Even though indigenous knowledge (IK) is considered as one of the most effective strategies in response to climate change issues, this form is not being sufficiently integrated into the climate change planning and policy at both local and national levels in Vietnam. This study investigates the role of the traditional agricultural practices of the Xo Dang ethnic minority groups in Central Vietnam and provides insights into the factors that influence farmers to adopt these practices in response to climate change. Primary data was obtained through three focus group discussions and 87 household surveys involving the Xo Dang people through face-to-face semi-structured interviews in the Tra Doc commune, Bac Tra My district, Quang Nam province, Central Vietnam. The binary logistic regression model was used to examine the factors which have influenced the choices made by this community in response to climate change. The results showed that Xo Dang people were highly aware of climate change risks and had, in response, employed their current adaptation practices. The major adaptation strategies implemented by the Xo Dang people included the use of flora and fauna indicators, native plant varieties, the adjustment of planting calendars, irrigation practices, and the application of intercropping. The results indicated that the living years, their monthly farm incomes, and farmer's perceptions of ongoing climate change effects on their environment were the factors that significantly affected farmers' adaptation decisions. Understanding indigenous knowledge plays a fundamental role in the processes of deciding the appropriate adaptation techniques more effectively and making use of human resources. Therefore, policy makers should pay much attention to indigenous knowledge to combat climate change in future national policies and projects.

Magnetic phase transition, magnetocaloric effect and critical parameters of Ni50-xCoxMn50-yAly (x = 5 and 10; y = 17, 18 and 19) rapidly quenched ribbons have been studied. X-ray diffraction patterns exhibit a coexistence of the L21 and 10M crystalline phases of the ribbons. Magnetization measurements show that all the samples behave as soft magnetic materials with a low coercive force less than 60 Oe. The shape of thermomagnetization curves considerably depends on Co and Al concentrations. The Curie temperature (TC) of the alloy ribbons strongly increases with increasing the Co concentration and slightly decreases with increasing the Al concentration. The Ni45Co5Mn31Al19 and Ni40Co10Mn33Al17 ribbons reveal both the positive and negative magnetocaloric effects. Under magnetic field change (ΔGH) of 13.5 kOe, the maximum magnetic entropy change (|ΔSm|max) of the Ni45Co5Mn31Al19 ribbon is about 2 and -1 J·kg−1·K−1 for negative and positive magnetocaloric effects, respectively. Basing on Arrott - Noakes and Kouvel - Fisher methods, critical parameters of the Ni45Co5Mn31Al19 ribbon were determined to be TC ≈ 290 K, β ≈ 0.58, γ ≈ 0.92 and δ ≈ 2.59. The obtained values of the critical exponents indicate that the magnetic order of the alloy ribbon is close to the mean-field model.

Soil erosion is a considerable concern in the upland areas of Central Vietnam. This situation is most serious in regions, where the terrain is sloped and subjected to heavy rainfall. Our research was conducted in a mountainous area, belonging to Central Vietnam, the area of Song Kon commune in the Dong Giang district. The objective of this study is first to estimate the impact of soil erosion risk in these areas, and second to assess the capacity of farming systems which are based on indigenous knowledge (IK) to respond to soil erosion. Our data were collected by Participatory Rural Appraisal (PRA) and processed using Geographical Information System (GIS) methods. We then interpreted this research using the Universal Soil Loss Equation (USLE) in order to calculate the soil erosion rate. The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were also used as measurements to compare the difference of land surface covers between different farming systems. The results showed that the lowest soil erosion rate was found in the narrow valley regions, which are populated by both agricultural and residential areas. On the other hand, soil erosion was extremely high in the more northerly quadrant of our research area. Our findings also indicate that local farmers are highly aware of soil erosion, which has positively influenced the adoption of adaptation measures (AMs) in their agricultural activities. The most common AMs are as follows: changes in cropping patterns, the adjustments of their planting calendars, the use of native varieties, and intercropping methods. These AMs are mediated by the cultural observances of the local ethnic minority peoples in relation to their IK. We have concluded that when farmers apply IK in their farming systems, the soil erosion rate tends to decrease as compared with non-indigenous knowledge (NIK) practices. We hope to bring a better understanding of the processes that shape farmers’ AMs and thereby to develop well-targeted adaptation policies that can then be applied at the local level. Our findings may be instrumental in future adaptation planning and policies in regard to climate change, and that they will help to increase awareness not only in matters of the soil erosion but also in other interconnected aspects of climate change in these areas.

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Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that is being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Systems (GIS) has provided the potential for more accurate spatial prediction of SOC content. This research was conducted in a relatively small-scale capacity in the Central Vietnam region. The aim of this study is to compare the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Random Forest (RF) methods for SOC interpolation, with a dataset of 47 soil samples for an area of 145 hectares. Three environmental variables including elevation, slope, and the Normalized Difference Vegetation Index (NDVI) were used for the RF model. In the RF model, the values of the number of variables randomly sampled as candidates at each split, (mtry), and the number of bootstrap replicates, (ntree), were determined in terms of 1 and 1,000 respectively The results at our research site showed that using IDW is the most accurate method for SOC mapping, followed by the methods of RF and OK respectively. Concerning SOC mapping based-on auxiliary variables, in areas where there is human activity, the selection of auxiliary variables should be carefully considered because the variation in the SOC may not only be due to environmental variables but also by farming technologies.