The introduction of the chapter highlights the importance of geostatistics in environmental science and precision farming. It begins with a farmer's request for detailed soil phosphorus content data to optimize fertilizer application, emphasizing the need for spatially continuous information. The chapter explains how geostatistics can provide this by estimating values between sample points, balancing cost and accuracy. It discusses the challenges of environmental data, which are often continuous but measured at finite points, and how geostatistics addresses these issues by quantifying spatial autocorrelation and providing error estimates. The text also addresses the importance of probability in geostatistics, particularly in assessing risks associated with over- or under-fertilizing. It contrasts geostatistics with classical methods, noting that geostatistics relies on spatial models and assumes random processes, while classical methods focus on unbiased estimation through random sampling designs. The chapter then provides a historical overview of geostatistics, tracing its roots back to early agricultural and geological studies. It highlights key contributions from researchers like Fisher, Kolmogorov, Matérn, Krige, and Matheron, who developed the foundational theories and methods that have evolved into modern geostatistics. Finally, the chapter outlines the structure of the book, which is designed to guide practitioners through a geostatistical project, covering topics from data summarization to advanced techniques like kriging, cokriging, and disjunctive kriging.The introduction of the chapter highlights the importance of geostatistics in environmental science and precision farming. It begins with a farmer's request for detailed soil phosphorus content data to optimize fertilizer application, emphasizing the need for spatially continuous information. The chapter explains how geostatistics can provide this by estimating values between sample points, balancing cost and accuracy. It discusses the challenges of environmental data, which are often continuous but measured at finite points, and how geostatistics addresses these issues by quantifying spatial autocorrelation and providing error estimates. The text also addresses the importance of probability in geostatistics, particularly in assessing risks associated with over- or under-fertilizing. It contrasts geostatistics with classical methods, noting that geostatistics relies on spatial models and assumes random processes, while classical methods focus on unbiased estimation through random sampling designs. The chapter then provides a historical overview of geostatistics, tracing its roots back to early agricultural and geological studies. It highlights key contributions from researchers like Fisher, Kolmogorov, Matérn, Krige, and Matheron, who developed the foundational theories and methods that have evolved into modern geostatistics. Finally, the chapter outlines the structure of the book, which is designed to guide practitioners through a geostatistical project, covering topics from data summarization to advanced techniques like kriging, cokriging, and disjunctive kriging.