E-ISSN: 2456-2033

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IJAREM: Volume 04 - No. 11, 2018

 

1. Valorization of Leucaena Leucocephala (L) leaves in the broilers feed (strain Cobb500) in Lubumbashi: Effect on growth and economic yield
Kabemba Tshiakamona James, KilembaMukangala Benjamin, Kilela Mwanasomwe Jacques, LengewaIlunga Stephane, Nsenga Mpanda Gauthier and Tshibangu Muamba Innoncent
Abstract
The present study was focused on Leucaena leucocephala leaves valorization in the broiler meat feed (strainCobb500) in the Lubumbashi. One hundred and twenty unsexed chicks were used in this study. Four treatments based on different foods were setfor this experiment i.e.the control: 100% of soya flour (T0), mere Leucaena leaves (MLL), Sulphated Leucaenaleaves (SLL) and soaked Leucaena leaves (SoLL). These leaves were incorporated at a rate of 0%, 5%, 10% and 15% respectively in replacement of soya flower. The obtained results reveal a significant difference on zootechnical parameters i.e. the daily feed consumption (DFC), daily mean increase and the consumption index (CI) with the low cost of production observed on Leucaena leaves treatment soaked in water and Leucaena leucocephala leaves chemically treated with ferric sulphate.

 

2. New family of estimators using two auxiliary attributes
Tolga Zaman
Abstract
Utilizing the estimators in Singh and Malik (Appl. Math. Computed. 219, (2013), 10948)and Kadilar and Cingi (Appl. Math. Computed. 162 (2005), 902), It is proposed estimators using two auxiliary attributes in simple random sampling. It is obtained mean square error (MSE) equation of these estimators.Theoretically, it is compared with the MSE of proposed estimators and the MSE of traditional regression estimators using two auxiliary attributes. As a result of these comparisons, it is observed that the proposed estimators give more efficient results than the traditional regression estimators. Also, under all conditions, all of the proposed estimators more efficient than the traditional regression estimators. And, these theoretical results are supported by an application with original data.

 

 

 

 

 

 

 

 

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