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Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc’s) of >4 mm, 2–4 mm, 0.5–2 mm, and n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts.

Nitrogen (N) fertilization effects on Eucalyptus growth and soil carbon (C) stocks are still controversial. We set up an N fertilization experiment in southern Brazil to evaluate initial tree growth and changes in soil organic matter (SOM). Four N levels (24-Reference, 36, 48 and 108 kg ha-1 of N) were tested and tree growth was assessed during the first two years. Afterwards, representative trees were chosen to evaluate fine-root biomass (FRB) and its spatial distribution. Soil was sampled to a 40-cm depth and SOM was fractionated in Particulate (POM) and Mineral-Associated Organic Matter (MAOM) for C and N content, and δ13C determination. Positive N effect on tree growth was seen only for tree height. N addition resulted in higher FRB. Changes in SOM were more expressive in top-soil layers. Overall, afforestation had positive effects on soil C. Increasing reference N dose resulted in higher C and N content in both SOM fractions. C and N dynamics were tightly correlated, especially in MAOM. Eucalypt-derived C was on average three-fold higher in POM. In summary, we showed that N fertilization may have positive but limited effects on tree growth, nevertheless it enhances fine-root biomass and C and N accumulation in SOM pools. [ABSTRACT FROM AUTHOR]

Isolation and quantification of soil organic matter (SOM) pools under the influence of management practices is needed for assessing the changes in soil fertility. However, the knowledge on how the active, slow and passive pools of SOM respond to long-term fertilization is scarce. Therefore, the present study was designed to isolate the active, slow, and passive pools of soil organic matter through physical fractionation under long-term fertilization. The treatments included; inorganic fertilization (NPK) either alone or combined with a normal dose of manure (MNPK) or a high dose of manure (1.5MNPK) with an unfertilized control (CK) for comparison. The isolated pools were analyzed and compared for their sizes, SOC and TN storage and their contribution to total SOC and TN sequestration. The results revealed that the fertilization enhanced the active, slow and passive pools of SOC and TN and their storage under applied treatments was patterned as 1.5MNK > MNPK > NPK > CK. The highest SOC and TN storage was observed in the active pool, while, greater response to fertilization (in terms of response ratio) was associated with the slow pool. Results show that fertilization enhanced the proportion of SOC and TN stocks to bulk SOC and TN stocks in active and slow pools, while a diminishing trend was found for passive pools. Moreover, the highest response ratio was found for TN sequestration in each pool as compared to SOC, suggesting preferential accumulation of TN over SOC in the studied soil. Nevertheless, the highest SOC and TN storage took place in the active pool. The slow pool showed greater response to applied fertilizer, with the highest values being observed under 1.5MNPK. This study concluded that long-term manure + inorganic fertilization is crucial for enhancing C and N sequestration by altering the size and response of SOM pools. [ABSTRACT FROM AUTHOR]

Abstract: Long-term no-tillage (NT) leads to profile stratification of soil organic matter (SOM) pools, and the soil organic carbon (SOC) stratification ratio (SR) is an indicator of soil quality. The objective of this report is to assess the feasibility of using SOC-SR as an index for estimating SOC sequestration in NT soils. The effect of a plow tillage (PT) and NT chronosequence on the SR of SOM pools was assessed in an Oxisol in Southern Brazil (50°23′W and 24°36′S). The chronosequence consisted of six sites: (i) native field (NF); (ii) PT of the native field (PNF-1) involving conversion of natural vegetation to cropland; (iii) NT for 10 years (NT-10); (iv) NT for 20 years (NT-20); (v) NT for 22 years (NT-22); (vi) conventional tillage for 22 years (CT-22). Soil samples were collected from four depths (0–5cm; 5–10cm; 10–20cm; 20–40cm layer) and soil parameters comprised by SOM pools [i.e., C, N, S, particulate organic C (POC), particulate N (PN), stable C (SC) and stable N (SN), microbial biomass C (MBC) and microbial biomass N (MBN), basal respiration (BR), dissolved organic C (DOC), total polysaccharides (TP) and labile polysaccharides (LP)] were measured. In undisturbed NF soil, the SR of all parameters increased with increase in soil depth. In contrast, the SR decreased in PT, and the SOM was uniformly distributed in the soil profile. All NT treatments restored the SR, and were characterized with higher values of all measured parameters compared to NF. The SR for SOC ranged from 1.12 to 1.51 for CT-22 compared with 1.64–2.61 SR for NT surface and sub-soil layers, respectively. The SR for POC and PN were higher than those for stable C and N. However, SR for the biological pools (e.g., MBC, MBN and BR) were the highest and strongly correlated with the rate of SOC sequestration. An increase in SR of SOC was also positively correlated with the rate and amount of SOC sequestered. Regression analyses indicated a strong correlation between SR of SOC and all parameters monitored in this study. The data showed that the SR of SOC is an efficient indicator of C sequestration in long-term NT management. [Copyright &y& Elsevier]

Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc's) of >4 mm, 2–4 mm, 0.5–2 mm, and <0.5 mm. A universal SVM model was then developed for all OA and psc's (n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts. [ABSTRACT FROM AUTHOR]