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Abstract:
In the pursuit of enhancing yield efficiency, mitigating environmental impact, and reducing costs of fertilizers and fuel, precision farming emerges as a pivotal strategy. The application of nutrients must be tailored according to spatial and temporal variations. This requires a comprehensive understanding of the nutrient composition of organic fertilizers, the nutrient supply of plants, and the soil’s capacity. To optimize fertilizer application in the field, it is recommendable to subdivide the field into management zones, thereby identifying distinct zones characterized by uniform growing conditions. To establish management zones, we combined satellite-based phenological-dependent timeseries of vegetation, with proximal soil sensor data from a multi-sensor platform. The zones were generated through a multi-step clustering algorithm, based on hierarchical clustering, which results were combined by a consensus clustering algorithm. Four different scenarios of input datasets were tested. The first scenario incorporates all scenes during the timeseries, followed by the one with selected scenes during specific phenology stages. Another scenario was based solely on soil information. The fourth scenario involves phenologically distributed vegetation and soil information. For the validation we calculated the variance for the input datasets per cluster, lying under one scenario. Our hypothesis that the clustering based on soil and phenology separated vegetation data would improve the management zones was refuted. The vegetation cluster, which was based on the entire Sentinel-2 timeseries, exhibited optimal performance, for one field in Groß Kreutz, Germany. The management zones are interpreted as recommendations for farmers to adapt the management practices within the framework of possibilities.
