Farming System Characterization and Analysis of East Wollega Zone, Oromia, Ethiopia
Kifle Degefa,
Getachew Biru,
Galmessa Abebe
Issue:
Volume 6, Issue 2, June 2020
Pages:
14-28
Received:
30 May 2020
Accepted:
15 June 2020
Published:
4 August 2020
Abstract: The study was characterizing and analyze the existing farming system and identify the production and marketing constraints of the East Wollega zone with cross-sectional data of 156 sample respondents. The farming system of the study area is characterized as mixed farming systems with 56.21% and 28.44% contribution of crop and livestock, respectively for livelihood activities. The survey result shows that low productivity, shortage/lack of improved varieties, weed infestation, high cost of inputs was identified as main important constraints in crop production while high transaction cost, lack of marketing linkage, low price of output and shortage of market information were reported as main constraints in crop marketing. Disease, feed shortage, grazing land shortage, and lack of improved breed were identified as main important constraints in livestock production whereas high transaction cost, low price output, shortage of market information, unorganized marketing system, and lack of market linkage were reported as main livestock marketing constraints. Besides, soil erosion, soil fertility decline, waterlogging, soil acidity, and termite were reported as the main important constraints in natural resources. To improving crop and livestock productivity access improved varieties and breed, capacitate farmers’ awareness on the disease, minimizes transaction cost, focus on the high-value crops, expanding soil and water conservation, strengthening market information and linkage where must the urgent concentration for interventions.
Abstract: The study was characterizing and analyze the existing farming system and identify the production and marketing constraints of the East Wollega zone with cross-sectional data of 156 sample respondents. The farming system of the study area is characterized as mixed farming systems with 56.21% and 28.44% contribution of crop and livestock, respectivel...
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An Algorithm for Clustering Input Variables in a Fuzzy Model in a FLC Process
Issue:
Volume 6, Issue 2, June 2020
Pages:
29-46
Received:
18 September 2020
Accepted:
6 October 2020
Published:
13 October 2020
Abstract: The input and output variables in fuzzy systems are linguistic variables. The base of the fuzzy rule represents the central part of a fuzzy controller, and the fuzzy rule represents its basic part, and it has the following form: "if R then P", where R and P represent the fuzzy relation, i.e. the proposition. Complex systems described by fuzzy relations generate a large number of inference rules. Grouping the states into clusters on the basis of which we make conclusions about the value of the output variable is performed by an expert based on his or her experience and knowledge. Ideally, the number of clusters should correspond to the number of attributes by which the value of the output variable is classified, which, in reality is not the case. In the absence of experts, we perform grouping on the basis of some of the criteria. One way of grouping descriptive states into clusters is presented in this paper. It presents a construction of the method of grouping descriptive states of fuzzy models, with the aim of drawing conclusions about the value of the output variable described by a given state. The presented method of grouping descriptive states is based on defined characteristic values associated with fuzzy numbers by which the input variables of the model are evaluated. They represent the basis for defining the characteristic value of the descriptive state of the output variable of a fuzzy model. For the presented method, a mathematical logical argumentation of the application is given, as an algorithm for the application of the constructed method. The application of the algorithm is demonstrated in measuring the economic dimension of the sustainability of tourism development, measured by comparative evaluation indicators.
Abstract: The input and output variables in fuzzy systems are linguistic variables. The base of the fuzzy rule represents the central part of a fuzzy controller, and the fuzzy rule represents its basic part, and it has the following form: "if R then P", where R and P represent the fuzzy relation, i.e. the proposition. Complex systems described by fuzzy relat...
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