This paper explores the challenge of defining the scope of a systems model, emphasising three aspects: boundary, granularity and conceptual scope. The significance of these is illustrated by reference to a model of land-use decisions made in villages bordering on the Mafungautsi forest in zimbabwe. The purpose of this model was to help policy players (Forestry Commission staff, non-governmental organisations, researchers and local people) to understand the impact of policy interventions on local people’s livelihoods. Scoping decisions that were made in building the Mafungautsi model were deliberately liberal, to encompass the inerests of all participants in the modelling process. These decisions now present a range of serious challenges: the difficulty of model calibration, the computational expense of running simulations, and the difficulty for new users to understand the model. Facilitators of modelling teams need to consider the serious implications of giving everyone what they want and including all participants’ ideas in a model. In the long run, it may be better to be tough and reject many suggestions at the outset. The former approach is unlikely to lead to a tractable model, while the latter may ultimately offer greater satisfaction for all.
Tag: calibration
Increasing DryDev’s effectiveness and efficiency through probabilistic decision modelling
This working paper describes the Decision Analysis work done on the Drylands Development Programme (DryDev) in Sub-Saharan Africa. The programme was designed to address water management, food security and rural economic development in the drylands of Kenya, Ethiopia, Burkina Faso, Mali and Niger. The initiative was geared towards supporting the transition of smallholder households from subsistence farming and reliance on emergency aid towards more sustainable agribusiness enterprises. Decision Analysis was used as a quantitative ex-ante impact assessment tool to prioritize interventions based on their projected impacts. The assessment simulated the potential of four interventions in six project sites of Eastern Kenya, incorporating risk and uncertainty in decision modelling. The result delivered to the decision makers was a range of plausible outcomes from a cost benefit analysis and a description of the variables with the highest critical uncertainties whose measurement would most facilitate decision-making. This paper describes the modelling process, which was both participatory and probabilistic, for each intervention. It gives details on the quantitative approach used for each of the four interventions separately, highlighting the benefit, cost and risk variables and the interactions between them. It then discusses the results of each decision model and from these makes recommendations to the decision makers. The penultimate section highlights the limitations and constraints faced by the analysis, and this is followed by general conclusions. The DryDev Programme is funded by the Netherlands’ Ministry of Foreign Affairs, with ICRAF as the lead implementing agency and SNV, CARITAS, ADRA and World Vision as implementing partners.
Limitations to use of infrared spectroscopy for rapid determination of Carbon-Nitrogen and wood density for tropical species
Infrared (IR) spectroscopy was used as a rapid and non-destructive method to determine, carbon (C), nitrogen (N) and tree wood density.A total of 82 sample cores were scanned in the reflectance mode from 4000 to 400 cm-1 for mid-infrared (MIR) spectra and from 8000 to 4000cm-1 and 11000-4000cm-1 for near infrared (NIR) spectra. The reference values for C and N were measured using combustion method while wood density was calculated using auger method. Calibration equations were developed using partial least-squares and first derivative spectra. Root mean square error (RMSEP) was used to calculate prediction error. Prediction of Cusing MIR spectra gave R2 = 0.59, RMSEP = 0.02; NIR spectra R2 = 0.50, RMSEP = 0.02, whileN prediction usingMIR spectra had R2 = 0.54, RMSEP = 0.22; NIR spectra R2 = 0.48, RMSEP =0.24. Wood density prediction was fair for MIR (R2= 0.79, RMSEP = 0.14); NIR (R2= 0.69, RMSEP = 0.17).Improved predictions using NIR were for extendedspectral range;though accuracies were inferior to MIR. Both MIR and NIR models showed good potentials to be used as rapid and cost effective method of predicting C-N and wood density.
Calibration and validation of the Modified Universal Soil Loss Equation for estimating sediment yield on sloping plots: A case study in Khun Satan catchment of Northern Thailand
In this study, model testing, calibration, and validation of the Modified Universal Soil Loss Equation (MUSLE) model were carried out in Khun Satan catchment, Thailand, for the estimation of sediment yield in plots of different slopes using the S factor from the classic Universal Soil Loss Equation (USLE) and the McCool model, as the calibration parameter. In situ experimental plots were established with five different inclinations (9, 16, 25, 30, and 35%), with the other model parameters (e.g., erodibility, conservation practice, etc) being treated as constants. Sediment yields were recorded from 27 rainfall events between July and October 2003. It was found that both the classic USLE and the McCool models over-estimated sediment yields at all slope angles. However, the classic USLE produced a smaller relative error (RE) than the McCool model at plots with slopes of 9 and 16%, while the McCool model performed better at plots with slopes over 16% inclination. The calibration of the model using the S factor was then made for two slope range intervals, and the slope algorithm was later modified. The calibrated S factors were used in the prototype model for slope ranges of 9 to 16% using classic USLE and for slopes from 16 to 35% using the McCool model. The results revealed that an acceptable accuracy can be obtained through model calibration. The model validation based on paired t-test, on the other hand, showed that there was no difference ( = 0.05) between measured and estimated sediment yield using both models. This result indicates that if data on various slope gradients are limited, MUSLE needs to be calibrated before application, especially with respect to topographic factors, in order to obtain an accurate estimate of the sediment yield from individual rainfall events.
Transferability of a large mid-infrared soil spectral library between two Fourier-transform infrared spectrometers
Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) mid-infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid low-cost estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a secondary spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC-KSSL MIR library to a secondary instrument. For the soil properties, total carbon (TC), pH, and clay content, we found that good performance (ratio of performance to deviation [RPD] = 4.9, 2.0, and 3.6, respectively) could be achieved on an independent test set with Savitzky-Golay smoothing and first derivative preprocessing of the secondary spectra using a memory-based learning chemometric approach. We tested three calibration transfer techniques (direct standardization [DS], piecewise direct standardization [PDS], and spectral space transformation [SST]) using different size transfer sets selected to be representative of the entire NSSC-KSSL library. Among the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest that calibration transfer may not always be necessary, but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.
Portable X-ray fluorescence (pXRF) calibration for analysis of nutrient concentrations and trace element contaminants in fertilisers
With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97–0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0–100 mg kg-1 (R2 values were from 0.78–0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10–0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump. Copyright: © 2022 Acquah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Exploring mid-infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset
Infrared spectroscopy is increasingly being adopted as a technology for soil analysis. However, laboratories worldwide are equipped with different infrared spectrometers, leading to variations that hinder the global application of soil spectroscopy. This study evaluates the transferability of soil spectra from a global dataset collected using four mid-infrared spectrometers. To evaluate the efficacy of five spectral transfer functions (direct standardization, piecewise direct standardization, spectral space transformation [SST], principal components-canonical correlation analysis [PC-CCA], and domain-invariant partial least square [DIPLS] regression), two datasets were used: dataset A (n = 224; standardized samples) was scanned using one primary spectrometer and three secondary spectrometers; dataset B (n = 1904; legacy samples) was scanned only using the primary spectrometer. The first set of chemometrics models was developed using dataset A to compare the performance of different spectrometers. The second set of models was developed using dataset B to evaluate the effectiveness of spectral transfer functions. Both models were developed using partial least squares regression. Spectral transfer functions developed using dataset A indicate that the PC-CCA method was the best in converging spectra collected from four instruments into a similar space projected using Uniform Manifold Approximation and Projection. Spectral transfer did not result in consistent improvement in the prediction of soil properties compared to the direct use of spectra collected from different spectrometers. These findings carry significant implications for the utilization of legacy models, enabling laboratories to concentrate on acquiring new samples and spectral measurements using established protocols without the need for spectral transfer.