Mohammad Sadegh Askari

Grassland soil carbon and nitrogen stocks under temperate livestock grazing

Sustainable grassland management is critical to many agricultural economies because of the significant proportion of agricultural commodities derived from grass-fed livestock (milk and meat). Mismanagement will result in a cycle of soil quality deterioration and reduced productivity. This study estimated carbon (C) and nitrogen (N) stocks in relation to grazing management in Ireland, with a focus on understanding the role of management intensity derived from the interaction of stocking rate, N fertiliser rate and reseeding frequency. Soil samples were taken from depths 0–10, 10–20 and 20–30 cm. Soil physical properties, C and N concentrations, and microbial biomass C were measured. Significant increases in C and N concentrations were observed along the texture gradient: sandy loam < loam < sandy clay loam < clay loam < silty clay loam. However, there was little difference in C and N stock according to soil texture class. Soil with 10–20-year-old grass sward contained the lowest soil C and N content, indicating that proper reseeding is necessary to maintain soil C and N storage capacity. Increased chemical N fertiliser rate did not cause changes of soil C and N content, whereas intensified stocking rate caused great changes in soil C and N content by re-locating soil C and N at depth. Moderately intensive management was associated with significantly lower C and N stocks, and highly intensive management was associated with greater capacity of soil C and N, but no interaction between texture and management intensity was found.

A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon:

This study evaluated whether the accuracy of soil organic carbon measurement by laboratory hyperspectral imaging can match that of standard point spectroscopy operating in the visible–near infrared. Hyperspectral imaging allows a greater amount of spectral information to be collected from the soil sample compared to standard spectroscopy, accounting for greater sample representation. A total of 375 representative Irish soils were scanned by two-point spectrometers (a Foss NIR Systems 6500 labelled S-1 and a Varian FT-IR 3100 labelled S-2) and two laboratory hyperspectral imaging systems (two push broom line-scanning hyperspectral imaging systems manufactured by DV optics and Spectral Imaging Ltd, respectively, labelled S-3 and S-4). The objectives were (a) to compare the predictive ability of spectral datasets for soil organic carbon prediction for each instrument evaluated and (b) to assess the impact of imposing a common wavelength range and spectral resolution on soil organic carbon model accuracy. These objectives examined the predictive ability of spectral datasets for soil organic carbon prediction based on optimal settings of each instrument in (a) and introduced a constraint in wavelength range and spectral resolution to achieve common settings for instruments in (b). Based on optimal settings for each instrument, the deviation (root-mean square error of prediction) from the best fit line between laboratory measured and predicted soil organic carbon, ranked the instruments as S-1 (26.3 g kg−1) < S-2 (29.4 g kg−1) < S-3 (34.3 g kg−1) < S-4 (41.1 g kg−1). The S-1 model outperformed in all partial least squares regression performance indicators, and across all spectral ranges, and produced the most favourable outcomes in means testing, variance testing and identification of significant variables. It is assumed that a larger wavelength range produced more accurate soil organic carbon predictions for S-1 and S-2. Under common instrument settings, the prediction accuracy for S-3 that was almost equal to S-1. It is concluded that under standard operating procedures, greater soil sample representation captured by hyperspectral imaging can equal the quality of the spectra from point spectroscopy. This result is important for the development of laboratory hyperspectral imaging for soil image analysis.

Rapid Evaluation of Soil Quality Based on Soil Carbon Reflectance

Many studies have considered spectroscopy for measurement of soil carbon (SC), and there is potential for spectroscopy to be used as a cost and time effective approach to assess soil quality (SQ). In this research, the relationship between SC and SQ in Irish grassland soils was studied; particularly the efficiency of spectroscopy and chemometric techniques for assessing SC and its contribution to SQ. The study was conducted using 20 sites with 5 replicates per site (n = 100 soil samples). Twenty soil properties were measured using standard methods as soil quality indicators. Management intensity was classified using K-means clustering, and the results reflected a trend in soil properties indicative of poorer SQ under more intensive management. Soil porosity, CN ratio and SC were selected as a minimum data set using principal component analysis and SC was the most discriminating indicator of the impact of management intensity on SQ. Soil visible and near-infrared spectra showed a good efficiency (R2 = 0.91, RMSE = 0.4, RPD = 2.94) for prediction of SC. Spectroscopy and chemometric analysis allowed rapid evaluation of SC, and because of the strong relationship with management intensity, can provide a rapid, low cost, quantitative method for evaluating SQ under grassland management.