The effect of biofertilizer in enhancing nutrient quality and antioxidant property of rice grain was investigated. The experiment was carried out in a randomized complete block design with 3 replications and 7 treatments namely : RF = N-P2O5-K2O(11-5.5-4.8kg~;10a-1); half of the recommended fertilizer rate, HRF=N-P2O5-K2O(5.5-2.75-2.4kg~;10a-1): HRF+Bio 250=HRF combined with 250 kg Biofertilizer 10 a-1 ; HRF+Bio 500=HRF combined with 500 kg Biofertilizer 10 a-1; Bio 250=250 kg Biofertilizer 10 a-1; Bio 500=500 kg Biofertilizer 10 a-1; and NF=No Fertilizer. Results showed that HRF+Bio 500 obtained a significantly higher protein content but a significantly lower amylose content compared with RF and NF treatments. Highest phytic acid content was recorded in NF treatment while the lowest was observed in HRF+500 treatment. The highest values in both electron donating ability and reducing power were obtained in HRF+Bio 500 treatment. All treatments obtained higher reducing power than that of the RF treatment and that NF treatment showed comparable values in both electron donating ability and reducing power with those of the treated plots. Highest antimutagenicity property was also observed in HRF+Bio 500 treatment followed by Bio 500 treatment. This study showed the possibility of using biofertilizer to enhance nutritional quality and antioxidant property of rice.

The effect of biofertilizer (compound of microbial inoculants or groups of micro-organisms) on growth and yield of rice was investigated. The experiment was carried out in a randomized complete block design with 3 replications and 7 treatments namely: RF=N-P2O5-K2O (11-5.5-4.8 kg 10a-1 ); half of the recommended fertilizer rate, HRF=N-P2O5-K2O (5.5-2.75-2.4 kg 10a-1 ); HRF+Bio 250=HRF combined with 250 kg biofertilizer 10a-1 ; HRF+Bio 500=HRF combined with 500 kg biofertilizer 10a-1 ; Bio 250=250 kg biofertilizer 10a-1 ; Bio 500=500 kg biofertilizer 10a-1 ; and NF = No Fertilizer. Results showed that the recorded values of plant height, tiller number and chlorophyll content at 40 to 60 days after transplanting (DAT) in HRF+Bio 500 were significantly higher than those recorded in the RF treatment. Similar observations between these two treatments were only recorded from 60 DAT onwards. Yield components were also superior in HRF+Bio 500 treatment and comparable to that of RF. The highest grain yield obtained in HRF+Bio 500 treatment (785.8 kg 10a-1 ) was statistically similar to that of RF (739.8 kg 10a-1 ) but significantly higher than that of NF (506.7 kg 10a-1 ). Finally, head grain recovery (90.9) was low while chalkiness (0.03) was high at HRF+Bio 500 treatment as compared with RF, which were (96.1) and (0.3), respectively. Results showed that combined treatment of HRF and 500 kg biofertilizer 10a-1 has similar effects on the growth and yield of rice with that of RF.

ice yield and plant growth response to nitrogen (N) fertilizer may vary within a field, probably due to spatially variable soil conditions. An experiment designed for studying the response of rice yield to different rates of N in combination with variable soil conditions was carried out at a field where spatial variation in soil properties, plant growth, and yield across the field was documented from our previous studies for two years. The field with area of 6,600 m2 was divided into six strips running east-west so that variable soil conditions could be included in each strip. Each strip was subjected to different N application level (six levels from 0 to 165kg/ha), and schematically divided into 12 grids (10m ~times10m~;for~;each~;grid) for sampling and measurement of plant growth and rice grain yield. Most of plant growth parameters and rice yield showed high variations even at the same N fertilizer level due to the spatially variable soil condition. However, the maximum plant growth and yield response to N fertilizer rate that was analyzed using boundary line analysis followed the Mitcherlich equation (negative exponential function), approaching a maximum value with increasing N fertilizer rate. Assuming the obtainable maximum rice yield is constrained by a limiting soil property, the following model to predict rice grain yield was obtained: Y=107651-0.4704*EXP(-0.0117*FN)*MIN(I-clay,~;Iom,~;Icec,~;ITN,~; ISi) where FN is N fertilizer rate (kg/ha), I is index for subscripted soil properties, and MIN is an operator for selecting the minimum value. The observed and predicted yield was well fitted to 1:1 line (Y=X) with determination coefficient of 0.564. As this result was obtained in a very limited condition and did not explain the yield variability so high, this result may not be applied to practical N management. However, this approach has potential for quantifying the grain yield response to N fertilizer rate under variable soil conditions and formulating the site-specific N prescription for the management of spatial yield variability in a field if sufficient data set is acquired for boundary line analysis.

Rice yield and protein content have been shown to be highly variable across paddy fields. In order to characterize this spatial variability of rice within a field, two-year experiments were conducted in 2002 and 2003 in a large-scale rice field of 6,600m2 In year 2004, an experiment was conducted to know if variable rate treatment (VRT) of N fertilizer, that was prescribed for site-specific management at panicle initiation stage, could reduce spatial variation in yield and protein content of rice while increasing yield compared to conventional uniform N topdressing (UN, 33kg N/ha at PIS) method. VRT nitrogen prescription for each grid was calculated based on the nitrogen (N) uptake (from panicle initiation to harvest) required for target rice protein content of 6.8~% , natural soil N supply, and recovery of top-dressed N fertilizer. The required N uptake for target rice protein content was calculated from the equations to predict rice yield and protein content from plant growth parameters at panicle initiation stage (PIS) and N uptake from PIS to harvest. This model· equations were developed from the data obtained from the previous two-year experiments. The plant growth parameters for the calculation of the required N were predicted non-destructively by canopy reflectance measurement. Soil N supply for each grid was obtained from the experiment of year 2003, and N recovery was assumed to be 60~% according to the previous reports. The prescribed VRT N ranged from 0 to 110kg N/ha with an average of 57kg/ha that was higher than 33 kg/ha of UN. The results showed that VRT application successfully worked not only to reduce spatial variability of rice yield and protein content but also to increase rough rice yield by 960kg/ha. The coefficient of variation (CV) for rice yield and protein content was reduced significantly to 8.1~% and 7.1~% in VRT from 14.6~% and 13.0~% in UN, respectively. And also the average protein content of milled rice in VRT showed very similar value of target protein content of 6.8~% . In conclusion the procedure used in this paper was believed to be reliable and promising method for reducing within-field spatial variability of rice yield and protein content. However, inexpensive, reliable, and fast estimation methods of natural N supply and plant growth and nutrition status should be prepared before this method could be practically used for site-specific crop management in large-scale rice field.

For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. 6,600m2 in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of 10~times10m . The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay (7.6~%) and the largest in Si (21.4~%) . The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to 38~% , and that of rice yield ranged from 11 to 21~% . CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as 76~% of yield variability, of which 21.6~% is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about 50~% of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.