Pro-poor intervention strategies in irrigated agriculture in Asia: poverty in irrigated agriculture: issues and options: Vietnam

Irrigated farming / Poverty / Farm income / Irrigation management / Institutions / Legal aspects / Water rates / User charges / Participatory management / Privatization / Participatory rural appraisal / Performance indexes / Irrigation programs / Irrigation systems / Pumping / Irrigation canals / Social aspects / Economic aspects / Rivers / Hydrology / Dams / Households / Income / Regression analysis / Drainage / Cooperatives / Water delivery / Water distribution / Rice / Financing / Drought / Vietnam / Red River Delta / Nam Duong Irrigation System / Nam Thach Han Irrigation System / Han River

Technical efficiency of smallholder banana production: a case study in Viet Nam

Bananas are considered one of the leading trading crops due to their high demand all over the globe. Since the increasing demand leads to the expansion of global import, the existing literature is in dire need of updating, especially from the producing economies that fall in the category of developing nations. The study, thus, intends to estimate the critical efficiency of said area. Along with it, the study aims to determine the elements of banana production in the context of Vietnam using a stochastic frontier approach and technical efficiency technique. The sample of the study is the province of Vietnam named Hung Yen, and it made sure to collect the data from 160 farmers in 2022. Results of the study reveal that the farmers' technical efficiency fluctuates between the range of 89.68- 97.81%. However, the average technical efficiency of banana farmers was reported to be 95.92%. From the result, it is gauged that factors such as potassium, manure, distance, capital, and training showed positive signs at a 0.01 significance level. Also, the education and area coefficient show a positive sign at a 0.05 significance level. Finally, distance and district variables, which were the dummy variable, show a negative sign at 0.01 and 0.05 significance levels, respectively.Hoang Van Hung (Hung Yen University of Technology and Education (UTEHY)), Nguyen Van Huong (Hung Yen University of Technology and Education (UTEHY)), Le Thi Thuong (Hung Yen University of Technology and Education (UTEHY)), Thai Thi Kim Oanh (Vinh University (VU)), Nguyen Van Chuong (University of Financial – Business Administration (UFBA)), Nguyen Cong Tiep (Viet Nam National University of Agriculture (VNUA)), Thai Van Ha (Ha Noi University of Business and Technology (HUBT)), Nguyen Thi Luong (Can Tho University (CTU))Includes bibliographical references

Rice farmers' perception and determinants of climate change adaptation measures: a case study in Vietnam

The study used Mann Kendall's and Sen's slope tests to elicit rice farmers' perceptions of climate change due to extreme weather occurrences and compared them to hydro-meteorological data. According to the findings, temperatures increased by 0.4 degrees during the last 35 years. While rainfall has increased, the pattern has been difficult to discern. The test results corroborated farmers' perceptions of increased heat spells, but rainfall frequency and intensity vary and are difficult to anticipate. Three adaptation strategies are frequently employed in the Nong Cong district: adjusting the seasonal calendar to alter transplanting and harvesting timing; increasing fertiliser and pesticide application; and changing variety to short-time kinds. Due to the interdependence of adaption techniques, the study used a multivariate probit model. The regression findings indicated that several relevant variables influence the decision to apply adaption methods. Numerous policy ideas for enhancing adaptation to climate change can be derived from the results of this study. District governments must improve their capacity to forecast weekly weather and train how to adapt production to climate change.Le Phuong Nam (Viet Nam National University of Agriculture (VNUA)), Nguyen Dang Que (National Academy of Public Administration (NAPA)), Nguyen Van Song (Viet Nam National University of Agriculture (VNUA)), Tran Thi Hoang Mai (Vinh University (VU)), Nguyen Thi Minh Phuong (Vinh University (VU)), Nguyen Thi Xuan Huong (Viet Nam National University of Forestry (VNUF)), Nguyen Cong Tiep (Viet Nam National University of Agriculture (VNUA)), Tran Ba Uan (Dien Bien Technical Economic College)Includes bibliographical references

Assessing the impact of EU policies on recycling supply chain: a system dynamics perspective on advancing packaging recycling capacity

Recycling stands as a crucial strategy in mitigating climate change and advancing towards carbon neutrality. Within the European Union (EU), the development of a resilient recycling supply chain is of paramount importance, particularly in response to global disruptions such as the widespread ban on solid waste imports by numerous countries like China, Malaysia, Thailand, and Vietnam. Such disruptions have exposed the vulnerabilities of EU member states, notably their overreliance on waste export and limited domestic recycling capacities. This study integrates primary data from diverse public sources into a system dynamics simulation model to assess the effectiveness of three policy types used to enhance EU domestic recycling capacities: Innovation-focused (IF), Subsidy-focused, (SF) and Market-based (MB) policies. Our findings show that IF policies exert the most considerable impact in the short term and continue to play a crucial role in the EU’s recycling capacity expansion over the medium and long term. Conversely, MB policies are identified as most effective for immediate capacity enhancement in response to abrupt disruptions. Finally, the result suggests the optimal policy mix where 84% government resources should be allocated to IF policies and 16% to MB policies to ensure the EU achieves the deliberate balance between short-term market stabilisation and long-term transformation of its domestic recycling capacity for economic, environmental, and social sustainability. This research represents a pioneering effort in examining the efficacy of a diverse array of policy types within an optimised mix, thereby encompassing a broader range of policy considerations

Unsupervised deep learning-based Reconfigurable Intelligent Surface aided broadcasting communications in Industrial IoTs

This paper presents a general system framework which lays the foundation for Reconfigurable Intelligent Surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) is blocked completely. In this context, an RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, the DNN technique relies on unsupervised deep learning. Our numerical results show that the both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using deep learning reduces the computational latency by more than 10 times with an almost identical total ergodic spectral efficiency achieved. These numerical results reveal that while using conventional RCG method may provide unsatisfactory latency, DNN technique shows much promise for enabling RIS in ultra reliable and low latency communications (URLLC) in the context of IIoTs

RIS-aided smart manufacturing : information transmission and machine health monitoring

This paper proposes a novel industrial Internet-of-Things framework to monitor the machine health conditions(MHCs) in a smart factory. The framework utilises reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy(secondary information) in terms of the RIS size (K) and the transmit power (P). We observe that the accuracy of detecting MHCs does not change significantly with K and P, implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. By contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: i) reasonably high data rate for primary transmission and ii) high accuracy for secondary MHC information

Study and simulation of the electric field-induced spin switching in PZT/NiFe/CoFe nanostructured composites

In this work, we have studied the electric field-induced spin switching in the PZT/NiFe/CoFe nanostructured composites by sputtering ferromagnetic layers on a horizontal polarized piezoelectric PZT substrate. The electric field-induced change in the magnetization orientation was investigated systematically using a vibrating sample magnetometer and analytical simulations. The results revealed that electric field applications could indirectly control the magnetic spin orientations. Moreover, the magnetization change depends not only on the electric field but also on the direction of the electric field applying against the magnetic field. The images of magnetic moment orientations under various electric field applications are modeled by the Monte Carlo and NMAG simulations. In particular, a critical electric field of Ecr ≈ 300 kV/cm, which makes a 90o spin switching, was determined. These results are proposed to offer an opportunity for random access memory applications