Residual effects of cultivation methods on weed ... - Wiley Online Library

Weed Biology and Management 16, 93–107 (2016)

RESEARCH PAPER

Residual effects of cultivation methods on weed seed banks and weeds in Cambodia AKIHIKO KAMOSHITA1*, HIROYUKI IKEDA2,3, JUNKO YAMAGISHI2, BUNNA LOR4 and MAKARA OUK4 1 Asian Natural Environmental Science Center, The University of Tokyo, Nishitokyo, Japan 2Graduate School of Agricultural and Life Sciences, The University of Tokyo, Nishitokyo, Japan 3Rural Development Department, Japan International Cooperation Agency, Tokyo, Japan 4 Cambodian Agricultural Research and Development Institute, Phnom Penh, Cambodia The effects of the planting method (transplanting vs. direct seeding), rice cultivar (Sen Pidao vs. Phka Rumduol), and herbicide application (admixture of bentazone and cyhalofop-butyl) on weeds and weed seed banks were quantified in unflooded, shallowly flooded, and deeply flooded paddy fields in Cambodia in 2005 and 2006. Broad-leaved weeds infested more toward maturity in 2006 than in 2005, particularly in directly seeded plots. Weed dry weights at pre-heading and/or maturity were consistently reduced by herbicide application and Phka Rumduol cultivar, while weed numbers increased under unflooded condition. The proportion of sedges was consistently larger in directly seeded and non-herbicide plots at pre-heading. Larger numbers and dry weights of sedges and total weeds in 2005 caused larger seed bank sizes of sedges and total weeds in 2006, which further caused their infestation in 2006. A greater weed dry weight at 62 days after sowing in 2005 resulted in larger seed banks of Cyperus iria and Fimbristylis miliacea, which were most severe under non-herbicide, direct-seeded treatment, while that at rice maturity resulted in larger seed banks of Cyperus difformis, Scirpus juncoides, and Lindernia antipoda. Overall, sedges shared the majority of the total weed seed bank, followed by broad-leaved weeds and then grasses. A lower yield in 2005 led to significantly larger seed bank sizes of sedges, but not of grasses or broad-leaved weeds. Rice yield reduction was consistently related to larger numbers of sedges by heading and those of grasses at maturity. Keywords: Cambodia, cultivar, direct seeding, transplanting, weed management..

Rapid economic development in Asia, including Cambodia, has induced the migration of rural labor to urban areas. In the production of paddy rice (Oryza sativa L.), the diminished supply and increased cost of agricultural labor has led to a trend away from the traditional labor-

Communicated by T. Yoshioka. *Correspondence to: Akihiko Kamoshita, Asian Natural Environmental Science Center, The University of Tokyo, 1-1-1 Midoricho, Nishitokyo 188-0002, Japan. Email: [email protected] The authors have no commercial interest in the findings presented. Received 3 July 2015; accepted 15 July 2016

intensive practice of manual transplanting and towards direct seeding. Direct seeding, particularly the broadcasting of dry seed, requires much less labor than transplanting because it obviates the need for a nursery, the pulling of seedlings, and transplanting (Rickman et al. 2001; Kamoshita et al. 2009). Direct seeding has increased substantially in rice-producing countries in Asia (Pandey & Velasco 2002; Dawe 2005), including Cambodia, in which there was 10–30% of direct seeding in rice cultivation areas during the 1990s (Rickman et al. 1995). The broadcasting of dry seed is suited to rain-fed lowland environments because it affords farmers more flexibility in scheduling their crops without concerns about water shortages during

doi:10.1111/wbm.12097 © 2016 The Authors. Weed Biology and Management published by John Wiley & Sons Australia, Ltd on behalf of Weed Science Society of Japan This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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the transplanting time and its consequent problems of poor rooting and delayed transplanting (Ikeda et al. 2007; Serraj et al. 2009). A number of studies have reported and assessed the reduction in grain yield that is associated with the shift from transplanting to direct seeding (Naklang et al. 1996; Pandey & Velasco 2002; Savary et al. 2005; Hayashi et al. 2007). Increased weed competition is one of the most serious consequences of replacing transplanting with direct seeding (Ikeda et al. 2008; Hayashi et al. 2009). Ikeda et al. (2008) showed that, in Cambodia, the grain yield declined to an 84% level on average of 24 combinations of management in an on-station experiment with a move from transplanting to direct seeding due to the less intensive management of the direct seeding production system, but also found that appropriate cultivation methods could minimize the yield reduction and even achieve yields similar to those of transplanting systems (i.e. a range of direct-seeded yield from 48 to 115% of the transplanted yield among the 24 combinations). It is imperative that appropriate cultivation methods for maintaining yields under direct seeding be identified to ensure that rice production does not decline over time (May 2001) and to remove the risks of land degradation from uncontained weed infestations. The effect of direct seeding on weed ecology was examined by Tomita et al. (2003a,2003b,2003c), but no study has examined its relationship with weed seed banks. The size and composition of weed seed banks and weed populations can be altered by the planting methods, herbicides, and rice cultivars that are used by farmers (Bhagat et al. 1999a,1999b). Manipulation of the weed population through the weed seed bank is an important option for weed management (McIntyre 1985; Wilson et al. 1985; Sago 2000,2001), but has received little study in tropical, rain-fed rice production systems. This study aimed to assess the effects of different crop and weed management practices on weed species composition and rice yield reduction, including the relationship between weed seed banks and weed infestation in Cambodia, where the traditional transplanting method is shifting to direct seeding and where rice is produced mostly under a rain-fed system with wide ranges of water conditions. The variation in weed seed banks in a sample of paddies on Cambodian farms under varying levels of water availability to the farmers concerned was reported separately (Kamoshita et al. 2010). Variation in covariate factors confounded the results to a considerable extent. In the current study, a more robust assessment of the weed species, composition of weed groups, and their dynamics was

undertaken in an on-station agronomic experiment, with an emphasis on the effects of the previous year0 s treatments on the weed seed bank.

MATERIALS AND METHODS Replicated agronomic experiments were conducted in paddy fields at the Cambodian Agricultural Research and Development Institute (CARDI, Phnom Penh, Cambodia) (11 280 N, 104 480 E) during the June-toNovember rainy seasons of 2005 and 2006. The experiments previously have been described by Ikeda et al. (2008), which contained a factorial combination of three different water conditions (unflooded, shallowly flooded, and deeply flooded), two planting methods (direct seeding and transplanting), two rice cultivars (Sen Pidao, a short, early-maturing cultivar, and Phka Rumduol, a taller, later-maturing cultivar), and two levels of weed control (with and without a herbicide application). The three different water conditions represented the diversity of water conditions of Cambodian paddy fields (e.g. Kamoshita et al. 2010), most of which do not have irrigation and drainage facilities. The combination of transplanting without a herbicide application has been regarded as the traditional, present Cambodian system, while direct seeding with a possible use of herbicide would be seen as a future system. Each combination was replicated three times, yielding 72 plots in total. Herbicide was sprayed once as an admixture of bentazone (2 kg ai ha−1) and cyhalofopbutyl (0.3 kg ai ha−1) during the early crop establishment stage to control broad-leaved and Cyperus weeds (by the bentazone) and grasses (by the cyhalofop-butyl). The same treatments were repeated in the same plots in both years. The fields were plowed twice and puddled once before broadcasting seed or transplanting seedlings. The seeding rate in the direct-seeding method was 100 kg ha−1; in the transplanting method, the hill spacing was 20 cm × 20 cm and the seedling age was 25 days old. All the plots received 50 kg N ha−1 as urea and diammonium phosphate (DAP), 30 kg P2O5 ha−1 as DAP and 30 kg K2O ha−1 as potassium chloride. Half of the N fertilizer was applied as a basal dressing and half during the late vegetative-to-panicle initiation stage. All of the P and K fertilizer was added as a basal dressing. Weed seed bank The surface soils (0–5 cm depth, the depth layer with the most populously located weed seeds of the previous season) of each plot were sampled prior to cropping on June 5 2006. The sampled soils were air-dried

© 2016 The Authors. Weed Biology and Management published by John Wiley & Sons Australia, Ltd on behalf of Weed Science Society of Japan

Weed seed banks in Cambodian paddy fields and sieved with a 7 mm mesh to remove larger plant residues and then were kept at room temperature for ~2 months until germination testing started. A 200 mL subsample of each sampled soil was placed into 6 cm tall tapered plastic pots with top and bottom diameters of 13 and 11 cm respectively, providing a soil depth in the pots of ~2 cm. The soils were watered twice per day and kept saturated, but without standing water. The germinated seeds were counted weekly by weed species and removed from the pots; the germination observations were continued for 4 weeks after the commencement of watering. Whenever species identification was uncertain, a selection of the seedlings was transplanted into larger containers and grown until flowering in order to permit their identification. The species were grouped into sedges, grasses, and broad-leaved weeds and only the data for those species with high frequencies of germination number were reported individually. The soil was discarded and replaced with new soil twice to give a germination count from a total of three replicates. The weed seed bank size in the top 5 cm soil layer was calculated as the average of the three replicates per soil sample and then converted into the number of seeds per square meter. Weed population and rice yield The individual numbers and dry weights of the weeds and their species grouping of sedges, grasses, and broadleaved weeds were recorded for all 72 plots before heading (referred to as “pre-heading” and was carried out at 62 days after sowing [DAS] in 2005 and 75 DAS in 2006), at heading, and at maturity. The 36 directly seeded plots also were measured during the crop establishment stage (31 DAS in 2005 and 30 DAS in 2006). The weed dry weight was sampled from a representative 50 cm × 50 cm quadrat within each plot, while the number was recorded from a 25 cm × 25 cm subquadrat inside the quadrat. The grain yield, total dry weight, and their ratio (harvest index) of rice also were estimated from the above-ground sample of a 1 m × 1 m quadrat at maturity for all 72 plots (Ikeda et al. 2008). Statistical analysis The effects of the treatments and interactions were analyzed by ANOVA using Microsoft Excel (Microsoft, Redmond, WA, USA). Normality by the Shapiro– Wilk test and homogeneity of variance also were tested in Microsoft Excel. When the variables did not distribute normally, they were transformed by Box–Cox transformation, as below:

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X 0 = X λ − 1 =λ, where X is a variable and λ is a transformation parameter. An ANOVA also was conducted for the transformed variables X0 in order to better qualify the analysis using the raw data and more conservative criteria were shown when the results of the ANOVA for the raw data and transformed data differed. A correlation analysis of the variables of seed bank size in 2006 was conducted with the: (i) variables of weed number or dry weight in 2005; (ii) variables of rice production in 2005; (iii) variables of weed number or dry weight in 2006; and (iv) variables of rice production in 2006 by using the mean data of 24 treatments and Microsoft Excel. The significance of correlation was tested from the P-values that were calculated from the correlation coefficient, t-values, and degrees of freedom in Microsoft Excel. When the variables of the mean data did not show a normal distribution, the transformed data by Box–Cox transformation were used for the analysis. RESULTS The rainfall level at CARDI was 1005 and 693 mm from the June-to-November rainy season in 2005 and 2006, respectively, with the mean air temperature and average minimum and maximum daily temperatures at 28.5, 23.3, and 34.6 C, respectively (measured only in 2006). Crop and weed management effects on weed growth The average weed dry weights at pre-heading and maturity were significantly larger in 2006 than in 2005 (Table 1). The weed dry weight decreased from pre-heading to maturity in 2005, but not in 2006. Interactions were detected between the year and planting method at both pre-heading and at maturity, with the direct seeding in 2006 having much larger values. The weed dry weight and weed number at 30 DAS were 48 g m−2 and 2294 m−2, respectively, on average in the non-herbicide plots for the 2 years, which were much higher than the values in the herbicide plots, of 5 g m−2 and 273 m−2, respectively (Supporting Information Table S1). The herbicide effect on the weed dry weight and weed number was strongly and consistently observed thereafter until maturity (Table 1). The weed numbers decreased sharply from ~30 DAS until rice maturity in both years; this was true also in the non-herbicide plots, which had an average reduction rate of 41.3 and 20.7 m−2 day−1 between ~30 DAS


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Table 1. Weed infestation data in 2005 and 2006 Planting method

Unflooded Direct seeding

Cultivar

Sen Pidao Phkar Rumduol

Transplanting


Shallowly flooded Direct seeding


Transplanting


Deeply flooded Direct seeding


Transplanting


Main effects Year (Y)

2005 2006 Water condition (WA) Unflooded Shallowly flooded Deeply flooded Planting method (P) Direct seeding Transplanting Cultivar (C) Sen Pidao Phkar Rumduol Weed management Herbicide method (WM) Non-herbicide Interactive effects between Y and P 2005 Direct seeding Transplanting 2006 Direct seeding Transplanting Interactive effects between WA and P Unflooded Direct seeding Transplanting

Weed management method

Weed dry weight (g m−2)

Weed number (m−2)

Pre-heading

Maturity

Pre-heading

Maturity

Herbicide Non-herbicide Herbicide Non-herbicide Herbicide Non-herbicide Herbicide Non-herbicide

9 169 17 166 17 113 9 59

29 117 10 51 47 190 15 42

172 1456 237 1379 341 2741 216 2347

69 585 32 253 264 1027 125 403


40 218 13 150 13 37 9 26

52 164 4 27 3 40 3 11

245 1755 200 1037 8 83 19 40

120 315 21 131 8 43 32 37


15 197 10 121 0 76 44 38

35 273 7 72 14 51 8 62

49 888 16 603 3 453 35 139

48 424 123 200 48 101 43 80

55 75 70 63 63 94 37 75 55 16 114

22 88 63 38 65 70 40 85 26 19 92

757 448 1111 423 273 670 535 683 522 129 1077

123 254 345 88 133 193 184 254 123 78 300

80 31 108 43

22 23 118 58

726 788 613 282

92 154 294 213

90 50

52 74

811 1411

235 455


Weed seed banks in Cambodian paddy fields

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Table 1 (continued) Planting method

Shallowly flooded Deeply flooded

Cultivar

Weed management method

Weed dry weight (g m−2) Pre-heading

Maturity

Pre-heading

Maturity

105 21 86 39

62 14 97 34

809 37 389 158

147 30 199 68

* NS ** NS ***

*** NS NS *** ***

NS *** NS * ***

*** *** NS *** ***

Direct seeding Transplanting Direct seeding Transplanting

ANOVA Main effects Y WA P C WM Interactive effects ***

Y×P

Y×P

Y × WM

WA × P, P×C

** *

Weed number (m−2)

Y × P, Y × WA × WM Y × WA, Y × WM

Y × P, WA × P

Y × WA, Y × C, WA × P ×C

*, ** and *** indicate that the effect of the treatment or the interaction of the treatments was significant at the 5, 1, and 0.1% level, respectively. NS, no significant effect at the 5% level. The dry weight and number of all the weeds at pre-heading (62 and 75 days after sowing of rice in 2005 and 2006, respectively) and at rice maturity, on average of each level of years, water conditions, planting methods, cultivars, and weed management methods. An ANOVA was conducted, including the transformed variables by Box–Cox transformation, to show the main effects and interactions that were significantly detected in both analyses (conservative judgment was made). The average values of the main effects and the interactive effects (between the year and planting method and between the water condition and planting method) also are shown.

and the pre-heading measurement in 2005 and 2006, respectively. The weed dry weight in the direct-seeding treatment at pre-heading 94 g m−2, was higher than that under the transplanting treatment, 37 g m−2, while that for the cultivar, Sen Pidao, at maturity, 85 g m−2, was higher than that for Phka Rumduol, at 26 g m−2 (Table 1). The directly seeded Sen Pidao had 112 g m−2 of weed dry weight at maturity, whereas the directly seeded Phka Rumduol had a much smaller value of 28 g m−2, but the difference was smaller in the transplanting treatment, with Sen Pidao at 57 g m−2 and Phka Rumduol at 23 g m−2. The water conditions strongly affected the weed number at pre-heading and at maturity, with the weed numbers in the unflooded condition being significantly higher than in the two flooded treatments. There were significant interactions between the water condition and planting method for the weed dry weight and weed number at maturity, where transplanting can better minimize weed

infestation under the two flooded conditions, compared with direct seeding, but both practices suffered from the largest weed infestation under the unflooded condition. Under the non-herbicide treatment, direct seeding led to 170 g m−2 of the weed dry weight at preheading, compared with 58 g m−2 for transplanting, while at maturity, the plots of the Sen Pidao cultivar included 139 g m−2 of weed dry weight, compared with 44 g m−2 in the Phka Rumduol cultivar plots, although these values were not significant at P < 0.05. More than 70% of the weeds were sedges at ~30 DAS (measured in the directly seeded plots only) in both years. In 2005, the percentage of weeds that were sedges was 78% at pre-heading and 62% at maturity, whereas in 2006, the corresponding values were 57 and 28%, respectively, with the proportion of broad-leaved weeds being at 37 and 49% before heading and at maturity, respectively. On a dry weight basis, the percentages of sedges, grasses, and broad-leaved


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weeds in 2006 were 49, 15, and 37% at 30 DAS and 11, 49, and 40% at maturity, respectively. At the pre-heading measurement in both 2005 and 2006, the percentage of weeds that were sedges was higher for the direct-seeding and non-herbicide treatments than for the transplanting and herbicide treatments (Fig. 1b,d,f,h). The percentage of sedges decreased toward maturity, but was maintained at a high level in the unflooded treatment in 2005 (Fig. 1a). The percentage of broad-leaved weeds was higher in 2006 than in 2005. In the treatments in which the weed numbers were generally lower, such as in the shallow and deep flooding, transplanting, Phka Rumduol, and herbicide treatments in 2005, the percentage of the weeds that were grasses tended to be higher.

(a)

(b)

Percentage of weed number

2005 100 80 60 40 20 0 UF SF DF Pre-heading

(c) 100 80 60 40 20 0

2005

UF SF DF Maturity

SP PR Pre-heading

Percentage of weed number

2006 100 80 60 40 20 0 UF SF DF Pre-heading

100 80 60 40 20 0

SP PR Maturity

100 80 60 40 20 0

(f)

UF SF DF Maturity

100 80 60 40 20 0

(h)

2006

SP PR Pre-heading

100 80 60 40 20 0

(d)

2005

(e)

(g)

The dominant weed species were: the sedges, Cyperus iria L., Cyperus difformis L., and Fimbristylis miliacea (L.) Vahl; the broad-leaved weeds, Lindernia antipoda Alston and Ludwigia hyssopifolia (C. Don) Exell; and the grasses, Echinochloa colonum (L.) Link and Leptochloa chinensis (L.) Nees. A negative correlation was obtained between the grain yield and the sedge number at heading and the grass number at maturity in both 2005 and 2006 (Fig. 2). An additional 10 sedge plants per square meter at heading could cause a 70% of the total weed seed bank consisted of sedges, followed by broad-leaved weeds of ~20% and grasses of 5%, in the on-station


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experiments (cf. values calculated from Table 2), as well as in a companion study in the farmer fields of Cambodia (Kamoshita et al. 2010), herbicides that are effective on sedges may be sufficient at present. At the initial stage of transition from transplanting to direct-seeding methods, such herbicides may be adequate, as sedges by the heading stage were identified as the most dominant weed group in direct seeding (Fig. 1b,f ). However, herbicide use changed the weed species composition, with a larger proportion of grasses and broad-leaved weeds (Fig. 1d,h), and hence the more widespread adoption of herbicide use for routine weed control could change the future weed composition in Cambodian paddy fields, leading to the need for herbicides with a wider spectrum of activity. Herbicide application changed the weed species composition in Japanese paddies (Sago 2000; Sago & Takeshita 2004) and the introduction and application of 2,4-D resulted in a transition from sedges to grass species in paddy fields in Malaysia (Azmi & Mortimer 2002; Karim et al. 2004; Azmi et al. 2005) and other tropical south-eastern Asian countries (Itoh 1994; Kim 1996). The results of this study also indicate the importance of the choice of suitable rice cultivars for maintaining the rice yield: the Phka Rumduol cultivar, which was 20–30 days longer in growth duration than Sen Pidao in these experiments (Ikeda et al. 2008), significantly reduced weed infestation (Table 1) (and also seed bank accumulation in Table 2). Moreover, the nonherbicide plots tended to suffer greater weed infestation using Sen Pidao, but much less for Phka Rumduol (P = 0.07). The higher plant height and longer growth duration of Phka Rumduol (Ikeda et al. 2008) could have helped to make a larger canopy coverage by the rice plants for a longer period (shade to the weeds) to minimize weed infestation, even without a herbicide. This interactive effect was not observed in the weed seed bank sizes. There was a number of efforts to develop suitable cultivars to cope with the weed problems in the direct-seeding practice; as desired traits, coleoptile length, seedling vigor for good establishment, rapid canopy development, allelopathy, and lodging resistance were nominated (Rao et al. 2007), which would be considered for future Cambodian cultivar development. Quantification of the weed seed bank and its relationship with weed infestation The estimated total seed bank in the top 5 cm soil layer from the experimental station amounted to 13.9 × 103 m−2 on average (calculated from Table 2),

which was comparable to the values at the farmers0 fields in three provinces in Cambodia (8.5 × 103 m−2; Kamoshita et al. 2010). Given that the weed seeds within a depth of the top 1 cm soil layer can actually germinate in a single cropping duration (cf. Chauhan & Johnson 2009), the germinable weed seed number was 2.8 × 103 m−2, with the values for the herbicide plots and the non-herbicide plots at 2.1 × 103 and 3.4 × 103 m−2, respectively. The difference (1.3 × 103 m−2) between the herbicide and the nonherbicide plots indicates that the weed seed bank can increase or decrease, depending on the weed management method. The ratios of the observed weed number in the field at pre-heading (cf. Table 1) divided by the estimated total weed seed bank size were 32% (=1077/ 3400) and 6% (=129/2100) in the non-herbicide and herbicide plots, respectively. Within the non-herbicide plots, the ratios ranged from 1% (=40/3400 for the shallowly flooded Phka Rumduol cultivar under transplanting) to 81% (=2741/3400 for the unflooded Sen Pidao cultivar under transplanting) (cf. Table 1), showing the importance of the choice of cultivar and field water condition. This ratio of the observed weed number in the field was higher (i.e. about two-thirds) in the earlier growth stage (i.e. 30 DAS; Supporting Information Table S1) in the non-herbicide plots. In a study of Japanese paddy fields, Sago et al. (2001) estimated that a weed seed bank size >3 × 103 m−2 is likely to cause crop loss due to weed infestation. A comparison of Tables 1 and 2 show the number of similarities of the main effects of the treatments (i.e. water condition, planting method, cultivar, and weed management method) between weed infestation (measured as dry weights and numbers at pre-heading and maturity) and the total weed seed bank size, while Table 3 and Supporting Information Figure S1 show the interrelationship between weed infestation in the previous and the following years and seed bank accumulation for total weeds and sedges. Larger numbers and dry weights of sedges and total weeds in 2005 caused larger seed bank sizes of sedges and total weeds in 2006, which further caused their infestation in 2006 (Table 3). A greater weed dry weight at pre-heading in 2005 resulted in a larger seed bank of C. iria and F. miliacea, which was most severe under the non-herbicide, directly seeded treatment, while that at rice maturity resulted in a larger seed bank of C. difformis, S. juncoides, and L. antipoda. No interrelationship was found for the grass and broad-leaved weed groups. In addition, a lower rice yield in 2005 led to a significantly larger seed bank size of the sedges, but not of the grasses or broadleaved weeds.


Weed seed banks in Cambodian paddy fields Cultivation method and the weed seed bank This study presented quantitative evidence of the planting method by weed management interaction (P < 0.001) for two sedge species, F. miliacea and C. iria, and because of their dominance in number, causing the interaction in the total weed seed bank. Weed infestation without a herbicide application, which is common among a number of farmers of rain-fed rice in Cambodia, was more severe under the directseeding practice, but the weed seed bank sizes also greatly increased under the direct-seeding practice, compared with the traditional transplanting practice. This aspect should be considered when introducing the direct-seeding planting method as a labor-saving practice in Cambodia and other countries. The directseeding practice caused a greater overall accumulation of the weed seed banks; in particular, of F. miliacea, C. iria,and C. difformis. However, this effect of direct seeding was less obvious in the grasses and L. antipoda and the opposite relationship of larger seed bank sizes in transplanting was observed in S. juncoides and M. vaginalis (Table 2). Tomita et al. (2003b) also reported that dry direct seeding prompted the more frequent appearance of F. miliacea but not C. difformis, compared with transplanting in the rain-fed lowlands in northeastern Thailand. The different responses of F. miliacea, C. difformis, and C. iria to water stress and intermittent flooding from germination to the seedling stage were known from a laboratory experiment, with a greater reduction in the germination rate of C. difformis under the mild water stress (i.e. osmotic potential of the solution ranging from −0.1 to −0.4 MPa) and with a greater reduction in the growth of F. miliacea under flooding of a 10 cm depth (Chauhan & Johnson 2009). The results of this study showed significant interactive effects of the water condition with the planting method and the weed management method (Table 2), in which deep flooding also reduced the seed bank size of F. miliacea, except for the directly seeded, non-herbicide plot; in the field experiment, the average depth of the deeply flooded conditions during the rice growth duration were 21 and 10 cm in 2005 and 2006, respectively, but with some shallower depth periods during the establishment stage of direct seeding (particularly in 2006) (Ikeda et al. 2008), which might have allowed the infestation of F. miliacea in this treatment. Tomita et al. (2003a,2003b) showed a higher frequency of appearance of hydrophytes, such as Ludwigia adscendens (L.) (not identified in this study) and M. vaginalis, in the transplanted fields, compared with the dry, directly seeded fields, with M. vaginalis confirmed in this study too.

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It is interesting that the on-station experiments clearly showed significant effects of the treatments (i.e. water condition, planting method, rice cultivar, and herbicide application) on the weed seed bank sizes and compositions even only after a single cropping cycle (Table 2). It has been known that after several cropping cycles, dominant weed species and their composition in paddy fields change dramatically, depending on the planting method and weed control method, such as transplanting compared to direct seeding and herbicide use compared to manual weeding (e.g. Tuong et al. 2000). The water condition and herbicide application, as well as the planting method to a smaller degree, greatly affected the size of the seed banks of the sedges, with some differences among the four major sedge species (e.g. S. juncoides not being affected by the herbicide application) (Table 2, Supporting Information Figure S1). The results of the onstation experiments were different from the authors0 previous survey in the 22 Cambodian farmer fields, in which herbicide application did not result in clear differences in the species composition of the weed seed banks (Kamoshita et al. 2010). The 22 farmer fields were grouped into upstream or downstream within the irrigated paddy fields and into toposequentially high or low within the rain-fed rice fields, but the planting methods and cultivar choices were not consistent, whereas the on-station experiments were with factorial designs that enabled the researchers to detect the effects of each treatment in more reliable ways. Water conditions in Cambodian paddy fields often differ so greatly (e.g. Nguyen et al. 2011; Kamoshita et al. 2014) that other crop and weed management effects must have not been detected in the previous farmer field survey (Kamoshita et al. 2010). Weed effects on the rice yield This study indicates that sedge weeds are more likely to crop loss from the establishment to the heading stage, whereas the grasses and broad-leaved weeds are more likely to crop loss after the heading stage (cf. Figs 1–2). As discussed earlier, the majority of the weed seed bank in the Cambodian paddy is sedges (Kamoshita et al. 2010, 2014), but the growth of sedge-dominated weeds in the early growth stages can be neglected up to a limit: Ikeda et al. (2008) presented data that suggest that the effect of sedges on the rice yield can be neglected at dry weights of ≤48 g m−2 in the pre-heading stage, but sedge dry weights of ≤94 g m−2 lead to a 10% reduction in the yield. It is proposed that sedges should be controlled, so as not to exceed these limits in soil types that are


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similar to the experimental site in this study; that is, sandy soil, named “Prateah Lang.” From Table 1, under the condition that herbicides are not used, the promising combinations of cultivation treatments without rice yield penalty would be transplanted Phka Rumduol under the deeply flooded condition and transplanted both cultivars under the shallowly flooded condition, but any choice of cultivar and planting method would not stop the yield reduction under the unflooded condition. The negative effects of weeds differ depending on the soil fertility, as Inamura et al. (2003) showed in their study in Laos for rain-fed paddies on infertile soil: a weed biomass of 100 g m−2 would reduce the rice biomass by 274 g m–2 from 839 g m−2 in a zero weed condition; for fertile irrigated paddies, the rice biomass reduction was 100 g m−–2 from 876 g m−2. The current authors also propose that grasses and broad-leaved weeds should be controlled with caution, so as not to present many during the later rice-growing periods, as a single grass weed at maturity has a more than 15-fold larger impact on rice yield reduction than a single sedge weed at heading (Fig. 2).

ACKNOWLEDGMENTS We thank Hidejiro Shibayama, Saga University, Saga, Japan, for assisting in the identification of the weed species. We thank Leang Sambath, Prek Leap National School of Agriculture, Phnom Penh, Cambodia, for assisting with the measurement of the weed seed banks.

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SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s website: Figure S1. Relationship between seed bank sizes and weed number at pre-heading in 2006 for four major sedge species (F. miliacea, C. difformis, C. iria, S. juncoides) in all the 24 treatments. Linear regression lines for F. miliacea, C. difformis, and C. iria are drawn when significant at P < 0.05. Table S1. Weed infestation data of direct seeded plots on 30 days after sowing in 2005 and 2006. Dry weight (g m−2) and number (m−2) of all the weeds on average of each level of years (Y) (2005, 2006), water conditions (WA) (non-flooded, shallow flooded, deep flooded), cultivar (C) (Sen Pidao, Phka Rumduol), weed managment (WM) (herbicide, nonherbicide).
