RESEARCH ARTICLE

Increasing forage yield and effective weed control of corn-soybean mixed forage for livestock through using by different herbicides

Yowook Song1https://orcid.org/0000-0002-1311-9399, Muhammad Fiaz2https://orcid.org/0000-0002-3398-5243, Dong Woo Kim1https://orcid.org/0000-0003-3697-909X, Jeongtae Kim1https://orcid.org/0000-0001-8080-9114, Chan Ho Kwon1,*https://orcid.org/0000-0003-4602-0093
Author Information & Copyright
1Department of Animal Science and Biotechnology, Kyungpook National University, Sangju 37224, Korea
2Department of Livestock Management, Cholistan University of Veterinary and Animal Sciences Bahawlpur, Punjab, Pakistan
*Chan Ho Kwon, Department of Animal Science and Biotechnology, Kyungpook National University, Sangju 37224, Korea. Tel: +82-54-530-1226, E-mail: chkwon@knu.ac.kr

© Copyright 2019 Korean Society of Animal Science and Technology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Apr 10, 2019 ; Revised: May 29, 2019 ; Accepted: Jul 01, 2019

Published Online: Jul 31, 2019

Abstract

The aim of this study was to evaluate different herbicides for optimum growth, yield and nutritive value of corn-soybean mixed forage under randomized complete block design. The experimental site was selected and divided equally into 3 blocks. Each block was further divided into 5 plots that each plot had 15 square meter space (3 × 5). Five herbicidal treatments were randomly applied over 5 plots and herbicides were used under 5 herbicidal treatments, viz. 1) No herbicide (control); 2) Pendimethalin; 3) Linuron; 4) S-metolachlor and 5) Ethalfluralin. The collected data were analyzed using ANOVA through SAS 9.1.3 software. The results indicated that growth characteristics were not influenced (p > 0.05) by any herbicide. However, arithmetically corn stalk height was highest in the field of Pendimethalin treatment, whereas highest soybean height was found in the field of S-metolachlor. Arithmetically dry matter (DM) yield was increased with herbicidal treatments as compared to that of control treatment. Relatively highest DM yield (130%) was recorded in the treatment of Ethalfluralin followed by Pendimethalin (126%), S-metolachlor (126%) and Linuron (108%) as compared to that of control treatment. The weed emergence was significantly reduced in all herbicidal treatments as compared to that of control (p > 0.05), but the difference among herbicidal treatments was non-significant. It was concluded that weed emergence can be effectively controlled by use of any tested herbicide. However, optimum DM yield can be achieved through using herbicides; Ethalfluralin, Pendimethalin and S-metolachlor.

Keywords: Corn; Soybean; Intercropping; Herbicides; Productivity

Background

The consumption trend of animal origin food has been increasing in Korea for last few years. It has become a good driving force to increase the production status of livestock products in the country as depicted by Korean livestock statistics. The livestock statistic’s comparison of years 2018 and 2014 showed adequate increase in number of different livestock species; beef cattle +5% (3,104 to 3,117), dairy cattle–1.0% (442 to 407), pig +1.3% (9,966 to 11,641), layer +5% (65,263 to 71,227) and broiler +3.4% (75,846 to 83,278) thousands [1].

Feeding resources and specifically forage is the prime requirement of rearing variety of livestock species. The researchers and academicians are embarked to enhance the self-sufficiency of forage production under limited land resources in country through innovative forage production techniques. Corn and soybean intercropping or mixed cropping is well suited innovative forage production technique under South Korean conditions [2]. However, native farmers face unwanted allied weeds with their crop plants and bear considerable losses in terms of forage productivity because weeds always compete crop plants for nutrients to grow parallel. Hence, weeds have been authenticated serious issue which may cause forage productivity losses [3]. The range 40% to 60% of production losses in corn has been already documented due to weed infestation [4]. Manual weed removal at optimal time is best possible solution to reduce forage productivity but rarely adopted as it is tedious and time-consuming job.

As rapid industrialization and urbanization, enormous young labor force have been shifted to urban area for better employment opportunities. Consequently, the labor gets limitedly available for weeding and other farm operations. Therefore, use of herbicides for weed removal gets popularized to reduce eventual forage production losses and have been promising weed control method [58]. However, haphazard usage of herbicide may cause adverse effects because some herbicides are well suited for corn but not for soybean in intercropping field [9]. Therefore, present study plan was designed with objective to evaluate different herbicides and find-out most suitable herbicide for optimum growth, yield and nutritive value of corn-soybean mixed forage to be fed as basal diet for livestock.

Materials and Methods

Location of study

The experiment was carried out at research farm located in Cheongri, Sangju-si, Gyeongsangbuk-do, Korea from 23rd May to 1st September, 2016. Geographical coordinates of research site were 36.3323°N, 128.1287°E.

Climate of research site

Climate in terms of temperature and rainfall of research site recorded during trial with 10 years history is given in Table 1.

Table 1. Comparative average temperature and total rainfall in field area of Sangju-si, Gyeongsangbuk-do, Korea
Climate Year May June July August
Temp (°C) 2016 19.8 23.7 25.9 27.1
2006–2015 18.41 22.4 24.7 25.1
Rainfall (mm) 2016 52.8 47 313.6 116.3
2006–2015 91 112.2 255.4 230.3

Korea Meteorological Administration, 2016 [10].

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Experimental treatments

The chemical properties of the experimental site is given in Table 2. The experiment was conducted under randomized complete block design (RCBD) and the design of experimental was shown in Fig. 1. An area of land (length 17 m and width 15 m) is selected and divided equally into 3 blocks; Block A, Block B, and Block C. Each block was further divided into 5 plots so that each plot had 15 square meter space (3 × 5). Five herbicidal treatments were randomly applied over 5 plots in each block. The blocks were defined as 3 replicates. Different herbicides were used under 5 herbicidal treatments, viz. 1) No herbicide (control); 2) Pendimethalin; 3) Linuron; 4) S-metolachlor and 5) Ethalfluralin.

Table 2. Chemical properties of the experimental field soil
p (1:5) EC (ds/m) Available phosphate (mg/kg) Organic matter (g/kg) N (%) Exchangeable cations (cmolc/kg)
Ca K Mg Na
6.22 0.25 214.34 10.64 0.2 7.44 0.85 1.81 0.84

EC, electrical conductivity; N, nitrogen; Ca, calcium; K, kalium; Mg, magnesium; Na, natrium.

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jast-61-4-185-g1
Fig. 1. Experimental design in Sangju-si, 2016.
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Land preparation

The soil of experimental land was sandy in nature having chemical properties given in Table 2. The chemical fertilizer N-P-K (21:17:17) was applied at the rate 1,000 kg per hectare before seeding for experiment.

Seeds and seeding

The variety of corn seed used was Pioneer-32P75, whereas variety of soybean seed was Chookdu-1 which selected from a cross between PI483463 and Hutcheson were used for experiment. Seeding of corn and soybean was carried out on 2 equally spaced rows in each plot as per experimental treatments. The space between corn and soybean rows was 0.75 meter. The seed to seed distance within a row from soybean to soybean was 10 cm and from corn to corn was 20 cm as fixed by seeding machine named Hwangeum (HG10A model). Two border rows around experimental area were examined to check the border effect.

Parameters studied

The effect of different herbicides on growth characteristics and productivity of corn-soybean intercropping forage was studied in terms of following parameters.

  1. Height of corn stalk & ear and soybean plant (cm)

  2. Corn-soybean coupling (No.)

  3. Dry matter (DM) yield of corn, soybean & total (tons/ha)

  4. Neutral detergent fiber (NDF), acid detergent fiber (ADF), relative feed value (RFV) and total digestible nutrients (TDN) (%)

  5. Yield of TDN in corn, soybean & total (tons/ha)

  6. Emergence of following weeds (No./m2)

    • - Chenopodium album

    • - Digitaria sanguinalis

    • - Portula caoleracea

    • - Echinochloa crus-galli

Data collection

Number of survived plants was counted 2 weeks post seeding on 21st June, 2016. The counting of following weeds was also executed on 28th June, 2016 for weed emergence, viz. 1) Chenopodium album, 2) Digitaria sanguinalis, 3) Portula caoleracea and 4) Echinochloa crus-galli. After counting of weed emergence, we left them to investigate how the growth of weeds affects the corn and soybean growths. Height of corn stalk, corn ear & soybean was recorded on the day of harvesting, 1st September 2016. The corn stalk height was measured from ground to the top of plant stamen, whereas height of corn ear was taken from ground to the stigma of ear evolved. Similarly, soybean height was measured from ground to the top of plant. Five average plants were taken from each replicate for measuring data regarding height. Number of stalk, ear & soybean as well as corn-soybean coupling was recorded through counting on harvesting date.

Laboratory analysis

Samples of corn stalk, ear and soybean from each replicate were randomly taken for DM yield, initially weighed, dried in oven at 70°C for 72 hours & then again weighed after drying. The kilogram of DM yield was also converted into tons per hectare. The percentage of DM was just calculated using fresh yield and DM yield information. Fiber analysis (NDF & ADF) were performed as per procedure of Van-Soest et al. in the laboratory of “Forage Production and Utilization”, Department of Animal Sciences and Biotechnology, Kyungpook National University Sangju Campus, Korea. The RFV and TDN were calculated through following equations [11,12],

R F V  =  R e l a t i v e   F e e d   V a l u e  =  ( D D M × D M I ) / 1 . 29   DDM = Digestible Dry Matter = 88.9 ( 0.779 × % ADF ) DMI = Dry Matter Intake ( % of BW ) = 120 / ( % NDF ) %   T D N   =   T o t a l   D i g e s t i b l e   N u t r i e n t s =   4 . 898   +   ( 89 . 796   × N E L ) NEL (Mcal / lb) = Net energy for lactation = 1 .0876  ( 0.0127 × ADF )
Statistical analysis

The collected data were analyzed using ANOVA technique through SAS 9.1.3 software. The difference among five treatment means was tested through Duncan Multiple Range Test [13].

Results

Growth characteristics

Effect of intercropping corn with soybean under different herbicides was determined in terms of following parameters. Fig. 2 shows the growth pattern under different herbicide of each experimental site. Results in Table 3 depicted that all growth characteristics were not influenced (p > 0.05) by any treatment. However, arithmetically corn stalk height was highest in the field of Pendimethalin treatment, whereas highest soybean height was found in the field of S-metolachlor. In case of corn-bean coupling number, it was decreased (p > 0.05) in the field of control treatment, however, the difference among other treatments was not significant (p > 0.05).

jast-61-4-185-g2
Fig. 2. Experimental sites and growth characteristics about each treatment in Sangju-si, 2016.
Download Original Figure
Table 3. Effect of different herbicides on growth characteristics of plants in corn-soybean mixed cropping forage (Mean)
Growth parameters Treatments SEM p-value
No herbicide Pendimethalin Linuron S-metolachlor Ethalfluralin
Corn stalk height (cm) 299 312 300 297 306 10.6 0.940
Corn ear height (cm) 139 139 141 138 138 5.5 0.998
Soybean height (cm) 102 107 102 124 112 13.2 0.872
Corn-bean coupling (No./7.5 m2) 13 36 31 35 39 3.6 0.018

a,b Variables having different superscripts in the same rows are significantly different (p < 0.05).

SEM, standard error of the mean.

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Dry matter yield

The overall results in Table 4 indicated that no doubt DM yield was not different (p > 0.05) statistically among different treatment groups. However, arithmetically DM yield was increased with herbicidal treatments as compared to that of control treatment. Findings regarding total DM yield and relative DM yield depicted that relatively highest DM yield (130%) was found in the treatment of Ethalfluralin followed by Pendimethalin (126%) and S-metolachlor (126%). Whereas relative DM yield in case of Linuron was also better (108%) than that of control treatment with herbicide use. Similarly, the soybean proportion was not significantly different, but control treatment was arithmetically lowest, whereas the impact of Ethalfluralin is the highest in among different treatment groups. The results shown that Ethalfluralin had less damage on soybean and ultimately produced higher level of soybean.

Table 4. Effect of different herbicides on dry matter yield in corn & soybean mixed cropping
Dry matter yield Treatments SEM p-value
No herbicide Pendimethalin Linuron S-metolachlor Ethalfluralin
Corn stalk (ton/ha) 5.1 6.1 5.5 6.1 6.1 0.313 0.405
Corn ear (ton/ha) 1.8 2.2 1.7 2.3 2.4 0.421 0.832
Soybean (kg/ha) 383 904 680 808 963 121.0 0.146
Total (ton/ha) 7.3 9.2 7.9 9.2 9.5 0.758 0.475
Soybean proportion (%) 4.9 9.5 9.2 8.9 10.1 1.3 0.274
Relative DM yield (%) 100 126 108 126 130 - -

a,b Variables having different superscripts in the same rows are significantly different (p < 0.05).

SEM, standard error of the mean.

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NDF, ADF, RFV and TDN

The effect of different herbicidal treatment on NDF, ADF, RFV and TDN was presented in Table 5. The statistical analysis showed that there was no difference (p > 0.05) among different herbicidal treatments. However, arithmetic difference was observed. The NDF in corn stalk was found lowest in Pendimethalin, whereas it was highest in control treatment. Similarly, ADF trend was lower in Pendimenthalin and Lunuron but Ethalfluralin and S-metolchlor were in higher levels on both corn stalk and soybean. In case of RFV and TDN values, highest trend was also found in Pendimethalin treatment except TDN% in soybean.

Table 5. Effect of different herbicide on NDF, ADF, RFV, TDN percentage in corn soybean intercropping forage
Treatments NDF ADF RFV TDN
Corn (%) Soybean (%) Corn (%) Soybean (%) Corn (%) Soybean (%) Corn (%) Soybean (%)
Non-herbicide 74.1 58.0 45.7 44.5 111.6 142.7 56.1 56.9
Pendimethalin 65.4 57.8 39.3 45.1 126.6 143.3 60.5 56.5
Linuron 67.5 57.7 40.2 44.5 123.1 143.2 59.9 56.9
S-metolachlor 69.3 60.1 42.7 46.5 119.6 137.6 58.1 55.5
Ethalfulralin 67.8 59.7 41.7 47.0 122.3 138.5 58.8 55.2
SEM 1.7 0.9 1.2 0.9 3.0 2.2 0.8 0.6
p-values 0.15 0.43 0.11 0.49 0.16 0.46 0.11 0.49

NDF, neutral detergent fiber; ADF, acid detergent fiber; RFV, relative feed value; TDN, total digestible nutrients; SEM, standard error of the mean.

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Emergence of following weeds

The influence of different herbicidal treatments on emergence of various weeds is mentioned in Table 6. The emergence of Chenopodiumalbum was found absent in case of Pendimethalin and Linuron herbicides. However, the difference among various treatments was non-significant (p > 0.05). In case of weed named Digitaria sanguinalis, its emergence was found lower (p < 0.05) in Pendimethalin, S-metolachlor and Ethalfluralin as compared to that of control treatment. However, emergence in case of Linuron was not different (p > 0.05) with all other treatments. The emergence of other weed named, “Portulaca oleracea” was also found absent in case of Linuron herbicide but its difference with all treatment was not significant (p > 0.05). The emergence of last weed named, “Echinochloa crus-galli” was found zero in S-metolachlor herbicide. Its emergence was found higher (p < 0.05) in control treatment than all other treatments. However, weed emergence was significantly reduced in all herbicidal treatments as compared to that of control but the difference among herbicidal treatments was non-significant.

Table 6. Effect of different herbicides on following weed emergence in corn-soybean mixed cropping fields
Weed emergence parameters Treatments SEM p-value
No herbicide Pendimethalin Linuron S-metolachlor Ethalfluralin
Chenopodium album (No/m2) 159.3 0 0 105.6 27.8 30.8 0.062
Digitaria sanguinalis (No/m2) 509.3 31.5 266.7 16.7 9.3 60.8 0.004
Portulaca oleracea (No/m2) 35.2 20.4 0 22.2 25.9 7.5 0.208
Echinochloa crus-galli (No/m2) 335.2 64.8 1.9 0 61.1 16.7 <.0001

a,b Variables having different superscripts in the same rows are significantly different (p < 0.05).

SEM, standard error of the mean.

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Discussion

Findings regarding influence of different herbicides over corn-soybean coupling depicted that the effect of tested herbicides in this study was comparable in terms of improved corn-soybean coupling which was higher to control treatment. The possible factor might be effective control of weeds through herbicides as documented by previous studies [1416]. The findings of this study regarding impact of Pendimethalin on coupling was also in line to previous study by Kim et al. [14]. The possible reason might be attributed to factor of effectiveness against weeds through its inhibitory action on cell division of broader leaves and finally improved corn-soybean coupling [17]. Pandey and Prakash [18] also substantiated improvement in yield of corn-soybean intercropping through Pendimethalin treatment. However, arithmetic increase in other growth characteristics of corn-soybean forage over control treatment was found but in fact not influenced statistically. The possible reason might be little variation in data generated in this study. In case of optimum total DM yield, although effect of tested herbicides was also comparable. Possible reason for highest DM yield in Ethalfluralin treatment followed by Pendimethalin and S-metolachlor might be the same pattern of corn-soybean coupling number in this study. The weed emergence of Pendimethalin and Ethalfuralin were higher levels on whole groups and the total number of weeds were 116.7 and 124.1 no/m2, respectively. The effective control of Linuron against weed emergence might be due to the factor of its inhibition of photosystem-II which was important for photosynthetic electron transport in plants [17]. However, relative DM yield in current study was found lowest in case of Linuron herbicide. The possible reason might be its less suitability factor for corn-soybean mixed forage. The highest relative DM yield in case Ethalfluralin treatment might be due its effective control over weed emergence. Ethalfluralin is dinitroaniline herbicides in nature with inhibition of cell mitosis [19], whereas effect of Pendimethalin over DM yield was also documented previously by Kim et al. [14]. Similarly, the S-metolachlor was already substantiated and environmentally friendly herbicide used for sustainable weed management which enhanced the relative DM yield 126% in current study [20].

Keeping in view the above discussion, it was concluded that weed emergence can be effectively controlled by use of any tested herbicide. However, optimum DM yield can be achieved through using herbicides; Ethafluralin, Pendimethalin and S-metolachlor. However, in present study results variation indicated that whole herbicides are not able to recommend yet. The finding high stability herbicide is generally important on sustainable agriculture.

Competing interests

No potential conflict of interest relevant to this article was reported.

Funding sources

Not applicable.

Acknowledgements

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agri-Bio industry Technology Development Project, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (314024-3).

Availability of data and material

Upon reasonable request, the datasets of this study can be available from the corresponding author.

Authors’ contributions

Conceptualization: Kwon CH.

Data curation: Fiaz M.

Formal analysis: Song Y, Fiaz M.

Methodology: Kwon CH, Fiaz M.

Software: Song Y, Fiaz M.

Validation: Kwon CH.

Investigation: Kim DW, Kim J.

Writing - original draft: Song Y.

Writing - review & editing: Kwon CH.

Ethics approval and consent to participate

This article does not require IRB/IACUC approval because there are no human and animal participants.

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