EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 523 Economic Threshold Density of Multi Species Weed for Direct Seeded Rice Md. Abdullah Al Mamun Senior Scientific Officer; Agronomy Division, Bangladesh Rice Research Institute, Bangladesh Rakiba Shultana, Md. Masud Rana, Abdul Jalil Mridha Bangladesh Rice Research Institute, Bangladesh Abstract We conducted two experiments in 2009 and 2010 at central part of Bangladesh to examine the effects of multispecies weeds on grain yield and to determine the economic threshold (ET) of weeds in direct seeded rice (DSR). The treatments consisted of 0, 5, 10, 20, 40, 80, 160 weeds m -2 and control (unweeded). Scirpus maritimus L. and Cyperus difformis L. were the most dominant weed species in year 1 and 2, respectively. Grain yield losses due to weed interference increased with weed population density increase. Panicle m -2 , grains panicle -1 , 1000-garin weight and grain yield varied significantly due to different weed density in both years of the study. Estimated ET were 5 and 7 weeds m -2 in year 1 and 2, respectively, assuming a weed free rice grain yield 5 ton ha -1 , a crop price $ 210 ton -1 , and weed control cost $ 30 ha -1 . Keywords: Economic thresholds, Direct seeded rice, Yield loss, Weeds Introduction 1 Irrigated rice cultivation depends on water supply but scarcity of water threatens the sustainability of the irrigated rice production system and food security (Turmuktini et al., 2012). Direct seeded rice (DSR) is water and labor saving technique of cultivation (Mahajan et al., 2006). It eliminates the need of seedling rising, maintaining and subsequent transplant- ing. Weeds are considered as a serious problem in DSR (Johnson et al., 1998) because they emerge before or at the same time as the rice (Oerke et al., 1994; Johnson et al., 1998; Mallik, 2001). Due to presence of weeds, yield losses occur in lowlands situation whatever it is rainfed or irrigated systems. This situation is more severe in direct seeded systems where weed management is hampered by labour shortages, 1 Corresponding author’s details: Name: Md. Abdullah Al Mamun Email address: aamamunbrri@yahoo.com limited water control, scarce resources and inadequate management practices. Rice yields were reduced by 63% in a transplanted system and 70-76% in DSR (Singh et al. 2005). Weeds cause rice yield losses by 30-40% in Bangladesh (BRRI, 2006; Mamun, 1990), 36 to 56% in the Philippines (Rao and Moody, 1994) and 40 to 100% in South Korea (Kim and Ha, 2005). Thus weed must be controlled in economic means. In recent time, use of herbicides is one of the most promising and cost effective farmer-initiated weed control method in Bangladesh. Mazarura (2013) reported that herbicides controlled weeds well after transplanting and more so with delay in the application. The economics of herbicide use must be determined by a combination of yield loss, crop price, crop yield productivity, and herbicide and application costs. These factors must be considered in order to apply weed yield loss models in Bangladeshi agriculture. Weeds compete for water, nutrients and light and cause the reduction in both Asian Journal of Agriculture and Rural Development journal homepage: http://aessweb.com/journal-detail.php?id=5005 mailto:aamamunbrri@yahoo.com Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 524 quantity and quality of rice yield. Effective weed control is therefore important especially during the critical weed free period, which is the first 4- 6 weeks of the crop’s production cycle (Bertha et al., 2013). Concentrated efforts must be made to achieve food security by increasing food grain production (Mahendran, 2013). Again, weed density is one of the most important factors in competition with the crop. Determination of weed economic damage threshold is an important component for dynamic decision of integrated weed management. The weed economic threshold is the weed population at which the cost of control is equal to the crop value increase of the present weeds control (Weaver and Ivany, 1998; Pester, 2000). In Germany, weed management is done successfully through threshold approach (Gerowitt and Heitefuss, 1990). The threshold values in cereal crops were 20-30 plants m -2 for grass weeds and 40-50 plants m -2 for broadleaved weeds [excluding Galium aparine L. and Fallopia convolvulus (L.) A. Love]. The economic threshold of wild oat (Avena fatua L.) in wheat was estimated between 3 to 30 and 8 to 12 plants m -2 in USA and UK, respectively (Cousens, 1987). We have hypothesized that a complementary economic simulation including the crop grain prices and cost of weed control. Furthermore, most of the studies evaluate the interference between a single weed species and the crop. But, there are several weed species that could reduce crop yield in the field situation. Therefore, most of these studies have not had an important role in control decision-making (Swinton et al., 1994). Therefore, the objectives of this study were: i) to evaluate the effect of multispecies weeds on rice yield and its components, and ii) to determine the economic threshold of weeds in DSR using regression equations. Materials and Methods Experimental site We executed two field experiments at Agronomy field of Bangladesh Rice Research Institute fields in the Gazipur district of Bangladesh during aman season (June to November) in 2009 and 2010. Same field was used in both years (90 0 33´ E longitude and 23 0 77´ N latitude). The field was infested by natural weed species. Soil of the experimental field belongs to the Shallow Red Brown Terrace Soils. The experiential soil characters in the two seasons are shown in Table 1. The region belongs to sub-tropical humid climate. Weather conditions during experimental periods are presented in Fig. 1. Table 1: Soil description of experimental site in year 1 and 2 Year Soil texture Organic matter Sand (%) Silt (%) Clay (%) pH Y-1 Loamy 1.4 47 35 18 6.2 Y-2 Loamy 1.5 45 38 17 6.3 Figure 1: Weekly rainfall distribution and temperature during growing seasons 0 25 50 75 100 125 150 175 200 225 250 275 7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119 126 133 140 147 154 161 168 Days after seeding (DAS) R ai n fa ll (m m ) 0 5 10 15 20 25 30 35 A ir te m pe ra tu re ( 0C ) Rainfall ( year 1) Rainfall (year 2) Temperature ( year 1) Temperature (year 2) Asian Journal of Agriculture and Rural Development, 3(5) 2013: 234-248 514 Experimental setup Rice (cv. RRRI dhan49, popular variety of Bangladesh, was sown on 05 and 07 July in year 1(2009), year 2 (2010), respectively. Unit plot size was 1m × 1m and total twenty four plots were used for the experiment in both years. Before sowing, the plot was kept weed-free by hand weeding. Basal application of fertilizer at a rate of 90 kg N ha -1 as urea, phosphorus of 20 kg P ha -1 as triple superphosphate and 35 kg K ha -1 as muriate of potash were broadcast uniformly and incorporated into the soil of all plots. Except urea all other fertilizers were applied before rice sowing and urea were top dressed in three installments at 15, 30 and 45 days after sowing (DAS). Pesticides and herbicides were not applied during crop growth. A randomized complete block design with three replicates was used. Rice seeds at a rate of 40 kg ha -1 were sown by hand broad-casting. Treatments consisted of 0, 5, 10, 20, 40, 80, 160 weeds m -2 and control (unweeded). Weeds of different species were maintained from 10 to 50 DAS at 10 days interval. Newly emerged weeds were uprooted ton maintain desired weed number m -2 . Biological measurement Weed height and rice plant height were taken at 10 days interval from 30 to 60 (DAS). Then relative weed height (RWH) was calculated. Weed sample were collected at 60 DAS. Weeds samples were classified by species, counted, dried and the dry weight of each species was recorded. Absolute number and dry weight of each species were used for computing the community characters. The contribution of an individual weed species to the weed community was determined by its two-factor summed dominance ratio (SDR) (Janiya and Moody, 1989). This was calculated using relative weed density (RD) and relative dry weight (RDW), as follows: RD (%) = 010 community in the species weedall ofdensity Total community in the species weedindividual ofDensity  ….........…… [1] RDW (%) = 010 species weeddriedoven all of Dry weight species weeddriedoven given a of Dry weight  ………….............. [2] SDR = 2 RD RDW …………………………..…….............. [3] RWH = heightPlant height Weed ………………………….............................. [4] Estimation of yield loss The percentage of yield loss (YL) of each infested plot was calculated by following equation: YL (%) = 010 Ywf Y-Ywf  ……......… [5] Where, Ywf is the grain yield in weed-free plots and Y is the grain yield from each infested plot. The relationship between yield loss and weed density for each year was described using the rectangular hyperbolic model proposed by Cousens (1985). The model is as follows: Y = Ywf [ id/a)(1 100 id 1   ] ......…… [6] Where, Y is the predicted crop yield, Ywf in the estimated weed-free crop yield, i is the initial slop, d is the weed density (no. m -2 ) and a is the asymptote. Estimation of economic threshold weeds density As the response of crop yield to weed density was nonlinear, single-season threshold was calculated by extending Eq. [6] as described by O'Donovan (1991) as E = [ H)/CP}-s{(CPs)-(r H)/CP-(CP-1  ] ...….......… [7] Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 526 Where, E is the economic threshold weed density (weeds m -2 ), C is the expected weed-free crop yield (ton ha -1 ), P is the crop market price ($ ton -1 ), H is the cost of the herbicide and its application ($ ton -1 ), and r is i/100 and s is i/a. Statistical analysis The data on yield and yield parameters were recorded. Grain yield was adjusted at 14% moisture content. Constant straw weight was recorded after repeated sun drying. Collected data were analyzed for analysis of variance following MSTAT-C and means were compared by DMRT (Gomez and Gomez, 1984). Results Vegetative growth of rice and weed Relative weed height (RWH) at 40, 50 and 60 DAS significantly affected by different weed densities in year 1 but not in year 2 (Table 2). During both years, RWH more or less increased with the increases of weed densities and advances of crop growing period (from 30 to 60 DAS). Relative weed height was increased by 19 and 25 % at 30 DAS, 18 and 42 % at 40 DAS, 23 and 11% 50 DAS and 16 and 6 % at 60 DAS in control over 5 weeds m -2 in year 1 and 2, respectively. Except 30 DAS, comparatively higher RWH was recorded in year 1 than that of year 2 with the advance of growing period. Numerically higher RWH was recorded from control plot whereas lower from 10 and 20 weeds m -2 in year 1 and 5, 20, 40 weed m -2 in year 2 at 30 DAS. Significantly maximum RWH was obtained from control plot which was identical with that of 80 weeds m -2 at 40 DAS in year 1. Relative weed height obtained from 5, 20, 40 and 160 weeds m -2 was statistically similar whereas minimum from 10 weeds m -2 in year 1 at 40 DAS. In year 2, higher RWH was found in control plot whereas lower in 5 weeds m -2 plot at 40 DAS. At 50 DAS, the highest RWH was recorded from control plots which was statistically similar with that of 20, 40, 80 and 160 weeds m -2 whereas lowest from 5 weeds m -2 that was statistically similar with that of 10 weeds m -2 in year 1. In year 2, higher RWH was found in control plot whereas lower in 10 weeds m -2 plot at 50 DAS. At 60 DAS, the highest RWH was found in 80 weeds m -2 plots which was statistically similar with that of 10, 20, 40, 160 weeds m -2 and control plot whereas lowest from 5 weeds m -2 in year 1. In year 2, numerically higher RWH was found in control plot whereas lower in 5 weeds m -2 plot at 60 DAS. Table 2: Relative weed height at 30, 40, 50 and 60 days after sowing (DAS) in year 1 and 2 Weeds (no.m -2 ) Relative weed height 30 DAS 40 DAS 50 DAS 60 DAS Year-1 Year-2 Year-1 Year-2 Year-1 Year-2 Year-1 Year-2 5 0.62 0.38 0.71ab 0.76 0.75b 1.12 0.95b 1.21 10 0.60 0.39 0.69b 0.90 0.78b 1.10 1.02ab 1.25 20 0.60 0.38 0.78ab 0.85 0.83ab 1.12 1.09ab 1.27 40 0.64 0.38 0.77ab 0.90 0.85ab 1.18 1.06ab 1.27 80 0.70 0.40 0.87a 0.97 0.84ab 1.22 1.12a 1.24 160 0.67 0.49 0.79ab 1.10 0.86ab 1.25 1.04ab 1.27 Control 0.74 0.51 0.87a 1.08 0.92a 1.24 1.10ab 1.29 Values of a column followed by same letter are statistically similar at 5% probability Weed vegetation Twelve weed species inhabited control plot of direct seeded rice field. The infesting weed species were belonging to 6 families and 11 genera. This weed flora was ecologically categorized into 4 broadleaf species, 4 sedges and 4 grasses. The number of weed species was higher in year 2 (10 species) than in year 1 (9 species) (Table 3). The weed density and biomass was higher in year 1 than that of year 2. The sedges were Scirpus maritimus L., Cyperus diffornis L., Fimbristylis miliacea L. and Cyperus iria L. Echinochloa crus-galli L., Leersia hexandra Sw., Cynodon dactylon L. Pers. and Leptochloa chinensis L. Ness. were grass and Monochoria vaginalis Burm. f., Marsilea minuta L. Ludwigia octovalvis Jacq. and Sphenoclea zeylanica Gaertn were broad Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 527 leaf weed. The monocot weed species were Scirpus maritimus L., Cyperus difformis L., Echinochloa crus-galli L., Monochoria vaginalis Burm. f., Fimbristylis miliacea L., Leersia hexandra Sw., Cynodon dactylon L. Pers., Marsilea minuta L., Cyperus iria L. and Leptochloa chinensis L. Ness. whereas dicot were Ludwigia octovalvis Jacq. Raven. and Sphenoclea zeylanica Gaertn. Table 3: Weed density, dry weight and population composition in the weedy treatment Weeds (no.m -2 ) Biomass (g m -2 ) Species Family Category Relative density (%) Relative biomass (%) Year 1 220 161.79 Scirpus maritimus L. Cyperaceae Sedge 44.40 45.01 Cyperus diffornis L. Cyperaceae Sedge 40.31 13.09 Echinochloa crus-galli L. Poaceae Grass 10.15 39.66 Monochoria vaginalis Burm. f. Pontederiaceae Broad leaf 2.42 0.97 Ludwigia octovalvis Jacq. Onagraceae Broad leaf 0.60 0.46 Fimbristylis miliacea L. Cyperaceae Sedge 0.76 0.20 Sphenoclea zeylanica Gaertn. Sphenocleaceae Broad leaf 0.76 0.17 Leersia hexandra Sw. Poaceae Grass 0.30 0.30 Cynodon dactylon L. Poaceae Grass 0.30 0.14 Year 2 180 57.74 Cyperus diffornis L. Cyperaceae Sedge 63.15 50.55 Echinochloa crus-galli L. Poaceae Grass 15.19 20.83 Scirpus maritimus L. Cyperaceae Sedge 13.89 21.22 Monochoria vaginalis Burm.f. Pontederiaceae Broad leaf 4.26 3.15 Marsilea minuta L. Marsiliaceae Broadleaf 1.11 1.12 Ludwigia octovalvis Jacq. Onagraceae Broad leaf 0.92 1.28 Sphenoclea zeylanica Gaertn. Sphenocleaceae Broad leaf 0.74 0.73 Cynodon dactylon L. Poaceae Grass 0.37 0.79 Cyperus iria L. Cyperaceae Sedge 0.19 0.28 Leptochloa chinensis L. Poaceae Grass 0.18 0.05 Weed ranking In year 2, number of weed species was higher (10 species), in comparison with year 1 (9 species) (Figs. 1 and 2). Sedges were dominant weeds (0.22– 45 % SDR in year 1 and 0.25-57 % SDR in year 2) followed by grasses (0.3 – 25 % SDR in year 1 and 0.2-18% SDR in year 2) and broadleaf weeds (0.5 – 2 % SDR in year 1 and 0.75-4 % SDR in year 2). Three most dominant weeds in year 1 were Scirpus maritimus L., Cyperus difformis L., and Echinochloa crus-galli L. Dominant was Scirpus maritimus L. in year 1. In year 2, Cyperus diffornis L., Echinochloa crus-galli L. and Scirpus maritimus L. were the most dominant weeds. Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 529 Figure 2: Weed ranking in weedy plot (year 1) Figure 3: Weed ranking in weedy plot (year 2) SM= Scirpus maritimus L., CDI= Cyperus difformis L., EC= Echinochloa crus-galli L., MV= Monochoria vaginalis Burm. f., LO= Ludwigia octovalvis Jacq., FM= Fimbristylis miliacea L., SZ= Sphenoclea zeylanica Gaertn., LH= Leersia hexandra Sw., MM= Marsilea minuta L., CDA= Cynodon dactylon L. CI= Cyperus iria L. and LC= Leptochloa chinensis L. Yield components and grain production Number of panicles m -2 gradually decreased with the weed density in both seasons. The lowest number of panicles m -2 was recorded in the control plots for both years (121 in year 1 and 123 in year 2). The numbers of grains panicle -1 were significantly affected by the weed density in both years (Table 4). Significantly highest number of grains panicle -1 was recorded from weed free plots in both years (106 in year 1 and 114 in year 2). The number of grains panicle -1 gradually decreased with the weed density in both seasons. The lowest number of grains panicle -1 was recorded from control for both years. Weed density exerted significant influence on 1000-grain weight in both years (Table 4). The highest 1000-grain weight was recorded from weed free, while the lowest 1000- grain weight was recorded from control in both years. Similar 1000-grain weight was obtained from 5, 10, 20, 40, 80 and 160 weed m -2 in both years. The rice grain yield was significantly affected by the weed number m -2 in both years (Table 4). Rice grain yield decreased with the weed number m -2 in both years. Significantly the highest rice grain yields of 4.63 and 4.75 ton ha - 1 were recorded from weed free plots which were 50 and 47 % higher than that of control in year 1 and 2, respectively. Grain yield obtained from weed free was similar with that of 5 weed m -2 in year 1 but higher in year 2. More than 3 ton ha -1 grain yield was found upto 40 and 160 weed m -2 in year 1 and 2, respectively. The lowest grain yield was recorded from control in both seasons. The numbers of panicles m -2 were significantly affected by the weed density in both years (Table 4). Table 4: Grain yield and yield contributing characters of rice at harvest Weeds (no.m-2) Grain yield (ton ha-1) Panicles (no. m-2) Grains (no. panicle-1) 1000-grain weight (g) Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 0 4.63a 4.75a 227a 211a 106.00a 114.00abc 20.20a 18.87a 5 4.22ab 4.30b 194b 194b 103.00ab 120.00ab 18.93b 18.17ab 10 3.83bc 4.12c 184bc 182bc 102.00ab 123.33a 18.40bc 18.37ab 20 3.48cd 3.88d 166bcd 172c 99.00ab 114.00abc 18.42bc 18.28ab 40 3.12de 3.68e 156cd 174c 96.67ab 108.00bc 18.53b 18.55a 80 2.87def 3.45f 152d 173c 92.33ab 120.00ab 18.22bc 18.21ab 160 2.59ef 3.10g 141de 168c 89.00bc 103.67c 18.39bc 18.36ab Control 2.31f 2.54h 121e 123d 76.33c 102.33c 17.51c 17.47b Values of a column followed by same letter are statistically similar at 5% probability (p = 0.05) 0 6 12 18 24 30 36 42 48 SM CDI EC MV LO FM SZ LH CDA Name of weeds S D R ( % ) 0 6 12 18 24 30 36 42 48 54 60 CDI EC SM MV MM LO SZ CDA CI LC Name of weeds S D R ( % ) Asian Journal of Agriculture and Rural Development, 3(5) 2013: 234-248 529 As weed density increased, rice grain yield decreased hyperbolically. The relation between grain yield m -2 and weed density was represented by a rectangular hyperbola in both years. Significant yield reduction was noticed due to competition from different densities of weeds in year 1 and 2. The highest reduction (47.7% in year 1 and 34.9 % in year 2) was occurred when 119 and 130 weeds present m -2 in year 1 and 2, respectively. The percent reduction increased progressively with the weed density upto 119 and 130 weeds m -2 in year 1 and 2, respectively. Weed economic threshold density The equation described by O'Donovan (1991) was used to determine economic threshold of weed density. The estimated economic threshold of multispecies weed was 5 and 7 weeds m -2 in years 1 and 2, respectively, assuming a weed free rice grain yield 5 ton ha -1 , a crop price $ 210 ton -1 , and weed control cost $ 30 ha -1 (Table 5). Table 5: Economic threshold weed density of direct seeded rice Years Economic threshold weed density (no.m -2 ) Year 1 5 Year 2 7 Discussion Yield of direct seeded rice (DSR) was greatly affected by weeds. Determination of economic threshold (ET) of weed density consisted of the number of weeds in DSR is one possible way to improve farming methods. Obtained results from both seasons have clearly showed the effects of weed density on yield reduction of DSR. Rice grew faster than weeds at early stage in both years. Rice plants grew rapidly upto 40 DAS than weeds. This offers an apparent advantage to the rice plants in terms of light interception. After 40 DAS weeds grew rapidly than rice plants. In DSR, the rice seeds are sown in saturated soil moisture. Tanaka (1976) found that sedges and grasses accounted for more than 90% of the total dry weight in saturated condition. In addition, Bhagat et al. (1999) also reported that the dominance of Echinochloa crus-galli and Lepthocloa chinensis was favoured by the saturated condition. Mamun et al. (2011) reported that Paspalum disticum and Echinochloa crusgalli was the dominant weed species in translated rice. In our field trial sedges were observed as main weeds in both years followed by broadleaves. This succession was due to ability of the weeds to produce more seeds which contribute to additional soil seed bank. Increasing weed density reduced the number of panicle m -2 of rice plants in both years. Sultana (2000) observed about 52% reduction in tillers due to competition from weeds. Fazlul et al. (2003) also observed significantly highest number of total tillers produced in weed-free treatments. Begum et al. (2009) found that significantly higher percentage of fertile grains per panicle was produced in weed free plots and weed density of 250 m -2 compared with the rest of weed density treatments. Similar result was observed for other species e.g., red rice density of 5 plants m -2 were not effective to reduce fertile rice grain, but weed densities to 108 and 215 plants m -2 reduced fertile rice grain (Diarra et al., 1985). The 1000-grain weight is a genetic character widely used in yield estimation and varietal selection in rice (Iqbal et al., 2008). The highest 1000-grain weight was obtained in weed-free, while 1000-grain weights were significantly lowered in the treatments with higher weed densities. Islam et al. (1980) also reported variation in 1000- grain weight due to weed infestation, while Rao and Moody (1992) reported that weed competition did not affect the grain index of the rice. It is now generally acknowledged that at low weed population densities the yield loss response tends towards linearity. As density increases, weeds begin to compete intraspecifically, and yield loss approaches an asymptote (Cousens 1985). The wisdom of continuing to conduct labor-intensive weed interference experiments, for the sole purpose of reinforcing this well-established conclusion, has been questioned (Cousens 1987; Norris 1992). The rice plants produced the highest grain yield m -2 when grown in the absences of weeds. Grain yield was reduced due to competition from weeds. After a certain weed density the magnitude of yield reduction was reduced; in other words the yield m -2 was increased slightly. In this concept the determination of economic threshold is a basic requirement for Integrated Weed Management System. Again, technologies of rice cultivation with biological crop management and organic Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 530 based fertilizers are required that can increase rice production and sustainable agriculture (Turmuktini et al., 2012). Conclusion Scirpus maritimus L. and Cyperus difformis L. were the most dominant weed species in year 1 and 2, respectively. The density of 40 or more weeds per m 2 is competitive to rice. The weed infestation levels 40 or more weeds per m 2 offered higher competition and affected rice yield. Estimated economic threshold of multispecies weed was 5 and 7 weeds m -2 in years 1 and 2, respectively, assuming a weed free rice grain yield 5 ton ha -1 , a crop price $ 210 ton -1 , and weed control cost $ 30 ha -1 . Reference Begum, M., Juraimi, A. S., Rajan, A., Omar, S. R. S. and Azmi, M. (2009). Competition of Fimbristylis miliacea weed with rice. Int. J. Agric. Biol. 11 (2): 183-187. Bertha, K. Mashayamombe, Upenyu Mazarura and Albert Chiteka (2013). Effect of Two Formulations of Sufentrazone on Weed Control in Tobacco (Nicotiana Tabacum L). Asian Journal of Agriculture and Rural Development, 3 (1): 1-6. Bhagat, R. M., Bhuiyan, S. I., Moody, K. and Estorninos, L. E. (1999). Effect of water, tillage and herbicides on ecology of weed communities in intensive wet- seeded rice system. Crop Prot. 18: 293 – 303. BRRI (2006). Bangladesh Rice Knowledge Bank. Bangladesh Rice Research Institute (BRRI) http://riceknowledgebank.brri.org Cousens, R. (1985). A simple model relating yield loss to weed density. Ann. Appl. Biol., 107: 239-252. Cousens, R. (1987). Theory and reality of weed control thresholds. Plant Prot. Quart., 2: 13-20. Diarra, A., Smith, Jr R. J. and Talbert, R. E. (1985). Interference of red rice (Oryza glaborima L.) with rice (Oryza sativa L.). Weed Sci., 33: 644–649. Fazlul, I., Karim, S. M. R., Haque, S. M. A. and Islam, M. S. (2003). Effect of population density of Echinochloa colonum on rice. Pakistan J. of Agron., 2(3): 120-125. Gerowitt, B. and Heitefuss, R. (1990). Weed economic thresholds in cereals in the Federal Republic of Germany. Crop Prot., 9: 323-331. Gomez, K. A. and Gomez, A. A. (1984). Statistical Procedures for Agricultural Research, 2nd Ed., John Wiley and Sons Inc., New York. Iqbal, S., Ahmad, A., Hussain, A., Ali, M. A., Khaliq, T. and Wajid, S. A. (2008). Influence of transplanting date and nitrogen management on productivity of paddy cultivars under variable environments. Int. J. Agric. Biol., 10: 288–292. Islam, M. A., Khan, M. N. H. and Rahman, M. M. (1980). Effect of cultural weed control practice on Aus (summer) rice. Bangladesh J. Agric. Sci., 7: 43-46. Janiya, J. D. and Moody, K. (1989). Weed populations in transplanted and wet- seeded rice as affected by weed control method. Tropical Pest Management, 35 (1): 8-11. Johnson, D. E., Dingkuhn, M., Jones, M. P. and Mahamane, M. C. (1998). The influence of rice plant type on the effect of weed competition on O. glaberrima and O. sativa. Weed Res., 38: 207-216. Kim, S. C. and Ha, W. G. (2005). Direct seeding and weed management in Korea. In: Rice is Life: Scientific Perspectives for the 21 st Century. Proc. of the World Rice Res. Conf., 4-7 November 2004, Tsukuba, Japan, pp. 181-184. Mahajan, G., Sardana, V., Brar, A. S. and Gill M. S. (2006). Effect of seed rate, irrigation intervals and weed pressure on productivity of direct seeded rice (Oryza sativa L.). Indian J. of Agric. Sci., 76(12): 756-759. Mahendran, A. (2013). A Case on Problems in Quality and Reasons in Rice -Under the Scheme of UPDS in Tamil Nadu. Asian Journal of Agriculture and Rural Development, 3(4): 169-175. http://riceknowledgebank.brri.org/ Asian Journal of Agriculture and Rural Development, 3(8) 2013: 523-531 531 Mallik, M. A. B. (2001). Selective isolation and screening of soil microorganisms for metabolites with herbicidal potential. J. Crop Prot., 4: 219-236. Mamun, A. A. (1990). Weeds and their control: A review of weed research in Bangladesh. Agricultural and Rural Development in Bangladesh. Japan Intl. Co-operation Agency, Dhaka, Bangladesh. JSARD. 19: 45-72. Mamun, M. A. A., Shultanal, R., Bhuiyanl, M. K. A., Mridha, A. J. and Mazid, A. (2011). Economic weed management options in winter rice. Pak. J. Weed Sci. Res. 17(4): 323-331 Norris, R. F. (1992). Case history for weed competition / population ecology: Barnyardgrass (Echinochloa crus-galli) in sugarbeets (Beta vulgaris). Weed Technol., 6: 220-227. O'Donovan, J. T. (1991). Quackgrass (Elytrigia repens) interference in canola (Brassica campestris). Weed Sci., 39: 397-401. Oerke, E. C., Dehne, H. W., Schonbeck, F. and Weber, A. (1994). Crop Production and Protection-Estimated Losses in Major Food and Cash Crops. Elsevier, Amsterdam, p. 808. Pester, T. A. (2000). Secale cereale interference and economic thresholds in winter Triticum aestivum. Weed Sci., 48(6): 720-727. Rao, A. N. and Moody, K. (1992). Competition between Echinochloa glabrescens and Oryza sativa. Trop. Pest Mang., 38: 25- 29. Rao, A. N. and Moody, K. (1994). Ecology and Management of Weed in Farmer’ Direct seeded Rice (Oryza sativa L.) Fields. IRRI, Los Banos, Philippines. Singh, V. P., Singh, G., Singh, V. P. and Singh, A. P. (2005). Effect of establishment methods and weed management on weed and rice in rice- wheat cropping system. Indian J. Weed Sci., 37: 51-57. Sultana, R. (2000). Competitive ability of wet- seeded boro rice against Echinochloa crusgalli and Echinochloa colonum. M.S. Thesis, pp: 36–50. Bangladesh Agricultural University, Mymensingh, Bangladesh Swinton, W. M., Buhler, D. D., Forcella, F., Gunsolus, J. L. and King, R. P. (1994). Estimation of crop yield loss due to interference by multiple weed species. Weed Sci., 42: 103–109. Tanaka, I. (1976). Climatic influence on photosynthesis and respiration of rice plants. Climate and Rice, International Rice Research Institute, Los Banos, Laguna, Philippines. Tien Turmuktini, Endang Kantikowati, Betty Natalie, Mieke Setiawati, Yuyun Yuwariah, Benny Joy and Tualar Simarmata (2012). Restoring the Health of Paddy Soil by Using Straw Compost and Biofertilizers to Increase Fertilizer Efficiency and Rice Production with Sobari (System of Organic Based Aerobic Rice Intensification) Technology. Asian Journal of Agriculture and Rural Development, 2(4): 519 - 526. Tien Turmuktini, Tualar Simarmata, Benny Joy and Ania Citra Resmini (2012). Management of Water Saving and Organic based Fertilizers Technology for Remediation and Maintaining the Health of Paddy Soils and To Increase the Sustainability of Rice Productivity in Indonesia. Asian Journal of Agriculture and Rural Development, 3(4): 536 – 551 Upenyu Mazarura (2013). Effect of Sulfentrazone Application Method and Time, on Weed Control and Phyto- toxicity in Flue-Cured Tobacco. Asian Journal of Agriculture and Rural Development, 3(1): 30-37. Weaver, S. E. and Ivany, J. A. (1998). Economic threshold for wild radish, wild oat, hemp nettle, and corn spurry in spring barley. Canadian J. Plant Sci., 78(2): 357-361.