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Weed composition and maize yield in a former tin-mining area: A case study in Malim Nawar, Malaysia

  • Pei Sin Tong ORCID logo EMAIL logo and Tuck Meng Lim
Published/Copyright: June 25, 2022
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Open Agriculture
From the journal Open Agriculture Volume 7 Issue 1

Abstract

Weed species composition has been assessed for major crops such as rice, rubber, and oil palm but not for cash crops in Malaysia. In this study, we determine the associations between maize yields and weed species, weed density, mean temperature, and mean rainfall. Annual field surveys of weeds were conducted in maize (Zea mays L.) in a former tin-mining land in Malim Nawar, Perak, Malaysia, during June of 2017, 2018, and 2020 to determine the effects of weeds on maize yields. The field surveys in 2017, 2018, and 2020 involved 120 quadrats (0.5 m × 0.5 m) with 40 replicates. Fifteen species were observed, representing 14 genera and 9 families and consisted of 9 broadleaves, 3 grasses, and 1 sedge. Phytosociological characteristics, namely, frequency, relative frequency, density, relative density, abundance, and relative abundance, were used to analyze weed species composition at the study site. The species with the highest mean density and relative abundance were Cyperus sp., followed by Amaranthus viridis, Eleusine indica, Hedyotis corymbosa, and Phyllanthus amarus. These five species accounted for 65% of the total relative abundance. Individual broadleaf, sedge, and grass weed types were compared between paired years using a two-proportion z-test. The variation in number of individuals in each group was significant between 2017 and 2018, 2018 and 2020, and 2017 and 2020. The relationship between maize yield and mean rainfall, mean temperature, and weed species was analyzed using a general linear model, none of which affected maize yields. The results of this study provide a foundation for practical weed management in maize fields in Malaysia, thereby contributing to sustainable agriculture and food security.

Keywords: maize; weeds; competition; growth; yield; phytosociology

1 Introduction

Maize (Zea mays L.) is a cash crop that takes 60–70 days from sowing to harvest in lowland Malaysia. Maize, wheat, and rice are the three most important cereal staple foods globally [1]. From 1994 to 2018, corn was the sixth most globally produced crop with an average production of one giga ton per year [2]. Maize production in Southeast Asia has increased steadily, owing to the increasing demand for both food and livestock feed. In Malaysia, the total planted vegetables and cash crop areas in 2018 was 77,828 ha, with maize occupying the largest area (10,361.6 ha). Perak is the highest maize production state [3], and the Malim Nawar subdistrict is one of the main planting areas in Perak, consisting of small holders. Small rural farms have been an essential part of food security systems in terms of production [4]. Weeds are the main constraint hindering plant yields and result in varying yield losses depending on the crop type [5].

Weed surveys have become a prerequisite for pest management programs because of the emerging issues related to herbicide resistance. Weed inventories are conducted at all levels (from local to regional, to national) to determine the presence and abundance of weed species for weed management. The standard duration period in weed surveys is 3 years, whereas the interval between assessments varies from 17 to 47 years in Europe [6]. Weed density has been identified as a key element that affects maize yields [7,8]. Low to medium weed densities can result in crop yields that are equivalent to those achieved under weed-free conditions, whereas high-density weed infestations reduce maize yields by 12–15%. For example, high densities of Amaranthus blitum, Eleusine indica, and Borreria latifolia at a ratio of 2:1:1 (total of 338,980 plants ha−1) reduced plant heights and cob yields compared with weed-free conditions [9]. Furthermore, van Heemst [8] ranked the competitive abilities for 25 crops using weed–crop competition, in which maize was ranked seventh because weeds moderately reduced its yields.

In Malaysia, weed composition studies for different land use types have been conducted in forest reserves [10], turf grass areas [11], paddy fields [12,13,14], rubber plantations [15], and oil palm plantations [16]. Most of the research has been conducted in paddy fields because rice is a major crop and, therefore, remains an extensive research focus. In contrast, there is a lack of information regarding weeds in cash crop fields, particularly those located in former mining areas. Only one such study has been conducted on vegetable farms in Selangor but land use type was not known [17].

Malim Nawar, located in the Kinta Valley conurbation, Perak, was previously a tin-mining site. Since the collapse of the tin industry in 1985, small-scale farmers have cultivated this land, with maize as a common crop. However, weed competition can reduce maize yields. In this study, we assess the associations between maize yields and weed species, weed density, mean temperature, and mean rainfall. The results provide a foundation for practical weed management in this region. To the best of our knowledge, this is the first study to correlate weed survey data with maize yield data in Malaysia.

2 Materials and methods

2.1 Field study

The weed composition survey was conducted in Malim Nawar, Kampar District, Perak, Malaysia, where maize is a major cash crop that covers an area of 2,480.87 ha (Figure 1). The survey was conducted over 3 years, during June of 2017, 2018, and 2020. The same planting site was used throughout the study; however, the cropping area varied, with areas of 0.99 ha in 2017, 0.86 ha in 2018, and 0.63 ha in 2020.

Figure 1 The location of the study site: Malim Nawar in Kampar, Perak, Malaysia.
Figure 1

The location of the study site: Malim Nawar in Kampar, Perak, Malaysia.

The study site was located on a small-scale farm. Maize seeds were sown at a density of 8 plants m−2. Two seeds per planting hole were sown at 0.4 m × 0.4 m. Two rows of maize were planted on a soil bed. Soil bed width was 0.6 m, and distance between rows of soil beds was 0.9 m. Herbicides were applied twice during the planting cycle. One week before planting, the planting site was treated with glyphosate herbicides, with the active ingredient isopropylamine salt, mixing 250 mL glyphosate with 20 L water. One month after germination, the planting site was applied with the atrazine herbicide, with the mixture of 150 mL atrazine and 20 L water. Both herbicides were applied at 500 L water mixture for 1 acre. A pesticide containing the active ingredient emamectin benzoate was applied on the 30th day after sowing and then on the 46th, 55th, and 59th day.

A fertilizer was applied five times throughout the planting season, starting with the 12th day after sowing, followed by the 20th, 34th, 46th, and 58th day. An N:P:K fertilizer with a ratio 15:15:15 was applied to the crops during the first three rounds of fertilization at the 12th, 20th, and 34th day, whereas an N:P:K fertilizer in the ratio 12:12:17 was applied during the final two rounds at the 46th and 58th day. Fertilizer quantity was 2 g in the first round placed at 2 inches distance around the germinated maize. For second to fourth rounds, the farmer had applied 30 g of fertilizer between two planting holes along the same row. For the last and fifth round, the farmer applied 40 g of fertilizer, also between the planting holes. Small farmers provided total corn yield data (including kernel, ears, and silk) in units of t (metric ton), on a fresh weight basis. In turn, average mean temperature (°C) and mean rainfall (mm) data were provided by the Malaysian Meteorological Department.

The annual weed inventory was conducted 30 days after planting. Sampling was conducted along transects in the soil beds. The sampled soil beds were randomly selected. The sample quadrats were 0.5 m × 0.5 m. The first and last quadrats of each sample were located 5 m from each end of the soil bed. The 5 m placing was to avoid edge effects as the weed composition at the fringe may not be representative of the weed composition on the farm [13]. The quadrat was placed at 5 m interval. Forty quadrats were each surveyed in 2017, 2018, and 2020, for a total of 120 quadrats. All the weeds growing in each quadrat were identified at the species level, and the number of individuals was recorded.

2.2 Statistical analysis

Phytosociological characteristics have gained widespread use in agriculture [18]. Weed composition and distribution was analyzed using frequency, relative frequency, density, relative density, abundance, and relative abundance adopted from Thomas [19]:

(1) F k = ∑ n Y i / n × 100 ,

where F k is the frequency of the weed species k, Y i is the presence (1) or absence (0) of the weed species k in field i, and n is the number of fields surveyed.

(2) U k = ∑ n ∑ 40 X i j / 40 n × 100 ,

where U k is the field uniformity for the weed species k, X ij is the presence (1) or absence (0) of weed species k in quadrat j in field i, n is the number of fields surveyed, and the number of quadrats per field is 40.

The difference between mean field density (MFD) and mean occurrence field density (MOFD) is that the former computes the mean number of plants m−2 for all fields, whereas the latter calculates the mean within fields where the species is found.

(3) MFD k = ∑ n D i / n ,

where MFD k is the mean field density of the weed species k (expressed as the number of weeds m−2) for all the surveyed fields and n is the number of fields surveyed.

(4) MOFD k = ∑ n D i / n – a ,

where MOFD k is the mean occurrence field density of weed species k (expressed as the number of weeds m−2), which is obtained by dividing the sum of the density of weed species k by the number of fields in which the species k is present, n is the number of fields surveyed, and a is the number of fields in which species k is absent.

The total relative abundance value was 300 for all species in the study area. The relative abundance of each species is the sum of the relative frequency, relative field uniformity, and relative MFD, according to the following formula:

(5) RF k = Frequency for weed species k /Sum of frequency for all weed species × 100 ,

where RF k is the relative frequency for weed species k.

(6) RU k = Field uniformity for weed species k /Sum of field uniformity for all weed species × 100 ,

where RU k is the relative field uniformity for weed species k.

(7) RD k = MFD for weed speciesk/Sum of MFD for all weed species × 100 ,

where RD k is the relative MFD for weed species k.

Total number of plants of grass, sedge, and broadleaf populations in 2017, 2018, and 2020 were analyzed using two-proportion z-test with a Bonferroni correction to determine whether two multinomial probability distributions (i.e., pairwise for 2017 and 2018; 2017 and 2020; and 2018 and 2020) were distributed equally for each weed type. Bonferroni correction involved the adjustment of the alpha (α) level to mitigate Type 1 error, which is rejecting the null hypothesis falsely.

The adjusted alpha level = 0.05/3 (three groups consisting of broadleaves and others, grasses and others, and sedges and others) = 0.016667.

When the p values were lower than the adjusted alpha level, populations compared were statistically significant between the pair showing variation in the number of individuals. The relationships between different weed species, average mean temperature, mean rainfall, and the response variable, which was maize yields, were determined using a general linear model. A p value < 0.05 was considered statistically significant.

3 Results

3.1 Weed composition

Fifteen species, representing 14 genera and 9 families, were distributed in the 120 quadrats (Table 1). The number of species ranged from 8 to 11 over the 3 years, with 10 species observed in 2017, 8 in 2018, and 11 in 2020. The following five weed species were consistently present during all 3 years: Amaranthus viridis, Cleome rutidosperma, Cyperus sp., E. indica, and Phyllanthus virgatus. We observed 11 broadleaf species belonging to families Acanthaceae, Amaranthaceae, Cleomaceae, Commelinaceae, Euphorbiaceae, Phyllanthaceae, and Rubiaceae, as well as one sedge species of family Cyperaceae and three grass species belonging to Poaceae/Gramineae. In addition, four weed species belonging to Rubiaceae, three to Poaceae, and two to Phyllanthaceae were observed. Other families were represented by a single species.

Table 1

Phytosociological parameters of weed species in a corn field in 2017, 2018, and 2020

Weed species Habit F k U k ∑ D i MFD k MOFD k RF k RUk RD k RA k
Amaranthus viridis Broadleaf 100.00 36.67 32.68 10.89 10.89 10.34 9.57 19.64 39.55
Asystasia gangetica Broadleaf 33.33 3.33 0.38 0.13 0.38 3.45 0.87 0.23 4.54
Borreria latifolia Broadleaf 33.33 0.83 0.10 0.03 0.10 3.45 0.22 0.06 3.73
Cleome rutidosperma Broadleaf 66.67 24.17 3.80 1.27 1.90 6.90 6.30 2.28 15.48
Commelina sp. Broadleaf 33.33 0.83 0.20 0.07 0.20 3.45 0.22 0.12 3.79
Cyperus sp. Sedge 100.00 71.67 44.58 14.86 14.86 10.34 18.70 26.79 55.83
Digitaria longiflora Grass 66.67 9.17 0.60 0.20 0.30 6.90 2.39 0.36 9.65
Eleusine indica Grass 100.00 58.33 16.45 5.48 5.48 10.34 15.22 9.89 35.45
Euphorbia hirta Broadleaf 66.67 30.00 6.00 2.00 3.00 6.90 7.83 3.61 18.33
Hedyotis corymbosa Broadleaf 66.67 45.83 23.68 7.89 11.84 6.90 11.96 14.23 33.08
Mitracarpus hirtus Broadleaf 66.67 10.83 1.78 0.59 0.89 6.90 2.83 1.07 10.79
Oldenlandia corymbosa Broadleaf 33.33 4.17 0.53 0.18 0.53 3.45 1.09 0.32 4.85
Panicum dichotomiflorum Grass 33.33 1.67 1.00 0.33 1.00 3.45 0.43 0.60 4.48
Phyllanthus amarus Broadleaf 66.67 38.33 24.83 8.28 12.41 6.90 10.00 14.92 31.82
Phyllanthus virgatus Broadleaf 100.00 47.50 9.80 3.27 3.27 10.34 12.39 5.89 28.63
Total 100 100 100 300

F k  – frequency of weed species k; U k  – field uniformity of the weed species k; ∑D i  – sum of weed density; MFD k  – mean field density of weed species k; MOFD k  – mean occurrence field density of weed species k; RF k  – relative frequency of weed species k; RU k  – relative field uniformity of weed species k; RD k  – relative mean field density of weed species k; RA k  – relative abundance.

Table 1 lists the weed composition data, which indicated that the broadleaf weed type was the most dominant group. Cyperus spp. and E. indica had the highest field uniformities (>50%), which indicated their distributions for a given year. Cyperus spp. had the highest mean density (14.86 plants m−2), followed by A. viridis (10.89 plants m−2). The other weed species had densities below 10 plants m−2. The relative abundance indicated the overall weed abundance of a species compared with those of other species. Cyperus spp. had the highest relative abundance, followed by A. viridis, E. indica, Hedyotis corymbosa, and Phyllanthus amaru. These five species accounted for 65% of the total relative abundance.

Weed composition was categorized into three weed types, namely, broadleaf, sedge, and grass weeds. Calculated p values were lower than the adjusted alpha level. The variations in the number of individuals in the broadleaf, sedge, and grass weed types differed significantly between 2017 and 2018, between 2018 and 2020, and between 2017 and 2020 (Table 2). Broadleaf types increased from 2017 and 2018, while both grass and sedge types decreased in the same period. From 2018 to 2020, the number of broadleaf and grass species decreased, whereas the number of sedge species increased.

Table 2

A comparison of broadleaves, grasses, and sedges in 2017, 2018, and 2019 using two-proportion z-tests

Year Type of weed Number of plants in year mentioned Adjusted α level p value
2017 2018 2020
2017 and 2018 Broadleaf 1,203 2,793 0.016667 1.9029 × 10−267
Grass 149 32 0.016667 3.9698 × 10−27
Sedge 900 125 0.016667 4.6232 × 10−226
2018 and 2020 Broadleaf 2,793 154 0.016667 0
Grass 32 0 0.016667 0.000068
Sedge 125 1,299 0.016667 0
2017 and 2020 Broadleaf 1,203 154 0.016667 9.6325 × 10−154
Grass 149 0 0.016667 1.4031 × 10−23
Sedge 900 1,299 0.016667 1.4008 × 10−196

The p value was obtained from pairwise comparisons using two-proportion z-test with a Bonferroni correction.

3.2 Corn yield

On a fresh-weight basis, maize yields were 20.1 t from 0.99 ha, 19.8 t from 0.86 ha, and 15.7 t from 0.63 ha in 2017, 2018, and 2020, respectively. The yields obtained during the 3 years were higher than the average maize yield reported by the Department of Agriculture [3], which was 8 t per ha. However, maize planting density information for the average yield is limited. Temperature, rainfall, and weed density did not have a significant effect on corn yield (Table 3). A small sample size of 3 years may be a limitation of the results; however, the data can serve as a preliminary assessment.

Table 3

Effect of mean temperature, mean rainfall, and weed species on corn yield using General Linear Model

Parameters Estimate Standard error t value p value
Mean rainfall –0.000407283 0.00059901 –0.68 0.6199
Average mean temperature 0.003201266 0.00128208 2.5 0.2425
Amaranthus viridis 7.67 × 10−8 0.00000082 0.04 0.9769
Cleome rutidosperma 2.57 × 10−7 0.00000708 0.04 0.9769
Cyperus spp. –3.447 × 10−7 0.00000138 –0.25 0.8437
Eleusine indica 1.6722 × 10−6 0.00000181 0.92 0.5258
Euphorbia hirta –8.997 × 10−7 0.00000728 –0.12 0.9217
Hedyotis corymbosa –1.3281 × 10−6 0.00000038 –3.54 0.1754
Mitracarpus hirtus –0.000013825 0.00000790 –1.75 0.3304
Phyllanthus spp. –1.487 × 10−7 0.00000115 –0.13 0.9178
Others 8.3603 × 10−6 0.00000977 0.86 0.5495

“Others” include Asystasia gangetica, Borreria latifolia, Commelina spp., Digitaria longiflora, Oldenlandia corymbosa, and Panicum dichotomiflorum.

4 Discussion

A low number of species diversity (15 species) was recorded in this study. Raya et al. [17] identified 40 weed species in five vegetable farms in Selangor, Malaysia. Weed species diversity composition may be lower in maize farms than those in other crop farms [20]. Ahmad et al. [21] identified 29 species in 65 maize farms in Pakistan during a 2-month survey. Maize fields have dense canopies and a high crop density that can shade weed species, thereby reducing the growth of weeds as well as their seed production [22,23].

Species composition did not vary substantially during the study, with 5 of 15 species observed during all 3 years. This is consistent with a 9-year weed survey in maize farms in Germany, in which weed composition remained almost invariable [24]. Most weed species are replaced by other species after 30 years of maize cultivation, whereas 8 of 81 weed species remained on maize farms in France over a 30-year period [25]. Weeds have acquired different competitive abilities through sympatric evolution, which can affect weed abundance [26].

Based on weed type, weed communities changed in different years. The composition of broadleaf, grass, and sedge weeds changed in abundance in 2017, 2018, and 2020. Weed community diversities and abundances vary over time in response to selection pressures (e.g., environmental factors) and agronomic practices (e.g., fertilizer application) [27], which may have single, simultaneous, or cumulative effects [28,29]. However, weeds that are not persistently present every year may still have seed banks in the soil that fail to germinate in a particular season [30]. Therefore, the number of individuals of broadleaf, sedge, and grass species may vary from year to year, as their seeds may persist in the seed bank.

The weed species we observed were similar to those observed in other maize studies. For example, Cyperus was the most abundant species in maize farms in Pakistan [31]. Sedge weeds, such as Cyperus, are distributed widely in tropical and subtropical regions [32]. In addition, A. viridis, E. indica, and Euphorbia hirta, which were identified in this study, are associated with maize [33]. Sandy soil is the most important variable that contributes to the abundance of A. viridis [31]. Another study site on former tin-mining land in Kampar had a predominately sandy soil texture to a depth of 25–50 cm [34]; therefore, it is likely that the study site used herein also had sandy soil. A. viridis is a broadleaf plant that originated in Africa and is known for its widespread dispersal in tropical and subtropical areas, as well as in temperate regions [35]. It is adapted to various habitats, ranging from wet to extremely dry, resulting in an extensive geographical distribution [36]. Two genera identified in this study, Phyllanthus spp. and Oldenlandia spp., were present in maize farms in Pakistan [33]. The origin of each weed plays a crucial role; tropical weeds have broad and expanding ecological niches and therefore tend to be ubiquitous.

Mean density and relative abundance reflect the degree of difficulty to attain weed control. Weed densities of 32 plants m−2 and below would result in maize yields equivalent to those obtained in weed-free conditions [18]. We recorded 55.57 weeds m−2, which is considered a high weed density [7]. However, the parameters studied associating with maize yields were statistically insignificant. Planting density is an important factor in maize yields. The higher the density, the higher the expected yield [37]. In modern plant varieties, high maize yields correlate with high population density, i.e., between 6 and 9 plants m−2 [38,39,40]. The maize planting density in this study was 8 plants m−2. Optimum corn plant density may be advantageous for outcompeting weeds [39,41]. In addition, new maize varieties have improved tolerances to biotic and abiotic stresses; for example, they have crop crowding, weed interference, and heat and drought response. Maize varieties also differ in their physiological efficiency to uptake and utilize resources, including water and nutrients [39,41,42,43]. One limitation of this study is that we only investigated one maize planting density and one maize variety. These factors could affect maize competition with weeds and yields. Further research is needed to confirm our results using different maize varieties and planting densities.

In this study, weed surveys were conducted in 2017, 2018, and 2020. Fifteen weed species belonging to 14 genera and 9 families were identified. Cyperus sp. and A. viridis were the two most common weed species based on MFD and relative abundance. The numbers of broadleaf, sedge, and grass weed species varied significantly between years. Mean rainfall, mean temperature, and weed species did not have significant effects on maize yields. Weed management is a critical component of farming and sustainable agriculture to control yield loss. Long-term weed composition studies on maize farms in relation to yields are needed to provide an effective weed management framework for achieving food security and sustainable agriculture, through reviewing herbicide applications.

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Acknowledgments

The authors would like to express their gratitude to the farmer Mr. Chai Yoon Fook and the Malaysian Meteorological Department for their generous support in providing data.

  1. Funding information: The authors are grateful to the University Tunku Abdul Rahman (UTAR) for the research grant IPSR/RMC/UTARRF/2017-C2/T10.

  2. Conflict of interest: The authors state no conflict of interests.

  3. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] Ranum P, Peña-Rosas JP, Garcia-Casal MN. Global maize production, utilization, and consumption. Ann N Y Acad Sci. 2014;1312:105–12. 10.1111/nyas.12396.Search in Google Scholar

[2] Food and Agriculture Organization. FAOSTAT. Crops; 2020. [2020/12/20]. http://www.fao.org/faostat/en/#data/QC/visualize.Search in Google Scholar

[3] Department of Agriculture. Vegetables and cash crops statistics. Malaysia: Department of Agriculture Peninsular Malaysia; 2018. p. 24.Search in Google Scholar

[4] Galli F, Grando S, Adamsone-Fiskovica A, Bjørkhaug H, Czekaj M, Duckett DG, et al. How do small farms contribute to food and nutrition security? Linking Europeans small farms, strategies and outcomes in territorial food systems. Glob Food Sec. 2020;2020:26. 10.1016/j.gfs.2020.100427.Search in Google Scholar

[5] Gharde Y, Singh PK, Dubey RP, Gupta PK. Assessment of yield and economic losses in agriculture due to weeds in India. Crop Prot. 2018;107:12–8. 10.1016/j.cropro.2018.01.007.Search in Google Scholar

[6] Hanzlik K, Gerowitt B. Methods to conduct and analyse weed surveys in arable farming: A review. Agron Sustain Dev. 2016;36(1):11. 10.1007/s13593-015-0345-7.Search in Google Scholar

[7] Myers MW, Curran WS, Vangessel MJ, Majek BA, Scott BA, Mortensen DA, et al. The effect of weed density and application timing on weed control and corn grain yield. Weed Technol. 2005;19(1):102–7. 10.1614/WT-03-263R.Search in Google Scholar

[8] van Heemst HDJ. The influence of weed competition on crop yield. Agric Syst. 1985;18(2):81–93. 10.1016/0308-521X(85)90047-2.Search in Google Scholar

[9] Mohamed Zain S, Syed H, Miro MS. The effect of annual weed density and nitrogen fertilization on the yield of maize (Zea mays var. Bakti-I) [Malaysia]. Pertanika J Trop Agric Sci. 1984;7(1):61–5.Search in Google Scholar

[10] Asyraf M, Mashhor M. Weedy plants of Ayer Hitam Forest Reserve, Selangor. Pertanika J Trop Agric Sci. 2001;24(1):1–5.Search in Google Scholar

[11] Kamal-Uddin MD, Juraimi AS, Begum M, Ismail MR, Rahim AA, Othman R. Floristic composition of weed community in turf grass area of West Peninsular Malaysia. Int J Agric Biol. 2009;11(1):13–20.Search in Google Scholar

[12] Azmi M, Baki BB. Weed flora landscapes of the Muda rice granary in the new millennium: A descriptive analysis. JTAFS. 2007;35:319–31.Search in Google Scholar

[13] Hakim MA, Juraimi AS, Ismail MR, Hanafi MM, Selamat A. Distribution of weed population in the coastal rice growing area of Kedah in Peninsular Malaysia. J Agron. 2010;9(1):9–16.10.3923/ja.2010.9.16Search in Google Scholar

[14] Hakim MA, Juraimi AS, Ismail MR, Hanafi MM, Selamat A. A survey on weed diversity in coastal rice fields of Seberang Perak in Peninsular Malaysia. J Anim Plan Sci. 2013;23(2):534–42.Search in Google Scholar

[15] Adnan NS, Abdul Karim MF, Mazri NH, Fikri NA, Saharizan N, Mohd Ali NB, et al. Plants diversity in small rubber plantations at Segamat, Johor. In IOP Conference Series: Earth and Environmental Science, 2nd International Conference on Tropical Resources and Sustainable Sciences. Malaysia: Universiti Malaysia Kelantan, City Campus. 549(1), IOP Publishing; 2020. p. 012033. 10.1088/1755-1315/549/1/012033.Search in Google Scholar

[16] Mohamed MS, Seman IA. Occurrence of common weeds in immature plantings of oil palm plantations in Malaysia. Planter. 2012;88(1037):537–47.Search in Google Scholar

[17] Raya KB, Ahmed SH, Juraii AS, Bakar RA, Uddin MK. Floristic composition of weed community in selected vegetable fields in Selangor, Malaysia. J Food Agric Env. 2013;11(3–4):1659–63.Search in Google Scholar

[18] Concenço G, Tomazi M, Correia IVT, Santos SA, Galon L. Phytosociological surveys: Tools for weed science? Planta Daninha. 2013;31(2):469–82. 10.1590/S0100-83582013000200025.Search in Google Scholar

[19] Thomas AG. Weed survey system used in Saskatchewan for cereal and oilseed crops. Weed Sci. 1985;33(1):34–43. 10.1017/S0043174500083892.Search in Google Scholar

[20] Kamuti M, Mazsu N, Csathó P, Lehoczky É. Effects of nutrient supply on the weed flora composition in early growth stage of maize. Növénytermelés. 2015;64(Suppl):75–8.Search in Google Scholar

[21] Ahmad Z, Khan SM, Abd Allah EF, Alqarawi AA, Hashem A. Weed species composition and distribution pattern in the maize crop under the influence of edaphic factors and farming practices: A case study from Mardan, Pakistan. Saudi J Biol Sci. 2016;23(6):741–8. 10.1016/j.sjbs.2016.07.001.Search in Google Scholar PubMed PubMed Central

[22] Bhowmik PC. Weed biology: Importance to weed management. Weed Sci. 1997;45(3):349–56. 10.1017/S0043174500092973.Search in Google Scholar

[23] Jastrzębska M, Jastrzębski WP, Holdyński C, Kostrzewska MK. Weed species diversity in organic and integrated farming systems. Acta Agrobot. 2013;66(3):113–24. 10.5586/aa.2013.045.Search in Google Scholar

[24] de Mol F, von Redwitz C, Gerowitt B. Weed species composition of maize fields in Germany is influenced by site and crop sequence. Weed Res. 2015;55(6):574–85. 10.1111/wre.12169.Search in Google Scholar

[25] Fried G, Chauvel B, Munoz F, Reboud X. Which traits make weeds more successful in maize crops? Insights from a three-decade monitoring in France. Plants. 2020;9(1):40. 10.3390/plants9010040.Search in Google Scholar PubMed PubMed Central

[26] de Wet JMJ, Harlan JR. Weeds and domesticates: Evolution in the man-made habitat. Econ Bot. 1975;29(2):99–108. 10.1007/BF02863309.Search in Google Scholar

[27] Nkoa R, Owen MDK, Swanton CJ. Weed abundance, distribution, diversity, and community analyses. Weed Sci. 2015;63(SP1):64–90. 10.1614/WS-D-13-00075.1.Search in Google Scholar

[28] Nagy K, Lengyel A, Kovács A, Türei D, Csergő AM, Pinke G. Weed species composition of small-scale farmlands bears a strong crop-related and environmental signature. Weed Res. 2018;58(1):46–56. 10.1111/wre.12281.Search in Google Scholar

[29] Zhu J, Wang J, DiTommaso A, Zhang C, Zheng G, Liang W, et al. Weed research status, challenges, and opportunities in China. Crop Prot. 2020;134:104449. 10.1016/j.cropro.2018.02.001.Search in Google Scholar

[30] Borgy B, Reboud X, Peyrard N, Sabbadin R, Gaba S. Dynamics of weeds in the soil seed bank: A hidden Markov model to estimate life history traits from standing plant time series. PLoS One. 2015;10(10):e0139278. 10.1371/journal.pone.0139278.Search in Google Scholar PubMed PubMed Central

[31] Ahmad Z, Khan SM, Abd Allah EF, Alqarawi AA, Hashem A. Weed species composition and distribution pattern in the maize crop under the influence of edaphic factor and farming practices: a case study from Mardan, Pakistan. Saudi J Biol Sci. 2016;23:741–8.10.1016/j.sjbs.2016.07.001Search in Google Scholar PubMed PubMed Central

[32] Xu Z, Zhou G. Identification and control of common weeds. 1, Netherlands: Springer; 2017. p. 367.10.1007/978-94-024-0954-3_2Search in Google Scholar

[33] Hossain A, Islam MT, Islam MS, Ahmed S, Sarker KK, Gathala MK. Chemical weed management in maize (Zea mays L.) under conservation agricultural systems: An outlook of the Eastern Gangetic Plains in South-Asia. In: Hossain A, editor. Maize: Production and use. IntechOpen; 2019. p. 117–30.10.5772/intechopen.89030Search in Google Scholar

[34] Shamshuddin J, Mokhtar N, Paramananthan S. Morphology, mineralogy and chemistry of an ex-mining land in Ipoh, Perak. Pertanika. 1986;9(1):89–97.Search in Google Scholar

[35] Grubben GJH, Denton OA, editors. Plant resources of tropical Africa 2: Vegetables. Wageningen, Netherlands: PROTA foundation/Backhuys Publishers/CTA; 2004. p. 667.Search in Google Scholar

[36] Xu Z, Deng M. Identification and control of common weeds. 2, Netherlands: Springer; 2017. p. 279.10.1007/978-94-024-1157-7Search in Google Scholar

[37] Gözübenli H. Influence of planting patterns and plant density on the performance of maize hybrids in the eastern Mediterranean conditions. Int J Agric Biol. 2010;12:556–60.Search in Google Scholar

[38] Abuzar MR, Sadozai GU, Baloch MS, Baloch AA, Shah IH, Javaid T, et al. Effect of plant population densities on yield of maize. J Anim Plant Sci. 2011;21(4):692–95.Search in Google Scholar

[39] Amiri Z, Tavakkoli A, Rastgoo M. Responses of corn to plant density and weed interference period. Middle East J Sci Res. 2014;21(10):1746–50.Search in Google Scholar

[40] Greveniotis V, Zotis S, Sioki E, Ipsilandis C. Field population density effects on field yield and morphological characteristics of maize. Agriculture. 2019;9(7):160. 10.3390/agriculture9070160.Search in Google Scholar

[41] Wilson BJ, Wright KJ, Brain P, Clements M, Stephens E. Predicting the competitive effects of weed and crop density on weed biomass, weed seed production and crop yield in wheat. Weed Res. 1995;35(4):265–78. 10.1111/j.1365-3180.1995.tb01789.x.Search in Google Scholar

[42] Duvick DN. Genetic progress in yield of United States maize (Zea mays L.). Maydica. 2005;50(3/4):193–202.Search in Google Scholar

[43] Tollenaar M, Wu J. Yield improvement in temperate maize is attributable to greater stress tolerance. Crop Sci. 1999;39(6):1597–604. 10.2135/cropsci1999.3961597x.Search in Google Scholar
Received: 2021-09-05
Revised: 2022-03-15
Accepted: 2022-06-07
Published Online: 2022-06-25

© 2022 Pei Sin Tong and Tuck Meng Lim, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  3. Impacts of adopting specialized agricultural programs relying on “good practice” – Empirical evidence from fruit growers in Vietnam
  4. Evaluation of 11 potential trap crops for root-knot nematode (RKN) control under glasshouse conditions
  5. Technical efficiency of resource-poor maize farmers in northern Ghana
  6. Bulk density: An index for measuring critical soil compaction levels for groundnut cultivation
  7. Efficiency of the European Union farm types: Scenarios with and without the 2013 CAP measures
  8. Participatory validation and optimization of the Triple S method for sweetpotato planting material conservation in southern Ethiopia
  9. Selection of high-yield maize hybrid under different cropping systems based on stability and adaptability parameters
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  14. Competitiveness and impact of government policy on chili in Indonesia
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  16. Effect of potassium fertilizer application in teff yield and nutrient uptake on Vertisols in the central highlands of Ethiopia
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https://doi.org/10.1515/opag-2022-0117
Keywords for this article
maize; weeds; competition; growth; yield; phytosociology
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Foliar application of boron positively affects the growth, yield, and oil content of sesame (Sesamum indicum L.)
  3. Impacts of adopting specialized agricultural programs relying on “good practice” – Empirical evidence from fruit growers in Vietnam
  4. Evaluation of 11 potential trap crops for root-knot nematode (RKN) control under glasshouse conditions
  5. Technical efficiency of resource-poor maize farmers in northern Ghana
  6. Bulk density: An index for measuring critical soil compaction levels for groundnut cultivation
  7. Efficiency of the European Union farm types: Scenarios with and without the 2013 CAP measures
  8. Participatory validation and optimization of the Triple S method for sweetpotato planting material conservation in southern Ethiopia
  9. Selection of high-yield maize hybrid under different cropping systems based on stability and adaptability parameters
  10. Soil test-based phosphorus fertilizer recommendation for malting barley production on Nitisols
  11. Effects of domestication and temperature on the growth and survival of the giant freshwater prawn (Macrobrachium rosenbergii) postlarvae
  12. Influence of irrigation regime on gas exchange, growth, and oil quality of field grown, Texas (USA) olive trees
  13. Present status and prospects of value addition industry for agricultural produce – A review
  14. Competitiveness and impact of government policy on chili in Indonesia
  15. Growth of Rucola on Mars soil simulant under the influence of pig slurry and earthworms
  16. Effect of potassium fertilizer application in teff yield and nutrient uptake on Vertisols in the central highlands of Ethiopia
  17. Dissection of social interaction and community engagement of smallholder oil palm in reducing conflict using soft system methodology
  18. Farmers’ perception, awareness, and constraints of organic rice farming in Indonesia
  19. Improving the capacity of local food network through local food hubs’ development
  20. Quality evaluation of gluten-free biscuits prepared with algarrobo flour as a partial sugar replacer
  21. Effect of pre-slaughter weight on morphological composition of pig carcasses
  22. Study of the impact of increasing the highest retail price of subsidized fertilizer on rice production in Indonesia
  23. Agrobiodiversity and perceived climatic change effect on family farming systems in semiarid tropics of Kenya
  24. Influences of inter- and intra-row spacing on the growth and head yield of cabbage (Brassica oleracea var. capitata) in western Amhara, Ethiopia
  25. The supply chain and its development concept of fresh mulberry fruit in Thailand: Observations in Nan Province, the largest production area
  26. Toward achieving sustainable development agenda: Nexus between agriculture, trade openness, and oil rents in Nigeria
  27. Phenotyping cowpea accessions at the seedling stage for drought tolerance in controlled environments
  28. Apparent nutrient utilization and metabolic growth rate of Nile tilapia, Oreochromis niloticus, cultured in recirculating aquaculture and biofloc systems
  29. Influence of season and rangeland-type on serum biochemistry of indigenous Zulu sheep
  30. Meta-analysis of responses of broiler chickens to Bacillus supplementation: Intestinal histomorphometry and blood immunoglobulin
  31. Weed composition and maize yield in a former tin-mining area: A case study in Malim Nawar, Malaysia
  32. Strategies for overcoming farmers’ lives in volcano-prone areas: A case study in Mount Semeru, Indonesia
  33. Principal component and cluster analyses based characterization of maize fields in southern central Rift Valley of Ethiopia
  34. Profitability and financial performance of European Union farms: An analysis at both regional and national levels
  35. Analysis of trends and variability of climatic parameters in Teff growing belts of Ethiopia
  36. Farmers’ food security in the volcanic area: A case in Mount Merapi, Indonesia
  37. Strategy to improve the sustainability of “porang” (Amorphophallus muelleri Blume) farming in support of the triple export movement policy in Indonesia
  38. Agrarian contracts, relations between agents, and perception on energy crops in the sugarcane supply chain: The Peruvian case
  39. Factors influencing the adoption of conservation agriculture by smallholder farmers in KwaZulu-Natal, South Africa
  40. Meta-analysis of zinc feed additive on enhancement of semen quality, fertility and hatchability performance in breeder chickens
  41. Meta-analysis of the potential of dietary Bacillus spp. in improving growth performance traits in broiler chickens
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  43. Cross transferability of barley nuclear SSRs to pearl millet genome provides new molecular tools for genetic analyses and marker assisted selection
  44. Detection of encapsulant addition in butterfly-pea (Clitoria ternatea L.) extract powder using visible–near-infrared spectroscopy and chemometrics analysis
  45. The willingness of farmers to preserve sustainable food agricultural land in Yogyakarta, Indonesia
  46. Transparent conductive far-infrared radiative film based on polyvinyl alcohol with carbon fiber apply in agriculture greenhouse
  47. Grain yield stability of black soybean lines across three agroecosystems in West Java, Indonesia
  48. Forms of land access in the sugarcane agroindustry: A comparison of Brazilian and Peruvian cases
  49. Assessment of the factors contributing to the lack of agricultural mechanization in Jiroft, Iran
  50. Do poor farmers have entrepreneurship skill, intention, and competence? Lessons from transmigration program in rural Gorontalo Province, Indonesia
  51. Communication networks used by smallholder livestock farmers during disease outbreaks: Case study in the Free State, South Africa
  52. Sustainability of Arabica coffee business in West Java, Indonesia: A multidimensional scaling approach
  53. Farmers’ perspectives on the adoption of smart farming technology to support food farming in Aceh Province, Indonesia
  54. Rice yield grown in different fertilizer combination and planting methods: Case study in Buru Island, Indonesia
  55. Paclobutrazol and benzylaminopurine improve potato yield grown under high temperatures in lowland and medium land
  56. Agricultural sciences publication activity in Russia and the impact of the national project “Science.” A bibliometric analysis
  57. Storage conditions and postharvest practices lead to aflatoxin contamination in maize in two counties (Makueni and Baringo) in Kenya
  58. Relationship of potato yield and factors of influence on the background of herbological protection
  59. Biology and life cycle Of Diatraea busckella (Lepidoptera: Crambidae) under simulated altitudinal profile in controlled conditions
  60. Evaluation of combustion characteristics performances and emissions of a diesel engine using diesel and biodiesel fuel blends containing graphene oxide nanoparticles
  61. Effect of various varieties and dosage of potassium fertilizer on growth, yield, and quality of red chili (Capsicum annuum L.)
  62. Review Articles
  63. Germination ecology of three Asteraceae annuals Arctotis hirsuta, Oncosiphon suffruticosum, and Cotula duckittiae in the winter-rainfall region of South Africa: A review
  64. Animal waste antibiotic residues and resistance genes: A review
  65. A brief and comprehensive history of the development and use of feed analysis: A review
  66. The evolving state of food security in Nigeria amidst the COVID-19 pandemic – A review
  67. Short Communication
  68. Response of cannabidiol hemp (Cannabis sativa L.) varieties grown in the southeastern United States to nitrogen fertilization
  69. Special Issue on the International Conference on Multidisciplinary Research – Agrarian Sciences
  70. Special issue on the International Conference on Multidisciplinary Research – Agrarian Sciences: Message from the editor
  71. Maritime pine land use environmental impact evolution in the context of life cycle assessment
  72. Influence of different parameters on the characteristics of hazelnut (var. Grada de Viseu) grown in Portugal
  73. Organic food consumption and eating habit in Morocco, Algeria, and Tunisia during the COVID-19 pandemic lockdown
  74. Customer knowledge and behavior on the use of food refrigerated display cabinets: A Portuguese case
  75. Perceptions and knowledge regarding quality and safety of plastic materials used for food packaging
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  77. Liquefaction and chemical composition of walnut shells
  78. Validation of an analytical methodology to determine humic substances using low-volume toxic reagents
  79. Special Issue on the International Conference on Agribusiness and Rural Development – IConARD 2020
  80. Behavioral response of breeder toward development program of Ongole crossbred cattle in Yogyakarta Special Region, Indonesia
  81. Special Issue on the 2nd ICSARD 2020
  82. Perceived attributes driving the adoption of system of rice intensification: The Indonesian farmers’ view
  83. Value-added analysis of Lactobacillus acidophilus cell encapsulation using Eucheuma cottonii by freeze-drying and spray-drying
  84. Investigating the elicited emotion of single-origin chocolate towards sustainable chocolate production in Indonesia
  85. Temperature and duration of vernalization effect on the vegetative growth of garlic (Allium sativum L.) clones in Indonesia
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  87. Prediction model for agro-tourism development using adaptive neuro-fuzzy inference system method
  88. Special Issue of International Web Conference on Food Choice and Eating Motivation
  89. Can ingredients and information interventions affect the hedonic level and (emo-sensory) perceptions of the milk chocolate and cocoa drink’s consumers?
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