to be higher than AJ, DC, KC and WL. Again, K2O content has been found to be higher under DC compared to AJ, KC and WL. Zn content under EF and AJ was found to be higher than all other LULC classes. CJ, DF, DC, KC and WL have been found to have lower Zn content. It has been observed that P2O5 content is significantly higher in inceptisols irrespective of LULC classes. The study has highlighted the spatial distribution of available soil nutrients as a function of soil orders and LULC. This will be a useful input in sustainable land management programmes.

Spatial distribution of litter yield in an evergreen broadleaf forest and its effects on the heterogeneityof soil nutrients

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
戴雯笑，楼晨阳，许大明，张亚芬，黄国栋，骆争荣 DAI Wenxiao
Keyword(s):  
Spatial Distribution ◽  
Soil Nutrients ◽  
Broadleaf Forest

scholarly journals The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

2021 ◽  
Vol 10 (4) ◽  
pp. 243
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Ruslan Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Precision Agriculture ◽  
Large Scale ◽  
Soil Nutrient ◽  
Steppe Zone ◽  
Shuttle Radar Topography Mission ◽  
Coefficient Of Determination ◽  
Support Vector

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

scholarly journals Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1430
Author(s):  
Yingying Li ◽  
Zhengyong Zhao ◽  
Sunwei Wei ◽  
Dongxiao Sun ◽  
Qi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Forest Soil ◽  
Soil Nutrients ◽  
Predictive Power ◽  
Remote Sensing Data ◽  
Soil Nutrient ◽  
Soil Maps ◽  
Soil Layers ◽  
Coarse Resolution

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

Spatial Distribution of Soil Nutrients in a Northern Everglades Marsh: Water Conservation Area 1

1997 ◽  
Vol 61 (4) ◽  
pp. 1275-1283 ◽  
Author(s):  
S. Newman ◽  
K. R. Reddy ◽  
W. F. DeBusk ◽  
Y. Wang ◽  
M. M. Fisher ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Nutrients ◽  
Water Conservation ◽  
Conservation Area ◽  
Water Conservation Area
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