Agricultural Health and Safety Measures by Fuzzy ahp and Prediction by Fuzzy Expert System: Agricultural Risk Factor

Agricultural Health and Safety Measures by Fuzzy ahp and Prediction by Fuzzy Expert System: Agricultural Risk Factor

Suchismita Satapathy (KIIT University (Deemed), India) and Debesh Mishra (KIIT University (Deemed), India)
Copyright: © 2020 |Pages: 22
DOI: 10.4018/978-1-5225-9175-7.ch012

Abstract

Farming is an ancient traditional business, but still it is not a profitable business sector due to risk factor attached to it. It is a high-risk business. Although profit is lucrative, loss rate is also high. Occupational safety is a big issue of discussion for agricultural workers. The methods of working in field in extreme climate (heat, rain) totally depends on environmental factors. Due to rain and droughts, the loss of profit impacts on economic condition and market. Extreme weather condition, heavy workload during their working procedure gives them early old age, bone and muscle problems. So to attain better efficiency of performance and to improve productivity of the worldwide farmers in the agricultural sector it is essential to minimize risk factors. Agricultural workers need sufficient precaution and safety measures at the time of field and machine work to minimize risk factors. Still risk is major discussion topic in agricultural business. So, an effort is taken to prioritize safety majors by fuzzy ahp, and prediction are done by fuzzy logic modelling.
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Introduction

Agriculture assumes an imperative role in the development of Indian economy, and it additionally contributes around 15% to the nation's GDP, offering work chances to around half of its population. Diverse devices and supplies implied for farming machines are utilized in farming processes which are either manually or mechanically operated. In spite of the fact that there have been advancements in new technologies, still sustainability is the most important issue in farming. Now a days modern farming process and advanced machineries have solved OHS(occupational health and safety) problems of farming .But modern equipment smoke, dust, chemicals and fertilizers both in manual driven farming and modern farming are major environmental issue. Sustainability is a very critical issue in farming, if the farming is traditional manures of animal waste is used instead of chemicals and fertilizers to improve farming prouctivity. But use of wastes of animals also creates pollution. Two of the many possible practices of sustainable agriculture are crop rotation and soil amendment, both designed to ensure that crops being cultivated can obtain the necessary nutrients for healthy growth. Soil amendments would include using locally available compost from community recycling centers. These community recycling centers help produce the compost needed by the local organic farms. Sustainable agriculture is a type of agriculture that focuses on producing long-term crops and livestock while having minimal effects on the environment. This type of agriculture tries to find a good balance between the need for food production and the preservation of the ecological system within the environment.

Key Terms in this Chapter

ARM Agricultural Risk Management: AARM is an innovative approach for improving the resilience of vulnerable rural households, and leveraging finance and investment. AARM allows farmers and businesses to be pro-active and increases their capacity to assess, prepare for, absorb and adapt to risks.

Platform for Agricultural Risk Management (PARM): PARM has the global mandate to contribute to sustainable agricultural growth, boost rural investment, reduce food insecurity, and improve resilience to climate change.

Fuzzy Rule: Fuzzy rule is a conditional statement. The form of fuzzy rules is given by IF THEN statements. If y is B THEN x is A, where x and y are linguistic variables, A and B are linguistic values determined by fuzzy sets.

Fuzzy Logic: Fuzzy logic is multi-valued and handles the concept of partial truth. A system of logic developed for representing conditions that cannot be easily described by the binary terms “true” and “false.”

Fuzzy Variable: A quantity that can take on linguistic values. For example, the fuzzy variable “disease” might have values such as “low” or “high.”

Fuzzy Set: Fuzzy set is expressed as a function and the elements of the set are mapped into their degree of membership. A set with the fuzzy boundaries are “hot,” “medium,” or “cold” for temperature.

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