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International Journal of Modern Agriculture ISSN 2305-7246

     
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IMPACT OF TRADITIONAL PROCESSING ON PESTICIDE RESIDUES IN ONION

 

Saghir Ahmed Sheikh1, Muhammad Shahnawaz2, Shafi Muhammad Nizamani1, Muhahid Laghari1, Asia Panwar1, and Shahid Abbas1

1Institute of Food Science and Technology, Sindh Agriculture University Tandojam, Pakistan

2Department of Food Technology, Karakoram International University, Gilgit, Pakistan

*Corresponding author (e-mail: nawaz_glt@yahoo.com)

ABSTRACT

This study was carried out in two parts, in the first part; a survey was conducted to determine the extent of pesticide usage. For this purpose thirty farmers were interviewed concerning pesticides usage. The results discovered that several pesticides are used by the farmers however Endosulfan and Profenofos are the most frequently used pesticides on onion crop. Both pesticides are fat soluble and belong to the Organochlorine and organophosphorus groups respectively. On the second part, three plots were selected of which each plot was half an acre. One plot onions were grown as a control (no pesticide was sprayed). On the other two plots onions were grown on which Endosulfan and Profenofos were sprayed for six days at a recommended dose. Traditional processing methods were applied to check the efficacy of the pesticide in the end crops. It was observed that traditional processing reduces the concentration of pesticide residues. Some of the processing methods were found highly effective while some were slight effective.  Peeling of unwashed onions reduced 20.92% of Endosulfan while 29.52% of Profenofos.  Washing of peeled onions with tap water reduced 32.11% Endosulfan and 38.52% Profenofos. Sun drying of peeled sliced onions reduced residues up to 90.88% of Endosulfan and 94.56% 0f Profenofos. Dehydration of peeled and sliced onions reduced 93.32% Endosulfan and 94.71% Profenofos. Frying of peeled followed by plain water washed onions reduced 88.24% of Endosulfan and 96.33% of Profenofos. Blanching of peeled onions reduced 54.01% Endosulfan and 59.83% Profenofos.  Washing of peeled onions with detergent followed by tap water washing reduced 36.49% of Endosulfan and 43.44% of Profenofos.  Washing of peeled onion with 5% NaCl solution followed by tap water washing reduced 38.44% of Endosulfan and 45.90% Profenofos, while washing with 10% NaCl solution reduced 40.63% Endosulfan and 48.36% Profenofos. FULL TEXT PDF

 

 

 

A NOVEL METHOD WITH MULTILAYER FEED-FORWARD NEURAL NETWORK FOR MODELING OUTPUT YIELD IN AGRICULTURE

Morteza Taki*1 and Meisam Haddad2

1 Young Researches Club Shahreza Branch, Islamic Azad University, Shahreza, Iran

2 Department of Economics and Management, Power and Water University of Technology, Tehran, Iran

*Corresponding author (e-mail: mtaki88@ms.tabrizu.ac.ir)

ABSTRACT

The aim of this study was to examine energy use pattern and predict the output yield for greenhouse tomato production in Esfahan province of Iran. The data used in this study were collected from growers by using a face to face survey. The results revealed that diesel fuel (40%), chemical fertilizer (30%), electricity (12%) and human power (10%) consumed the bulk of energy. In this study, several direct and indirect factors have been identified to create an artificial neural networks (ANN) model to predict greenhouse tomato production. The final model can predict output yield based on human power, machinery, diesel fuel, chemical fertilizer, water for irrigation, seed and chemical poisons. The results of ANNs analyze showed that the (7-10-10-1)-MLP, namely, a network having ten neurons in the first and second hidden layer was the best-suited model estimating the greenhouse tomato production. For this topology, MSE of training, MSE of cross validation, RMSE, MAPE and R2 were 0.027, 0.019, 0.009, 0.98 and 96%, respectively. The sensitivity analysis of input parameters on output showed that diesel fuel and seeds had the highest and lowest sensitivity on output energy with 27% and 6%, respectively. Comparison between the ANN model and a Multiple Linear Regression (MLR) model showed that the ANN model can predict output yield relatively better than the MLR multiple model on the selected training and validation set. FULL TEXT PDF

 

 

 

Evaluation of Land Suitability for Principal Crops in the Hendijan Region

 

M. Albaji*1, P.Papan2, M.Hosseinzadeh2,and S. Barani3

 

1Department of Irrigation and Drainage, Faculty of Water Sciences, Shahid Chamran University, Ahwaz, Iran.

2KWPA , Ahwaz, Iran.

3Department of Soil Sciences, Faculty of Agriculture, Shahid Chamran University, Ahwaz, Iran.

*Corresponding author (e-mail: m_albaji2000@yahoo.co.uk)

 

ABSTRACT

Today's excessive use of croplands and the resulting damages along with the ever-increasing demand for further crop productions have necessitated the best land management practices more than ever. Due to the current lack of any proper land management practices for Hendijan region in Khuzestan Province, southwest Iran, a land suitability evaluation study for key productions of the region, including wheat, alfalfa, maize, and barley, covering an area of 48956 ha was carried out in the region. Using the findings of the semi-detailed soil studies for this area, 19 soil series were identified. Qualitative evaluation was carried out by means of simple limitation and parametric methods (Storie and Square Root) and comparing land and climate characteristics with crop needs. The indexes obtained for barley, alfalfa and wheat were higher in comparison to that developed for maize. Limiting factors in different crop yield in the region along with climatic variables included soil physical properties, especially soil texture, salinity and alkalinity and drainage. From the two methods used, Square Root produced more realistic results in respect to the existing conditions of the region. FULL TEXT PDF

 

 
   

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