Data Science For Agricultural Innovation And Productivity
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Data Science For Agricultural Innovation And Productivity
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Author : S. Gowrishankar, Hamidah Ibrahim, A. Veena, K.P. Asha Rani, A.H. Srinivasa
language : en
Publisher: Bentham Science Publishers
Release Date : 2024-02-12
Data Science For Agricultural Innovation And Productivity written by S. Gowrishankar, Hamidah Ibrahim, A. Veena, K.P. Asha Rani, A.H. Srinivasa and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-12 with Technology & Engineering categories.
Data Science for Agricultural Innovation and Productivity explores the transformation of agriculture through data-driven practices. This comprehensive book delves into the intersection of data science and farming, offering insights into the potential of big data analytics, machine learning, and IoT integration. Readers will find a wide range of topics covered in 10 chapters, including smart farming, AI applications, hydroponics, and robotics. Expert contributors, including researchers, practitioners, and academics in the fields of data science and agriculture, share their knowledge to provide readers with up-to-date insights and practical applications. The interdisciplinary emphasis of the book gives a well-rounded view of the subject. With real-world examples and case studies, this book demonstrates how data science is being successfully applied in agriculture, inspiring readers to explore new possibilities and contribute to the ongoing transformation of the agricultural sector. Sustainability and future outlook are the key themes, as the book explores how data science can promote environmentally conscious agricultural practices while addressing global food security concerns. Key Features: - Focus on data-driven agricultural practices - Comprehensive coverage of modern farming topics with an interdisciplinary perspective - Expert insights - Sustainability and future outlook -Highlights practical applications Data Science for Agricultural Innovation and Productivity is an essential resource for researchers, data scientists, farmers, agricultural technologists, students, educators, and anyone with an interest in the future of farming through data-driven agriculture.
Big Data For The Greater Good
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Author : Ali Emrouznejad
language : en
Publisher: Springer
Release Date : 2018-07-13
Big Data For The Greater Good written by Ali Emrouznejad and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-13 with Technology & Engineering categories.
This book highlights some of the most fascinating current uses, thought-provoking changes, and biggest challenges that Big Data means for our society. The explosive growth of data and advances in Big Data analytics have created a new frontier for innovation, competition, productivity, and well-being in almost every sector of our society, as well as a source of immense economic and societal value. From the derivation of customer feedback-based insights to fraud detection and preserving privacy; better medical treatments; agriculture and food management; and establishing low-voltage networks – many innovations for the greater good can stem from Big Data. Given the insights it provides, this book will be of interest to both researchers in the field of Big Data, and practitioners from various fields who intend to apply Big Data technologies to improve their strategic and operational decision-making processes.
Artificial Intelligence And Advanced Analytics For Food Security
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Author : Chandrasekar Vuppalapati
language : en
Publisher: CRC Press
Release Date : 2023-07-17
Artificial Intelligence And Advanced Analytics For Food Security written by Chandrasekar Vuppalapati and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-17 with Technology & Engineering categories.
Climate change, increasing population, food-versus-fuel economics, pandemics, etc. pose a threat to food security to unprecedented levels. It has fallen upon the practitioners of agriculture and technologists of the world to innovate and become more productive to address the multi-pronged food security challenges. Agricultural innovation is key to managing food security concerns. The infusion of data science, artificial intelligence (AI), advanced analytics, satellites data, geospatial data, climatology, sensor technologies, and climate modeling with traditional agricultural practices such as soil engineering, fertilizers use, and agronomy are some of the best ways to achieve this. Data science helps farmers to unravel patterns in fertilizer pricing, equipment usage, transportation and storage costs, yield per hectare, and weather trends to better plan and spend resources. AI enables farmers to learn from fellow farmers to apply best techniques that are transferred learning from AI to improve agricultural productivity and to achieve financial sustainability. Sensor technologies play an important role in getting real-time farm field data and provide feedback loops to improve overall agricultural practices and can yield huge productivity gains. Advanced Analytics modeling is essential software technique that codifies farmers’ tacit knowledge such as better seed per soil, better feed for dairy cattle breed, or production practices to match weather pattern that was acquired over years of their hard work to share with worldwide farmers to improve overall production efficiencies, the best antidote to food security issue. In addition to the paradigm shift, economic sustainability of small farms is a major enabler of food security. The book reviews all these technological advances and proposes macroeconomic pricing models that data mines macroeconomic signals and the influence of global economic trends on small farm sustainability to provide actionable insights to farmers to avert any financial disasters due to recurrent economic crises.
Data Science In Agriculture And Natural Resource Management
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Author : G. P. Obi Reddy
language : en
Publisher: Springer Nature
Release Date : 2021-10-11
Data Science In Agriculture And Natural Resource Management written by G. P. Obi Reddy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-11 with Technology & Engineering categories.
This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.
Iot And Analytics For Agriculture
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Author : Prasant Kumar Pattnaik
language : en
Publisher: Springer Nature
Release Date : 2019-10-01
Iot And Analytics For Agriculture written by Prasant Kumar Pattnaik and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-01 with Technology & Engineering categories.
This book presents recent findings on virtually every aspect of wireless IoT and analytics for agriculture. It discusses IoT-based monitoring systems for analyzing the crop environment, and methods for improving the efficiency of decision-making based on the analysis of harvest statistics. In turn, it addresses the latest innovations, trends, and concerns, as well as practical challenges encountered and solutions adopted in the fields of IoT and analytics for agriculture. In closing, it explores a range of applications, including: intelligent field monitoring, intelligent data processing and sensor technologies, predictive analysis systems, crop monitoring, and weather data-enabled analysis in IoT agro-systems.
Digital Ecosystem For Innovation In Agriculture
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Author : Sanjay Chaudhary
language : en
Publisher: Springer Nature
Release Date : 2023-05-19
Digital Ecosystem For Innovation In Agriculture written by Sanjay Chaudhary and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-19 with Technology & Engineering categories.
This book presents the latest findings in the areas of digital ecosystem for innovation in agriculture. The book is organized into two sections with thirteen chapters dealing with specialized areas. It provides the reader with an overview of the frameworks and technologies involved in the digitalization of agriculture, as well as the data processing methods, decision-making processes, and innovative services/applications for enabling digital transformations in agriculture. The chapters are written by experts sharing their experiences in lucid language through case studies, suitable illustrations, and tables. The contents have been designed to fulfill the needs of geospatial, data science, agricultural, and environmental sciences of universities, agricultural universities, technological universities, research institutes, and academic colleges worldwide. It helps the planners, policymakers, and extension scientists plan and sustainably manage agriculture and natural resources.
Big Data And Climate Smart Agriculture Review Of Current Status And Implications For Agricultural Research And Innovation In India
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Author : NH. Rao
language : en
Publisher:
Release Date : 2018
Big Data And Climate Smart Agriculture Review Of Current Status And Implications For Agricultural Research And Innovation In India written by NH. Rao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Climate change will increase the vulnerability of agricultural production systems, unless scientists and farmers reorient their present approaches towards making them climate smart or climate resilient. The integration of recent developments in big data analytics and climate change science with agriculture can greatly accelerate agricultural research and innovation for climate smart agriculture (CSA). CSA refers to an integrated set of technologies and practices that simultaneously improve farm productivity and incomes, increase adaptive capacity to climate change effects, and reduce green house gas emissions from farming. It is a multistage, multiobjective, data-driven, and knowledge based approach to agriculture, with the farm as the most fundamental unit for both strategic and tactical decisions. This paper explores how big data analytics can accelerate research and innovation for CSA. Three levels at which big data can enhance farmer field level insights and actionable knowledge for the practice of CSA are identified: (i) developing a predictive capability to factor climate change effects to scales relevant to farming practice, (ii) speeding up plant breeding for higher productivity and climate resilience, and (iii) delivery of customized and prescriptive real-time farm knowledge for higher productivity, climate change adaptation and mitigation. The state-of-art on big data based approaches at each of the three levels is assessed. The paper also identifies the research and institutional challenges, and the way forward for leveraging big data in research and innovation aimed at climate smart agriculture in India.
Economics Of Research And Innovation In Agriculture
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Author : Petra Moser
language : en
Publisher: University of Chicago Press
Release Date : 2021-10-08
Economics Of Research And Innovation In Agriculture written by Petra Moser and has been published by University of Chicago Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-08 with Business & Economics categories.
Feeding the world’s growing population is a critical policy challenge for the twenty-first century. With constraints on water, arable land, and other natural resources, agricultural innovation is a promising path to meeting the nutrient needs for future generations. At the same time, potential increases in the variability of the world’s climate may intensify the need for developing new crops that can tolerate extreme weather. Despite the key role for scientific breakthroughs, there is an active discussion on the returns to public and private spending in agricultural R&D, and many of the world’s wealthier countries have scaled back the share of GDP that they devote to agricultural R&D. Dwindling public support leaves universities, which historically have been a major source of agricultural innovation, increasingly dependent on industry funding, with uncertain effects on the nature and direction of agricultural research. All of these factors create an urgent need for systematic empirical evidence on the forces that drive research and innovation in agriculture. This book aims to provide such evidence through economic analyses of the sources of agricultural innovation, the challenges of measuring agricultural productivity, the role of universities and their interactions with industry, and emerging mechanisms that can fund agricultural R&D.
The Innovation Revolution In Agriculture
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Author : Hugo Campos
language : en
Publisher: Springer Nature
Release Date : 2020-10-07
The Innovation Revolution In Agriculture written by Hugo Campos and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-07 with Technology & Engineering categories.
This open access book is an important reframing of the role of innovation in agriculture. Dr. Campos and his distinguished coauthors address the need for agriculture to feed a growing global population with a reduced environmental footprint while adapting to and mitigating the effects of changing climate. The authors expand the customary discussion of innovation in terms of supply driven R&D to focus on the returns to investors and most importantly, the value to end-users. This is brought to life by exploring effective business models and many cases from agricultural systems across the globe. The focus on converting the results of innovation in R&D into adoption by farmers and other end-users is its greatest contribution. Many lessons from the book can be applied to private and public sectors across an array of agricultural systems. This book will be of enormous value to agri-business professionals, NGO leaders, agricultural and development researchers and those funding innovation and agriculture across the private and public sectors. Tony Cavalieri, Senior Program Officer, Bill & Melinda Gates Foundation Hugo Campos, Ph.D., MBA, has 20+ years of international corporate and development experience. His distinguished coauthors represent a rich collection of successful innovation practice in industry, consultancy, international development and academy, in both developed and developing countries.”
Reflections On Agricultural R D Productivity And The Data Constraint
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Author : Julian Alston
language : en
Publisher:
Release Date : 2020
Reflections On Agricultural R D Productivity And The Data Constraint written by Julian Alston and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Sixty years ago, T.W. Schultz introduced the idea of the productivity “residual” to agricultural economics (1956). Schultz's main message was that growth in conventional inputs accounted for little of the observed growth in agricultural output, and that there was work to be done by agricultural economists to understand and ultimately eliminate this unexplained residual called “productivity.” Thus was launched the economics of agricultural productivity as a sub-field within agricultural economics, along with the economics of agricultural R&D and innovation and related government policy. Much progress has been made in the decades since. Still, critical issues remain unresolved. This matters because agricultural innovation and productivity matter, and so do the related policies that rest to some extent on our established understanding of the economic relationships. In this paper I review some unsettled issues related to economic models and measures applied to agricultural R&D and productivity, and some unfinished business in terms of economic and policy questions that are not yet well answered. Before doing that, I present some evidence on agricultural productivity and why it matters. Next, with a nod to “factology,” I present available productivity measures from the U.S. Department of Agriculture (USDA) and International Science and Technology Practice and Policy (InSTePP) Center, and compare them in the context of translog cost function models. In subsequent sections I use these and other data to develop new evidence related to two contentious questions: (a) Do farmers benefit from public agricultural R&D? (b) Has U.S. agricultural productivity growth slowed in recent decades? The answers are revealed within.