Research on Evaluation of Soil Fertility Resource Space Based on Regional Hotspots and Clustering Method

2019 ◽  
pp. 246-256
Author(s):  
Guifen Chen ◽  
Jian Lu ◽  
Ying Meng ◽  
Dongxue Wang
Keyword(s):  
Soil Fertility ◽  
Clustering Method ◽  
Research On Evaluation ◽  
Resource Space

Gray Clustering Evaluation Method of MIE on Combinative Weight-Fixing

2010 ◽  
Vol 455 ◽  
pp. 369-372
Author(s):  
Yan Feng Li ◽  
H.H. Mou ◽  
K. Lv
Keyword(s):  
Evaluation System ◽  
Evaluation Method ◽  
Construction Process ◽  
Clustering Method ◽  
Ahp Method ◽  
Minimum Error ◽  
Research On Evaluation ◽  
Advantages And Disadvantages ◽  
The Core ◽  
The Way

The research on evaluation method of manufacturing enteraprise informatization(MEI) is the core of informatization evaluation system. According to the characters of MEI, a gray clustering evaluation method on combinative weight-fixing is brought forward . In the way to fix the index weights, firstly the advantages and disadvantages of the subjective and objective weight-fixing method are analyzed compositively. Then the subjective weights are gained from Analytical Hierchy Process(AHP) method and the objective weights from Gray Relating Analyses(GRA) method. Last the final indexs’ weights are gained from the combinative weight-fixing method based on the minimum error of optimizing project, which takes in the full advantages of the two methodes. Finally the informatization construction process is devided into certain Gray Types, and the informatization benefits are calculated with Gray Clustering method based on Whiten Function. In the end Three enterprises are chosen to analyze with gray clustering evaluation method on combinative weight-fixing.

Research on Evaluation of Soil Fertilities of Farmland at County Level in Nongan Based on Data Mining

2014 ◽  
Vol 635-637 ◽  
pp. 1671-1674
Author(s):  
Li Ma ◽  
Gui Fen Chen ◽  
Li Ying Cao ◽  
Yue Ling Zhao
Keyword(s):  
Data Mining ◽  
Soil Fertility ◽  
Rough Set ◽  
Evaluation Model ◽  
Arable Land ◽  
Fertility Level ◽  
County Level ◽  
Research On Evaluation ◽  
Subjective Factor ◽  
Grade One

This research used the method of rough set and decision tree in data mining, building the evaluation model of soil fertility, to evaluate NongAn of Jilin province farmland productivity. Results show that the NongAn of arable land is divided into six grades, the fertility of grade one to six account for 6.77%, 14.29%, 23.78%, 23.51%, 20.13%, and 11.54% respectively. The farmland productivity difference is significant. The evaluation model of the evaluation results are in conformity with actual farmland productivity NongAn, avoiding the evaluation differences caused by the subjective factor, can effectively improve the soil fertility level classification accuracy and objectivity.

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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