The Utilization of Big Data in Land Management and Planting Patterns: Predictive Innovation Towards Sustainable Agriculture

Rasyid, Harun
Ningsih, Gumoyo Mumpuni
Ningsih, Natali
Pujotomo, Darminto
Suseno, Gijanto Purbo

Journal of Social Science
Vol. 6 No. 3 (2025): Journal of Social Science

Modern agriculture faces major challenges related to land degradation, climate change, and inaccuracies in planting patterns. Data-driven decision-making is an urgent need in realizing sustainable agriculture. This study aims to analyze the influence of the use of big data on the effectiveness of land management and farmers' planting patterns. The research approach used is quantitative with a survey method of 120 farmers in Central Java who have used a big data-based system to monitor land and weather conditions. The data is analyzed by linear regression to see the relationships between variables. The results show that the use of big data significantly improves planting timeliness, fertilizer use efficiency, and crop yields. These findings indicate that big data-based predictive technologies can be an important innovation for adaptive and efficient agricultural management. The conclusion of this study emphasizes the importance of strengthening data infrastructure and training farmers in utilizing digital technology to support sustainable food security.