Comparison of Different Spatial Regression Methods for Predicting Organic Matter Content in Soil

Dynamic changes in soil organic matter content affects the sustainable supply of soil water and fertilizer and impacts the stability of soil ecological function. Understanding the spatial distribution characteristics of soil organic matter will help deepen our understanding of the differences in soil organic matter content, soil formation law; such understanding would be useful for rational land use planning. Taking terrain data, meteorological data, and remote sensing data as auxiliary variables and the ordinary Kriging (OK) method as a control, this study compares the spatial prediction accuracies and mapping effects of various models (MLR, RK, GWR, GWRK, MGWR, and MGWRK) on soil organic matter. Our results show that the spatial distribution trend of soil organic matter predicted by each model is similar, but the prediction of composite models can reflect more mapping details than that of unitary models. The OK method can provide better support for spatial prediction when the sampling points are dense; however, the local models are superior in dealing with spatial non-stationarity. Notably, the MGWR model is superior to the GWR model, but the MGWRK model is inferior to the GWRK model. As a new method, the prediction accuracy of MGWRK reached 47.72% for the OK and RK methods and 40.08% for the GWRK method. The GWRK method achieved a better prediction accuracy. The influence mechanism of soil organic matter is complex, but the MGWR model more clearly reveals the complex nonlinear relationship between soil organic matter content and factors influencing it. This research can provide reference methods and mapping technical support to improve the spatial prediction accuracy of soil organic matter.

EVALUATING THE ECOPHYSIOLOGY OF SURVIVAL FOR MAPANIA CUSPIDATA (MIQ.) UITTIEN (CYPERACEAE) TRANSPLANTATION

SHABDIN, Z., NORI, H., MEEKIONG, K. & FAIZ, M. F. M. 2021. Evaluating the ecophysiology of survival for Mapania cuspidata (Miq.) Uittien (Cyperaceae) transplantation. Reinwardtia 20(2): 69–75. — This study aimed to investigate the ecology of the sedge Mapania cuspidata at three different locations in East Malaysia, namely Gunung Gading, Matang and Bengoh, and the survival of M. cuspidata transplanted in pots exposed to different light intensities in Universiti Malaysia Sarawak, East Malaysia. The highest species density was recorded in Matang with a total density of 1.98 individuals/ha followed by Bengoh (1.42) and Gunung Gading (0.96). In these locations, the soil pH ranged from 4.9 in Bengoh to 5.7 in Matang where as soil organic matter content was between 3.47% in Bengoh and 8.68% in Gunung Gading. The highest light intensity was recorded in Matang with 0.94 kLux, and produced plants with the highest chorophyll content (64.8 SPAD value). This study found that the transplanted M. cuspidata had 90% survival over a four month experiment, produced ~ 8 new leaves, took an average of 15.8 days to produce a new leaf and had a chlorophyll content of ~30.3 SPAD value regardless of the intensity of light where the plants were exposed to. The findings of this study suggests that M. cuspidata can grow well in any light conditions and therefore it is also possible to transplant and re-establish other Mapania species in new location. It is hoped that the initiative to relocateother Mapania species of concervation concern will be effective if adequate post-harvest handling methods are practiced.

Using Machine Learning Algorithms Based on GF-6 and Google Earth Engine to Predict and Map the Spatial Distribution of Soil Organic Matter Content

The prediction of soil organic matter is important for measuring the soil’s environmental quality and the degree of degradation. In this study, we combined China’s GF-6 remote sensing data with the organic matter content data obtained from soil sampling points in the study area to predict soil organic matter content. To these data, we applied the random forest (RF), light gradient boosting machine (LightGBM), gradient boosting tree (GBDT), and extreme boosting machine (XGBoost) learning models. We used the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) to evaluate the prediction model. The results showed that XGBoost (R2 = 0.634), LightGBM (R2 = 0.627), and GBDT (R2 = 0.591) had better accuracy and faster computing time than that of RF (R2 = 0.551) during training. The regression model established by the XGBoost algorithm on the feature-optimized anthrosols dataset had the best accuracy, with an R2 of 0.771. The inversion of soil organic matter content based on GF-6 data combined with the XGBoost model has good application potential.