Skip to main content

MIS capabilities

MIS in Agriculture



The Indian Council for Agricultural Research, in referring to its Agricultural Research Information System, stated that agricultural scientists can carry out research more effectively by having systematic access to research information available in India as well as in other countries, better project management of agricultural research, and modernization of the office tools. Information is the blood of an organisation, country or region and its management is vital for effectiveness, efficiency and economic and social stability. In many organizations, countries and regions, there have been increasing calls for the development of integrated, national (geo-) information infrastructure for management, resource planning and decision-making. ACP scientists who work in the various disciplines generating scientific data on climate, water, soils, land etc need to pay more attention to integrating the data sets to improve decision making at the policy and enterprise level including farms to contribute to socio-economic development.

MIS - capabilities

The term 'information system' is a general term for a system that facilitates access to information; however, a ' management information system' refers to integrated data sources and information systems, which meet the particular needs and requirements of planning and decision-making. In an ideal case, the major objectives of MIS are to:



  1. reach an understanding of the relevant processes on the basis of the available historic information. This element forms the basis for the development of models, required for forecasting and simulation.
  2. provide information on the current situation, especially for early warning purposes, for instance related to issues impacting on food security, water resources or pest and disease status.
  3. forecast changes and impacts, either natural or man-made , as an element in vulnerability assessments.
  4. forecast the consequences of policy decisions and measures before they are implemented in reality. This implies evaluating options for several given scenarios based on the possible results and predicted consequences, and selecting the most acceptable alternative.

Existing environmental information systems in ACP countries consist of isolated data sets (soil type, climate, land, water, forest and fisheries resources) and systems, aimed at management of specific resources: water, land or forest; they hardly reach the second objective mentioned above. The third and fourth objectives (forecasting changes and simulation) are currently almost only reached within one discipline mainly in meteorology (weather and climate forecast) and to a certain extent in oceanography. This is due to the fact that the ACP countries, in addition to operating within serious financial and human constraints, must grapple with inter-institutional competition (individuality of scientific disciplines and competition for resources), limited agreement on harmonized standards, formats or quality assurance and legal constraints (lack of common data policy).

The following table provides an overview of different phases of data/information systems.

CATEGORY

ELEMENTS

USAGE

environmental information systems

traditional knowledge

oral transmission and exchange

manuscript/printed archives

documents in files and folders

visual inspection and analysis

isolated digital data archives

databases, spreadsheets using different standards and formats

computer-aided review and synthesis

coordinated digital data archives

databases and spreadsheets, using common standards and formats, user interface

automatic review and synthesis

GIS (Geographic Information System)

databases and data layers with common (geographic) reference, user interface

integrated analysis

MIS (Management Information System)

databases, models, user interface

integrated analysis, extrapolation, forecast, simulation

DSS (Decision Support System)

databases, models, artificial intelligence, user interface

integrated analysis, extrapolation, forecast, simulation, weighted ?advice?


Labels:

Comments

Post a Comment

Popular posts from this blog

Advantages and Disadvantages of EIS Advantages of EIS Easy for upper-level executives to use, extensive computer experience is not required in operations Provides timely delivery of company summary information Information that is provided is better understood Filters data for management Improves to tracking information Offers efficiency to decision makers Disadvantages of EIS System dependent Limited functionality, by design Information overload for some managers Benefits hard to quantify High implementation costs System may become slow, large, and hard to manage Need good internal processes for data management May lead to less reliable and less secure data
1 comment
Read more

Inter-Organizational Value Chain

The value chain of   a company is part of over all value chain. The over all competitive advantage of an organization is not just dependent on the quality and efficiency of the company and quality of products but also upon the that of its suppliers and wholesalers and retailers it may use. The analysis of overall supply chain is called the value system. Different parts of the value chain 1.  Supplier     2.  Firm       3.   Channel 4 .   Buyer
Post a Comment
Read more

Big-M Method and Two-Phase Method

Big-M Method The Big-M method of handling instances with artificial  variables is the “commonsense approach”. Essentially, the notion is to make the artificial variables, through their coefficients in the objective function, so costly or unprofitable that any feasible solution to the real problem would be preferred, unless the original instance possessed no feasible solutions at all. But this means that we need to assign, in the objective function, coefficients to the artificial variables that are either very small (maximization problem) or very large (minimization problem); whatever this value,let us call it Big M . In fact, this notion is an old trick in optimization in general; we  simply associate a penalty value with variables that we do not want to be part of an ultimate solution(unless such an outcome is unavoidable). Indeed, the penalty is so costly that unless any of the  respective variables' inclusion is warranted algorithmically, such variables will never be p
Post a Comment
Read more