• Energy Research

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.

Environment and Natural Resources Journal, 16, 2, 45 to 57

Environmental and socio-economic evaluations that imply techniques for mitigating greenhouse gas (GHG) emissions from rice cultivation are a challenging and controversial issue. This study was designed to investigate the potential use of mitigation techniques for rice cultivation. Mid-season drainage (MD), using ammonium sulfate instead of urea (AS), and site-specific nutrient management (SSNM) were chosen as mitigation techniques. Data were collected using field surveys and structured questionnaires at the same 156 farms, covering four crop years. The GHG emissions were evaluated based on the concept of the life cycle assessment of the GHG emissions of products. The farmers’ assessments of mitigation techniques, with multiple criteria evaluation, were obtained by face-to-face interviews. Opinions on all mitigation techniques were requested two times covering four years with the same 156 farm owners. The multinomial logistic regression model was used to examine the factors influencing the farmers’ decisions. The results show that SSNM was evaluated as the highest abatement potential (363.52 kgCO2eq ha−1), the negative value of abatement cost (−2565 THB ha−1), and the negative value of the average abatement cost (−14 THB kgCO2eq−1). Among the different techniques, SSNM was perceived as the most suitable one, followed by MD and AS. Highly significant factors influencing decision making consisted of planted area, land size, farmer liability, farmer perception of yield, and GHG emissions. Subsidies or cost-sharing measures to convince farmers to adopt new techniques can enhance their practices, and more support for the development of water systems can increase their availability.

Organic rice farming is a sustainable rice cultivation system that eliminates chemical inputs and has the potential to reduce environmental impacts. This study aims to: 1) evaluate and compare the carbon footprint intensity and the value of carbon sequestration ecosystem services (VCSES) between organic rice farming (OF) and conventional rice farming (CF) and 2) estimate the impact of climate change on soil organic carbon (SOC), rice yield, and VCSES of two farming types in Phichit province, Thailand. The results showed that the carbon footprint intensity in OF and CF were significantly different with -0.13 and 0.82 kg CO2eq kg-1 rice yield, respectively. The differences in SOC stocks (ΔSOCS) were more significant in OF with the increase of 1107.6 kg C ha-1 year-1 (4061.2 kg CO2eq ha-1 year-1), while the ΔSOCS value in CF was 625 kg C ha-1 year-1 (2291.7 kg CO2eq ha-1 year-1). The VCSES in OF (541,196 US$ ha-1 year-1) was nearly two times higher than in CF (305,388 US$ ha-1 year-1). Under future climate change, rice yields of both farming types are expected to increase under Representative Concentration Pathway (RCP) 2.6, RCP4.5, and RCP6.0, and it will decline under RCP8.5. The SOC and VCSES values are predicted to increase, except under RCP8.5. The dramatic declines can be found from the near future (2020-2039) to the very far future (2080-2099) period. Our finding indicates that even though climate change will have negative effects on SOC and VCSES, the OF will have less impact compared with CF.

Coastal communities and small-scale fisheries are highly vulnerable to climate change. In this study, we aimed to examine fishers’ decisions to adapt to climate change and their expectations for their children to pursue the same profession. Data were obtained from fisher households covering 8 districts and 22 sub-districts in the coastal area of Chumphon Province, Thailand, using participatory observation, focus group discussion, and in-person field surveys. A binary logistic regression model was used to determine factors influencing the fishers’ decisions and their expectations for their children to inherit their occupation. Results showed that the fishers are aware of the increasing trends in air temperature, sea water temperature, inland precipitation, offshore precipitation, and storms. Increased fishing experience and fishing income increased the likelihood of the fishers applying adaptations to climate change. Looking to the future, fishers with high fishing incomes expect their children to pursue the occupation, whereas increased fishing experience, non-fishing incomes, and perceptions of storms likely discourage them from expecting their children to be fishers. Of the fishers interviewed, 58.06% decided to apply adaptations in response to climate change by incorporating climate-smart agriculture, particularly by cultivating rubber, oil palm, and orchards as a second income source. The adoption of climate-smart fisheries should be considered in relation to the body of local knowledge, as well as the needs and priorities of the fisher community. To cope with the impacts of current and future climate change on coastal communities, the national focal point of adaptation should be climate change, and related governmental agencies should pay more attention to these key factors for adaptation.

Identifying the optimal rice establishment option combined with specific fertilizer application can lower the global warming potential (GWP) and greenhouse gases intensity (GHGI) of rice production. In this study, methane (CH4) and nitric oxide (N2O) emissions and rice yields under different fertilizer application methods and two different planting methods, transplanted rice (TPR) and wet bed direct seeded rice (WDSR), was measured. Field experiments using a split plot design and closed chamber-GC method for gas flux measurements were conducted. CH4 and N2O emissions ranged from 1.83-4.68 mg/m2/h and 0.073-0.135 mg/m2/h, respectively. Minimum CH4 and N2O emissions were observed at 48-69 days after seedling (DAS) (tiller stage), while maximum emissions were generally found at 90 DAS or early primordial initiation (EPI) stage. It was found that TPR produced more CH4 and N2O than WDSR across fertilizers methods almost each growth stage throughout the growing period. Regarding GHGs emission factors, CH4 emissions were negatively correlated with soil pH (-0.35*, N=18). At higher soil pH, lower CH4 emissions were found in early growth stages. The N2O emissions did not correlate with soil pH (-0.04 ns, N=18). The highest average CH4 emission was reached in 90 days after seedling and EPI when the soil temperature was maximal at 34.8ºC. The correlation coefficient (r) between CH4 emission and soil temperature was 0.48*, N=18, indicating a positive correlation.

Understanding crop yield and water requirements in response to the future climate at the local scale is essential to develop more precise and appropriate adaptation strategies. From this perspective, repeated drought and flood events in the lower north of Thailand were investigated. The objectives of the study were to evaluate the impact of climate change on major crop yields and the water footprint (WF). Five global circulation model datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), known as Shared Socioeconomic Pathways (SSPs), were selected. Three future periods: near (2015-2039), mid (2040-2069), and far future (2070-2100) under SSP245 and SSP585 scenarios were used to predict the major crop yields and WF changes in the future. The precipitation and maximum and minimum temperatures were projected to increase in all periods under both scenarios. Rice yields in irrigated areas were predicted to rise gradually over the three projection periods under SSP245 but decline in mid and far-future periods under SSP585. There was a predicted reduction of first and second rice crop yields by -6.0% to -17.7% under SSP585. Fortunately, those rice yields were expected to increase in the near-future period under SSP245 by 3.0% to 4.3%. Growing maize, soybean, or mung bean instead of a second rice crop will have a less negative impact on future climate change. Changing from growing rice to be planting maize twice per year and growing cassava had increased favorability in rain-fed areas. The WF changes in the future were associated with future crop yield changes; therefore, the decrease in WFs was due to an increase in crop yield and vice-versa. The total WFs of maize, soybean, mung bean, and cassava production were roughly half that of rice production, indicating that these crops are suitable alternatives in the dry season.

Large-scale farming (participation in large-scale agricultural extension program) and individual farming (no participation) are two farming management practices of rice cultivation in Thailand, both of which cause significant water consumption and degradation and are vulnerable to climate change. However, given that climate change will influence both grain yield and water resource availability, it is not fully understood which type of farming management practice is more adaptive to climate change. This study aims to evaluate the adaptation capabilities of large-scale and individual farming by simulating rice yield changes under future climatic conditions and estimating the climate change impact on the water footprint (WF) of rice production. Rice management practices were obtained from large-scale and individual farming. Five General Circulation Models of RCP4.5 and RCP8.5 scenarios under four future time periods were used as future climate projections. Simulation results show a remarkable increase in rice yield of individual and large-scale farming under RCP4.5, ranging from 1.3 to 29.8% and 2.0 to 30.8%, respectively, whereas it fluctuates from 11.7 to -29.0% and 8.3 to -20.8% under RCP8.5 for individual and large-scale farming, respectively. The projected total WF of rice production under RCP4.5 will decline, ranging from -10.0 to -43.0% and -0.5 to -67.0% for individual and large-scale farming, respectively. Conversely, the RCP8.5 shows a fluctuation in projected total WF of -26.5 to 63.3% and -51.1 to 60.0% for individual and large-scale farming, respectively. The total WF, mainly grey WF, in large-scale farming is lower than in individual farming. The increase of rice yield under RCP4.5 is due to an increment of temperature and precipitation, resulting in a decrease of the total WF and vice versa for RCP8.5. The large-scale farms are highlighted as adopting appropriate management practices for rice production in which they can maintain rice yield and reduce grey WF.