Analyzing UNO Statistics on Land Use of Agricultural Practices by Using k-Means Clustering and SARIMA: Irrigated, Organic, and Overall Agricultural Activities on a Global Scale

Ongoing and self-enhancing climate change, population growth, and urbanization put a lot of pressure on worldwide agriculture. To ensure food production, agriculture must become either more efficient or grow in size. The United Nations offers a broad range of statistics on global land usage, irrigated land, and certified organic land. This paper aims to analyze the development of overall land usage and the interest shifting of modern-day agriculture by using the Silhouette score, k-Means Clustering, and SARIMA. It looks on ongoing trends concerning organic food production as well as the growing amount of irrigated land for growing crops. An outlook of worldwide, French, Nigerien, and Indian land use is applied with the SARIMA algorithm. As results, we identified clear clusters of countries that irrigate their farmland and saw a rise in organic farmlands all around the globe. Additionally, a clear land loss for all agricultural proposes was detected in most of countries inside the dataset. Especially in high-developed countries such as ones in Western Europe, land surface is declining massively for decades. This trend will most likely continue in the future, as we discovered by using the predictive SARIMA algorithm.