Soil water retention curves (SWRCs) are crucial for characterizing soil moisture dynamics, and are particularly relevant in the context of irrigation management. Inverse modelling is one of the methods used to parameterize models representing these curves, which are closest to the field reality. The objective of this study is to estimate the soil hydraulic properties through inverse modelling using the HYDRUS-1D code based on soil moisture and potential data acquired in the field. The in situ SWRCs acquired every 30 min are based on simultaneous soil water content and soil water potential measurements with 10HS and MPS-2 sensors, respectively, in five experimental fields. The fields were planted with drip-irrigated lettuces from February to March 2016 in the Chrey Bak catchment located in the Tonlé Sap Lake region, Cambodia. After calibration of the van Genuchten soil water retention model parameters, we used them to evaluate the performance of HYDRUS-1D to predict soil moisture dynamics in the studied fields. Water flow was reasonably well reproduced in all sites covering a range of soil types (loamy sand and loamy soil) with root mean square errors ranging from 0.02 to 0.03 cm³ cm⁻³.

Setting up water-saving irrigation strategies is a major challenge farmers face, in order to adapt to climate change and to improve water-use efficiency in crop productions. Currently, the production of vegetables, such as lettuce, poses a greater challenge in managing effective water irrigation, due to their sensitivity to water shortage. Crop growth models, such as AquaCrop, play an important role in exploring and providing effective irrigation strategies under various environmental conditions. The objectives of this study were (i) to parameterise the AquaCrop model for lettuce (Lactuca sativa var. crispa L.) using data from farmers’ fields in Cambodia, and (ii) to assess the impact of two distinct full and deficit irrigation scenarios in silico, using AquaCrop, under two contrasting soil types in the Cambodian climate. Field observations of biomass and canopy cover during the growing season of 2017 were used to adjust the crop growth parameters of the model. The results confirmed the ability of AquaCrop to correctly simulate lettuce growth. The irrigation scenario analysis suggested that deficit irrigation is a “silver bullet” water saving strategy that can save 20–60% of water compared to full irrigation scenarios in the conditions of this study.

This research executes a development of prototype of smart irrigation system using IoT technology aiming to assist agriculture individual on reducing labor force and time to increase yield production as well as modernize traditional agriculture method. It is consisted of one controller and two sensor nodes communicate using star topology of Zigbee communication protocol. The system uses Xbee s2c module for data transferring from each sensor node to the controller. Each sensor node equips with sensors to collect significant parameters such as humidity, ambient temperature, soil moisture and solar radiation which influence crop’s production. All of the harvested data are presented on LCD display at the controller node for real-time monitoring and irrigation task based on soil moisture level. It also uploads data to cloud platform service (ThingSpeak) via internet enabled by Wi-Fi module (Node MCU) for further data analysis. The system has already been implemented in two greenhouses at Institute of Technology of Cambodia (ITC) and Royal University of Agriculture (RUA). As a consequence, the crops have been grown healthy with less effort and time requirement, thanks to the automatic irrigation system. In the future, data collection and control module will be improved using industrial-grade microcontroller on a solid surface mounted PCB module with Human Machine Interface (HMI) for the display to enhance user experience.

Rice is a staple crop best known for its crucial role in feeding humans. In Cambodia, the human population depends on rice as its staple food. Increased rice production results in an increase in straw residue. The extensive residue caused by straw burning significantly contributes to high concentrations of air pollution, which are associated with critical health issues. As such, our objective in this study was to assess the human health impact of rice straw production in Cambodia. In this assessment, we focused on primary and secondary fine particulate matter formation (PM_2.5, NH₃, NO_x, and SO₂) from fertilizer, combustion of fuel in rice production processing, and burning of straw. We assessed endpoint impact by the following methods: (1) We estimated the proportion of rice straw from the annual rice production of the Ministry of Agriculture, Forestry, and Fisheries (MAFF) of Cambodia subjected to open burning in all 25 Cambodian provinces. (2) We calculated air pollutants emissions in terms of kilograms of pollutants per ton of rice produced by following the air pollutant emission inventory (EI) in the EMEP/EEA Guidebook for 2019, using data for fertilizer and fuel combustion that was applied in the field, based on a face-to-face survey of farmers. (3) We analyzed health impact in terms of DALYs using the characterization factor (CF) adapted from the ReCiPe 2016 v1.1 method. The results showed that the total health impact of rice straw production was 13,093.50 DALYs. Our analysis showed that open burning alone contributed 98.55% of the total health impact considered in this study, while fertilizers and combustion contributed only 0.12% and 1.33%, respectively. As the major of the total health impact arose from open burning, a policy of zero open burnings should be an effective way to reduce health impacts. These findings provide information for policymakers on how to alleviate air quality issues caused by the practice of open straw burning by adopting alternative techniques of rice straw management.

The Tonle Sap Lake (TSL) Basins of the Lower Mekong are one of the world’s most productive ecosystems and have recently been disturbed by climate change. The SWAT (Soil & Water Assessment Tool) hydrological model is utilized to investigate the effect of future climate scenarios. This study focused on two climate scenarios (RCP2.6 and RCP8.5) with three GCMs (GFDL-CM3, GISS-E2-R-CC, and IPSL-CM5A-MR) and their impact on the hydrological process and extremes in the Sen River Basin, the largest tributary of the TSL basin. The annual precipitation, surface runoff, lateral flow, groundwater flow, and total water yield are projected to decrease in both the near-future (2020–2040) and mid-future period (2050–2070), while actual evapotranspiration is projected to increase by 3.3% and 5.3%. Monthly precipitation is projected to increase by 11.2% during the rainy season and decrease by 7.5% during the dry season. Two climate models (GISS and IPSL model) lead to decreases in 1-day, 3-day, 7-day, 30-day, and 90-day maximum flows and minimum flows flow. Thus, the prediction results depend on the climate model used.