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.
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.
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]:
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.
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.
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.
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:
where RF k is the relative frequency for weed species k.
where RU k is the relative field uniformity for weed species k.
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.
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.
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.
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.
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.
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Funding information: The authors are grateful to the University Tunku Abdul Rahman (UTAR) for the research grant IPSR/RMC/UTARRF/2017-C2/T10.
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Conflict of interest: The authors state no conflict of interests.
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Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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