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SJA_36_4_1103_1111

 

 

 

Research Article

GIS Applications in Surveying and Mapping of Rice Weeds in Guilan Province, Iran

Mohammad Javad Golmohammadi1, Hamid Reza Mohammaddoust Chamanabad1*, Bijan Yaghoubi2 and Mostafa Oveisi3

1Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Resource University of Mohaghegh Ardabili; Ardabil, Iran; 2Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization, Rasht, Iran; 3Department of Agronomy and Plant Breeding, Faculty of Agriculture University of Tehran; Karaj, Iran.

Abstract | Present study was carried out in rice farms of Guilan province for mapping and weed distribution using Geographic Information System (GIS) technologies for a period of three years (2014-2016). Echinochloa crussgalli was the more important species in 10 regions of Guilan province. The Langarud and Rudsar regions each with 47 species and Anzali and Shaft with 25 species (with 71.2 and 37.9 percent, respectively) had the highest and lowest species diversities, species diversity in 2015 was more than 2014 and 2016 years. The frequency of weed species in Guilan rice farms in 2014-2016 was 3.38, 3.87 and 4.82percent, respectively. Cyperus esculentus and P. distichum were the most abundant species with 9 and 8 plants/m2, respectively. Echinochloa crussgalli and C. difformis with a density of 5 plants/m2. Weeds were non-uniformly distributed in various families including Cyperaceae, Poaceae, Lythraceae, Polygonaceae, Asteraceae, and Salviniaceae that accounted for 36 species (54.5 percent). Accurate and specific weed maps are the key to achieving all the benefits of weed management. When in an area the distribution and consumption of inputs is based on accurate information from the weed composition of the farms in that area, the efficiency of these inputs is improved and weed damage is reduced.


Received | January 29, 2018; Accepted | September 4, 2020; Published | October 21, 2020

*Correspondence | Hamid Reza Mohammaddoust Chamanabad, Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Resource University of Mohaghegh Ardabili; Ardabil, Iran; Email: [email protected]

Citation | Golmohammadi, M.J., H.R.M. Chamanabad, B. Yaghoubi and M. Oveisi. 2020. GIS applications in surveying and mapping of rice weeds in Guilan Province, Iran. Sarhad Journal of Agriculture, 36(4): 1103-1111.

DOI | http://dx.doi.org/10.17582/journal.sja/2020/36.4.1103.1111

Keywords | Accurate agriculture, Geographic information system, New systems, Rice farming


Introduction

The culture of man and rice has a historical and profound relationship. Rice has an inevitable role in providing food, economy, religion and civilization in many countries of the world (Jabran and Chauhan, 2015; Dass et al., 2016; Golmohammadi et al., 2018). More than half of the world’s rice is produced in Asia (Awan et al., 2015). In Iran also rice is the main staple food in most parts of the country. There are almost 600,000 hectares of rice plantation in Iran with a total production of 2.5 milion tons (FAO, 2016). More than 75percent of the rice crop is grown in the Provinces of Mazandaran and Guilan, north of Iran. Weeds are considered an important challenge to rice production (Tshewang et al., 2016; Yaghoubi et al., 2010). The mean reduction in rice yield caused by weeds competition is 40-60percent, which may reach 94-96percent if weeds are not properly controlled (Chauhan and Johnson, 2011). Rice yield reduction resulting from weed competition may vary depending on rice planting methods, type of weed, degree of importance of the weed, agricultural operations, and weather conditions (Jabran and Chauhan, 2015).

Determining the distribution pattern of weeds and their density at each point is very important. Therefore, before any action, we need to draw up maps of the weed distribution pattern and their density. The preparation of these maps is done using GPS and GIS technology. The ability to use GPS as an effective tool for estimating weed infested areas and the evaluation of different weed control methods has been proven (Nelson et al., 1999; Wilson et al., 1993). The GIS system is a complex system that provides organized information to a specific location. The GIS is a geographic coordinate system that determines the accuracy of GPS information, dominance map, frequency, distribution, diversity of weed species, or paths that have invasive species (Nkoa et al., 2015). GIS maps are digital and effective on each other with information. The GIS system can complete the data collected from a place of information layers, and on the other hand, researchers use the information obtained to further explore them. GIS technology can be used to make maps that theoretically predict the time of weed emergence from weather data (Main et al., 2004). The distribution, density and frequency of rice weeds from 8 documentary areas were determined through the GIS system. GIS has been applied in agriculture for ahe spatial analysis of weeds (Nelson et al., 1999).

Using the GIS system and collecting accurate information, can be used to compare species distribution and location change from year to year and to summarize infestations by area (Korejo et al., 2010). The most popular research techniques used by weed scientists are the use of the GIS system. Most studies of weed science are through the use of GIS with the geographic location of particular weed species and their populations. GIS is used to analyze the issues of what weed species are in the region, and what is the relationship between distribution and dominance with climate, environment and management factors. Tools like ArcGIS can help geographically spread weed science, cluster patterns, and geographic ratio analysis. The use of GIS in analytical ratios, such as the presence and absence of weed, distribution, dominance, abundance, aquatic and non-aquatic conditions, is finally used to better understand (Nkoa et al., 2015; Shanwad et al., 2002). Using GPS and GIS, we can identify contaminated weed maps and accurately manage farm information accurately. Spatial information is provided by GPS receivers for land border mapping, roads, irrigation systems, and problematic areas such as weed or disease susceptibility. The GPS system helps farmers navigate soil samples or crop conditions year after year to accurately navigate in special farms on farms (Yousefi and Razdari, 2015). Given the presence of weeds in a region, it is possible to decide on their control methods and plan; otherwise, the application of different control methods will not have the desired effects, and on the other hand, inadvertently contaminated in the chemical control of the environment, and even a number of weeds Weeds will also be resistant to herbicides. Therefore, regular weed monitoring will improve the status of the existing GIS technology.

 

Materials and Methods

In Iran, rice is the main staple in most parts of the country, and more than 75 percent of the rice crop is grown in the Provinces of Mazandaran and Guilan, north of Iran (Ministry of Agricalture Jihad, 2019). During the springs and summers of 2014, 2015 and 2016, when the current study was being done, temperature ranged from 16 to 29.4 °C, monthly average rainfall recorded 213 to 853 mm, average sunshine hours 3320, and relative humidity averaged 45-87percent. The study area is Guilan province of Iran, which situated in the north of Iran (Figure 1). Same is here located 36°34’ to 38°27’ latitude and 48°53’ to 50°34’ longitudes (Table 1). Guilan province is divided into 16 counties including Astara, Astaneh-ye Ashrafiyeh, Anzali, Amlash, Talesh, Rasht, Rudsar, Rudbar, Rezvanshahr, Siahkal, Shaft, Foman, Sowme’eh Sara, Lahijan, Langarud and Masal (Figure 1).

Guilan province is the wettest province in Iran and also the wettest region of the southern shores of the Caspian Sea. The average annual rainfall is 1300 mm, the average annual temperature and humidity are 16 °C and 80 percent, respectively (Guilan Meteorological Organization, 1398). Rainfall, rivers, temperature and evaporation of Guilan province are shown in Figure 2. The total cultivated area is about 220,000 hectares of cultivated land, accounting for 33percent of the land area of the country (Ministry of Agriculture Jihad, 2019). Sampling operation was performed by a quadrat of 0.25 m2 (0.5× 0.5 m). Accordingly, the higher the area under cultivation of the mentioned crop in the regions, the higher the number of samples will be. In order to obtain complete flora from farm weeds, separate quadrates were used, depending on the new species observed. In each city, according to the area under rice cultivation (in farms with an area of up to 2 hectares, 7 quadrats, farms of 2-5 hectares, 9 quadrats and more than 5 hectares, 13 quadrats) from 10 days after transplanting to the end of the rice clustering stage. Farms were randomly selected and sampled. In the evaluated farms, geographical coordinates (latitude and longitude and altitude) were recorded by Garmin GPSMap 62s. These coordinates were used to prepare and produce weed distribution maps in Guilan province by GIS system. A total of 481 farms in Guilan province were visited during the rice growing period. Weeds in each box were identified and counted separately by genus and species. The frequency and density of weeds in each farm and each city were calculated using the following equations (Thomas, 1985).

 

Table 1: Latitude and longitude in regions of Gilan province.

Longitude

Latitude

Min

Max

Min

Max

minutes

degrees

minutes

degrees

minutes

degrees

minutes

degrees

34

48

53

48

15

38

27

38

Astara

46

49

11

50

11

37

28

37

Astaneh-e Ashrafiyeh

0

50

17

50

48

36

8

37

Amlash

11

49

41

49

23

37

34

37

Anzali

32

48

3

49

33

37

16

38

Talesh

27

49

55

49

1

37

27

37

Rasht

40

48

13

49

25

37

40

37

Rezvanshahr

11

49

5

50

33

36

7

37

Rudbar

6

50

37

50

38

36

12

37

Rudsar

43

49

9

50

41

36

11

37

Siahkal

10

49

32

49

57

36

18

37

Shaft

3

49

31

49

15

37

30

37

Sowmee sara

52

48

27

49

1

37

17

37

Fouman

45

49

13

50

5

37

23

37

Lahijan

55

49

16

50

56

36

19

37

Langarud

43

48

11

49

15

37

29

37

Masal

 

 

Frequency is concerned with the presence or absence of a species in a quadrat, a farm, or a region, and does not refer to the number or quantity of the species.

In above equation, Fk represents the frequency of the species, Yi the presence or absence of the species, and “n” the number of visited farms (1).

Density refers to the counted individuals of each species m2 in the farm of interest:

Dki stands for the density of species k in farm i and Zj the number of plants in quadrat j (2). The coordinates of each farm, such as latitude, latitude and elevation, were recorded by the Garmin 62CX GPS device. After registering the point with the GPS device, it was converted to KMZ format, then these files became GIS file format for mapping. Then all information was created in the form of a database. The information in ArcMap software was processed from the ArcView GIS 3.1 suite of software, based on latitude and longitude.

 

Results and Discussion

Sixty- six weed species were found in the area, belonging to 43 genera and 29 families. The families with the highest number of species found were Cyperaceae, Poaceae and Lythraceae. Thirty- four of the identified species were annual grasses and 22 perennial species. Weeds have non-uniformly distributed in various families including Cyperaceae, Poaceae, Lythraceae, Polygonaceae, Asteraceae, and Salviniaceae that accounted for 36 species (54.5 percent). Among the grass weeds, E. crussgalli with 89.8 percent was the most common and most frequently species. P. distichum and E. oryzoides had frequencys of higher than 50 percent.

The most important weeds with high relative frequency value in rice farms were E. crussgalli (31.18), P. distichum (29.28), C. esculentus (25.56), E. prostrate (13.7), E. oryzoides (13.28), and, C. difformis (13.25). The dominant species in terms of frequency and density (m2) in 10 counties (62.5 percent of the

 

Table 2: Important weed species of field rice showing density and frequency% in Guilan province, Iran. (Astara, Astaneh-e-Ashrafiyeh, Amlash, Anzali, Talesh, Rasht, Rezvanshahr, Rudbar, Rudsar, Siahkal, Shaft, Sowmeesara,Fouman, Lahijan, Langarud and Masal).

Frequency

Density (m2)

Weeds

Area (ha)

Site

Item

3.326

0.138

Echinochloa crussgalli

3200

Astara

1

3.326

0.285

Paspalum distichum

2.495

37.000

Azolla filiculoides

2.287

0.157

Eclipta prostrata

2.079

0.093

Cyperus difformis

2.079

0.020

Echinochloa oryziodes

7.277

1.003

Echinochloa crussgalli

23570

Astaneh-e Ashrafiyeh

2

6.861

0.745

Paspalum distichum

5.198

0.613

Cyperus difformis

3.950

0.048

Echinochloa oryziodes

2.703

0.051

Eclipta prostrata

2.287

18.500

Algue blue-green

6.445

0.306

Echinochloa crussgalli

3500

Amlash

3

5.198

0.507

Paspalum distichum

4.574

0.167

Eclipta prostrata

4.366

0.054

Echinochloa oryziodes

3.950

0.936

Cyperus difformis

3.534

0.712

Monochoria vaginalis

2.287

0.332

Echinochloa crussgalli

4820

Anzali

4

1.871

0.125

Cyperus difformis

1.871

0.470

Paspalum distichum

1.455

0.011

Echinochloa oryziodes

1.247

0.182

Eclipta prostrata

1.040

0.021

Cyperus esculentus

4.990

0.480

Paspalum distichum

15987

Talesh

5

3.742

0.134

Echinochloa crussgalli

3.326

0.154

Alisma plantago-aquatica

2.911

24.000

Azolla filiculoides

2.703

0.141

Cyperus difformis

2.703

0.071

Cyperus serotinus

10.395

1.090

Echinochloa crussgalli

62338

Rasht

6

10.395

1.399

Paspalum distichum

7.900

0.132

Echinochloa oryziodes

7.484

1.423

Cyperus difformis

4.990

0.140

Cyperus serotinus

3.950

0.379

Cyperus esculentus

2.495

0.085

Alisma plantago-aquatica

10000

Rezvanshahr

7

2.287

0.085

Echinochloa crussgalli

2.287

0.207

Paspalum distichum

1.663

0.107

Cyperus difformis

1.663

0.047

Cyperus serotinus

1.455

16.600

Azolla filiculoides

5.198

1.332

Scirpus maritimus

3375

Rudbar

8

4.990

0.281

Echinochloa crussgalli

3.742

0.265

Paspalum distichum

3.326

0.352

Potamogeton nodosus

2.079

26.000

Najas marina

1.663

0.016

Echinochloa oryziodes

12.308

0.618

Echinochloa crussgalli

10689

Rudsar

9

9.813

1.101

Paspalum distichum

9.647

0.148

Echinochloa oryziodes

6.819

0.226

Eclipta prostrata

6.653

1.235

Cyperus esculentus

6.320

1.227

Cyperus difformis

5.613

0.641

Echinochloa crussgalli

4433

Siahkal

10

4.366

0.496

Paspalum distichum

3.742

0.645

Cyperus difformis

3.534

0.036

Eclipta prostrata

3.326

0.145

Echinochloa oryziodes

2.911

0.050

Cyperus esculentus

1.871

0.218

Paspalum distichum

14330

Shaft

11

1.247

0.147

Cyperus esculentus

1.247

0.046

Echinochloa crussgalli

1.247

0.179

Cyperus difformis

1.247

0.067

Eclipta prostrata

1.247

1.110

Azolla filiculoides

3.119

0.745

Cyperus difformis

27972

Sowmee Sara

12

3.119

0.164

Echinochloa crussgalli

2.703

0.137

Paspalum distichum

2.703

0.379

Eclipta prostrata

2.287

0.020

Echinochloa oryziodes

1.663

0.093

Cyperus serotinus

4.366

0.100

Echinochloa crussgalli

13870

Fouman

13

3.950

0.276

Paspalum distichum

2.703

0.322

Cyperus esculentus

2.703

0.111

Eclipta prostrata

2.287

0.258

Cyperus difformis

2.287

0.034

Echinochloa oryziodes

9.771

1.654

Echinochloa crussgalli

23816

Lahijan

14

7.692

0.094

Paspalum distichum

7.484

0.711

Cyperus difformis

6.237

0.138

Echinochloa oryziodes

6.029

1.360

Sagittaria trifolia

5.613

0.293

Eclipta prostrata

7.900

0.660

Echinochloa crussgalli

9100

Langarud

15

6.029

0.577

Paspalum distichum

5.198

0.066

Cyperus difformis

5.198

0.278

Eclipta prostrata

4.366

1.244

Echinochloa oryziodes

3.534

0.917

Monochoria vaginalis

2.079

0.165

Paspalum distichum

7000

Masal

16

1.663

0.053

Cyperus difformis

1.663

0.026

Cyperus serotinus

1.455

0.161

Echinochloa crussgalli

1.040

0.312

Alisma plantago-aquatica

1.040

0.021

Eclipta prostrata

 

Table 3: Frequency of weeds Guilan province, Iran (2014-2016).

2016

2015

2014

Species name

Family

Item

14.7

7.14

9.77

Sagittaria trifolia

Alismataceae

1

11.37

9.98

7.34

Alisma plantago-aquatica

Alismataceae

2

3.7

3.83

2.66

Alternanthera sessilis

Amaranthaceae

3

15.8

20.15

13.53

Eclipta prostrata

Asteraceae

4

6.33

4.39

4.65

Bidens tripartita

Asteraceae

5

4.45

3.66

2.48

Xanthium strumarium

Asteraceae

6

2.58

1.24

2.42

Butomus umbellatus

Butomaceae

7

0.26

0.16

0

Chara vulgaris

Characeae

8

3.89

2.63

3.88

Nasturtium officinale

Cruciferae

9

7.17

4.63

5.46

Algue blue-green

Cyanophyceae

10

24.19

17.12

15.23

Cyperus esculentus

Cyperaceae

11

22.53

7.01

5.19

Cyperus serotinus

Cyperaceae

12

13.44

7.78

10.38

Cyperus difformis

Cyperaceae

13

5.43

1.95

2.69

Scirpus maritimus

Cyperaceae

14

5.98

3.17

0

Cyperus fuscus

Cyperaceae

15

1.84

1.06

0.64

Cyperus rotundus

Cyperaceae

16

1.97

1.36

0

Cyperus longus

Cyperaceae

17

1.44

0.72

0.96

Scirpus mucronatus

Cyperaceae

18

1.49

1

0

Cyperus strigosus

Cyperaceae

19

0.95

0.51

0

Pycreus flavescense

Cyperaceae

20

0.64

0.4

0

Cyperus odoratus

Cyperaceae

21

0

0.33

0.5

Pycreus lanceolatus

Cyperaceae

22

0.31

0.31

0

Fimbristylis miliacea

Cyperaceae

23

0.21

0.21

0

Cyperus glomeratus

Cyperaceae

24

0.34

0.08

0

Cyperus iria

Cyperaceae

25

1.16

0.5

0

Bergia capensis

Elatinaceae

26

1.04

0.55

0.71

Equisetum palustre

Equistmaceae

27

0

0.21

0

Equisetum arvense

Equistmaceae

28

26.5

34.99

28.31

Echinochloa crussgalli

Gramineae

29

30.17

22.22

27.03

Paspalum distichum

Gramineae

30

19.15

23.9

17.24

Echinochloa oryziodes

Gramineae

31

2.41

4.23

0

Echinochloa colona

Gramineae

32

0

0.21

0

Eleocharis palustris

Gramineae

33

0

0.21

0

Coix lacrima-jobi

Gramineae

34

0

0.21

0

Digitaria sanguinalis

Gramineae

35

6.23

2.56

3.27

Najas marina

Hydrocharitaceae

36

1.29

0.52

0.89

Najas minor

Hydrocharitaceae

37

4.32

1.56

5.14

Schoenoplectus juncoides

Juncaceae

38

0

0.83

0

Mentha aquatica

Labiatae

39

10.39

9.68

8.19

Lemna minor

Lemnaceae

40

5.89

7.24

3.71

Ammannia multiflora

Lythraceae

41

4.25

6.35

0

Ammannia baccifera

Lythraceae

42

2.18

0.73

1.25

Rotala indica

Lythraceae

43

0

3.95

0

Ammannia senegalensis

Lythraceae

44

0

0.83

0

Ammannia gracilis

Lythraceae

45

2.94

1.11

2.19

Marsilea quadrifolia

Marsileaceae

46

4.29

2.87

1.26

Ludwigia epilobioides

Onagraceae

47

2.35

0.37

0.19

Ludwigia palustris

Onagraceae

48

3.7

6.49

5.19

Polygonum persicaria

Polygonaceae

49

2.45

1.55

2.45

Polygonum hydropiper

Polygonaceae

50

0

0.4

0.64

Polygonum hydropiperoides

Polygonaceae

51

0

0.21

0.21

Rumex crispus

Polygonaceae

52

9.68

3.94

5.71

Potamogeton nodosus

Potamogetonaceae

53

0

0.21

0

Potamogeton crispus

Potamogetonaceae

54

6.75

4.48

5.4

Monochoria vaginalis

Potederiaceae

55

0

0

0.21

Samolus valerandi

Primulaceae

56

0

1.2

1.92

Ranunculus aquatilis

Rananculaceae

57

1.87

0.62

0

Riccia glauca

Ricciaceae

58

0

0.21

0

Galium aparine

Rubiaceae

59

15.43

6.65

12.43

Azolla filiculoides

Salviniaceae

60

0.87

0.16

0.63

Azolla pinnatae

Salviniaceae

61

0.38

0.15

0.3

Salvinia natans

Salviniaceae

62

1.12

0.64

0.32

Veronica anagalis-aquatica

Scrophulariaceae

63

0

0.83

0.63

Typha minima

Typhaceae

64

0

0.83

0

Berula angustifolia

Umbelliferae

65

0.77

0.06

0

Hydrocotyle ranunculoide

Umbelliferae

66

 

16 counties of Guilan province) were E. crussgalli (Tables 2 and 3). In 2014 growing season, the average species frequency was 3.38percent and 42 species (63.6 percent of all species) were identified. Species diversity in 2015 was higher than in 2013 and 2016. In 2015, the average species frequency was 3.87 percent, 35 percent higher than 2014 years old, and 65 species (98.5 percent) were identified from 66 species. In 2016, the average frequency of species was 4.82 percent, which was 29.8 percent higher than 2014 years and 19.7 percent more than 2015 years old and 53 species (80.3 percent) were identified from 66 species. Species diversity in 2015 was more than 2014 and 2016 years (Table 3). The first and most important step in precision agriculture is the plan for the distribution of weeds.

Maps are often generated by manually drawing polygon around areas where the species is known to occure some automated interpolation procedures. The distribution map of several important and important weed species of Guilan rice farms is shown in Figure 3. In this research, weeds of rice farms in different years

 

 

have frequency and species diversity. In 2014, the average frequency of species was 38.3 percent and 42 species (63.6 percent of all species) were identified. The highest frequency of species in the year 2014 consisted of E. crussgalli, P. distichum, E. oryzoides, C. esculentus, E. prostrata, A. filiculoides, S. trifolia, L. minor and A. plantago-aquatica. In 2015, the average species frequency was 3.87 percent which was 35 percent higher than 2014 years old and 65 species (98.5 percent) were identified from 66 species. The highest amount of weed species in year 2015 was in E. crussgalli, E. oryzoides, P. distichum, E. prostrata, C. esculentus, A. plantago-aquatica, L. minor, C. difformis, A. multiflora and S. trifolia. In 2016, the average frequency of species was 4.82 percent, which was 29.8 percent higher than 2014 years old and 19.7 percent of the year 2015 and 53 species (80.3 percent) were identified from 66 species. The highest frequency

 

 

of weed species in the year 2016 was in frequency including P. distichum, E. crussgalli, C. esculentus, C. serotinus, E. oryzoides, E. prostrata, A. filiculoides, S. trifolia, C. difformis and A. plantago-aquatica (Table 3). This suggests that the species occurs throughout the area represented by the map polygons. Most of the weeds that had the highest frequencies, uniformity, and mean density in the farm are hard to control (Hakim et al., 2013). Weed flora composition in agricultural systems results from seasonal changes, crop rotation, and long-term environmental changes such as soil erosion and climate change. Agricultural operations such as plowing, the crop plant species grown, weed control methods, and fertilizer application change the natural distribution pattern and availability of resources and, hence, change the structure and composition of plant species (Ahmadvand, 2005; Kraemher et al., 2016; Lal et al., 2014; Noroz zade et al., 2008). Accurate determination of weed distribution pattern and density at each location is of great importance. Therefore, before any action is taken, the necessary maps of weed distribution pattern and density must be prepared (Lass et al., 1993).

 

Conclusions and Recommendations

Every year, due to insufficient knowledge in the farm of identification and factors affecting weed control, their population increases and therefore the role of management in changing the structure of communities and diversity of weed species can be useful in developing strategies and weed management. The most important weeds of rice farms in terms of frequency recorded from study area were E. crussgalli (89.81%), P. distichum (79.42%), E. oryziodes (60.29%), C. esculentus (56.5%), E. prostrate (49.5%), C. serotinus (34.73%), A. filiculoides (34.1%), C. difformis (31.6%), S. trifolia (31.61%), A. plantago-aquatica (28.69%), and Lemna minor (28.26%). Due to the importance of rice in Guilan and the lack of basic information about the status of weeds in this product, identification of weed flora and distribution map of weed species in the GIS system by each city to important information to improve weed management methods we will achieve this important agriculture.

 

Novelty Statement

The identification of weeds in Guilan rice fields was done for the first time and according to the up-to-date technology of the GIS, a distribution map for the optimal management of weeds was prepared and made available to users.

 

Author’s Contribution

Mohammad Javad Golmohammadi: Conducted the research.

Hamid Reza Mohammaddoust Chamanabad: Helped in article writing.

Bijan Yaghoubi: Proposal preparation and article writing and visit farms.

Mostafa Oveisi: Helped in data analysis.

Conflict of interest

The authors have declared no conflict of interest.

 

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Sarhad Journal of Agriculture

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Vol.40, Iss. 3, Pages 680-1101

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