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To meet the increasing demands of precision agricultural and environmental management, more abundant and accurate information is needed to describe soil organic carbon (SOC) vertical variation. Based on 923 soil profiles (collected at the depths of 0–15, 15–30, 30–60, 60–90, 90–120, and 120–150 cm) in the central area of Changhua County, Taiwan, the distribution curve of the SOC content of each profile was fitted by the equal-area spline model, and it was possible to obtain the SOC content at all depths. Taking the 0–5 cm (L1), 5–10 cm (L2), and 10–15 cm (L3) sub-layers as examples, their SOC contents and stocks were compared to the mean values of the average 5-cm-thick sub-layers (Lm) derived from the value of the 0–15 cm layer. The results indicated that the SOC contents and stocks both reduced with increasing soil depths. The mean SOC contents of L1, L2, and L3 were 22.1, 21.0, and 18.7 g·kg−1, respectively, with significant variation, and the values of L2 and L3 were 5.0% and 15.4% lower than that of L1. Similarly, the mean SOC stocks were 1.29, 1.25, and 1.16 kg·m−2 of the L1, L2, and L3 layers, also with significant variation, and the values of L2 and L3 were 4.0% and 10.1% lower than that of L1. Meanwhile, it was found that the SOC content and stock of Lm were both close to the corresponding values in L2, but were significantly different to that of L1 and L3. Furthermore, the interpolation contours of the SOC contents and stocks in L1, L2, and L3 by digital soil mapping also presented regular variation with increasing soil depths, while the contours of Lm had nearly identical patterns to that of L2. The results demonstrate that the typically used mean SOC contents with certain thicknesses calculated from the sampling layer can only approximately inflect the SOC situation at intermediate depths, but the SOC content in the upper and lower parts within the sampling layer varies greatly. Therefore, the actual distribution of SOC varies gradually depending on the soil depth. This study indicates that the combination of the equal-area spline model and digital soil mapping can greatly enrich the current soil SOC database and provide more abundant and accurate SOC content and stock information for precision agricultural and environmental management based on legacy soil database.

Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1–12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991–1994 for 38 months and the driest one in 2015–2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition.

The phenotypic and genotypic characterization of eight rhizobial isolates obtained from Arachis hypogaea nodules grown under stress environment was performed. Isolates were screened for their ability to tolerate different abiotic stresses (high temperature (60° C), salinity (1–5% (w/v) NaCl), and pH (1–12). The genomic analysis of 16S rRNA and housekeeping genes (atpD, recA, and glnII) demonstrated that native groundnut rhizobia from these stress soils are representatives of fast growers and phylogenetically related to Rhizobium sp. The phenotypic characterization (generation time, carbon source utilization) also revealed the isolates as fast-growing rhizobia. All the isolates can tolerate NaCl up to 3% and were able to grow between 20 and 37 °C with a pH between 5 to 10, indicating that the isolates were alkali and salt-tolerant. The tested isolates effectively utilize mono and disaccharides as carbon source. Out of eight, three rhizobial isolates (BN-20, BN-23, and BN-50) were able to nodulate their host plant, exhibiting their potential to be used as native groundnut rhizobial inoculum. The plant growth promoting characterization of all isolates revealed their effectiveness to solubilize inorganic phosphate (56–290 µg mL−1), synthesize indole acetic acid (IAA) (24–71 µg mL−1), and amplification of nitrogen fixing nifH gene, exploring their ability to be used as groundnut biofertilizer to enhance yield and N2-fixation for the resource poor farmers of rainfed Pothwar region.

Exploring the sensitivity of rural households’ livelihood strategies to livelihood capital is of great significance for improving rural households’ livelihood levels. This paper selects 23 livelihood capital measurement indicators and conducts an in-depth survey of rural households. In addition, the entropy method and a weighted comprehensive model are used to explore the basic characteristics of rural households’ livelihood capital in the upper reaches of the Min River, China, in 2017. Furthermore, econometric models are used to analyze the sensitivity of rural households’ livelihood strategies to livelihood capital. As indicated from the research, the livelihood capital levels of different types of rural households in the study area are not equivalent. The types of rural households with different livelihood strategies can be ordered in terms of quantity as follows: non-agricultural type > non-agricultural dominant type > agricultural dominant type > pure agricultural type. Livelihood strategies have different sensitivities to different livelihood capital measurement indicators. Among these indicators, cash income, the number of relatives and friends available for financial assistance, and the number of civil servants have positive effects on the livelihood strategy selection of non-agricultural dominant rural households and non-agricultural rural households. However, the average age of laborers, area of cultivated land and gardens, number of livestock and poultry, and present value of production tools have negative effects. These evaluation results can provide a scientific decision-making basis for the formulation of poverty alleviation policies by relevant government departments.

In the karst area of southern China, karst water is important for supporting the sustainable production and home living for the local residents. Consequently, it is of significance to fully understand the water cycle, so as to make full use of water resources. In karst areas, epikarst and conduits are developed, participating in the hydrological cycle actively. For conventional lumped hydrologic models, it is difficult to simulate the hydrological cycle accurately. These models neglect to consider the variation of underlying surface and weather change. Meanwhile, for the original distributed hydrological model, the existence of epikarst and underground conduits as well as inadequate data information also make it difficult to achieve accurate simulation. To this end, the framework combining the advantages of lumped model–reservoir model and distributed hydrologic model–Soil and Water Assessment Tool (SWAT) model is established to simulate the water cycle efficiently in a karst area. Xianghualing karst watershed in southern China was selected as the study area and the improved SWAT model was used to simulate the water cycle. Results show that the indicators of ENS and R2 in the calibration and verification periods are both above 0.8, which is evidently improved in comparison with the original model. The improved SWAT model is verified to have better efficiency in describing the hydrological cycle in a typical karst area.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

The issue of achieving sustainable livelihoods (SL) is a persistent problem that has gained significant interest for all countries. Even though contexts of vulnerability have been highlighted to be critical to SL, the difference of SL under vulnerability contexts, particularly disaster, has been ignored. As one disaster-prone area, there is an urgent need to conduct studies on SL in Shenzha, within the context of the construction of a national park. This paper proposes to address this research gap by evaluating SL under various disaster contexts in Shenzha, China. According to the frequency of natural disasters, towns in Shenzha can be divided into three groups: Snowstorm and windstorm-dominated towns (SWT), mixed towns (MT) and drought-dominated towns (DT). The results showed that (1) a great disparity of SL can be observed among the three vulnerability groups. The scores of these SL were sorted into descending order as: DT > SWT > MT. (2) In detail, herdsmen in DT have a high value of SL because they have high livelihood assets, livelihood strategies and disaster management capabilities. (3) Herdsmen in SWT have high livelihood assets, particularly human and financial assets, and livelihood strategies. (4) The low livelihood assets and livelihood strategies have restricted the SL of herdsmen in MT. An analysis of SL under various disaster contexts helped to depict the characteristics of SL. Accordingly, targeted policies were developed for the development of SL under various disaster contexts.

The rapid increase in urbanization has brought about a great deal of ecological problems, and thus the systematic protection of the environment is vital. Ecological security patterns are important for maintaining regional ecological stability and sustainable urban development. Human disturbance is a key factor affecting the stability and sustainable development of ecosystems. This paper constructs an ecological security pattern and evaluates the degree of human disturbance in Fuzhou City. Through a comprehensive analysis of both of these factors, the ecological priority restoration areas in Fuzhou were identified. The study shows that (1) there are 40 ecological source areas in Fuzhou, with a total area of 4556.48 km2; 83 ecological corridors, with a total distance of 179.33 km; and 30 ecological nodes. (2) The human disturbance degree score in the study area is between 0 and 0.8. The degree of human disturbance forms two larger major cores in Cangshan District, Gulou District, and Fuqing City. (3) The scores for the degree of human disturbance with ecological sources range from 0 to 0.42. The high-priority areas in the study area are distributed at the edges of ecological sources and form two high-scoring aggregation areas in Fuqing City and Jinan District. These corridors have a high degree of human disturbance with scores between 0 and 0.56. The I and II priority areas are mostly found in longer corridors in Fuqing City and Cangshan District near coastal or urban centers, and the III priority areas are mainly distributed in ecological corridors near the inland. The human disturbance degree scores of the nodes range from 0.01 to 0.27, and the nodes with higher grades were mainly distributed in the northeast, southeast, northwest and southwest of the study area.