In the last 30 years, severe soil acidification has been found in China due to acid deposition and nitrogen fertilizer overuse. Understanding the spatial pattern and vertical variations in base saturation percentage (BSP) and exchangeable cations (Ca²⁺, Mg²⁺, K⁺, Na⁺, H⁺ and Al³⁺) can directly benefit fertilization management and ecological protection. Here, 1253 soil profiles were surveyed in tropical and subtropical regions in China to investigate the spatial variations in BSP and exchangeable cations at three soil depths of 0–20 cm, 20–50 cm and 50–100 cm. The spatial distributions were interpolated by using advanced machine learning techniques. We found that the exchangeable Ca²⁺ (Exch. Ca), Mg²⁺ (Exch. Mg) and BSP were significantly higher in paddy fields and uplands than in forests and gardens, regardless of soil depth, while the exchangeable K (Exch. K) did not significantly differ between various land-use types. The Exch. Ca and BSP in Anthrosols were significantly higher than those in Ferrosols, Argosols and Cambosols in the three soil layers. The spatial prediction results indicated that exchangeable cations and BSP were generally characterized by strong heterogeneity, and the Exch. Ca, Exch. K and exchangeable H⁺ (Exch. H) contents and BSP declined with increasing soil depth. This study helps us understand the spatial variation in BSP and exchangeable cations in the study area and benefits fertilization management and environmental protection.

Fertilization decisions depend on the measurement of a large set of soil fertility indicators, usually through laboratory determination, which is costly and time-consuming. Visible and near-infrared (vis-NIR) spectroscopy combined with machine learning can simultaneously predict various soil fertility indicators. Spectroscopy is inherently less accurate than direct laboratory determination. However, in many fertilization recommendation contexts, farmers mainly fertilize according to classified fertility indicators, rather than by continuous soil property values. These classes have defined limits of property values. We hypothesized that the additional inaccuracy from spectroscopy may not be important for properties grouped into classes. This study compared the indirect and direct prediction of soil fertility classes. Indirectly, by (1) using vis-NIR spectra with machine learning to predict 20 soil fertility indicators (pH, soil organic matter (SOM), cation exchange capacity (CEC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), calcium (Ca), magnesium (Mg), silicon (Si), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), Zinc (Zn), molybdenum (Mo) and chlorine (Cl)) and (2) allocating the indicators to soil fertility classes. Directly, by predicting soil fertility classes directly from vis-NIR spectra using machine learning. The prediction accuracy of these two methods were compared and the accuracies needed for the acceptable class allocation of the fertility indicators were determined. The example dataset is a soil spectral library from the Guizhou Province, southwest China. The model performance was evaluated by the overall allocation accuracy and tau index, which accounts for class imbalance. For direct allocation based on three fertility classes (low, medium and high), the overall allocation accuracy of eight properties (CEC, Cu, Si, Zn, S, Mn, Ca and Mg), nine properties (B, AN, TK, AK, SOM, TN, TP, Fe and Mo) and three properties (Cl, AP and pH) were within the range of 0.80–1.0, 0.60–0.80 and 0.40–0.60, respectively. For indirect allocation based on the same classes, the allocation accuracy of nine properties (TN, CEC, Cu, S, Zn, Si, Mn, Ca and Mg), nine properties (B, TK, pH, TP, AK, AN, Fe, Mo and SOM) and two properties (Cl and AP) were within the range of 0.80–1.0, 0.60–0.80 and 0.40–0.60, respectively. We conclude that vis-NIR spectroscopy was fairly successful for soil fertility class allocation for most of the soil properties, using either direct or indirect models. The advantage of indirect models is that both specific property values and soil fertility classes can be obtained at no increase in cost, while direct models are suggested when only soil fertility class information are available.

While Qiaocheng district currently is the main tobacco planting region in Northern Anhui Province, China, the quantitative assessment on spatial suitability for tobacco planting is necessary for the scientific instruction in the adjustment of planting regionalization and fertilization. In this paper, soil samples of the plough layers were collected from 1224 typical farmlands in 2008, and 18 types of soil properties, pH, clay, silt, sand, organic matter, total nitrogen, available phosphorus, slowly available potassium, rapidly available potassium, exchangeable Ca2+ and Mg2+, Cl-, HCO3-, SO42-, available Fe, Mn, Cu and Zn were measured. Meanwhile irrigation water samples of 90 typical pumping wells were collected, and Cl-, HCO3-, SO42-, K+, Ca2+ and Mg2+ were measured. The above measured properties were used as the assessing indexes for spatial suitability for tobacco planting, in which the weights were generated by the method of principal component analysis and the fuzzy membership functions were produced based on the practical experiences with the related literatures. The soil suitabilities were quantitatively assessed using the ArcGIS 10.0 platform. The experimental results showed that: (1) areas of the highest, higher, middle, lower and lowest levels of suitability are 4.28×104, 4.36×104, 4.82×104, 4.47×104 and 4.81×104 hm2. respectively, constituted of 18.84%, 19.16%, 21.20%, 19.66% and 21.14% of the total area of the farmland, respectively; (2) in general, the Northeast, Northwest, Southeast and Southwest regions are the most suitable areas, and (3) for tobacco farmland in the North region, more potassium fertilizers should be applied due to the low available potassium content in soil, and the flood irrigation should be prevented due to the high content of Cl- in irrigation water. Chengfu in the Sourtheast, Shihe and Feihe in the Southwest, and Longyang and Gucheng in the South could be regarded as the new potential tobacco planting regions.

Article Crystal structure of bis(benzo-1,4,7,10,13-pentaoxacyclopentadec- 2-ene)-bis(thiocyanato)disodium(I), Na2(SCN)2(C14H2oO5)2, a benzo-15-crown-5 complex was published on April 1, 2004 in the journal Zeitschrift für Kristallographie - New Crystal Structures (volume 219, issue 1-4).