Time filter

Source Type

Wetterlind J.,Precision Agriculture and Pedometrics Group | Stenberg B.,Precision Agriculture and Pedometrics Group
European Journal of Soil Science

The advantage of using near-infrared spectroscopy to increase sample point density in soil mapping on farms relies on the number of conventional laboratory analyses for the calibrations being kept to a minimum. This study compared the performance of small farm-scale calibrations (25 samples) with a larger national soil library (396 samples) and tested whether a site-specific sample set selected from the national library, consisting of the 50 samples that were spectrally most similar to those of the local sites, could increase performance. In addition, the national library and selected subsets were augmented ('spiked') with up to 25 local calibration samples to test whether that had any additional effect on prediction errors and bias. Calibrations were made for predicting within-field variation in clay, silt, sand, soil organic carbon (SOC), pH and phosphorus. Selecting a subset of samples from the national library did not improve the results compared with using the entire national library. However, spiking both libraries with local samples reduced the root mean squared error of prediction (RMSEP) considerably, mainly through a decrease in bias, and often resulted in comparable results to the local calibrations. There was a tendency for better clay and SOC predictions from spiking a reduced national library compared with spiking the entire national library, sometimes even resulting in better predictions than using the local calibrations. However, using local calibrations seems to be the best alternative for predicting soil properties at the farm or field scale, even with as few as 25 samples. © 2010 The Authors. Journal compilation © 2010 British Society of Soil Science. Source

Wetterlind J.,Precision Agriculture and Pedometrics Group | Stenberg B.,Precision Agriculture and Pedometrics Group | Soderstrom M.,Precision Agriculture and Pedometrics Group

For use as decision support for variable rate applications in precision agriculture, the commonly used sample point density of one sample per hectare is often not enough. However, increasing the sampling density using laboratory analyses is too expensive for farmers to implement. It is therefore important to find methods for rationalisation. To this end, farm-scale visible and near infrared reflection (vis-NIR) calibrations were established on two farms in southern Sweden (Hacksta and Sjöstorp) for soil texture, soil organic matter, total N, pH and plant-available P, K and Mg. By keeping the laboratory analyses to a minimum to be used for vis-NIR calibrations and only collecting vis-NIR spectra from the vast majority of the samples, the sampling density could be increased without significantly increasing the cost. In this study 25 samples were used in the calibrations. Six different calibration sample selection methods were compared, selected from three different datasets originating from a larger context aiming at covering soil variations. Using only 25 calibration samples resulted in good predictions for clay at both farms, r2 values of 0.81 and 0.89 and RMSEP values of 3.6 and 3.9%. Sand, soil organic matter and total nitrogen were well predicted at Hacksta (r2 = 0.87, 0.90 and 0.89 and RMSEP = 3.0, 0.28 and 0.018% respectively) but 25 samples proved to be too few at the geologically divided farm Sjöstorp. For predicting pH and plant-available P, K and Mg, more than 25 calibration samples were needed at both farms, although with 75% of all reference samples (92 and 94 at Hacksta and Sjöstorp respectively) in the calibration these parameters also showed potential for building useful NIR calibrations (RPD values between 2.3 and 2.8 except for the predictions for pH at one of the farms resulting in an RPD value of 1.6). However, predictions for silt content were less reliable and the small number of calibration samples was not the limiting factor in this case. The promising results are encouraging for further development of cost-effective high resolution farm soil maps using NIR spectroscopy. © 2010 Elsevier B.V. All rights reserved. Source

Discover hidden collaborations