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• Prediction ofWater Requirementsfor Land Use Types in Srepok River Basinin the Context of Climate Change

  Nguyen Thi Ngoc Quyen

  Date Received: 30-11-2018 / Date Accepted: 11-04-2019

   Abstract PDF (VIETNAMESE)

  In agricultural production, irrigation water is at the forefront, especially in the context of climate change. The objective of this study wasto predictthe irrigation waterrequirementsfor the major land use types in the Srepok basin and to propose agricultural land-use planning to support decision makers in integrated river basin management towards sustainable development. The modeling approach under thesupport ofCROPWAT 8.0and assessment of natural land adaptation process were applied in the study. Irrigation demand for coffee, pepper, cashew, rubber, tea, rice, maize and cassava were calculated in 2015with highest value of 7.746 m3.ha-1.crop-1forwinter-spring rice. Theirrigation water wasforecast for the future (2045) with decreasing trend in three scenarios (11.7% for low scenario, 18.59% for average scenario and 4.25% in the high scenario) compared to the presentperiod. Furthermore, three maps for agricultural land-use thatdefine suitable areasand spatial allocation of land use plans, were proposed with climatechange scenarios as a response solution.

• SOIL ORGANIC MATTER DETERMINATION USING WIRELESS SENSOR NETWORKS

  Nguyen Van Linh

  Date Received: 10-11-2015 / Date Accepted: 08-03-2016 / Date Published: 12-06-2025

   Abstract PDF (VIETNAMESE)

  The paper addresses the problem of predicting soil organic matter content in an agricultural field using information collected by a low-cost network of mobile, wireless and noisy sensors that can take discrete measurements in the environment. In this context, it is proposed that the spatial phenomenon of organic matter in soil to be monitored is modeled using Gaussian processes. The proposed model then enables the wireless sensor network to estimate the soil organic matter at all unobserved locations of interest. The estimated values at predicted locations are highly comparable to those at corresponding points on a realistic image that is aerially taken by a very expensive and complex remote sensing system.

  DOI: https://doi.org/10.31817/tckhnnvn.2016.14.3.