Implementation Web Decision Support Model for Predicting Performance of Field Machinery Operation (DWDSS)

Farm machinery planning, design and operation are complicated undertaking due to time and cost constraint and due to prevalence of complicated interacting and overlapping field operations involving capacity constraints and cooperating units. The classical DSS models that applied in the past to machinery planning and policy analysis as well as to performance assessment and simulation of machinery demand, and supplies are criticized by limitations in programming and the difficulty in manipulation and storing the bulky data usually encountered in machinery records. In contrast by application of a web-based decision support system (DWDSS) the user can enjoy the facility to store the data in the server. (DWDSS), is a user-friendly interactive program which permits the user to interact by entering the required input records. The model estimates machinery performance of various farm machines. It consists of one model, which helps the farm manager to take the correct optimum selection of his agricultural machinery. DWDSS predicts field efficiency, field capacity, draft power required to operate machines and PTO power. The DWDSS was successfully validated statistically in comparison to the published data from the ASAE (2009). The comparison indicated that there were no significant differences (probability = 0.05) between them in the calculations that were executed. The DWDSS model was applied to real case conditions in Wad Salma and Rahad irrigated schemes in the central clay plains under similar treatments. The DWDSS results of field efficiency, theoretical field capacity, working rate and draw bar power was found fairly identical to the actual Wad Salma and Rahad data. The results indicated that, generally, the actual field efficiencies of the studied machines were found to be lower by 7% than ASAE published data and t-test comparison between Wad Salma  and Rahad schemes in working rate of the three tillage implement, indicated no significant difference between the two means at probability level =0.05. In general, the results indicated that the DWDSS could be applied to any real-life case successfully and with confidence. This is reached by helping the decision maker in planning and operation of a farm fleet by deciding size of farm power.

Mohamed Y.M .Hassana: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China, Email: mohamedyosuif@hotmail.com, Mobile  :008613912926529

   Zhu Sihong: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China.

   Ding Weimin: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China.

   Ding Qishuo: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China.

   B.Eisa Adam: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China.

  Cedric .S.Okinda: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031, China.

  Gamareldawla .H.D.Agbnad: College of Water Conservancy and Hydropower,Hohai University, Nanjing 210098, China)Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil-Water Environment.

Mohamed Y.M .Hassan, Zhu Sihong, Ding Weimin, Ding Qishuob, B.Eisa Adamc, Cedric .S.Okinda, Gamareldawla .H.D.Agbnad, "Implementation Web Decision Support Model for Predicting Performance of Field Machinery Operation (DWDSS)" International Journal of Engineering Works, Vol. 2, Issue 7, PP. 69-78,  July 2015.                                                                                  

[1]     Akinnuli, B. O., F. O. Akerele and A. B. Nathaniel (2014). "Model developed for farm tractors and implements selection for optimum utilization." Journal of Emerging Trends in Engineering and Applied Sciences 5(4): 248-255.

[2]     Belel, M. and M. Dahab (1997). "Effect of Soil Condition on a Two-wheel Drive Tractor Performance Using Three Types of Tillage Implements."

[3]     Bower, A. S., H. T. Rossby and J. L. Lillibridge (1985). "The Gulf Stream-barrier or blender?" Journal of Physical Oceanography 15(1): 24-32.

[4]     Crossley, C. and J. Kilgour (1978). "Field performance of a winch-powered cultivation device in Central Africa." Journal of agricultural engineering research 23(4): 385-396.

[5]     Donnell, H. (2001). "Farm Power and Machinery Management Wiley." Technology & Engineering-368 pages.

[6]     Feenstra, J. F., I. Burton, J. B. Smith and R. S. Tol (1998). Handbook on methods for climate change impact assessment and adaptation strategies, UNEP.

[7]     Isik, A. and A. Sabanci (1993). "Computer model to select optimum sizes of farm machinery and power for mechanization planning." AMA, Agricultural Mechanization in Asia, Africa and Latin America 24(3): 68-72.

[8]     Olmstead, A. L. and P. W. Rhode (2007). "Not on my farm! Resistance to bovine tuberculosis eradication in the United States." The Journal of Economic History 67(03): 768-809.

[9]     Ozpinar, S. and A. Isik (2004). "Effects of tillage, ridging and row spacing on seedling emergence and yield of cotton." Soil and Tillage Research 75(1): 19-26.

[10]  Santhi, C., J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan and L. M. Hauck (2001). VALIDATION OF THE SWAT MODEL ON A LARGE RWER BASIN WITH POINT AND NONPOINT SOURCES1, Wiley Online Library.

[11]  Schrock, M. D. (2002). "Extracting Machinery Management Information from GPS Data."

[12]  Serrano, J. M., J. O. Peça, J. M. da Silva, A. Pinheiro and M. Carvalho (2007). "Tractor energy requirements in disc harrow systems." Biosystems engineering 98(3): 286-296.

[13]  Taylor, R. K., M. D. Schrock, S. A. Staggenborg and R. Inn (2001). Using GPS technology to assist machinery management decisions. ASAE Meeting Paper No. MC01-204. St. Joseph, Mich.: ASAE, Citeseer.

[14]  Yohanna, J., S. Ode and M. Ibrahim (2014). "FIELD CAPACITIVE PERFORMANCE STUDY OF PLOUGHING OPERATION IN LAFIA LGA, NASARAWA STATE, NIGERIA." NIGERIAN INSTITUTION OF AGRICULTURAL ENGINEERS 22(4): 11.

[15]  Zaied, M. B., A. M. El Naim and T. E. Mahmoud (2014). "Computer Modeling for Prediction of Implement Field Performance Variables." World Journal of Agricultural Research 2(2): 37-41.

[16]  Zoz, F. M. (1970). Predicting tractor field performance, American Society of Agricultural Engineers.