Mapping Indicators of Machinery Utilization Predicted by an Artificial Neural Network

A methodology is presented to generate digital maps containing values of Mechanization Indicators (Mechanization Index and Machinery Energy Ratio), predicted without direct calculation, using a multilayered ANN model. The inputs to the ANN model were simple data obtained from local databases. Complementarily there were processed digital maps related to parameters on land slope, farm size, soil texture, water supply for crop production and distribution of the land productivity potential for the main crops in the region of study. Overlapping among the generated maps assisted to analyze the mechanization conditions in every production unit of the Mexican State of Guanajuato, in order to estimate the intensity and suitability of mechanization as well as to identify which farms in the region would benefit more from machinery use. The developed methodology can facilitate the analysis to prioritize areas for the introduction or replacement of agricultural machinery. It is concluded that the present methodology would be a good tool to assess mechanization sustainability of agricultural activities; this in turn providing policy-makers and planners with tools with which to judge the best use of land in the near future. Planning the intensity and suitability of mechanization using this approach would contribute to optimize the use of inputs from oil sources.

[1]  Gustavo Best,et al.  Effective Energy Use and Climate Change: Needs of Rural Areas in Developing Countries , 2001 .

[2]  S. O. Jekayinfa.  Energy Consumption Pattern of Selected Mechanized Farms in Southwestern Nigeria , 2006 .

[3]  B. Sims,et al.  RD—Rural Development: An Engineering Perspective on Sustainable Smallholder Farming in Developing Countries , 2002 .

[4]  W. Chancellor.  Global Energy Flows and their Food System Components , 2001 .

[5]  P. Andrade,et al.  Identification of Patterns of Farm Equipment Utilization in Two Agricultural Regions of Central and Northern México , 2002 .

[6]  R. Adams.  Reasons for Steel Price Increases and Impact on the AgriculturalMachinery Industry , 2006 .

[7]  Vilas M. Salokhe,et al.  Energy Consumption Analysis for Selected Cropsin Different Regions of Thailand , 2006 .

[8]  Marco Fiala,et al.  On the Development of Agricultural Mechanization to Ensure a Long- Term World Food Supply , 2002 .

[9]  W. Chancellor.  Synergistic Cooperation In The Food System , 2001 .

[10]  Lawrence Clarke,et al.  Farm Power - Present and Future Availability in Developing Countries , 2002 .

[11]  Axel Munack,et al.  Agriculture and the Environment: New Challenges for Engineers , 2002 .

[12]  Pietro Picuno,et al.  Historical Cartography and GIS for the Analysis of Carbon Balance in Rural Environment: a Study Case in Southern Italy , 2006 .

[13]  Hiroshi Nakashima,et al.  Mechanization Index and Machinery Energy Ratio Assessment by means of an Artificial Neural Network: a Mexican Case Study , 2007 .

[14]  Jorge López Blanco,et al.  Enfoque de límites difusos (fuzzy) para clasificación de tierras en especies sin datos de producción , 2004 .

[15]  V. P. Chaudhary,et al.  Auditing of Energy Use and Output of Different Cropping Systems in India , 2006 .

[16]  Jorge López Blanco,et al.  ENFOQUES FUZZY Y BOOLEANO CONVENCIONAL PARA CLASIFICAR LA APTITUD AGRÍCOLA DE LAS TIERRAS , 2001 .