Influence of Agricultural Land Use Types on Some Soil Properties in Midwestern Nigeria

2006 ◽  
Vol 5 (3) ◽  
pp. 387-390 ◽  
Author(s):  
J.S. Ogeh . ◽  
P.C. Ogwurike .
RSC Advances ◽  
2018 ◽  
Vol 8 (57) ◽  
pp. 32588-32596 ◽  
Author(s):  
Beidou Xi ◽  
Zhurui Tang ◽  
Jie Jiang ◽  
Wenbing Tan ◽  
Caihong Huang ◽  
...  

Agricultural land-use types could affect the transformation and decomposition of HS in soils, and thus further change the intrinsic chemical structures associated with ETC.


Soil Science ◽  
2014 ◽  
Vol 179 (9) ◽  
pp. 433-445 ◽  
Author(s):  
Huawei Pi ◽  
Gary Feng ◽  
Brenton S. Sharratt ◽  
Xinhu Li ◽  
Zehao Zheng

2018 ◽  
Vol 36 (4) ◽  
pp. 567-575
Author(s):  
Hee-Rae Cho ◽  
Yong-Seon Zhang ◽  
Kyung-Hwa Han ◽  
Jung-Hun Ok ◽  
Seon-Ah Hwang ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shin Ae Lee ◽  
Jeong Myeong Kim ◽  
Yiseul Kim ◽  
Jae-Ho Joa ◽  
Seong-Soo Kang ◽  
...  

Abstract Biogeographic patterns in soil bacterial communities and their responses to environmental variables are well established, yet little is known about how different types of agricultural land use affect bacterial communities at large spatial scales. We report the variation in bacterial community structures in greenhouse, orchard, paddy, and upland soils collected from 853 sites across the Republic of Korea using 16S rRNA gene pyrosequencing analysis. Bacterial diversities and community structures were significantly differentiated by agricultural land-use types. Paddy soils, which are intentionally flooded for several months during rice cultivation, had the highest bacterial richness and diversity, with low community variation. Soil chemical properties were dependent on agricultural management practices and correlated with variation in bacterial communities in different types of agricultural land use, while the effects of spatial components were little. Firmicutes, Chloroflexi, and Acidobacteria were enriched in greenhouse, paddy, and orchard soils, respectively. Members of these bacterial phyla are indicator taxa that are relatively abundant in specific agricultural land-use types. A relatively large number of taxa were associated with the microbial network of paddy soils with multiple modules, while the microbial network of orchard and upland soils had fewer taxa with close mutual interactions. These results suggest that anthropogenic agricultural management can create soil disturbances that determine bacterial community structures, specific bacterial taxa, and their relationships with soil chemical parameters. These quantitative changes can be used as potential biological indicators for monitoring the impact of agricultural management on the soil environment.


2016 ◽  
Vol 101 ◽  
pp. 47-56 ◽  
Author(s):  
A.J. Thougnon Islas ◽  
K. Hernandez Guijarro ◽  
M. Eyherabide ◽  
H.R. Sainz Rozas ◽  
H.E. Echeverría ◽  
...  

2021 ◽  
Vol 13 (2) ◽  
pp. 289
Author(s):  
Misganu Debella-Gilo ◽  
Arnt Kristian Gjertsen

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.


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