Browsing by Author "Mouazen, Abdul Mounem"
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Item Open Access Analysis of petroleum-contaminated soils by diffuse reflectance spectroscopy and sequential ultrasonic solvent extraction-gas chromatography(Elsevier Science B.V., Amsterdam., 2014-01-31T00:00:00Z) Okparanma, R. N.; Coulon, Frederic; Mouazen, Abdul MounemIn this study, we demonstrate that partial least-squares regression analysis with full cross-validation of spectral reflectance data estimates the amount of polycyclic aromatic hydrocarbons in petroleumcontaminated tropical rainforest soils. We applied the approach to 137 field-moist intact soil samples collected from three oil spill sites in Ogoniland in the Niger Delta province (5.317N, 6.467E), Nigeria. We used sequential ultrasonic solvent extractionegas chromatography as the reference chemical method. We took soil diffuse reflectance spectra with a mobile fibre-optic visible and near-infrared spectrophotometer (350e2500 nm). Independent validation of combined data from studied sites showed reasonable prediction precision (root-mean-square error of prediction ¼ 1.16e1.95 mg kg1, ratio of prediction deviation ¼ 1.86e3.12, and validation r2 ¼ 0.77e0.89). This suggests that the methodology may be useful for rapid assessment of the spatial variability of polycyclic aromatic hydrocarbons in petroleum-contaminated soils in the Niger Delta to inform risk assessment and remediation.Item Open Access Application of an on-line sensor to map soil packing density for site specific cultivation(Elsevier, 2016-04-24) Shamal, S. A. M.; Alhwaimel, Saad A.; Mouazen, Abdul MounemTillage is the most energy consuming operation in the primary production in agriculture. The majority of farmers worldwide adopt homogeneous tillage operations to optimise crop establishment, reduce weeds and compaction, where soil disturbance took place across the entire field including areas where no soil preparation is needed. This practice consumes high energy and leads to decrease soil resistance to water and air erosion. This paper investigates the potential of a previously developed on-line soil bulk density (BD) sensor to map packing density for the delineation of management zones for site specific tillage. The on-line sensor consisting of a multi-sensor platform pulled by a tractor was used to measure soil BD in two experimental fields with potato in East Anglia, UK. It consisted of a load cell to measure subsoiler draught, a wheel gauge to measure depth and a visible and near infrared (vis-NIR) spectrophotometer for the measurement of moisture content (MC). Based on these three on-line measured parameters, BD was calculated using a previously developed model with a hybrid numerical and multivariate statistical analysis. The packing density (PD) was then calculated for all on-line measured points as a function of BD and clay content (CC). Maps of soil BD and PD were produced, and both fields were divided into management zones with different tillage recommendations. Results, in the studied fields, showed that the on-line BD sensor can map not only the spatial distribution in BD but enable estimation of PD too. Classifying the PD into three compaction classes revealed that only 4.8% of the field needs aggressive tillage (primary and secondary tillage) and about 34.8% of the field requires harrowing or surface loosening with a cultivator (reduced tillage), while the remaining area of the field do not need any sort of tillage. Virtual calculations of fuel consumption and CO2 emission in one field based on the three PD classes confirmed that site specific tillage would significantly reduce energy consumption and CO2 emission, as compared to reduced and conventional tillage practices. By this it can be concluded that the on-line multi sensor platform for the assessment of PD holds a great potential for mapping and managing soil compaction site specifically. A future study is needed to relate soil compaction to actual plant growth and yield, and evaluate cost of production and practical limitations of this approach.Item Open Access Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra-weighting on the spiking subset(Wiley, 2014-01-17) Guerrero, C.; Stenberg, B.; Wetterlind, J.; Viscarra Rossel, R. A.; Maestre, F. T.; Mouazen, Abdul Mounem; Zornoza, R.; Ruiz-Sinoga, J. D.; Kuang, Boyan Y.Spiking is a useful approach to improve the accuracy of regional or national calibrations when they are used to predict at local scales. To do this, a small subset of local samples (spiking subset) is added to recalibrate the initial calibration. If the spiking subset is small in comparison with the size of the initial calibration set, then it could have little noticeable effect and a small improvement can be expected. For these reasons, we hypothesized that the accuracy of the spiked calibrations can be improved when the spiking subset is extra-weighted. We also hypothesized that the spiking subset selection and the initial calibration size could affect the accuracy of the recalibrated models. To test these hypotheses, we evaluated different strategies to select the best spiking subset, with and without extra-weighting, to spike three different-sized initial calibrations. These calibrations were used to predict the soil organic carbon (SOC) content in samples from four target sites. Our results confirmed that spiking improved the prediction accuracy of the initial calibrations, with any differences depending on the spiking subset used. The best results were obtained when the spiking subset contained local samples evenly distributed in the spectral space, regardless of the initial calibration's characteristics. The accuracy was improved significantly when the spiking subset was extra-weighted. For medium- and large-sized initial calibrations, the improvement from extra-weighting was larger than that caused by the increase in spiking subset size. Similar accuracies were obtained using small- and large-sized calibrations, suggesting that incipient spectral libraries could be useful if the spiking subset is properly selected and extra-weighted. When small-sized spiking subsets were used, the predictions were more accurate than those obtained with ‘geographically-local’ models. Overall, our results indicate that we can minimize the efforts needed to use near-infrared (NIR) spectroscopy effectively for SOC assessment at local scales.Item Open Access Combining frequency domain reflectometry and visible and near infrared spectroscopy for assessment of soil bulk density(Elsevier Science B.V., Amsterdam., 2014-01-01T00:00:00Z) Al-Asadi, Raed A.; Mouazen, Abdul MounemThis paper introduces a new approach for the assessment of soil bulk density (BD), which relies on an existed model to predict BD as a function of a visible and near infrared spectroscopy (vis-NIRS) measured gravimetric moisture content (v) and a frequency domain reflectometry (FDR) measured volumetric moisture content (uv). A total of 1013 soil samples collected from England and Wales, from 32 arable and grassland fields with different soil types were measured with a vis-NIR spectrophotometer (LabSpec1Pro Near Infrared Analyzer, Analytical Spectral Devices, Inc., USA) after in situ measurement with a ThetaProbe FDR (Delta-T Device Ltd.). Two calibration methods of the vis-NIRS were tested, namely, partial least squares regression (PLSR) and artificial neural network (ANN). ThetaProbe calibration was performed with traditional methods and ANN. ANN analyses were based on a single- variable input or multiple-variable input (data fusion). During ANN - data fusion analysis, vis-NIRS spectra and ThetaProbe output voltage (V) were fused in one matrix with or without laboratory measured texture fractions and organic matter content (OM). For the vis-NIRS and ThetaProbe traditional calibration, samples were divided into calibration (75%) and prediction (25%) sets, whereas for the ANN analyses these were divided into calibration (65%), test (10%) and independent validation (25%) sets. Results proved that high measurement accuracy can be obtained for v and uv with PLSR and the best performing traditional calibration method of the ThetaProbe with R2 values of 0.91 and 0.97, and root mean square error of prediction (RMSEp) values of 0.027 g g1 and 0.019 cm3 cm3, respectively. However, the ANN - data fusion resulted in improved accuracy (R2 = 0.98 and RMSEp = 0.014 g g1 and 0.015 cm3 cm3, respectively). This data fusion approach led to the best accuracy for BD assessment when vis-NIRS spectra and ThetaProbe V only were used as input data (R2 = 0.81 and RMSEp = 0.095 g cm3). It can be concluded that BD can be measured by combining the vis-NIRS and FDR techniques based on ANN-data fusion approach.Item Open Access Determination of Total Petroleum Hydrocarbon (TPH) and Polycyclic Aromatic Hydrocarbon (PAH) in Soils: A Review of Spectroscopic and Nonspectroscopic Techniques(Taylor & Francis, 2013-08-31T00:00:00Z) Okparanma, R. N.; Mouazen, Abdul MounemIn the analysis of petroleum hydrocarbon-contaminated soils for total petroleum hydrocarbons (TPHs) and polycyclic aromatic hydrocarbons (PAHs), the roles of spectroscopic and nonspectroscopic techniques are inseparable. Therefore, spectroscopic techniques cannot be discussed in isolation. In this report, spectroscopic techniques including Raman, fluorescence, infrared, and visible and near-infrared (Vis-NIR) spectroscopies, as well as mass spectroscopy (coupled to a gas chromatograph) and nonspectroscopic techniques such as gravimetry, immunoassay, and gas chromatography with flame ionization detection are reviewed. To bridge the perceived gap in coverage of the quantitative applications of Vis-NIR spectroscopy in the rapid determination of TPHs and PAHs in soils, a detailed review of studies from the period 1999-2012 are presented. This report also highlights the strengths and limitations of these techniques and evaluates their performance from the perspective of their attributes of general applicability, namely economic portability, operational time, accuracy, and occupational health and safety considerations. Overall, the fluorescence spectroscopic technique had the best performance (85% total score) in comparison to the others, and the gravimetric technique performed the least (60% total score). Method-specific solutions geared toward performance improvement are also suggested.Item Open Access Development of a framework for the evaluation of the environmental benefits of controlled traffic farming(MDPI, 2015-07-03) Mouazen, Abdul Mounem; Palmqvist, MartinAlthough controlled traffic farming (CTF) is an environmentally friendly soil management system, no quantitative evaluation of environmental benefits is available. This paper aims at establishing a framework for quantitative evaluation of the environmental benefits of CTF, considering a list of environmental benefits, namely, reducing soil compaction, runoff/erosion, energy requirement and greenhouse gas emission (GHG), conserving organic matter, enhancing soil biodiversity and fertiliser use efficiency. Based on a comprehensive literature review and the European Commission Soil Framework Directive, the choice of and the weighting of the impact of each of the environmental benefits were made. The framework was validated using data from three selected farms. For Colworth farm (Unilever, UK), the framework predicted the largest overall environmental benefit of 59.3% of the theoretically maximum achievable benefits (100%), as compared to the other two farms in Scotland (52%) and Australia (47.3%). This overall benefit could be broken down into: reducing soil compaction (24%), tillage energy requirement (10%) and GHG emissions (3%), enhancing soil biodiversity (7%) and erosion control (6%), conserving organic matter (6%), and improving fertiliser use efficiency (3%). Similar evaluation can be performed for any farm worldwide, providing that data on soil properties, topography, machinery, and weather are available.Item Open Access Effect of side-wings on draught: The case of Ethiopian Ard plough (maresha)(Elsevier, 2016-06-15) Gebregziabher, Solomon; De Swert, Karel; Saeys, Wouter; Ramon, Herman; De Ketelaere, Bart; Mouazen, Abdul Mounem; Gebray, Petros; Gebrehiwot, Kindeya; Bauer, Hans; Deckers, Jozef; De Baerdemaeker, JosseEthiopian farmers have been using an ox-drawn breaking plough, known as ard plough – maresha, for thousands of years. Maresha is a pointed, steel-tipped tine attached to a draught pole at an adjustable shallow angle. It has narrow side-wings, attached to the left and right side of it, to push soil to either side without inverting. The aim of this paper is to explore the effect of side-wings on draught using a field soil bin test facility. To this end, a mobile and an in-situ soil bin test system, for online measurements of draught, was designed and developed. This research considered tool geometry (maresha plough with and without side-wings) and rake angle (shallow – 8°, medium deep – 15°, and deep – 24°, representing primary, secondary and tertiary tillage processes in Ethiopia, respectively). Maresha plough with side-wings has greater contact area, between the moving soil and tool, than its wingless counterpart. When the ploughshare surface and soil slide relative to one another, the draught expected to increase with contact area, as adhesion and friction resistance increases with area. However, experimental analysis indicated that the maresha with side-wings required less draught compared to maresha without side-wings (ρ < 0.001). This might be attributed to the effect of side-wings on crack propagation by a wedging effect to enhance and facilitate subsequent ploughing. This paper also dealt with the effect of rake angle on draught. Though the depth setup was getting smaller d1 < d2 < d3 for the successive tillage runs, analysis showed increment in draught force (ρ < 0.001) with rake angle. This might be attributed to higher soil compaction that comes with depth and downward force resulting from repeated use of maresha every season to the same depth for thousand years. Although more and rigorous studies should be undertaken considering soil, tool, and operational parameters to arrive at conclusive results, this paper gave some insights regarding effect of side-wings on maresha plough and rake angle on draught. This shows that there is still room for improvement of maresha plough geometry for minimum draught requirement and optimum soil manipulation.Item Open Access Effect of spiking strategy and ratio on calibration of on-line visible and near infrared soil sensor for measurement in European farms(Elsevier Science B.V., Amsterdam., 2013-04-02T00:00:00Z) Kuang, Boyan Y.; Mouazen, Abdul MounemA previously developed on-line visible and near infrared (vis-NIR) spectroscopy-based soil measurement system was implemented for the measurement of soil organic carbon (OC), total nitrogen (TN) and moisture content (MC) in three fields at three European farms. The on-line sensor platform was coupled with a mobile, fibre type, vis-NIR spectrophotometer (AgroSpec from tec5 Technology for Spectroscopy, Germany), with a measurement range of 305-2200 nm, to acquire soil spectra in diffuse reflectance mode. A general calibration set of 425 soil samples, spiked with different number of spectra from the three validation fields were used to establish calibration models for the studied soil properties using partial least squares (PLS) regression analysis. Different spiking strategies and spiking ratios were investigated and results revealed that the best prediction accuracy was obtained after 20% spiking ratio with samples whose spectra were measured in the laboratory. Evaluated by the values of residual prediction deviation (RPD), which is the ratio of standard deviation to root mean square error of prediction (RMSEP), the accuracy of the on-line measurement was classified as excellent for MC (RPD = 2.76-3.96), good to very good for OC (RPD = 1.88-2.38) and good to excellent for TN (RPD = 1.96- 2.52). Reducing the number of samples used for spiking resulted in deteriorating the prediction accuracy, although 1-2 samples per ha were found to provide good predictions. There was a distinguishable spatial similarity between the on-line and laboratory measured maps for all studied properties, although the full-data point maps provided more detailed information about the spatial variation. This confirms that the on-line vis-NIR soil sensor provides correct and detailed information about soil OC, TN and MC at high sampling resolutions.Item Open Access Estimating the variability of tillage forces on a chisel plough shank by modeling the variability of tillage system parameters(Elsevier Science B.V., Amsterdam., 2011-08-01T00:00:00Z) Abo Al-Kheer, A.; Kharmanda, M. G.; El-Hami, A.; Mouazen, Abdul MounemIn this paper, a probabilistic approach is proposed for quantifying the variability of the tillage forces for the shank of a chisel plough with narrow tines and to estimate the failure probability. An existing three-dimensional analytical model of tool forces from McKyes was used to model the interaction between the tillage tools and the soil. The variability of tillage forces was modeled, taking into account the variability of soil engineering properties, tool design parameters and operational conditions. The variability of the soil engineering properties was modeled by means of experimental observations. The dispersion effect of each tillage system parameter on the tillage forces was determined by a sensitivity analysis. The results show that the variability of the horizontal and vertical forces follows a lognormal distribution (μ=0.872, ξ=0.449; μ=0.004, ξ=0.447) and the relationship between these forces is positive and quasi-linear (ρ(PH,Pv)=0.93).This lognormal variability was integrated into the estimation of the failure probability for the shank by using Monte Carlo simulation (MCS) and the first-order reliability method (FORM). The results obtained by these two methods, with the assumption of non-correlation between the horizontal and vertical forces, were almost identical. However, the FORM method was faster and simpler, compared to the MCS technique. Furthermore, the correlation between the horizontal and vertical forces has no significant effect on the failure probability, regardless of the correlation strength. Therefore, it is concluded that the FORM method can be used to estimate the failure probability without considering the correlation between horizontal and vertical foItem Open Access Evaluation of vis-NIR reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils(Cranfield University, 2018-01-18 08:49) Coulon, Frederic; Douglas, Reward K.; Alamar Gavidia, Maria del carman; Mouazen, Abdul Mounem ; Nawar, SaidUnderpinning data on1. hydrocarbons data analysis by GCMS - quantification and 2. vis-NIR spectra analysis and chemometricsItem Open Access Expanding implementation of an on-line measurement system of topsoil compaction in loamy sand, loam, silt loam and silt soils.(Elsevier Science B.V., Amsterdam., 2009-04-01T00:00:00Z) Mouazen, Abdul Mounem; Ramon, HermanA previously developed model for on-line prediction of soil compaction indicated as bulk density (BD), was limited in use for a sandy loam field. This study was undertaken to investigate the possibility of modifying this model for new soil textures, namely loamy sand, loam, silt loam and silt soils. Using the on-line measurement system of BD, measurements were carried out in four fields with different average textures of loam, sandy loam and silt loam and silt loam/silt fields. The on-line measurement system used consisted of a subsoiler, whom draught (D) was measured with a single shear beam load cell and depth (d) was measured with a wheel gauge consisted of a swinging arm metal wheel equipped with a linear variable differential transducer (LVDT). The soil gravimetric moisture content (MC) was measured with the oven drying method. The on-line measured BD was compared with measured BD with Kopecki rings (core sampling method) (736 samples), to validate the potential use of this sensor in the new studied soil textures.Item Open Access Glucose adulteration in Saudi honey with visible and near infrared spectroscopy(Taylor & Francis, 2014-07-14) Mouazen, Abdul Mounem; Al-Walaan, NouraThis article reports on the implementation of visible and near infrared spectroscopy for the detection of glucose concentration in a mixture of Saudi and imported honey samples adulterated by glucose syrup of five concentrations: 0, 5, 12, 19, and 33 g/100 g. Honey samples were scanned in trans-reflectance mode with an AgroSpec mobile, fibre type, visible and near infrared spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a measurement range of 305–2200 nm. The entire data set of 345 spectra was randomly divided into calibration (70%) and prediction (30%) sets. The first group was subjected to a partial least squares regression analysis with a leave-one-out cross-validation to establish a calibration model for the prediction of glucose concentration, whereas the second group was used to validate the partial least squares model. For the cross-validation, the values for root mean square error of prediction, coefficient of determination, and ratio of prediction deviation, which is the standard deviation divided by root mean square error of prediction were 4.52 g/100 g, 0.85, and 2.53, respectively. A slightly lower range of accuracy was obtained in the prediction set, with root mean square error of prediction, coefficient of determination, and ratio of prediction deviation values of 5.56 g/100 g, 0.78 and 2.06, respectively. The results achieved suggest that the visible and near infrared spectroscopy is a powerful technique for the quantification of glucose adulteration in Saudi honey.Item Open Access Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale.(Blackwell Publishing Ltd, 2013-01-23) Kuang, Boyan Y.; Mouazen, Abdul MounemAlthough visible and near infrared (vis-NIR) spectroscopy has proved to be a fast, inexpensive and relatively accurate tool to measure soil properties, considerable research is required to optimise the calibration procedure and establish robust calibration models. This paper reports on the influence of the number of samples used for the development of farm-scale calibration models for moisture content (MC), total nitrogen (TN) and organic carbon (OC) on the prediction error expressed as root mean square error of prediction (RMSEP). Fresh (wet) soil samples collected from four farms in Czech Republic, Germany, Denmark and the UK were scanned with a fibre type vis-NIR, AgroSpec spectrophotometer (tec5 Technology for Spectroscopy, Germany) with a spectral range of 305 - 2200 nm. Spectra were divided into calibration (two-third) and prediction (one-third) sets and the calibration spectra were subjected to a partial least squares regression (PLSR) with leave-one-out cross validation using Unscrambler 7.8 software (Camo Inc., Oslo, Norway). The RMSEP values of models with large sample number (46 - 84 samples from each farm) were compared with those of models developed using small sample number (25 samples selected from the large sample set of each farm) for the same variation range. Both large set and small set models were validated by the same prediction set for each property. Further PLSR analysis was carried out on samples from the German farm, with different sample number of the calibration set of 25, 50, 75 and 100 samples. Results showed that the large-size dataset models resulted in lower RMSEP values than the small-size dataset models for all the soil properties studied. The results also demonstrated that with the increase in sample number used in the calibration set, RMSEP decreased in almost linear fashion, although the largest decrease was between 25 and 50 samples. Therefore, it is recommended to chose the number of samples according to accuracy required, although 50 soil samples is considered appropriate in this study to establish calibration models of TN, OC and MC with smaller expected prediction errors as compared with smaller sample numbers.Item Open Access Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy(Elsevier, 2016-05-24) Morellos, Antonios; Pantazi, Xanthoula-Eirini; Moshou, Dimitrios; Alexandridis, Thomas; Whetton, Rebecca; Tziotzios, Georgios; Wiebensohn, Jens; Bill, Ralf; Mouazen, Abdul MounemIt is widely known that the visible and near infrared (VIS-NIR) spectroscopy has the potential of estimating soil total nitrogen (TN), organic carbon (OC) and moisture content (MC) due to the direct spectral responses these properties have in the near infrared (NIR) region. However, improving the prediction accuracy requires advanced modelling techniques, particularly when measurement is planned for fresh (wet and un-processed) soil samples. The aim of this work is to compare the predictive performance of two linear multivariate and two machine learning methods for TN, OC and MC. The two multivariate methods investigated included principal component regression (PCR) and partial least squares regression (PLSR), whereas the machine learning methods included least squares support vector machines (LS-SVM), and Cubist. A mobile, fibre type, VIS-NIR spectrophotometer was utilised to collect soil spectra (305–2200 nm) in diffuse reflectance mode from 140 wet soil samples collected from one field in Germany. The results indicate that machine learning methods are capable of tackling non-linear problems in the dataset. LS-SVMs and the Cubist method out-performed the linear multivariate methods for the prediction of all three soil properties studied. LS-SVM provided the best prediction for MC (root mean square error of prediction (RMSEP) = 0.457% and residual prediction deviation (RPD) = 2.24) and OC (RMSEP = 0.062% and RPD = 2.20), whereas the Cubist method provided the best prediction for TN (RMSEP = 0.071 and RPD = 1.96).Item Open Access Mapping polycyclic aromatic hydrocarbon and total toxicity equivalent soil concentrations by visible and near-infrared spectroscopy(Elsevier, 2014-06-19) Okparanma, Reuben; Coulon, Frederic; Mayr, Thomas; Mouazen, Abdul MounemIn this study, we used data from spectroscopic models based on visible and near-infrared (vis-NIR; 350–2500 nm) diffuse reflectance spectroscopy to develop soil maps of polycyclic aromatic hydrocarbons (PAHs) and total toxicity equivalent concentrations (TTEC) of the PAH mixture. The TTEC maps were then used for hazard assessment of three petroleum release sites in the Niger Delta province of Nigeria (5.317°N, 6.467°E). As the paired t-test revealed, there were non-significant (p > 0.05) differences between soil maps of PAH and TTEC developed with chemically measured and vis-NIR-predicted data. Comparison maps of PAH showed a slight to moderate agreement between measured and predicted data (Kappa coefficient = 0.19–0.56). Using proposed generic assessment criteria, hazard assessment showed that the degree of action for site-specific risk assessment and/or remediation is similar for both measurement methods. This demonstrates that the vis-NIR method may be useful for monitoring hydrocarbon contamination in a petroleum release site.Item Open Access Methods and procedures for automatic collection and management of data acquired from on-the-go sensors with application to on-the-go soil sensors.(Elsevier Science B.V., Amsterdam., 2012-02-01T00:00:00Z) Peets, Sven; Mouazen, Abdul Mounem; Blackburn, Kim; Kuang, Boyan Y.; Wiebensohn, JensSensors for on-the-go collection of data on soil and crop have become essential for successful implementation of precision agriculture. This paper analyses the potentials and develops general procedures for onthe- go data acquisition of soil sensors. The methods and procedures used to manage data with respect to a farm management information system (FMIS) are described. The current data communication standard for tractors and machinery in agriculture is ISO 11783, which is rather well established and has gained market acceptance. However, there are a significant number of non-ISO 11783 compliant sensors in practice. Thus, two concepts are proposed. The first concept is on-the-go data collection based on ISO 11783, which mostly covers data on parameters related to tractor and machine performance, e.g. speed, draught, fuel consumption, etc. Process data from sensors with Control Area Network (CAN) interfaces is converted into ISO 11783 XML and then imported into relational database at FMIS using RelaXML tool. There is also the export function from database to task controller (TC) to provide task management, as described in ISO 11783:10. The second concept is on- the-go data collection with non-ISO 11783 sensors. This data is likely to be recorded in many formats, which require an import service. An import service is based on local or public sharing or semantic mapping outputting a common format for FMIS (e.g. AgroXML). Import is best performed as close to the generation of sensor data as possible to maximise the availability of metadata. A case study of sensor based variable rate fertilisation (VRF) has been undertaken focussing on German fertilisation rules.Item Open Access A mobile, in-situ soil bin test facility to investigate the performance of maresha plough(Elsevier, 2016-09-30) Gebregziabher, Solomon; De Swert, Karel; Saeys, Wouter; Ramon, Herman; De Ketelaere, Bart; Mouazen, Abdul Mounem; Gebrehiwot, Kindeya; Deckers, Jozef; De Baerdemaeker, JosseEthiopia is well known for its use of an ard plough dating from antiquity – maresha – which fractures and disturbs the soil. However, hardly any notable progress of experimental research on this animal drawn tillage tool in the field has been made. The attendant problems in current practise are soil-maresha interaction, viz., uneven oxen strength along with different pace of walking, uncontrolled implement behaviour, and field conditions. Taking stock of the experimental research on animal drawn tillage tools in general, most of the documented works on the dynamics of the interaction between soil and animal drawn tillage tools tend to rely on trial-and-error based on factors mainly based on experience and cultural context. As such, no research tailored to systematically handle the link between maresha plough and soil bin experiments exists. To this aim, this study developed a mobile in-situ soil bin facility in which the system was calibrated, tested, and evaluated under outdoor experimental conditions, wherein online measurements of draught, speed, and depth of tillage were carried out. The insights and observations gained from the experimentation were discussed and reported in terms of smooth run, overload, cyclic forces, zero speed with minimal force, stoppage, speed measurement with no force, force measurement with no speed, and low speed with low force.Item Open Access A multi sensor data fusion approach for creating variable depth tillage zones(Cambridge University Press, 2017-06-01) Mouazen, Abdul Mounem; Waine, Toby; Whattoff, DavidIn this research a multi-sensor and data fusion approach was developed to create variable depth tillage zones. Data collected with an electromagnetic sensor was fused with measurements taken with a hydraulic penetrometer and conventionally acquired soil bulk density (BD) and moisture content (MC) measurements. Packing density values were then calculated for eight soil layers to determine the need to cultivate or not. From the results 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful approach to map layered soil compaction and guide VDT operations.Item Open Access Non-biased prediction of soil organic carbon and total nitrogen with vis-NIR spectroscopy, as affected by soil moisture content and texture(Elsevier Science B.V., Amsterdam, 2013-03-01T00:00:00Z) Kuang, Boyan Y.; Mouazen, Abdul MounemThis study was undertaken to evaluate the effects of moisture content (MC) and texture on the prediction of soil organic carbon (OC) and total nitrogen (TN) with visible and near infrared (vis-NIR) spectroscopy under laboratory and on-line measurement conditions. An AgroSpec spectrophotometer was used to develop calibration models of OC and TN using laboratory scanned spectra of fresh and processed soil samples collected from five fields on Silsoe Farm, UK. A previously developed on-line vis-NIR sensor was used to scan these fields. Based on residual prediction deviation (RPD), which is the standard deviation of the prediction set (S.D.) divided by the root mean square error of prediction (RMSEP), the validation of partial least squares (PLS) models of OC and TN prediction using on-line spectra was evaluated as very good (RPD = 2.01-2.24) and good to excellent (RPD = 1.86-2.58), respectively. A better accuracy was obtained with fresh soil samples for OC (RPD = 2.11-2.34) and TN (RPD = 1.91-2.64), whereas the best accuracy for OC (RPD = 2.66-3.39) and TN (RPD = 2.85-3.45) was obtained for processed soil samples. Results also showed that MC is the main factor that decreases measurement accuracy of both on-line and fresh samples, whilst the accuracy was greatest for soils of high clay content. It is recommended that measurements of TN and OC under on-line and laboratory fresh soil conditions are made when soils are dry, particularly in fields with high clay content.Item Open Access Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI(Elsevier, 2017-05-03) Whetton, Rebecca; Zhao, Yifan; Shaddad, Sameh; Mouazen, Abdul MounemThis paper explores the use of a novel nonlinear parametric modelling technique based on a Volterra Non-linear Regressive with eXogenous inputs (VNRX) method to quantify the individual, interaction and overall contributions of six soil properties on crop yield and normalised difference vegetation index (NDVI). The proposed technique has been applied on high sampling resolution data of soil total nitrogen (TN) in %, total carbon (TC) in %, potassium (K) in cmol kg−1, pH, phosphorous (P) in mg kg−1 and moisture content (MC) in %, collected with an on-line visible and near infrared (VIS-NIR) spectroscopy sensor from a 18 ha field in Bedfordshire, UK over 2013 (wheat) and 2015 (spring barley) cropping seasons. The on-line soil data were first subjected to a raster analysis to produce a common 5 m by 5 m grid, before they were used as inputs into the VNRX model, whereas crop yield and NDVI represented system outputs. Results revealed that the largest contributions commonly observed for both yield and NDVI were from K, P and TC. The highest sum of the error reduction ratio (SERR) of 48.59% was calculated with the VNRX model for NDVI, which was in line with the highest correlation coefficient (r) of 0.71 found between measured and predicted NDVI. However, on-line measured soil properties led to larger contributions to early measured NDVI than to a late measurement in the growing season. The performance of the VNRX model was better for NDVI than for yield, which was attributed to the exclusion of the influence of crop diseases, appearing at late growing stages. It was recommended to adopt the VNRX method for quantifying the contribution of on-line collected soil properties to crop NDVI and yield. However, it is important for future work to include additional soil properties and to account for other factors affecting crop growth and yield, to improve the performance of the VNRX model.