Microbial diversity as a biological indicator of soil quality for sustainable organic rice farming | |
| Author | Chunchara Thuithaisong |
| Call Number | AIT Diss. no.EV-11-03 |
| Subject(s) | Organic farming Rice--Soils |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Environmental Engineering and Management, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Abstract | Rice production is commonly associated with conventional farming (CF) practices that can have negative effects on soil quality. Alternative farming systems such as organic farming (OF) is the one of most impo1tant steps for sustainable agriculture, which is the maintenance of viable and diverse microbial communities in the soil. Therefore, the analyses of soil prope1ties and microbial diversity appear to be essential while monitoring environmental influences on soil quality. The research work is aimed at making use of some soil changes as a diagnostic warning indicator for sustainable rice production. The main objectives addressed are: (1) to select and evaluate suitable soil quality indicator from the soil prope1ties in organic rice field, (2) to compare soil quality including physical, chemical and biological soil properties among different agricultural practice in rice field. (3) to develop a predictive mathematical equation for soil quality using soil biological indicators in the monitoring program for rice field and validate the predictive mathematical equation under existing rice field, and (4) to investigate microbial diversity for serving as biological indicator. Fields following by laboratory experiment were carried out using a long-term existing rice field (Kao Dok Mali 105 variety) in Surin Rice Research Center, Thailand. The disse1tation began with soil characterization (physical, chemical and biological prope1ties) of four types of experimental rice farming plots (Control plot: CT, Rice straw: RS, Green manure: GM and Conventional farming: CF) in three different times (May, October and December) of a year. Key findings, soil physical properties, such as soil texture was identified as loam and sandy loam. Bulk densities seemed to have no effect on rice growth (1.50-1.58 g cm·3). Soil chemical and biological properties were found very low in all plots. Principal component analysis (PCA) was canies out with the different sets of soil characteristics/parameters, and important/significant parameters were selected. Pearson correlation analysis was carried out between rice yield and the selected parameters to evaluate significance of the parameters. Soil biological properties (MBC, MBN, and BR) showed the highest correlation while soil chemical characteristics (TOP, TK, TN, SOC and SOM) had the second highest correlation in terms of rice yield. Then, stepwise multiple linear regression analysis was carried out using rice yield as dependent variable and other selected soil parameters as independent parameters. A suitable linear mathematical model (Y=-1.685+0.333(MBN)+0.640(TK)- 0.282(SOC)) was selected based on accuracy in rice yield prediction. Microbial biomass nitrogen (MBN) was the most influencing parameter among the selected ones. Then it focused on various microbial diversity tests (e.g. functional, genetic diversities) in the selected four types of experimental paddy soils. Statistical analyses (Principal component analysis, Pearson correlation analysis) were carried out to evaluate microbial diversities and activities in the four types of experimental paddy soils at three different times of a year. To investigate microbial, molecular method was used through the analysis of 16S rDNA by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) approaches. In addition, quantification of total bacterial groups was determined by total plate count technique. An assessment of microbial functional diversity was conducted on conununity level physiological profiles (CLPP) by BIOLOG EcoPlate™ method. Two-way ANOV A of the results revealed that total plate counts were significantly (P<0.05) affected by the four different management practices. GM plot showed the highest microbial diversity and activities followed by RS plot. CF plot was close to RS plot in terms of microbial activities. The microbial diversity test results strengthened the importance of MBN in the rice yield prediction model. |
| Year | 2011 |
| Type | Dissertation |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Environmental Engineering (EV) |
| Chairperson(s) | Preeda Parkkpian ; Shipin, Oleg V. |
| Examination Committee(s) | Shrestha, Rajendra Prasad ;Kunnika Naklang ;Ozaki, Hiroaki |
| Scholarship Donor(s) | Rajamangala University of Technology Isan, Surin Campus, Thailand |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2010 |