Field radiometer with canopy pasture probe as a potential tool to estimate and map pasture biomass and mineral components: A case study in the Lake Taupo catchment, New Zealand

Abstract

Precision farming requires data on resource status at a very fine, within-paddock scale which is impractical to collect by traditional sampling methods. This paper demonstrates the potential of a field radiometer in conjunction with a canopy pasture probe (CAPP) and global positioning system (GPS) to predict and map the spatial distribution patterns of herbage biomass and mass of nutrients, such as nitrogen (N), phosphorous (P), potassium (K), and sulphur (S) in hill country A08070; Online publication date 4 November 2009 Received 28 October 2008 accepted 11 August 2009 grassland. The accuracy of the calibration model using partial least squares (PLS) regression was assessed by using coefficient of determination (R 2 ) and the ratio of prediction to standard deviation (RPD). Continuum-removed derivative reflectance (CRDR) data used in a PLS model gave an excellent prediction of the standing masses of N, P, and S (R 2 > 0.895, RPD > 3.0). Both first derivative reflectance (FDR) and CRDR datasets gave a good prediction of standing biomass (R 2 > 0.857, RPD > 2.5). Although relatively lower prediction accuracy was shown in standing K, it may still be possible to make a quantitative prediction using CRDR and FDR (RPD > 2.2). The semivariogram's parameter "range" of biomass was longer (58.7 m) than the ranges of the other parameters (10.6-17.4 m), suggesting that biomass values influenced neighbouring values of biomass over greater distances than the other pasture parameters (masses of N, P, K, and S).

Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system

“…They suggested that using mobile-sensor-based biomass estimation methods could be an effective alternative to the traditional clipping method for a rapid and accurate in-field biomass estimation. Different types of sensors both in static and mobile application have been used in recent grassland studies (Numata et al, 2008;Biewer et al, 2009a, b;Himstedt et al, 2009;Kawamura et al, 2009;Fricke et al, 2011;Pullanagari et al, 2012;Duan et al, 2014;Rahman et al, 2014;Reddersen et al, 2014). Particularly hyperspectral sensors, which measure reflectance signals over a wide range of wavelengths in discrete bands of 1-15 nm width, have raised considerable interest for the prediction of biomass and quality parameters.…”

Spectral indicators of forage quality in West Africa’s tropical savannas

“…In situ collected vegetation spectra have been found to be a time-saving, non-destructive and cost-effective alternative to laboratory analysis (Curran, 1989). The approach has been successfully applied in various biomes, including temperate grasslands (Biewer et al, 2009b;Kawamura et al, 2009;Sanches et al, 2013) and subtropical grasslands (Knox et al, 2011(Knox et al, , 2012.…”

“…Phosphorus is an important macro element in animal nutrition (Reid et al, 2015) and has therefore been investigated by several authors (e.g. Albayrak et al, 2011;Kawamura et al, 2009). Apart from nitrogen and phosphorus, Mutanga et al (2004) used in situ measured hyperspectral reflectance data to model potassium, calcium and magnesium concentrations.…”

Hyperspectral Reflectance Response of Seagrass (Enhalus acoroides) and Brown Algae (Sargassum sp.) to Nutrient Enrichment at Laboratory Scale

“…A wide range of plant physiological parameters and structures including leaf area index, biomass, water, pigment and nutrient content, crop canopy, and density have been investigated extensively (Kawamura et al, 2009;Mü ller et al, 2008;Ray, Singh, and Panigrahy, 2010).…”