Near infrared spectroscopy of plantation forest soil nutrients in Sabah, Malaysia, and the potential for microsite assessment

Abstract

Knowledge of soil physical and chemical properties is vital to the optimal growing performance of agricultural crops, including plantation forest trees. Near infrared (NIR) spectroscopy has been shown to be a tool that enables rapid and low-cost assessment of soils, however its use in forest plantations has been slow to develop. This study shows the development of calibrations for total organic carbon, total nitrogen and soil pH using a handheld NIR spectrometer for soils at three sites in Sabah, Malaysia. Soil samples were collected, dried, milled and scanned after which they were analysed using standard chemical methods to obtain total organic carbon (TOC) and total nitrogen (TN). Partial least squares regression was used to develop calibrations between reference data and NIR spectra and validated using an independent sample set. The calibration of soil pH is made using a subset of samples across A- and B-horizons for samples from two of the three sites. The most effective spectral pre-treatment was the standard normal variate for TOC and TN while the Savitzky-Golay first derivative was the best pre-treatment for predicting soil pH. Principal component analysis was performed on the raw NIR spectra of all samples to confirm that the samples from different sites were able to be used in a single regression analysis. Kennard-Stone selection was used to create calibration sets and validation sets from the combined spectra from all sites and both soil horizons. Calibrations were also developed independently on the A- and B-horizon samples, but there were insufficient sample numbers to utilize an independent validation set. The coefficients of determination for the validation set (r2p) were 0.77 and 53 for TOC and TN respectively while the root mean square error of prediction (RMSEP) was 0.44 g 100 g−1 for TOC and 0.051 g 100 g−1 for TN. In addition, it showcases the application of these calibrations to provide spatial assessment of two differing micro-sites within a single Eucalyptus pellita progeny breeding trial. Combined with the potential to monitor foliar nutrients, the ability to obtain high spatial details of soil composition will assist tree plantation growers and also other agricultural producers, such as oil palm plantation managers, to better manage their soil and fertiliser regimes.

Detecting the temporal trend of cultivated soil organic carbon content using visible near infrared spectroscopy

“…Also, this is consistent with results of Liu et al, 52 who found that stratifying samples by soil type improved prediction of SOC content. Moreover, Ahmad Jani et al 93 improved predictions of soil total organic carbon by stratifying samples of A and B horizons independently. In addition, Stenberg et al 18 stated that overall variability in soil properties would influence prediction accuracy to a small extent.…”

Large-scale measurement of soil organic carbon using compact near-infrared spectrophotometers: effect of soil sample preparation and the use of local modelling

“…When compared to Vis-NIR spectrophotometers, compact NIR instruments operate in a shorter range of the electromagnetic spectrum, [11][12][13] but in terms of performance in SOC determination, the current literature shows that models based on these instruments can present excellent accuracy. 10,[14][15][16] However, the performance of compact NIR spectrophotometers in SOC determination using large and heterogeneous data sets has seldom been studied 17 and, until now, methodological aspects such as soil sample preparation for using these instruments have not yet been addressed extensively.…”

Development of ecological management system for planted forest based on ELM deep learning algorithm

“…Human initial afforestation is mainly in order to production needs, as a part of the agricultural production, with the progress of social productivity, timber demand increasing, under the natural state of forest resource components are insufficient to satisfy the human production and life, people began planting plantation, planted forest ecological level and function is becoming more and more attention [20]. Therefore, this article is based on C/S architecture and the J2EE platform, in the Struts framework with the business model -user interface -controller programming model, under the support of construct artificial planting forest ecological management system, through the synergy evaluation module, the ecological function value of the principal component comprehensive analysis module and the depth of the extreme learning machine learning algorithm of planted forests demand forecast module, Various indicators and factors affecting the ecological function of planted forests were evaluated and analyzed in order to better understand the biodiversity of planted forests and its regulation approaches and mechanisms on ecological function and to provide scientific basis and technical means for guiding the ecological management and sustainable management of planted forests [21].…”