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Abstract
On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity.
Highlights
Precision agriculture (PA) technologies are increasingly applied on arable land to improve resource efficiency, reduce environmental impacts, and optimize agricultural productivity (e.g., [1,2,3]).This can only be achieved by understanding and controlling within-field variability of soil and/or vegetation properties [4,5,6]
The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and electrical conductivity (ECa) on the spatial distribution of biomass
A grassland field on sandy soils in Northeast Germany was mapped by proximal soil sensors and remote sensing
Summary
Precision agriculture (PA) technologies are increasingly applied on arable land to improve resource efficiency, reduce environmental impacts, and optimize agricultural productivity (e.g., [1,2,3]) This can only be achieved by understanding and controlling within-field variability of soil and/or vegetation properties [4,5,6]. Over the past decade numerous studies focused on that matter to close that knowledge gap (e.g., [11,12,13,14,15]) They monitored grassland biophysical parameters by means of optical remote sensing technologies [16,17,18,19,20], or mapped soil properties using proximal soil sensors, most of all applying geophysical methods to measure the apparent electrical conductivity (ECa) [11,13,21].
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