Geostatistics is a method used to predict and estimate spatially varying properties, such as soil phosphorus content, based on sparse data. It addresses the challenge of estimating values at unsampled locations, balancing cost and accuracy. The farmer's need for precise fertilizer application highlights the importance of geostatistics in precision farming. Geostatistics enables spatial prediction by quantifying spatial autocorrelation, minimizing errors, and estimating probabilities of exceeding thresholds. It is used in various fields, including environmental science, mining, and petroleum engineering. The method relies on spatial models, unlike classical methods that assume random sampling. Geostatistics accounts for spatial correlation, allowing for more accurate predictions and risk assessment. It has evolved from early studies in agriculture and mining, with key contributions from statisticians like Fisher, Kolmogorov, and Matheron. The book covers geostatistical methods, including variogram modeling, kriging, and cokriging, and discusses their applications in environmental and agricultural contexts. It emphasizes the importance of spatial data analysis for resource management and environmental protection.Geostatistics is a method used to predict and estimate spatially varying properties, such as soil phosphorus content, based on sparse data. It addresses the challenge of estimating values at unsampled locations, balancing cost and accuracy. The farmer's need for precise fertilizer application highlights the importance of geostatistics in precision farming. Geostatistics enables spatial prediction by quantifying spatial autocorrelation, minimizing errors, and estimating probabilities of exceeding thresholds. It is used in various fields, including environmental science, mining, and petroleum engineering. The method relies on spatial models, unlike classical methods that assume random sampling. Geostatistics accounts for spatial correlation, allowing for more accurate predictions and risk assessment. It has evolved from early studies in agriculture and mining, with key contributions from statisticians like Fisher, Kolmogorov, and Matheron. The book covers geostatistical methods, including variogram modeling, kriging, and cokriging, and discusses their applications in environmental and agricultural contexts. It emphasizes the importance of spatial data analysis for resource management and environmental protection.