Reply on RC1

Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale?

Using spectral indices and terrain attribute datasets and their combination in the prediction of cadmium content in agricultural soil

Soil surveys and mapping using conventional methods are time consuming and costly, particularly for large areas, while the demand for detailed soil data continues to grow for applications in land management and precision agriculture. In this study, a hybrid Random Forest Kriging (RFK) model combining the machine learning capabilities of Random Forest (RF) with the spatial prediction strength of the Kriging geostatistical model was employed alongside three other machine learning algorithms: Artificial Neural Networks (ANN), Gradient Boosting Machines (GBM), and K-Nearest Neighbor (KNN). These models were utilized to predict the spatial distribution of five soil physicochemical properties: potential of Hydrogen (pH), Electrical Conductivity (EC), Calcium Carbonate (CaCO₃), Bulk Density (BD), and Available Water Capacity (AWC) in a region located in Central Anatolia, Turkey. The physicochemical properties of 119 soil samples collected from the study area were analyzed. One of the objectives of this study was to evaluate the effectiveness of low-scale terrain variables as predictors for soil property estimation. For this purpose, three auxiliary terrain variables digital elevation model (DEM), slope, and topographic roughness index (TRI) were derived from remote sensing data and analyzed using GIS techniques. The predicted maps were evaluated utilizing three statistical metrics: Concordance Correlation Coefficient (CCC), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the hybrid RFK model outperformed other machine learning models in predicting soil properties, achieving the best CCC values and the lowest RMSE and MAE, while the performance of the other models remained relatively similar and weak. Furthermore, the findings indicated that the hybrid model's ability to delineate the spatial patterns of soil properties was primarily influenced by elevation and slope, despite the moderate effectiveness of low-scale terrain variables as auxiliary inputs in digital soil mapping.

Detailed soil maps are an essential tool for water resource, land management and agricultural planning. However, due to different soil processes and variation in geology, soil textural properties vary in space at different scales challenging the development of accurate soil maps. Despite the advances in sampling and efforts to produce accurate maps, uncertainties remain at many scales. Therefore, by implementing error evaluation methods such as comparing predictions and observations, it is possible to understand the performance of digital soil maps. The aim of this work was to obtain the uncertainties of soil categories from a Danish national soil map (AC map) based on measured contents of soil texture and soil organic matter. The AC maps describes the horizon characteristics (soil texture, soil organic matter, soil horizon depths and bulk density) at three depths corresponding to the A (0-30 cm), B (30-70 cm), and C (70-120 cm) horizon. In addition, it combines information on soil classification (soil texture), geology at a depth of ca. 1.5 meters as well as the national geological region. The map has a spatial resolution of 250 meters in the A and B horizons and 500 meters in the C horizon. The analysis is based on 38,000 textural points for the A horizon, 7,000 points for the B horizon, and almost 1,700 points for the C horizon. Considering mean and medians of the content of clay and organic matter together with the observed data, the AC map was validated. The uncertainties of the AC map, statistical correlation coefficient (R2), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) were used. The results showed that the AC map has an acceptable performance when predicting the clay content in the A horizon (R2 = 0.97, RMSE = 1.15%, and NSE = 0.91), B horizon (R2 = 0.95, RMSE = 1.85%, and NSE= 0.8), and C horizon (R2 = 0.74, RMSE = 5.32%, and NSE = 0.41, respectively. The performance of the prediction of organic matter content in the A horizon (R2 = 0.84, RMSE = 0.39%, and NSE = 0.65) and B horizon (R2 = 0.66, RMSE = 0.44%, and NSE = 0.32) horizons was acceptable as well. Also, the result of validation showed that the highest residual errors of AC maps for clay content and organic matter content has been related loamy (>15% clay) and peat soils. In conclusion, the AC maps, with its optimal accuracy especially in the A and B horizons, can be a suitable tool for use at variable scales in the analysis of crop growth and nitrate leaching modelling studies.Keywords: Soil texture, digital soil maps, soil variability, soil organic matter, clay, soil properties

Variability analysis of soil properties, mapping, and crop test responses in Southern Ethiopia

Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree

Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated.

Chapter 34 - Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran

Background and purposeThe study evaluated the differences in leaf positioning deviations by the log files of three advanced accelerators with two delivery techniques, and established specific assessment parameters of leaf positioning deviations for different types of accelerators.MethodsA total of 420 treatment plans with 5 consecutive treatment log files were collected from the Trilogy, TrueBeam and Halcyon accelerators. Millennium MLC was equipped on the Trilogy and TrueBeam accelerators. A jawless design and dual-layer MLC were adopted on the Halcyon accelerator. 70 IMRT and 70 VMAT plans were selected randomly on each accelerator. The treatment sites of all plans included head and neck, chest, breast, pelvis and other sites. The parsing tasks for 2100 log files were proceeded by SunCheck software from Sun Nuclear Corporation. The maximum leaf root mean square (RMS) errors, 95th percentile errors and percentages of different leaf positioning errors were statistically analyzed. The correlations between these evaluation parameters and accelerator performance parameters (maximum leaf speed, mean leaf speed, gantry and arc angle) were analyzed.ResultsThe average maximum leaf RMS errors of the Trilogy in the IMRT and VMAT plans were 0.44 ± 0.09 mm and 0.79 ± 0.07 mm, respectively, which were higher than the TrueBeam's 0.03 ± 0.01 mm, 0.03 ± 0.01 mm and the Halcyon's 0.05 ± 0.01 mm, 0.07 ± 0.01 mm. Similar data results were shown in the 95th percentile error. The maximum leaf RMS errors were strongly correlated with the 95th percentile errors (Pearson index > 0.5). The leaf positioning deviations in VMAT were higher than those in IMRT for all accelerators. In TrueBeam and Halcyon, leaf position errors above 1 mm were not found in IMRT and VMAT plans. The main influencing factor of leaf positioning deviation was the leaf speed, which has no strong correlation with gantry and arc angles.ConclusionsCompared with the quality assurance guidelines, the MLC positioning deviations tolerances of the three accelerators should be tightened. For both IMRT and VMAT techniques, the 95th percentile error and the maximum RMS error are suggested to be tightened to 1.5 and 1 mm respectively for the Trilogy accelerator. In TrueBeam and Halcyon accelerators, the 95th percentile error and maximum RMS error of 1 and 0.5 mm, respectively, are considered appropriate.

BackgroundThe multileaf collimator (MLC) is a critical component to accurate intensity-modulated radiotherapy (IMRT) delivery. This study examined MLC positional accuracy via MLC logs from Varian machines from six institutions and three delivery techniques to evaluate typical positional accuracy and treatment and mechanical parameters that affect accuracy. Typical accuracy achieved was compared against TG-142 recommendations for MLC performance; more appropriate recommendations are suggested.MethodsOver 85,000 Varian MLC treatment logs were collected from six institutions and analyzed with FractionCHECK. Data were binned according to institution and treatment type to determine overall root mean square (RMS) and 95th percentile error values, and then to look for correlations between those errors and with mechanical and treatment parameters including mean and maximum leaf speed, gantry angle, beam-on time, mean leaf error, and number of segments.ResultsResults of treatment logs found that leaf RMS error and 95th percentile leaf error were consistent between institutions, but varied by treatment type. The step and shoot technique had very small errors: the mean RMS leaf error was 0.008 mm. For dynamic treatments the mean RMS leaf error was 0.32 mm, while volumetric-modulated arc treatment (VMAT) showed an RMS leaf error of 0.46 mm. Most MLC leaf errors were found to be well below TG-142 recommended tolerances. For the dynamic and VMAT techniques, the mean and maximum leaf speeds were significantly linked to the leaf RMS error. Additionally, for dynamic delivery, the mean leaf error was correlated with RMS error, whereas for VMAT the average gantry speed was correlated. For all treatments, the RMS error and the 95th percentile leaf error were correlated.ConclusionsRestricting the maximum leaf speed can help improve MLC performance for dynamic and VMAT deliveries. Furthermore, the tolerances of leaf RMS and error counts for all treatment types should be tightened from the TG-142 values to make them more appropriate for clinical performance. Values of 1 mm for the 95th percentile of leaf RMS error and 1.5 mm for the 95th percentile leaf error are suggested as action levels for all treatment types.

The accuracy of lidar remote sensing in characterizing three-dimensional forest structural attributes has encouraged foresters to integrate lidar approaches in routine inventories. However, lidar point density is an important consideration when assessing forest biophysical parameters, given the direct relationship between higher spatial resolution and lidar acquisition and processing costs. The aim of this study was to investigate the effect of point density on mean and dominant tree height estimates at plot level. The study was conducted in an intensively managed Eucalyptus grandis plantation. High point density (eight points/m2) discrete-return, small-footprint lidar data were used to generate point density simulations averaging 0.25, one, two, three, four, five, and six points/m2. Field surveyed plot-level mean and dominant heights were regressed against metrics derived from lidar data at each simulated point density. Stepwise regression was used to identify which lidar metrics produced the best models. Mean height was estimated at accuracy of R2 ranging between 0.93 and 0.94 while dominant height was estimated with an R2 of 0.95. Root mean square error (RMSE) was also similar at all densities for mean height (∼1.0 m) and dominant height (∼1.2 m); the relative RMSE compared to field-measured mean was constant at approximately 5%. Analysis of bias showed that the estimation of both variables did not vary with density. The results indicated that all lidar point densities resulted in reliable models. It was concluded that plot-level height can be estimated with reliable accuracy using relatively low density lidar point spacing. Additional research is required to investigate the effect of low point density on estimation of other forest biophysical attributes.

Both excessive and deficient nitrogen (N) concentration in the growing media can affect the growth, development, yield, and quality of rice. Traditional methods of N determination of plant require destructive and rigorous sampling, which is also time-consuming and laborious. However, rapid and non-destructive nitrogen diagnosis has become an important area of research in precision agriculture. Heilongjiang Province, China is a cold climate rice growing area, where the growth and fertilization of rice follows a definite pattern. In the present study, two varieties of rice (Wuyoudao4 and Songjing9) and as location Heilongjiang Province were selected. Nitrogen diagnosis of rice was carried out based on airborne multi-spectrum methodology. Canopy spectral data of rice at key growth periods were obtained by using a UAV equipped with a multi-spectral camera, and agronomic parameters such as leaf N content and dry matter weight were obtained synchronically. An airborne multispectral canopy normalized vegetation index (NDVI) model for N diagnosis based on its critical concentration curve was established as a nondestructive N diagnosis of rice for cold region. Results showed that canopy NDVI can estimate rice nitrogen nutrition index (NNI) properly over the growth period. The coefficient of determination R2, root mean square error (RMSE) and standard root mean square error (nRMSE) were compared to determine the best effect of the index model. The interphase nitrogen diagnosis model of WYD-4 based on NDVI for cold region was as follows: NNI=0.3916e1.0809*NDVI (RMSE=0.12, nRMSE=12.43%), SJ-9: NNI=0.3325e1.2705*NDVI (RMSE=0.10, nRMSE =10.36%), indicating that the established model can better estimate the nitrogen status of rice. Bangladesh J. Bot. 52(2): 529-538, 2023 (June) Special

Accurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. Although deep learning-based methods have demonstrated strong potential in digital soil mapping, they typically require large amounts of data. However, the availability of site-level SOC observations is often limited, which poses a challenge for model performance. To address this, we propose a novel transfer learning approach based on a Convolutional Neural Network (CNN) model that does not rely on exogenous data. Specifically, when predicting SOC for a given soil layer, the model is first pre-trained on data from all layers and then fine-tuned using data from the target layer. This design enables more efficient use of limited site data. Experimental results show that the proposed transfer model consistently outperforms other machine learning models, including the Random Forest (RF), standard CNN, and Multi-Task CNN (MTCNN) models. The transfer model achieves a coefficient of determination (R2) of 0.374 and a root mean square error (RMSE) of 2.937%, indicating superior performance. These findings highlight the effectiveness of the proposed approach for digital soil mapping under data-scarce conditions and underscore its potential as a robust tool for accurate SOC estimation.

Soil Organic carbon (SOC) is vital to the soil’s ecosystem functioning as well as improving soil fertility. Slight variation in C in the soil has significant potential to be either a source of CO2 in the atmosphere or a sink to be stored in the form of soil organic matter. However, modeling SOC spatiotemporal changes was challenging due to lack of data to represent the high spatial heterogeneity in soil properties. Less expensive techniques, digital soil mapping (DSM) combined with space-for-time substitution (SFTS), were applied to predict the present and projected SOC stock for temperature and rainfall projections under different climate scenarios represented by the four Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6, and RCP8.5). The relationship between environmental covariates (n = 16) and measured SOC stock (148 samples) was developed using a random forest model. Then, the temporal changes in SOC stock over the baseline were developed for the top 30 cm soil depth of the selected districts (Chiro Zuria, Kuni, Gemechis and Mieso) of West Hararghe Zone at 30 m resolution. The model validation using the random sample of 20% of the data showed that the model explained 44% of the variance (R2) with a root mean square error (RMSE) of 8.96, a mean error (ME) of 0.16, and a Lin’s concordance correlation coefficient (CCC) of 0.88. Temperature was the most important predictor factor influencing the spatial distribution of SOC stock. An overall net gain of SOC stock over the present C stock was expected in the study area by 2050. The gain in areas with the lower baseline SOC stock counterbalanced the loss in areas with the higher baseline stock. The changes in the SOC stock depended on land use land cover (LULC), soil type, and agro-ecological zones. By 2050, cropland is supposed to lose its SOC stock under all RCPs; therefore, appropriate decisions are crucial to compensate for the loss of C.

The increasing demand for personalized information has resulted in the development of the Recommender System (RS). RS has been widely utilized and broadly studied to suggest the interests of users and make an appropriate recommendation. This paper gives an overview of several types of recommendation approaches based on user preferences, ratings, domain knowledge, users demographic data, users context and also lists the advantages and disadvantages of each RS approach. In this paper, we also proposed the movie recommendation based on collaborative filtering and singular value decomposition plus-plus (SVD++). The proposed approach is compared with well-known machine learning approaches namely k nearest neighbor (K-NN), singular value decomposition (SVD) and Co-clustering. The proposed approach is experimentally verified using MovieLens 100 K datasets and error of the RS is measured using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The result shows that the proposed approach gives a lesser error rate with RMSE (0.9201) and MAE (0.7219). This approach also overcomes cold-start, data sparsity problems and provides them relevant items and services.