The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. When compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better; therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modeling. This research could provide useful results for landslide modeling in landslide prone areas.

Coffee is considered a boom crop in Southeast Asia. However, while it bears typical boom crop characteristics in many places where it has been grown, in other places it has contributed to agrarian transformation. This paper examines the context of coffee development in the Northwestern Mountain Region of Vietnam and describes how smallholder coffee growing has triggered an agricultural transition process, and corresponding land use changes, from subsistence-based to commercialized agriculture production. The research was conducted in a commune located in Son La province. Interviews with 46 selected households and three focus group discussions (10–15 people each) were conducted to understand changes in crop systems, corresponding land use, and labor use, due to the adoption of coffee (the boom crop). The research found that coffee has replaced swidden crops and enables a multicrop system, with less land devoted to swidden land use. The income from coffee is used to hire labor and to pay for the inputs needed to mechanize rice farming. The research findings show that the coffee boom has brought about livelihood transformation, changed land use, and transformed local livelihoods from subsistence to production for the market.

This paper examines the transformation of agrarian livelihoods due to crop booms at the China-Myanmar borderland. A key finding is that local villagers have rented out their land to outside investors looking to make fruit boom investments. However, the villagers neither cultivated the same crops themselves, nor were they hired as wage laborers. Overall, this study finds that crop booms provide local villagers with opportunities to reallocate natural resources and adjust their livelihoods. We argue that the dynamics of agrarian livelihoods are co-produced as the result of transnational labor migration and state-led borderland repositioning under secure land tenure relations. This study contributes to the current discussion regarding the changes undergone by an agrarian society experiencing crop booms.