The book deals with the pricing of ecosystem services provided by agriculture. All provisioning, regulating, supporting and cultural services are being covered in this title. Chapters in this contributed volume cover topics such as pricing of services from the soil, water, and nutrient management. Quantified monetary values of carbon sequestration and renewable energy applications in agriculture are covered with clear-cut methodologies. This book also links ecosystem service-based pricing with crop insurance. Improving the farmers’ livelihood is the central goal of the agricultural production system throughout the world. Under the climate change context, farms’ produce is now climate-vulnerable and heavily dependent on weather conditions. Moreover, we often neglect the contribution of several positive impacts of agricultural practices on ecosystems and natural resources. Therefore, there is a need to quantify and value these ecosystem services in agriculture. However, valuation and pricing the services in agriculture both tangible and intangible is a challenge. It is necessary to have clear-cut methodologies for pricing ecosystem services of agriculture in terms of net monetary benefits. The ecosystem service-based pricing could be a solid basis for calculating the insurance to farmers in case of occurrence of natural hazard and associated crop damage. This book is of interest to scholars, teachers, researchers, environmental scientists, watershed managers, capacity builders, and policymakers. The book also serves as effective reading material for undergraduate and graduate students of agriculture economics, ecology, agronomy, and environmental sciences. National and international agricultural scientists, policymakers will also find this to be useful.
Master's Thesis from the year 2022 in the subject Mathematics - Statistics, grade: 9.0, , course: IMSc Mathematics and Computing, language: English, abstract: In any application that involve data, outlier detection is critical. In the data mining and statistics literature, outliers are sometimes known as abnormalities, discordants, deviants, or anomalies. The data in most applications are generated by one or more generating processes, which may reflect system activity or observations about entities. This monograph explains what an outlier is and how it can be used in a variety of industries in the first chapter of the report. This chapter also goes over the various types of outliers. Outlier analysis is an important part of research or industry that involves a large amount of data, as described in Chapter 2; it also describes how outliers are related to different data models. Chapter 3 covers Univariate Outlier Detection and methods for completing this task. Multivariate Outlier Detection techniques such as Mahalanobis distance and isolation forest are covered in Chapter 4. Finally, in Chapter 5, the Python programming language has been used to analyse and detect existing outliers in a public dataset. We hope this monograph would be useful to students and practitioners of statistics and other fields involving numerical data analytics.
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