Search

In this study, the spatial variability of nitrogen (N) balances and potential nitrate leaching were determined in heterogeneous arable fields in southern Germany using digital methods (tractor-mounted multispectral sensor, satellite data, vegetation indices and models) and measurements of nitrate stocks in deeper zones (1−2.5 m soil depth) with deep drilling. The aim of this study was to achieve precise localisation of N losses at high spatial resolution (sub field level; 10 × 10 m grid). The spatial variability of plant parameters within uniformly fertilised fields (crop yield, N uptake) was determined for the calculation of the N balances. The spatial variability of soil properties (e.g. soil organic carbon content, soil total nitrogen content) were determined to identify the causes of high or low N surpluses and nitrate leaching in sub-fields. N surplus determined using different digital methods and measured nitrate stocks showed similar spatial patterns. Site-specific N balancing identified zones with high N loss potential (N surplus up to 86 kg ha−1). Deep drilling showed zones with high nitrate loss (nitrate N up to 94 kg ha−1). N surplus and nitrate loss correlated with r = 0.49. This relationship could be impacted by many other soil and management factors. Soil properties showed considerable spatial variation within the fields. Soil organic carbon (SOC) and soil total nitrogen (TN) content were closely correlated in all fields (up to r = 0.96) and were most closely positively correlated with crop yield and N uptake (up to r = 0.74) and negatively correlated with N surplus (up to r = − 0.73). The sensor data and satellite data (crop yield, N uptake, N surplus) showed similar distribution patterns. Based on these results, digital technologies are suitable for the calculation of site-specific N surplus and estimation of nitrate leaching risk. Satellite or sensor based site-specific and yield-oriented fertilization is one approach to reduce N surplus on sub fields with low yield potential and high nitrate leaching risk. [ABSTRACT FROM AUTHOR]

This study describes a new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications. The relative biomass potential (rel. BMP) serves as an indicator for multiyear stable and homogeneous yield zones. The rel. BMP is derived from satellite data corresponding to specific growth stages and the normalized difference vegetation index (NDVI) to analyze crop-specific yield patterns. The development of this methodology is based on data from arable fields of two research farms; the validation was conducted on arable fields of commercial farms in southern Germany. Close relationships (up to r > 0.9) were found between the rel. BMP of different crop types and study years, indicating stable yield patterns in arable fields. The relative BMP showed moderate correlations (up to r = 0.64) with the yields determined by the combine harvester, strong correlations with the vegetation index red edge inflection point (REIP) (up to r = 0.88, determined by a tractor-mounted sensor system) and moderate correlations with the yield determined by biomass sampling (up to r = 0.57). The study investigated the relationship between the rel. BMP and key soil parameters. There was a consistently strong correlation between multiyear rel. BMP and soil organic carbon (SOC) and total nitrogen (TN) contents (r = 0.62 to 0.73), demonstrating that the methodology effectively reflects the impact of these key soil properties on crop yield. The approach is well suited for deriving yield zones, with extensive application potential in agriculture. [ABSTRACT FROM AUTHOR]