The nonlinear behavior of hillslope quick flow (QF) in response to variations in soil thickness and forest types remains unclear due to uncertainties or unknown features in field studies, hindering accurate interpretation of their impact on QF. To this end, three artificial physical models with different mineral soil thickness (0 cm, shallow 50 cm, and thick 150 cm) beneath a 50 cm root zone for each forest (i.e., the broadleaf and coniferous forests) were constructed and intensively monitored. Results showed that the threshold-based QF was a function of the sum of rainfall amount and antecedent soil wetness, which was isolated by two breakpoints-the generation and rising thresholds-across all artificial physical models. These breakpoints indicated the storage thresholds for QF generation and the transition from slow to fast QF in humid forests. The broadleaf forest required more rainfall to reach the generation threshold due to higher litter interception capacity and soil water deficit by plant water uptake compared to the coniferous forest. Above the rising threshold phase, QF increased rapidly and linearly due to the establishment of flow path connectivity driven by soil water content, with the broadleaf forest showing a faster increase in QF than the coniferous forest. The dominant preferential/unsaturated flow in humid mountainous areas was also confirmed. Preferential flow paths were well-connected in the root zone and extended to the shallow mineral soil, resulting in an insignificant difference in mean QF between forest types. The thick mineral soil disrupted preferential flow path connectivity, leading to a greater amount of water being laterally diverted and retained within the soil. Consequently, a sharp decrease in QF was observed in hillslope with the root zone capping thick mineral soil for both forests. Our results highlighted the application of threshold-based theory and controlled experiments in enhancing the comprehension of the fundamental physical mechanism governing runoff processes in humid forests.

A near real-time evaluation of the management system's impact on soil conditions requires a quick and straightforward framework to help farmers with a reliable on-farm assessment. The visual soil evaluation methods can potentially be used as a reliable on-farm approach for assessing soil physical quality. This research aims to better understand the importance of visual soil evaluation for productivity in semi-arid climates. The objectives were to evaluate the ability of Visual Evaluation of Soil Structure (VESS) and Double Spade (DS) under agricultural systems in a semi-arid region and to assess the relationship between soil structural quality and crop yield. Forty arable sites under rain-fed and irrigated farming with crop rotation and mono-cropping systems were studied, and twenty-one physical and bio-chemical soil properties were determined at two depths (0–20 cm and 20–40 cm). VESS and DS were conducted at each farm. The reliability of visual approaches was evaluated using measured soil properties and their capability to detect the impact of management systems. Soil quantitative measurements and crop yield information were deployed to assess soil productivity and its relationship with soil physical quality. The overall assessment of soil properties indicated a better soil condition for irrigated farming with crop rotation. The visual scores were in a range of 1.5–3.4 for VESS and a range of 1.9–3.5 for DS. The mean scores of VESS and DS were different between rain-fed and irrigated farms (P < 0.05), and visual methods were able to differentiate between mono-cropping and crop rotation (P < 0.05). The best soil structural quality was associated with the highest level of crop yield, and visual approaches were able to detect the differences in wheat yield levels (P < 0.05). The scores of VESS and DS were significantly correlated with soil properties (r > 0.6). Both VESS and DS indicated good efficiency for a practical on-farm assessment of soil conditions under agricultural management in northwest Iran and similar ecosystems. A close relationship between crop yield and soil structural quality was obtained, which indicated that the optimum soil structural condition plays an important role in the sustainability of crop production in semi-arid cultivation.

A clear understanding of soil-machine interaction is utilised in many areas, such as rational design and performance optimisation of soil-engaging tools/implements. This research developed a new soil bin to investigate the interaction between soil-narrow tines and soil failure. The new soil bin consisted of a chassis, a bin, a variable speed carriage and a bolt and nut type power transmission system between the motor and carriage. The design criteria of the new soil bin were based on the drive system's immediate acceleration of the carriage unit. A curved chisel tine was tested to evaluate the system's capabilities. Three parameters, including lift height, failure side area and forward failure distance, were investigated at two forward speeds of 0.037 and 0.05 m/s and three rake angles of 5°, 10° and 20°. An analysis of variance (ANOVA) test revealed the effect of rake angle on the lift height, failure side area, and forward failure distance was significant (P < 0.01). However, the forward speed did not have any significant influence on parameters. Also, the lift height and failure side area increased significantly (P < 0.01) by increasing the rake angle, while the forward failure distance decreased. Regarding soil failure, the results were in harmony with Godwin and Spoor's model. The regression and ANFIS models were developed to predict the output parameters by considering input parameters. The R2 values and ANFIS models were 0.4895, 0.7264, 0.9856, and 0.9999, 1, 1, respectively, for lift height, side area, and forward distance. Therefore, ANFIS approach was more accurate for predicting soil failure parameters.

The aim of this paper was to predict and optimize the overall energy efficiency (OEE) of a tractor-implement system in semi-deep tillage via response surface methodology (RSM) approach. The OEE was affected by two tillage tines (i.e., subsoiler and paraplow), four forward speeds (i.e., 1.8, 2.3, 2.9 and 3.5 km/h), three operating depths (i.e., 30, 40 and 50 cm) and vertical load imposed on tractor rear wheels at two levels (i.e., 225 kg and no-weight). All tests were replicated four times, forming 192 data points. Field test results revealed all variables were influential on the OEE except vertical load. An incre-ment of speed and depth increased OEE. The RSM approach displayed 3D views with higher accuracy for the OEE change due to changing tine, speed, depth and vertical load relative to regression prediction models. Another feature of the RSM approach was the output graphs with many small pixels. Accordingly, input variables changes and their influences on the OEE are more locational and visible. Moreover, the RSM model accurately predicted the OEE as 35.216% with the desirability of 0.843 at the optimum state as paraplow tine, 30 cm depth, 2.07 km/h forward speed, and 0.01 kg vertical load.(c) 2022 ISTVS. Published by Elsevier Ltd. All rights reserved.

Osmotically assisted reverse osmosis (OARO) has become an emerging membrane technology to tackle the limitations of a reverse osmosis (RO) process for water desalination. A strong membrane that can withstand a high hydraulic pressure is crucial for the OARO process. Here, we develop ultra-strong polymeric thin film composite (TFC) hollow fiber membranes with exceptionally high hydraulic burst pressures of up to 110 bar, while maintaining high pure water permeance of around 3 litre/(m 2 h bar) and a NaCl rejection of about 98%. The ultra-strong TFC hollow fiber membranes are achieved mainly by tuning the concentration of the host polymer in spinning dopes and engineering the fiber dimension and morphology. The optimal TFC membranes display promising water permeance under the OR and OARO operation modes. This work may shed new light on the fabrication of ultra-strong TFC hollow fiber membranes for water treatments and desalination.

This study was devoted to verifying the performance of various configurations of a winged chisel plough (WCP) in a soil bin. The performance of the new tool was assessed at three wing depths (5, 10 and 15 cm), three bend angles (10, 20 and 30 °), and three rake angles (7.5, 15 and 22.5 °) with three replications using a completely randomised design at a constant depth and speed of 30 cm and 1 m·s–1, respectively. The draught and vertical forces, soil disturbed and upheaved areas plus the efficiency of the soil loosening were measured during the tests. The results revealed that the draught and vertical forces were significantly increased by increasing the wing depth, bend and rake angles. The soil disturbance area increased with an increase in the wing depth, bend and rake angles. While the soil upheaving was decreased by increasing the wing depth and bend angle, the effect of the rake angle on the soil upheaving area was not significant. The maximum efficiency of the soil loosening of 268.1 cm2·kN–1 was achieved for a wing depth of 10 cm, a bend angle of 20 °, and a rake angle of 15 °. A significant improvement in the efficiency of the soil loosening along with maintaining a considerable portion of the residue on the soil surface suggest that the WCP should be adopted for conservation tillage.

The osmotically assisted reverse osmosis (OARO) has been recently proposed to increase the water recovery of the reverse osmosis (RO) process, in which RO has reached its limitations. Strong membranes are essential for both OARO and RO processes. Here, we have developed high mechanical strength thin-film composite (TFC) hollow fiber membranes for both RO and OARO processes. The newly developed TFC hollow fiber membranes consist of a polyamide layer synthesized via interfacial polymerization on the inner surface of PES hollow fiber substrates that have been optimized by controlling the bore and dope fluid flow rates, fiber dimension and morphology. The TFC-PES hollow fiber membranes have a pure water permeability (PWP) of around 2.5-3 L/(m(2) h bar) (LMH/bar) and a NaCl rejection of around 97.5-98% for brackish water desalination at 20 bar. The water permeability drops from 2.15 to 0.06 LMH/bar when the NaCl concentration increases from 0.035 mol/L (2000 ppm) to 1.2 mol/L at 30 bar for OARO due to the concentrative and dilutive concentration polarization (ECP and ICP) that decreases the effective driving force. The structural parameter (S) and burst pressure (P-B) of the newly developed membranes increase from 550 to 800 mu m and from 47 to 104 bar, respectively, with an increase in the dope to bore flow rate ratio (D/B ratio) because of the thicker substrate wall and reduction in porosity. The optimal TFC-PES hollow fiber membrane for OARO has a burst pressure of 95 bar, structural parameter of 795 mu m and water permeability of 0.09 LMH/bar when using 1.2 mol/L NaCl for OARO. To the best of our knowledge, this inner-selective TFC-PES hollow fiber membrane has the highest burst pressure to date, it has impressive high mechanical strength and good RO and OARO performance.

This study aimed to evaluate the ability of the response surface methodology (RSM) approach to predict the tractive performance of an agricultural tractor during semi-deep tillage operations. The studied parameters of tractor performance, including slippage (S), drawbar power (DP) and traction efficiency (TE), were affected by two different types of tillage tool (paraplow and subsoiler), three different levels of operating depth (30, 40 and 50 cm), and four different levels of forward speed (1.8, 2.3, 2.9 and 3.5 km h−1). Tractors drove a vertical load at two levels (225 kg and no weight) in four replications, forming a total of 192 datapoints. Field test results showed that all variables except vertical load, and different combinations of this and other variables, were effective for the S, DP and TE. Increments in speed and depth resulted in an increase and decrease in S and TE, respectively. Additionally, the RSM approach displayed changes in slippage, drawbar power and traction efficiency, resulting from alterations in tine type, depth, speed and vertical load at 3D views, with high accuracy due to the graph’s surfaces, with many small pixels. The RSM model predicted the slippage as 6.75%, drawbar power as 2.23 kW and traction efficiency as 82.91% at the optimal state for the paraplow tine, with an operating depth of 30 cm, forward speed of 2.07 km h−1 and a vertical load of 0.01 kg.

Long-term evaluation of the population change trend of different plant species in lagoons is of great importance in preserving the environment. Lagoons are the fundamental habitats for aquatic biodiversity conservation, which has at least one relatively narrow connection to the sea. In this research, Landsat time-series data are used to investigate the changes in Azolla (or green devil) expansion, and its effective factors over 30 years from the arrival of this aquatic fern from 1988 to 2018 in the International Lagoon of Anzali in the North of Iran using the spectral angular mapper (SAM) method and related field data. The results indicated that (1) the changes of Azolla plant expansion have a direct correlation with the volume of lagoon water and rainfall and an inverse relation with the region temperature, (2) The spread of Azolla has not been steady over three decades, increasing in some years and decreasing in others, (3) The high Azolla surface cover has caused environmental hazards in the lagoon and has reduced the value in the eastern, southwestern, and partly central parts of the area. Therefore, in order to maintain balance in the natural ecosystem of Anzali Lagoon, fundamental decisions must be made for determining the range and shelter of the lagoon and controlling the outflow of water resources.

For sustainable land management, it is necessary to understand the functions and characteristics of soils, and their spatial and temporal changes. Visible and near-infrared spectroscopy has a specific capability to identify and determine soil properties due to high accuracy and high-performance speed. The purpose of this study is to evaluate the accuracy of visible and near-infrared spectroscopy method in estimating soil organic matter and total neutralizing value. Therefore, 110 soil samples were collected from Khuzestan, Yazd and Tehran provinces, and spectral reflectance was performed using ASD FieldSpec3. The spectra obtained from the spectrometer were pre-processed using five methods including Savitzky-Golay filter (SG), the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variate (SNV), and Multiplicative scatter correction (MSC). Also, the performance of PLSR and SVMR methods was compared in terms of soil organic carbon and total neutralizing value estimation. The results indicated that the PLSR model in estimating both organic carbon properties and total neutralizing value had higher accuracy compared to the SVR model. In estimation of soil organic carbon, PLSR method and MSC preprocessing method had the best performance (R2VAL=0.59, RMSEVAL=0.19 and PRDVAL=1.47) and the second derivative method had the least performance (R2VAL=0.15, RMSEVAL=0.27 and PRDVAL=0.52). Also for estimation of total neutralizing value, the first derivative preprocessing method had the best performance (R2VAL=0.78, RMSEVAL=5.70 and PRDVAL=2.01) and the second derivative method had the least performance (R2VAL=0.1, RMSEVAL=11.13, and PRDVAL=0.31). The key wavelengths were observed for soil organic matter in the range of 421- 612 nm and for total neutralizing value in the range of 2315- 2151 nm. This study showed that the Vis-NIR spectroscopy method, due to its physical basis and considering the influencing factors, as a large-scale model, makes it possible to evaluate and predict soil OC and TNV.

CO2 separation has become an important global agenda because of the requirements of industrial products and CO2-induced global warming. Membrane-based technologies have provided an alternative for CO2 separation. To make membrane-based separation more competitive, membranes must have a high permeance and high selectivity. Herein, we have developed a new high-performance multiple-layer hollow fiber membrane of cellulose triacetate (CTA) and cellulose diacetate (CDA) blends for CO2 separation. CTA and CDA blends were chosen because they have similar chemical structures, good separation performance, economical and green nature. Both single-layer and dual-layer hollow fibers were fabricated by various spinning strategies. The optimized dual-layer membrane spun at outer dope flow rate of 1 mL/min exhibits a CO2 permeance of 45 GPU and an ideal CO2/CH4 selectivity of 30.3 at 2 bar. Comparing with the optimized single-layer hollow fiber spun at 5 cm air gap distance and 15 m/min take-up speed, the former has a 100% higher CO2 permeance without compromising the selectivity. The as-produced hollow fibers display a plasticization pressure of about 10 bar. In addition, they have impressive mixed gas CO2/CH4 selectivity of >40 at 2 bar. Therefore, the newly developed dual-layer hollow fiber membranes may have great potential for CO2/CH4 separation.

The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP.

GrassQ is a holistic grassland decision support system (DSS) that encapsulates a range of measurement technologies to provide yield and quality data to a cloud based platform, which can provide users with real time management information in the field. GrassQ aims to promote precision agricultural concepts within the pasture based livestock industry. Accurate measurement and allocation of fresh pasture to grazing herds on a daily basis is essential in increasing efficiency. Novel systems of measuring grass yield and quality were developed at the Moorepark Animal and Grassland Research Centre in Cork, Ireland, over the grass growing seasons of 2017 and 2018. Measurement systems included ground based and remote sensing techniques. The prototype GrassQ DSS was designed to process datasets uploaded from all proposed measurement systems. Measurement parameters were compressed sward height (CSH) (mm), herbage mass (HM) (kgDM/ha), dry matter (DM) (g/kg) and crude protein (CP) (g/kg). Ground based measurements were recorded using a smart rising plate meter (RPM) and lab based near infrared spectroscopy (NIRS). Multispectral remote sensing was carried out using an unmanned aerial vehicle (UAV), and data from the European Union's Sentinel-2 satellite (S2). Reference analyses for all prediction models were carried out at Moorpark's Grassland Laboratory and all sample locations were geo-tagged to enable spatial mapping of all parameters. The GrassQ prototype DSS is currently operational, including a number of preliminary grass quantity and quality prediction models. The complete Grass DSS will be is launched upon final validation.

In this study, the ability of adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) approaches for predicting the draft force of subsoiling tines was assessed. Results of ANFIS and RSM approaches were compared with the results of regression models, too. The draft force was evaluated as affected by the tines at three levels (subsoiler, paraplow, and bentleg), forward speed at four levels (1.8, 2.3, 2.9 and 3.5 km/h), depth at three levels (30, 40 and 50 cm) and wing width at two levels (with wing = 30 cm and no wing = 0 cm) at four replications. Test results show that tine types, speed, depth, and wing width were significant on the draft force but quadruplet interaction effect of them. Moreover, the increment of forwarding speed, tillage depth and adding wing increased the draft force of all tine types. Field data were applied for the development of the regression, ANFIS and RSM models. The results of ANFIS part showed that Gaussian membership function (gaussmf) configuration was found to denote MSE of 0.0156 and R-2 of 0.998, consequently, it was the best ANFIS model. The RSM and best regression models had a high correlation (R-2 = 0.9927 and 0.9968, respectively), too while ANFIS model was the better than them to predict the draft force of subsoiling tines with higher accuracy. The RSM graphs showed the changes of the output variable (draft force) caused by changes of input variables (tine type, speed, depth and wing width) better than ANFIS graphs for their surfaces with higher pixels. Moreover, the optimization process for prediction of the draft force was obtained 4.22 kN for depth of 35.19 cm, the forward speed of 1.9 km/h and wing width of 26.97 cm using the RSM approach.

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R-2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R-2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

The symbiotically interaction of arbuscular mycorrhiza (AM) with liquorice (Glycyrrhiza glabra) which is widely used in traditional Iranian medicine, may have a good potential to enhance host plant growth and resistance to salt stress and may regulate the synthesis and accumulation pattern of secondary metabolites in plant tissues. The aims of this study were to assess the effect of AM fungus Funneliforms mosseae on liquorice plant's growth and nutrition, and the expression of important genes participated in the glycyrrhizin biosynthesis under salinity stress. The salinity stress induced a decreased trend in root fungal colonization, growth parameters, K and P concentrations and K+/Na+ ratios. Moreover, salinity caused a significant increase in electrolyte leakage and Na+ concentration in shoot and root of liquorice. AM colonization considerably increased P and K concentrations under salinity stress and promoted shoot proline accumulation and a higher K+/Na+ ratio and concentration of glycyrrhizin. The expression of three genes including beta-amyrin synthas (bAS), squalene synthase1 (SQS1) and P450 under different treatments were assessed by qRT-PCR. The highest increase in bAS and P450 gene expression was observed in stressed mycorrhizal plants. The expression of SQS1 was higher in stress condition compared to non-stressed plants. This study indicated the ameliorating effects of arbuscular mycorrhizal symbiosis in liquorice plant under salinity stress. The symbiosis of liquorice with AM fungi can be suggested as an alternative to enhance liquorice quality for pharmaceutical purposes and a practical approach for exploiting saline soils.

Current management practices are thought to be having adverse impacts on soil quality for semi-arid agriculture. A multidimensional quantification of soil quality was developed and tested under irrigated and rain-fed agricultural systems in the northwest of Iran. Thirty-four chemical, biological and physical soil quality indicators were quantified at two depths with mono-cropping and crop rotation (n = 154). Discriminant analysis (DA) and principal component analysis (PCA) were applied to identify a minimum data set (MDS) for developing soil quality indices (SQI). Soil organic carbon (SOC), soluble sodium (Na), geometric mean diameter of soil aggregate (GMD) and available zinc (Zn) were identified using PCA, and GMD, Zn and soil microbial respiration (SMR) were identified using DA. Six SQIs were produced using non-linear and linear scoring equations and integration approaches based on two independent MDS. SQIs were significantly different between irrigated and dry farming at both depths (P-value < 0.05), although there was no impact of crop rotation under conventional tillage. The best index was produced using linear scoring and additive integration based the MDS selected using discriminant analysis. While the PCA is the conventional technique for reducing data redundancy, DA identified a more useful MDS than PCA. The importance of soil aggregate stability, heavy metal pollution, biological activity, organic carbon content and soil sodicity was noted for monitoring and assessing the main threats to SQ, these did not have to be directly quantified for a useful SQI, but need to be understood for interpretation. The SQI provided a rapid, reproducible and reliable method for multifaceted assessment of soil quality. The study indicated adverse impact on soil quality of management systems operating in the semi-arid regions of Iran under rain-fed farming.

ABSTRACT Equipping tines with the wings increases draft and soil loosening. Wing angles affect tine performance, soil aggregation and remained residue. In this research, conventional wing with no bent plus backward and forward bent wings with bend angles of 10 and 20° were attached to both a subsoiler and paraplow tine. The rake angle of all wings was 15°. The effects of tine and wing on draft, soil disturbance area, specific draft, remained residue, and mean weight diameter (MWD) were investigated in a clay loam soil at depth of 40 cm and speed of 1.6 km h−1. The effect of tine and wing plus interaction of them on all parameters was significant (p < 0.01) with the exception of remained residue. The bent-winged tines required higher draft and caused higher disturbance area, lower specific draft, and lower MWD than the conventional tines and those without wing. The highest draft, disturbed area and remained residue plus the lowest specific draft and MWD were obtained when applying the 10° forward bent-winged tines. In present work, the paraplow equipped with forward bent wings with a bend angle of 10° was suggested as a suitable tool for deep soil loosening under a conservation tillage system.

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.

In this study, the effect of tine type, adding wing, operating depth and forward speed on the draft requirement of subsoil tillage tines was investigated in clay loam soil. Three subsoil tillage tines (subsoiler, bentleg and paraplow), four levels of forward speed (1.8, 2.3, 2.9 and 3.5 km/h), three levels of depth (30, 40 and 50 cm) and winged and no-wing tines were examined with the exception of bentleg as it would not be winged. It was revealed that draft of the tines is less affected by forward speed but is much affected by tine type, depth and wing. It was observed that an increase of speed and depth plus adding wing results in an increase of draft in all tines. Additionally, it was found that in all depths and speeds, subsoiler required more draft than paraplow and paraplow required more draft than bentleg. Multiple regression models including the studied parameters were developed to predict the draft requirements for each tine with high accuracy.