The use of microscopic algae to maintain soil fertility and to increase the yielding capacity of farm crops

2006 ◽  
Vol 8 (2) ◽  
pp. 187-199
Author(s):  
L. A. Sirenko ◽  
T. V. Parshikova ◽  
E. V. Patsko
Keyword(s):  
Soil Fertility ◽  
Yielding Capacity

Soil fertility, fertilizers and herbicides influence on winter wheat yielding capacity and grain quality on various tilled predecessors

2018 ◽  
Vol 1 (74) ◽  
pp. 71-81
Author(s):  
Aleksei Kravtsov ◽  
◽  
Alexander Zagorulko ◽  
Natalia Kravtsova ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Soil Fertility ◽  
Grain Quality ◽  
Yielding Capacity

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

Establishment theoretical of recommendation fertilization guide of vegetable crops in Algeria: concept and validation

2015 ◽  
Vol 5 (1) ◽  
pp. 606-620
Author(s):  
Mahtali Sbih ◽  
Zoubeir BENSID ◽  
Zohra BOUNOUARA ◽  
Fouad DJAIZ ◽  
Youcef FERRAG
Keyword(s):  
Soil Fertility ◽  
Field Experiments ◽  
Soil Analysis ◽  
Critical Value ◽  
Yield Response ◽  
Published Data ◽  
Vegetable Crops ◽  
Fertilizer Recommendation ◽  
Continuous Models ◽  
Poor Soil

The goal of fertilization is to meet the nutritional needs of plants by completing the supply of soil nutrients in an economically profitable and environmentally friendly. Achieving on-farm optimum economic crop yields of marketable quality with minimum adverse environmental impact requires close attention to fertilization guide. The recommendations seek to do this by ensuring that the available supply of plant nutrients in soil is judiciously supplemented by additions of nutrients in fertilizers. The objective is that crops must have an adequate supply of nutrients, and many crops show large and very profitable increases in yield from the correct use of fertilizers to supply nutrients. The main objective of this work is to establishing a reference guide of fertilization of vegetable crops and cereal in Algeria. To meet this objective, we have processes in two steps: 1) Establishment of theoretical fertilizer recommendation from international guide of crop fertilization; 2) Validation of these developed theoretical fertilizer recommendation by trials in the fields. Sixteen fertilization guides of vegetable crops from the Canadian provinces (5 guides), USA (10 guides) and countries of northern Europe England (1 guide). Generally, the rating of these recommendation is ranging from poor soil to soil exceedingly rich; however, the numbers of fertility classes are very different. Indeed, Quebec Ontario, Minnesota, Wisconsin New England, Maryland and Kentucky and Florida guides are subdivided into 5 fertility classes, ranging from poor soil to soil exceedingly rich. The recommendation of New Brunswick and Manitoba contain six classes. The recommendation of Michigan, Nova Scotia and England contain 10 and 7 fertility classes respectively. The recommendation fertilizer of New York and New Jersey have 3classes. Unlike the systems of fertilization recommendation mentioned above, the recommendation fertilizer of Pennsylvania is based on continuous models of P, K and contains 34 classes for P and 22 classes K. Then we standardized the P soil analysis with conversion equations (Olsen method) and units of measurement (kg/ha, mg/kg…).Following this procedure we transformed discontinued systems of fertility classes in to continuous models to facilitate comparison between the different fertilization recommendation models in one hand, in other hand to obtain critical value (CV).Finally, we used statistics of the conditional expectation in order to generate the theoretical recommendation fertilization guide of fertilization with 7 fertility classes (VL, L, M, MH, OP, H and VH). The next step was calibrating soil tests against yield responses to applied nutrient in field experiments. A database (not published data) from agriculture and agri-food Canada, were used. Production of pumpkin responded positively and significantly to P or K soil fertility levels, increases being observed with P more often than with K. According to the Cate-Nelson methods, the critical value of Olsen-P in the top 20 cm of soil was about 25 mg/kg: at values of greater than or equal to 25 mg/kg, crops achieved about 80% of their maximal yield in the absence of fertilizer application. The CV of K in soil for this crop was about 140 mg/kg. The CV found was very close to this generated by the theoretical method for recommendation of fertilization guide. Finally, we used the procedure of Cope and Rouse in both sides of the CV in order to make subdivisions of different groups of soil fertility. One calibrates the soil-test value against yield response to tile nutrient to predict fertilizer requirement.

SOME ASPECTS OF CHANGE OF SOIL FERTILITY IN GARDENS OF INTENSIVE AND TRADITIONAL TYPE

2019 ◽  
Vol 12 (2) ◽  
pp. 84-100
Author(s):  
G.N. Pugachev ◽  
◽  
A.I. Kuzin ◽  
Keyword(s):  
Soil Fertility
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