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In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011–2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August. [ABSTRACT FROM AUTHOR]

We study studied the alpine meadow soil in Gannan Prefecture. Geostatistical analysis showed that Circular [soil total nitrogen (STN), soil total potassium (STK), soil available phosphorus (SAP)] model, index [bulk density (BD), soil water content (SWC), soil organic carbon (SOC), soil available nitrogen (SAN)] model and Gaussian [soil total phosphorus (STP), soil available potassium (SAK)] model is universal Kriging Method semi-variation function optimization fitting model. In the model, the spatial correlation is weak to medium, and SOC, STN, STP, STK, SAN, SAP, SAK, and SWC have specific spatial trend effects. Then principal component analysis was used to obtain three key factors (SOC, BD, and SWC) affecting soil quality, which accounted for 81.91% of the total variation of soil quality. These factors were taken as the minimum data set (MDS) and soil quality index (SQI) was calculated. According to SQI, the whole region was divided into three soil management zones (SMZs). Recommendations are made for different management areas to ensure resource conservation and reduce environmental footprint. [ABSTRACT FROM AUTHOR]

Wild licorice in China is mainly distributed in northern China, such as Gansu, Ningxia, and Inner Mongolia Provinces. The origin of wild licorice has varied among historical periods. The cultivated origin of planted licorice has the same as 59.26% of wild licorice. The distribution of cultivated licorice was shifted to the northwest relative to that of wild licorice. The yield and quality of cultivated licorice vary greatly from different origins, showing a certain pattern of variation from west to east. The same batch of licorice seedlings was planted at 8 sites overlapping the main licorice production areas in China. The yield and quality of licorice in the Baicheng experimental plot were low. The yield of licorice in the Jingtai and Altay experimental plots was high, but the quality was poor. The quality of licorice in Chifeng and Yuzhong experimental sites was high, but the yield was low. Principal component analysis of environmental and soil factors generated five characteristic roots with a cumulative contribution rate of 80%, three of which were related to soil and referred to as the soil charge factor, soil water factor, and soil nutrient factor, and the load coefficients of the water and nutrient factor were the largest. Soil conditions, especially water and nutrients, might have a substantial effect on the observed changes in the licorice production area. Generally, the regulation of water and nutrients merits special attention when selecting areas for the production and cultivation of licorice. This study can provide reference for the selection of cultivated licorice production areas and the research of high-quality cultivation techniques. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were analyzed to study the vegetation dynamics over different meteorological subdivisions in India for the period 2000–2016. Soil moisture (SM), rainfall (RF), and land use land cover (LULC) data were analyzed to identify the climatic and anthropogenic drivers that cause vegetation changes at the subdivision scale. Principal component analysis and MK (Mann-Kendall) test showed significant greening trend over semi-arid regions of Northwest India (NWI) and South India (SI) while slight browning trend seen over some of the subdivisions in Indo-Gangetic (IG) plains and Western Ghats (WG). It is found that the NDVI has superior correlation with soil moisture compared with rainfall and the croplands (CL) found to have significant increasing trend over the NWI and SI. Increasing trend in soil moisture over the NWI and SI may have contributed to increase in CL area and the greening trend. Over IG plains, the NDVI showed moderate correlation with SM and RF, and the greening trend (browning trend) in some regions can be attributed to increase in natural vegetation mosaic (decrease of CL). The NDVI has shown browning trend over the core monsoon regions of Madhya Pradesh (an increase of barren lands over west MP and decrease of CL over east MP) and Western Ghats (significant decrease of CL over Konkan and Goa). This study revealed that the soil moisture and LULC changes are the major driving factors for the vegetation changes over majority of the subdivisions in India. [ABSTRACT FROM AUTHOR]

The current study has been derived to understand morphometric parameters to pledge the reduction in the proclaimed drought influence for climate change. Watershed prioritization has been studied using the geographical information system and remote sensing techniques for soil erosion and water preservation measure at Plio–Pleistocene elevated tract in Bangladesh. Secondary data, i.e., SRTM DEM and the topographical sheets, have been used for the drainage network identification. Seventeen watersheds of fifth order and three watersheds of sixth order are selected for the whole study. Sixteen basic morphometric parameters that are categorized as areal, linear and relief aspects have been used for the study area. The study area comprises sixth stream order that has been obtained using a stream threshold value of 100-m length. Morphometric analysis is suitable for water and soil conservation practice where groundwater and relevant data sets are not available. For the categorization and correlation of the morphometric parameters, principal component analysis (PCA) has been used in the present study. PCA analysis is more suitable, well-known and widely used method for its simplicity to choose more influencing parameters (correlated parameters) that are responsible for watershed prioritization. Strongly correlated components (Dd, Rc, Re and Rb) are used for the compound parameter (Cp) and final priority (Rp) calculation. Low Cp value is suitable for water and soil conservation measure. Therefore, the corresponding low Cp has been taken as one for final priority ranking (Rp) purpose, then second lowest value as two, and so on. [ABSTRACT FROM AUTHOR]

Competition for light has driven forest trees to grow exceedingly tall, but the lack of a single universal limit to tree height indicates multiple interacting environmental limitations. Because soil nutrient availability is determined by both nutrient concentrations and soil water, water and nutrient availabilities may interact in determining realised nutrient availability and consequently tree height. In SW Australia, which is characterised by nutrient impoverished soils that support some of the world's tallest forests, total [P] and water availability were independently correlated with tree height ( r = 0.42 and 0.39, respectively). However, interactions between water availability and each of total [P], pH and [Mg] contributed to a multiple linear regression model of tree height ( r = 0.72). A boosted regression tree model showed that maximum tree height was correlated with water availability (24%), followed by soil properties including total P (11%), Mg (10%) and total N (9%), amongst others, and that there was an interaction between water availability and total [P] in determining maximum tree height. These interactions indicated a trade-off between water and P availability in determining maximum tree height in SW Australia. This is enabled by a species assemblage capable of growing tall and surviving (some) disturbances. The mechanism for this trade-off is suggested to be through water enabling mass-flow and diffusive mobility of P, particularly of relatively mobile organic P, although water interactions with microbial activity could also play a role. [ABSTRACT FROM AUTHOR]