Response of soil fertility to 25 years of experimental acidification in a temperate hardwood forest

2020 ◽  
Vol 49 (4) ◽  
pp. 961-972
Author(s):  
Frank S. Gilliam ◽  
Mary Beth Adams ◽  
William T. Peterjohn
Keyword(s):  
Soil Fertility ◽  
Hardwood Forest ◽  
Experimental Acidification

Herbaceous Layer and Soil Response to Experimental Acidification in a Central Appalachian Hardwood Forest

1994 ◽  
Vol 23 (4) ◽  
pp. 835-844 ◽  
Author(s):  
Frank S. Gilliam ◽  
Nicole L. Turrill ◽  
Staci D. Aulick ◽  
Dan K. Evans ◽  
Mary Beth Adams
Keyword(s):  
Hardwood Forest ◽  
Herbaceous Layer ◽  
Soil Response ◽  
Experimental Acidification

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

COUNTING TOTAL BACTERIA IN LAND ORGANIC WASTE HOUSEHOLD AND LAND INORGANIC WITH TOTAL PLATE COUNT METHOD (TPC)

2017 ◽  
Vol 4 (2) ◽  
pp. 87-91
Author(s):  
Ekamaida Ekamaida
Keyword(s):  
Soil Fertility ◽  
Bacterial Population ◽  
Organic Soil ◽  
Biological Properties ◽  
Plate Count ◽  
Bacterial Populations ◽  
Fertility Data ◽  
Plate Count Method ◽  
The Mean ◽  
Total Bacteria

The soil fertility aspect is characterized by the good biological properties of the soil. One important element of the soil biological properties is the bacterial population present in it. This research was conducted in the laboratory of Microbiology University of Malikussaleh in the May until June 2016. This study aims to determine the number of bacterial populations in soil organic and inorganic so that can be used as an indicator to know the level of soil fertility. Data analysis was done by T-Test that is by comparing the mean of observation parameter to each soil sample. The sampling method used is a composite method, which combines 9 of soil samples taken from 9 sample points on the same plot diagonally both on organic soil and inorganic soil. The results showed the highest bacterial population was found in total organic soil cfu 180500000 and total inorganic soil cfu 62.500.000

Strategies for Soil Fertility Improvement of Arable Land in China

2018 ◽  
Vol 20 (5) ◽  
pp. 84
Author(s):  
Yingjie Hu ◽  
Xiangbin Kong ◽  
Yuzhen Zhang
Keyword(s):  
Soil Fertility ◽  
Arable Land ◽  
Soil Fertility Improvement
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