Chapter2.docx

CHAPTER – 2 REVIEW OF RELATED LITERATURE 2.1 Introduction 2.2 Foreign Review of Related Literature 2.3 Local Review of Related Literature 2.4 Review of Related Literature from Journals/Books

CHAPTER – 2 REVIEW OF RELATED LITERATURE 2.1 INTRODUCTION

First chapter deals with the conceptual frame work of the present research problem and primary matters regarding the research. It had the statement of the problem, terms defined, objectives of the study, hypothesis, importance of the study and the limitation of the study. But, for any specific research to know more about the advancement of agriculture in terms of technology, the researcher must thoroughly familiar with both previous theory and research. To assure this familiarity a review of the research literature is done. It allows the researcher to know the amount of work done in the concerned area. The clarity of the problem is possible with the true understanding of the knowledge generation in the area of research. It provides the source for hypothesis. It avoids the replication. It suggests the method, procedure, sources of data and statistical technique appropriate to the solution of the problem. The review of the related literature provides some insight regarding strong points and limitation of the previous studies. It enables them to improve their own investigation and to arrive at the proper perspective of the study.

2.2 Foreign Review of Related Literature

Technological change in irrigated agriculture in a semiarid region of Spain Technological change plays a decisive role in irrigated agriculture, which is particularly challenging in semiarid regions. The main objective of this paper is to assess four kinds of alternative technological improvements aimed at dealing with future water availability, especially in the case of extreme events like drought. We evaluate these technologies for a better understanding of what form should be applied in irrigated agriculture in a context of limits on natural resources. We develop a dynamic computable general equilibrium (CGE) model, whose production structure distinguishes between rainfed and irrigated crops, and between a variety of irrigated crops. Land use changes are also evaluated. As well as technological change, we consider the Water Framework Directive (EC 2000/60), which establishes water cost recovery as a key goal. Thus, we assess strategies that combine irrigation water pricing strategies and improved technology. Our results show that policy strategies that focus on fostering technical progress can mitigate the long‐term economic effects of downward trends in water supplies, even in drought years. The study also confirms that the absence of price volatility achieved through a water pricing strategy could improve the sustainable use of water. (Jean‐ Marc, Philip Julio Sánchez‐Chóliz, Cristina Sarasa, 2014)

Advanced agricultural biotechnologies and sustainable agriculture Agricultural biotechnologies are anchored to a scientific paradigm rooted in experimental biology, whereas sustainable agriculture rests on a biological paradigm that is best described as ecological. Both biotechnology and sustainable agriculture are associated with particular social science paradigms: biotechnology has its foundation in neoclassical economics, but sustainability is framed by an emerging community-centered, problem-solving perspective. Fundamentally, biotechnology and neoclassical economics are reductionist in nature. Sustainability and community problem-solving, however, are nonreductionist. Given these differences, we might see the development of two rather distinct systems of food production in the near future. (Thomas A Lyson, 2002)

Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution Digital technology has the potential to change the way we are able to do science. This will be a challenge, but it can potentially reap large rewards in terms of speed of progress. The challenge then becomes: How do we, as scientists, leverage digitalization to accelerate the pace of our science given the pressing need to address the current challenges agriculture faces and the pace of technological progress? While traditional roles of data reduction and analysis will still have their place, the emerging science of big data, data veracity, and analytics will require a very different skill set. Digitalization will challenge the way we understand the problem, undertake or interpret data collected, and provide solutions. Deep learning will challenge the way we think about data and the role scientists have in understanding that data. The focus shifts from not necessarily fully understanding the data, but rather understanding the relationships between different data and processes. Digitalization challenges some of the fundamental ways we undertake science. Variability in the data is sought after and useful; fostering a move away from historical reductionism, where a problem is reduced to a single testable hypothesis. Rather, an understanding of the relationships between data is used not only to solve the problem, but also to identify additional or hidden issues. These relationships are likely to be better identified by AI than by humans. However, an understanding of human factors (in the case of potential training biases in AI), how aspirations and cultural values are incorporated, and human interpretation of difficult data points in data veracity (that may be considered either invalid or instead be valid points at the extreme edge of the acceptable range) is going to become increasingly important with any automatic systems tending towards less and less human intervention. Digitalization could also challenge the way we analyze literature and undertake experiments. The thinking is already that the sheer volume of knowledge produced is now impossible for scientists in a domain to keep up with, leading to a narrower disciplinary focus, which in turn risks missing potential solutions outside a discipline. It has been suggested that ‘literature‐related discovery’ has potential for

the identification of unexplored hypotheses by linking previously unconnected knowledge domains, especially if AI could be integrated into the scientific process. Digital technologies and their interconnectedness through the Internet of Things offer potential for solutions to produce more food more sustainably and link consumers and farmers. However, it is clear that embedding digital agriculture, and equitably sharing the potential benefits it has to offer, will require significant agricultural system changes and the need to address critical socio‐ethical, as well as technical issues. Science and scientists will play a critical role in navigating this change. Digitalization of science offers both a challenge and an opportunity to science playing this role. (Mark Shepherd, James A Turner, Bruce Small, David Wheeler, 2018)

Institutionalizing Participatory, Development in Agriculture

Client‐Driven

Research

and

Technology

This article identifies key characteristics of participatory research and development(R&D) in the agricultural sector: it is client‐driven, requires decentralized technology development, devolves to farmers the major responsibility for adaptive testing, and requires institutions and individuals to become accountable for the relevance and quality of technology on offer. Through case study material drawn from Latin America, Asia and Africa, the article then reviews ways by which institutions have responded to these characteristics and raises issues for further elaboration. Steps need to be taken, in particular, to safeguard equity, both between the more and less vocal groups of farmers, and between the requirements of present and future generations (the latter referring particularly to environmental concerns). It is argued that participatory R&D alone is insufficient to deliver innovations relevant to diverse client groups: policy mechanisms are required to define which clients are to participate, whose agendas are to drive the process, and what organizational innovations are needed to move agricultural R&D in these directions. (Jacqueline A. Ashby, Louise Sperling, 1995)

Precision Agriculture This chapter gives an account on the application of precision agriculture (PA) over the last 25 years, on the methods used, the results obtained, the adoption of the technology and the effects to crop management, to the environment and the sustainability of agricultural systems. Yield spatial distribution data, soil data, remote sensing data, data collected by crop scouting, as well as weather data can be collected for every field at site‐specific level to assist the farm manager in crop management. In agriculture, decision support system refers to the decision taken by the farmer for the management of the farm. Farmers expect new technologies to be proven and robust, cost‐effective, and when new equipment is employed for it, to be reliable and well supported for servicing and repair. New sensors that detect irregular reaction of the crops or the soil will improve productivity and profitability, and reduce effects on the environment. (Spyros Fountas, Katerina Aggelopoulou, Theofanis A. Gemtos, 2016)