Predicting glyphosate sorption across New Zealand pastoral soils using basic soil properties or Vis–NIR spectroscopy

Hermansen, Cecilie; Nørgaard, Trine; Jonge, Lis Wollesen de; Møldrup, Per; Müller, Karin; Knadel, Maria

doi:10.1016/j.geoderma.2019.114009

Cited by 23 publications

(18 citation statements)

References 54 publications

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“…The most accurate models are those that have a high R 2 and RPD, a low RMSE and Bias [12,28,37,46]. The coefficients of determination obtained in this study can be considered good for NDF (0.73 for the calibration model and 0.69 for the validation model, with RPD of 1.75) and moderate for CP (0.51 for calibration and 0.36 for internal validation, with RPD of 1.26).…”

Section: Nirs Models Accuracy: Calibration and Validationmentioning

confidence: 64%

Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

et al. 2021

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The Montado is a Mediterranean agro–forestry–pastoral ecosystem. Knowledge of pastures’ nutritional value is critical for farm managers’ decision-making. Laboratory determinations are very expensive, destructive and costly, in terms of time and labour. The objective of this experimental work was to calibrate and validate a portable near-infrared spectrometer (micro-NIR) to predict the nutritive value (neutral detergent fibre, NDF and crude protein, CP) of pastures in the peak of spring 2021. Thus, a total of 87 pasture samples were collected at eight experimental fields located in the Alentejo, Southern region of Portugal. The results show good correlations between in-situ micro-NIR measurements and pasture NDF reference values (R2 of 0.73 and 0.69 for calibration and validation models, respectively), and a moderate correlation between micro-NIR measurements and pasture CP reference values (R2 of 0.51 and 0.36 for calibration and validation models, respectively). These results show the potential of this tool for the quick evaluation of pasture quality and constitute a starting point for future work, which should include the monitoring of temporal variability (throughout the entire vegetative cycle of the pasture) and spatial (with geo-referenced information) diversity of pastures characteristic of the Montado ecosystem in the Mediterranean region.

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Section: Nirs Models Accuracy: Calibration and Validationmentioning

confidence: 64%

Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

et al. 2021

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“…Model performance evaluation was assessed by three parameters [17,18]: the coefficient of determination (R 2 ; an excellent indicator of the accuracy and robustness of a model), the root mean square error (RMSE; Equation (1)) for the calibration and external validation data sets, and the ratio of performance to deviation or residual predictive deviation (RPD; Equation (2). The model with the highest R 2 and the lowest RMSE is considered as the best model [19]. RPD corresponds to the ratio between standard deviation (SD) of the results obtained by the reference method and the corrected mean error of the prediction of the validation (SEP bias) and is commonly used to investigate the prediction error.…”

Section: Discussionmentioning

confidence: 99%

“…Phosphates are hardly detected by infrared radiation due to the low dipole moment between phosphorous and oxygen, which inhibits the direct detection of orthophosphate [29]. Notwithstanding, several works have presented interesting results in the validation of soil phosphorus (P) content based on the NIRS approach, as are the cases of Hermansen et al [19], Bogrekci and Lee [30], Maleki et al [31], Mouazen and Kuang [32], and Kawamura et al [33], with R 2 of, respectively, 0.79-0.84, 0.78-0.99, 0.73-0.75, 0.75, and 0.80, similar to those obtained in this study (0.76). Additionally, the RPD in our study (2.2) fits within the range of values obtained in these works (between 1.8 and 2.7).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Near Infrared Spectroscopy (NIRS) for Estimating Soil Organic Matter and Phosphorus in Mediterranean Montado Ecosystem

et al. 2021

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The Montado is an agro-silvo-pastoral ecosystem characteristic of the Mediterranean region. Pasture productivity and, consequently, the possibilities for intensifying livestock production depend on soil fertility. Soil organic matter (SOM) and phosphorus (P2O5) are two indicators of the evolution of soil fertility in this ecosystem. However, their conventional analytical determination by reference laboratory methods is costly, time consuming, and laborious and, thus, does not meet the needs of current production systems. The aim of this study was to evaluate an alternative approach to estimate SOM and soil P2O5 based on near infrared spectroscopy (NIRS) combined with multivariate data analysis. For this purpose, 242 topsoil samples were collected in 2019 in eleven fields. These samples were subjected to reference laboratory analysis and NIRS analysis. For NIRS, 165 samples were used during the calibration phase and 77 samples were used during the external validation phase. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantification of these soil parameters. The coefficient of determination (R2, 0.85 for SOM and 0.76 for P2O5) and the residual predictive deviation (RPD, 2.7 for SOM and 2.2 for P2O5) obtained in external validation indicated the potential of NIRS to estimate SOM and P2O5, which can facilitate farm managers’ decision making in terms of dynamic management of animal grazing and differential fertilizer application.

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“…The coefficient of determination (R 2 ), concordance correlation coefficient (CCC), ratio of performance to interquartile range (RPIQ), the root mean square error of calibration (RMSEC), and cross-validation (RMSECV) were used as statistical measures to assess the performance of estimation models. The RPIQ is defined as the ratio of IQ to RMSECV [42]. IQ is the interquartile range (IQ = Q3 − Q1) of the observed values.…”

Section: Accuracy Validationmentioning

confidence: 99%

Estimation of Soil Nutrient Content Using Hyperspectral Data

et al. 2021

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Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg−1 and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg−1 and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg−1 and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

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Predicting glyphosate sorption across New Zealand pastoral soils using basic soil properties or Vis–NIR spectroscopy

Cited by 23 publications

References 54 publications

Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

Evaluation of Near Infrared Spectroscopy (NIRS) for Estimating Soil Organic Matter and Phosphorus in Mediterranean Montado Ecosystem

Estimation of Soil Nutrient Content Using Hyperspectral Data

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