Structure of weed communities occurring in pea and wheat crops in the Rolling Pampa (Argentina)

Agriculture, Ecosystems and Environment 103 (2004) 225–235

Structure of weed communities occurring in pea and wheat crops in the Rolling Pampa (Argentina) Santiago L. Poggio∗ , Emilio H. Satorre, Elba B. de la Fuente Cátedra de Producción Vegetal, Departamento de Producción Vegetal, Facultad de Agronom´ıa, Universidad de Buenos Aires, Avda. San Mart´ın 4453 (C1417DSE), Ciudad Autónoma de Buenos Aires, Argentina Cátedra de Cerealicultura, Departamento de Producción Vegetal, Facultad de Agronom´ıa, Universidad de Buenos Aires, Avda. San Mart´ın 4453 (C1417DSE), Ciudad Autónoma de Buenos Aires, Argentina Cátedra de Cultivos Industriales, Departamento de Producción Vegetal, Facultad de Agronom´ıa, Universidad de Buenos Aires, Avda. San Mart´ın 4453 (C1417DSE), Ciudad Autónoma de Buenos Aires, Argentina Received 10 December 2002; received in revised form 12 September 2003; accepted 16 September 2003

Abstract Companion weed species in wheat and pea crops are influenced by the physiology, canopy structure, and management practices of these crops. Pea and wheat fields were surveyed in the central Rolling Pampa to compare the weed communities, and to determine the association among weed community structure, crop management practices, and yield. The floristic and functional structure of weed communities was evaluated for species richness and abundance. Floristic groups and their relationships with crop yield and management were analysed using multivariate methods. The weed community of pea crops was more diverse than that of wheat crops at both regional and field scales. Alpha diversity was 20 and 13 for pea and wheat, respectively, while gamma diversity was 91 for pea and 64 for wheat. In both crops, the structure of the weed communities was related to yield, chemical control practices, fertilization and the previous crop. Crop dominance, as accounted by crop growth and herbicides effects, was the main factor defining the weed community structure. Fertilization and previous crop had a secondary effect. Similar proportions of dicotyledonous and monocotyledonous species occurred in both crops, probably because of climatic factors operating at regional scale. © 2003 Elsevier B.V. All rights reserved. Keywords: Weed community structure; Vegetation surveys; Plant functional traits; Crop dominance; Multivariate analysis

1. Introduction Weed communities co-evolve with cropping systems, allowing the populations to adapt to highly, regularly disturbed environments (Mart´ınez-Ghersa et al., 2000). The floristic composition of weed communities results from seasonal variation, agricultural cycles, ∗ Corresponding author. Tel.: +54-11-4524-8039; fax: +54-11-4524-8737. E-mail address: [email protected] (S.L. Poggio).

and long-term environmental changes like soil erosion and climate change (Ghersa et al., 1996; Ghersa and León, 1999). Each year, choices in agricultural practices such as tillage, crop species, weed control methods and fertilization modify the natural patterns of disturbance and resource availability, affecting the natural colonisation processes of plant communities (Soriano, 1971). Regular and sequential changes in the environment and in agronomic practices inadvertently contribute to define a particular trajectory in the weed species shift and adaptation (Mart´ınez-Ghersa et al.,

0167-8809/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2003.09.015

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2000). Along its trajectory, a weed community follows successive states as a result of both biotic and abiotic constraints. The weed community is disassembled and reassembled in each state, in which some species are removed while others are introduced (Booth and Swanton, 2002). The importance of environmental and anthropogenic factors on weed communities structuring and functioning has been recognised by many authors (Ellenberg, 1950; León and Suero, 1962; Holzner, 1982). The relationship between weed community structure and cropping history of fields with Rolling Pampa summer crops has been studied using phytosociological approaches (Ghersa and León, 1999), but weed communities of winter crops have received much less attention. Management differences between maize (Zea mays L.) and soybean (Glycine max (L.) Merr.) had little effect on weed community structure (Suárez et al., 2001), however, environmental differences in crop yield potential were related to the presence of particular floristic groups (León and Suero, 1962; de la Fuente et al., 1999; Suárez et al., 2001). Wheat (Triticum aestivum L.) has been cropped for over 150 years and is still the most important winter crop in the Rolling Pampa. Pea (Pisum sativum L.) was introduced about 50 years ago and has more limited acreage. Pea is an interesting alternative crop that reduces the negative impact of wheat monoculture. Canopy structure, physiology and management practices differ between these winter crops, and different dynamics in resource availability and in the levels of physical factors of the environment are experienced by the companion weed species (Tilman and Pacala, 1993). Thus, pea and wheat crops constitute different environmental filters to the weeds. The structural and functional attributes of weed species communities were studied in wheat and pea crops aiming to understand how these crops may influence the weed shifting processes. The objectives of this work were to compare the weed communities of wheat and pea crops, and to determine the relationship between the weed community structure and agronomic management variables.

Pampa is located in the NE of the Pampean Region, from 34 to 36◦ south latitude and from 58 to 62◦ west longitude. This subregion is stretched along the Paraná River from NW to SE and is bordered to the S with the Flooding Pampa (Hall et al., 1992). The climate is temperate and humid, without dry season and with a very hot summer. Annual average rainfall ranges from 900 to 1000 mm, and mean annual temperature is 17 ◦ C (Soriano et al., 1991; Hall et al., 1992). Soils are mostly Molisols, Typic Argiudoll. A total of 74 fields were surveyed with 37 fields for each of pea and wheat crops. Eighteen of the wheat fields were surveyed in 1996. All the pea fields and the remaining wheat fields were surveyed in 1999 and 2000. In all cases, surveys were made between 15th October and 15th November and fields were chosen based on three criteria: (1) both autumn–winter and spring–summer weed communities were present; (2) weed chemical control had been applied; and (3) crops had achieved maximum ground-cover. The whole surface of a field was considered as the sampling area (30 ha on average). Sample stands fulfilled the following requirements (Mueller-Dombois and Ellenberg, 1974): (1) they were large enough to contain all species belonging to the plant community (25–100 m2 for agricultural weed communities); (2) the habitat was uniform within the stand area; (3) the plant cover was homogeneous. Field margins and negative topographic positions were avoided, because they may represent different habitats (e.g. different soil conditions). Furthermore, surveys were also restricted to those field areas which had homogeneous crop cover. Surveys were performed by two or more trained persons which walked across each field during at least 30 min, recording all species observed until no more new species were found. Abundance of each species was estimated considering the percentage of ground-cover, with the following scale intervals: 0–1, 1–5, 5–10, 10–25, 25–50, 50–75, 75–100% (Mueller-Dombois and Ellenberg, 1974). Farmers provided information concerning crop management and performance (i.e. tillage system, fertilization, weed control and grain yield).

2. Materials and methods

2.1. Data analyses

The study was done in the central area of the Rolling Pampa in Argentina (Soriano et al., 1991). The Rolling

Floristic structure was analysed in terms of species composition, and functional structure in terms of

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morphotypes and physiotypes. Since plant traits instead of species are adapted to a particular environment, arranging weed species in functional groups may give a better understanding of how weed communities assembled than species lists (Ghersa and León, 1999; Booth and Swanton, 2002). Each species was classified by its life cycle (annual, biennial, perennial), morphotype (monocotyledonous, dicotyledonous), origin (native, exotic, cosmopolitan), and season of emergence (autumn–winter and spring–summer). Data were summarised by calculating species constancy (proportion of fields in which a given species is present), and alpha, beta and gamma diversity (Whittaker, 1975). Alpha or local diversity is the number of species in a survey, also called species richness. Beta diversity [(gamma diversity/average alpha diversity) − 1] is the rate of change of species richness across surveys. Gamma or regional diversity is the total number of species occurring in a system (Whittaker, 1975; Magurran, 1988). Comparisons of means were carried out by unpaired Student’s t-test, after testing for normality (Shapiro– Wilk test) and homogeneity of variances (F-test). When variances were not equal, unpaired Student’s t-test with Welch’s correction was used. Data were analysed by non-parametric Mann–Whitney test when not normally distributed. The floristic and functional composition of both pea and wheat crops were compared by a multi-response permutation procedure (MRPP; Zimmerman et al., 1985). This is a non-parametric procedure for testing the hypothesis of no difference between two or more groups (McCune and Mefford, 1995). Crops were used as categorical variables. Squared Euclidean distance was used, with the following expression as weighting factor: (ni − 1) Ci =
(ni − 1)

(1)

where ni is the number of items in each group. This combination results in a procedure that is equivalent to a two-sample Student t-test or one-way analysis of variance F-test (Zimmerman et al., 1985). The use of more than one analysis technique in community data analyses has been recommended to minimize methodological effects (Crisci and López Armengol, 1983; Clements et al., 1994). Data were therefore subjected to multivariate and canonical

227

correspondence analysis (ter Braak, 1987a,b). Analyses were made only using species with constancy greater than 10%, and species with low constancy being considered accidental (Mueller-Dombois and Ellenberg, 1974). Sørensen coefficient of community (CC) (Magurran, 1988) was used as a distance measure for species and for fields: CC =

2W A+B

(2)

where W is the number of species present in two fields, A and B the total number of species in each field. Farthest neighbour (complete linkage) was used as a similarity measure, in which the distance between two clusters is given by the maximum distance between any pair of numbers of both clusters (van Torengen, 1987). Indicator species were defined as the most representative in a particular group of the classification occurring in most sites of that group (Dufréne and Legendre, 1997).This method was used to establish which species were present with high abundance, or high frequency or both in each crop. Indicator species analysis combines a species relative abundance with its relative frequency in the various groups of sites. This method is useful to identify where to stop dividing clusters into subsets (Dufréne and Legendre, 1997). Indicator value (Ind Val) was calculated as follows: Ind Val = SF × 100

(3)

where S is a measure of ‘specificity’ obtained as the division between the abundance of a species across sites of a particular group, as numerator, and the sum of the abundance of the same species over all groups, as denominator. F is a measure of ‘fidelity’ calculated as the division between the number of sites of a group in which a species is present, by the total number of sites in that group. Both, S and F, are combined through multiplication because they represent independent information about species distribution (Dufréne and Legendre, 1997). The index is expressed as a percentage. Statistical significance of maximum indicator value was evaluated by Monte Carlo test. Canonical correspondence analysis (CCA) was performed to determine the association between species composition and environmental information provided by the farmers. Information was complete in only 19

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Table 1 Latin binomial names, family names, functional groups and constancy for weeds species recorded in a survey of 74 fields, corresponding to 37 pea crops and 37 wheat crops in the central Rolling Pampa, Argentina Latin binomial name

Alternanthera philoxeroides (Mart.) Griseb. Amaranthus quitensis H., B., K. Ammi majus L. Ammi visnaga (L.) Lam. Anagallis arvensis L. var. arvensis Anoda cristata (L.) Schelcht. Anthemis cotula L. Apium leptophyllum (Pers.) F. Muell. ex Benth. Artemisia annua L. Artemisia verlotorum Lamotte Aster squamatus (Spreng.) Hieronymus Avena fatua L. Avena sativa L. Avena sterilis L. Baccharis medullosa De Candolle Baccharis pingraea De Candolle Bidens subalternans De Candolle Bowlesia incana Ruiz et Pav. Brassica rapa L. Bromus catharticus Vahl. Capsella bursa-pastoris (L.) Medicus. Carduus acanthoides L. Chenopodium album L. Cirsium vulgare (Savi) Tenore Convolvulus arvensis L. Conyza bonariensis (L.) Cronquist Coronopus didymus (L.) Smith Cotula australis (Sieber ex Spreng.) Hook. Cucurbita andreana Naudin Cynodon dactylon (L.) Pers. Cyperus spp. Datura ferox L. Dichondra repens J. R. Forst. & G. Forst. Digitaria sanquinalis (L.) Scopoli. Echinochloa colonum (L.) Link. Echinochloa crusgalli (L.) Beauvois Eleusine indica (L.) Gaertner Euphorbia maculata L. Facelis retusa (Lam.) Schultz Bip. Festuca arundinacea Schreb. Fumaria agraria Lag. Galinsoga parviflora Cavanilles. Gamochaeta pensylvanica (Will.) Cabr. Gamochaeta spicata (Lam.) Cabr. Glycine max (L.) Merr. Hypochoeris radicata L. Ibicella lutea (Lindl.) Van Eselt. Ipomoea indivisa (Vell.) Hallier. Lactuca serriola L. Lamium amplexicaule L.

Family name

Amaranthaceae Amaranthaceae Apiaceae Apiaceae Primulaceae Malvaceae Asteraceae Apiaceae Asteraceae Asteraceae Asteraceae Poaceae Poaceae Poaceae Asteraceae Asteraceae Asteraceae Apiaceae Brassicaceae Poaceae Brassicaceae Asteraceae Chenopodiaceae Asteraceae Convolvulaceae Asteraceae Brassicaceae Asteraceae Cucurbitaceae Poaceae Cyperaceae Solanaceae Convolvulaceae Poaceae Poaceae Poaceae Poaceae Euphorbiaceae Asteraceae Poaceae Fumariaceae Asteraceae Asteraceae Asteraceae Fabaceae Asteraceae Martiniaceae Convolvulaceae Asteraceae Lamiaceae

Functional groups

Constancy (%)

Mfa

Oa

LCa

Pea

Wheat

D D D D D D D D D D D M M M D D D D D M D D D D D D D D D M M D D M M M M D D M D D D D D D D D D D

N N E E E N E N E E N E E E N N N N E N E E C E E N N E N E C E N E E E E N N E E N N N E E N N E E

P A A A A A A A A P P A A A P P A A A B–P A A A A P A A–B A A P P A P A A A A A A P A A B–P P A P A A A–B A

22 5 3 – 5 68 16 19 32 5 3 – 8 5 3 5 8 46 8 22 3 81 76 65 22 43 51 68 3 51 16 32 – 76 3 41 3 3 3 8 5 5 41 8 14 8 3 3 5 51

24 3 5 11 8 73 8 24 11 0 0 27 0 0 0 0 11 27 14 16 5 41 73 49 5 3 43 3 14 5 3 46 11 65 0 38 0 16 0 3 5 16 16 3 38 0 0 0 0 14

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Table 1 (Continued ) Latin binomial name

Leersia hexandra Sw. Lolium multiflorum Lam. Matricaria chamomilla L. Medicago polymorpha L. Medicago sativa L. Melilotus albus Medicus. Nicotiana longiflora Cav. Descr. Oxalis chrysantha (Kunth.) Paspalum dilatatum Poir. Physalis viscosa L. Plantago tomentosa Lam. Poa annua L. Polygonum aviculare L. Polygonum convolvulus L. Portulaca oleracea L. Ranunculus platensis Spreng. Raphanus sativus L. Rumex crispus L. Senecio grisebachii Bak. Senecio magadascariensis Poir. Senecio vulgaris L. Setaria geniculata (Lam.) Beauv. Sida rhombifolia L. Sisyrinchium minutiflorum Klatt. Solanum sisymbrifolium Lam. Solanum sublobatum W. Solidago chilensis Meyen. Soliva pterosperma (Juss.) Less. Sonchus asper (L.) Hill. Sonchus oleraceus L. Sorghum halepense (L.) Pers. Stellaria media (L.) Vill. Tagetes minuta L. Taraxacum officinale Weber in Wiggers Trifolium pratense L. Trifolium repens L. Triodanis biflora (R. et P.) Greene. Triticum aestivum L. Urtica urens L. Veronica arvensis L. Veronica persica Poir. Veronica polita Fries. Viola arvensis Murray. Viola odorata L. Xanthium strumarium L. Zea mays L.

Family name

Poaceae Poaceae Asteraceae Fabaceae Fabaceae Fabaceae Solanaceae Oxalidaceae Poaceae Solanaceae Plantaginaceae Poaceae Polygonaceae Polygonaceae Portulacaceae Ranunculaceae Brassicaceae Polygonaceae Asteraceae Asteraceae Asteraceae Poaceae Malvaceae Iridaceae Solanaceae Solanaceae Asteraceae Asteraceae Asteraceae Asteraceae Poaceae Caryophyllaceae Asteraceae Asteraceae Fabaceae Fabaceae Campanulaceae Poaceae Urticaceae Scrophulariaceae Scrophulariaceae Scrophulariaceae Violaceae Violaceae Asteraceae Poaceae

Functional groups

Constancy (%)

Mfa

Oa

LCa

Pea

Wheat

M M D D D D D D M D D M D D D D D D D D D M D M D D D D D D M D D D D D D M D D D D D D D M

N E E E E E N N N N N E E E E N E E N E E N C N N N N N E E E E N E E E N E E E E E E E N E

P A A A P B P P P P P A A A A A A–B P A P A P P A P P P A A A P A A P B–P P A A A A A A A–B P A A

3 49 5 3 5 – 3 38 3 8 5 43 68 24 8 5 – 49 16 32 5 5 3 8 11 5 19 11 3 76 68 41 30 30 5 38 8 43 14 24 38 5 3 3 14 22

0 35 0 0 5 3 3 46 0 0 3 11 27 43 16 0 5 11 0 0 3 0 0 0 0 0 0 0 0 16 51 51 41 24 5 11 8 0 0 8 38 0 5 0 14 3

a Mf: morphotype; D: dicotyledons; M: monocotyledons. O: origin; N: native; E: exotic; C: cosmopolitan. LC: life cycle; A: annual; B: biennial; P: perennial.

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pea and 20 wheat fields, the rest of the survey being excluded. Three CCA were performed. The first analysis was made to compare wheat and pea crops, the environmental variables included being previous crop, tillage system, intensity of tillage, nitrogen and phosphorus fertilization (kg ha−1 ), mechanical and chemical weed control, and yield. Tillage was classified as either conventional, or no-tillage. Tillage intensity was expressed in energy units by tilled area (MJ ha−1 ). Chemical weed control was classified according to the time of application in pre-sowing and post-emergence, and according to the target species classified in grasses, broadleaved, and grasses-broadleaved. Grain yield was expressed as amount of glucose per unit area (kg ha−1 ), due to the different seed quality of wheat and pea crops (Sinclair and de Wit, 1975). The other two CCA analyses were made to order weed species in relation to the differences within pea and wheat crops. Grain yields were normalised by dividing the value of each field by the average yield of the survey year, which was defined as agronomic index (Suárez et al., 2001). The environmental matrix included the same variables described above, except for nitrogen and phosphorus fertilization.

3. Results A total of 96 weed species were recorded in the 74 surveys (Table 1). According to MRPP, floristic composition significantly differed between pea and wheat (P < 0.001). Gamma diversity of pea (91 spp.) was greater than that of wheat (64 spp.), the difference being mainly due to a greater number of rare species in pea (Table 1). Alpha diversity was significantly greater (P ≤ 0.001) in pea (19.8 ± 0.76 spp. per field) than in wheat (12.7 ± 0.82 spp. per field). Beta diversity did not differ (P < 0.10) between wheat (3.87 ± 0.200) and pea (5.34 ± 0.640). The number of dicotyledonous species (76 spp.) was greater than that of monocotyledonous (20 spp.) Poaceae (28 spp.) and Asteraceae (18 spp.) were the most numerous families from monocotyledons and dicotyledons, respectively. There were more exotic species (53 spp.) than native (38), and cosmopolitan species (5). Annual species were most numerous (56 spp.), followed by perennial (30), annual–biennial (6),

biennial–perennial (3) and biennial (1). Maize and soybean were found as volunteer crops in both pea and wheat surveys, while volunteer wheat plants were only identified in pea. Comparing crops at the regional scale, the total number of dicotyledonous species was similar in both pea (76 spp.) and wheat crops (72). Asteraceae and Poaceae were also more numerous in pea (28 and 15 spp., respectively) than in wheat surveys (17 and 10, respectively). At field level, the number of both Asteraceae and Poaceae species was also significantly greater (P < 0.001) in pea (6.5 ± 0.43 and 4.5 ± 0.29, respectively) than in wheat (2.6 ± 0.31 and 2.5 ± 0.22, respectively). Both pea and wheat had less native (37 and 22 spp., respectively) than exotics (49 and 38 spp., respectively). There were more annuals than perennials in both crops. Numbers of annuals and perennials were 54 and 29 for pea, and 40 and 15 for wheat, respectively. The proportion of both dicotyledons and annuals was higher than that of monocotyledons and perennials in both crops, but did not significantly differ between crops (Table 2). Exotics were the most abundant in both crops whereas the percentage of exotics, natives and cosmopolitans significantly differed between crops (Table 2). In pea, the proportion of species that emerged during autumn and winter was significantly greater (P < 0.05) than spring–summer species, yet no difference was observed in wheat (Table 2). Table 2 Relative abundance of weeds in pea (n = 37) and wheat (n = 37) crops, expressed as functional groups Functional groups

Relative abundance (%) Pea

Wheat

Morphotype

Dicotyledons Monocotyledons

74.0 a 26.0 b

71.0 a 29.0 b

Life cycle

Annuals Perennials

77.3 a 23.7 b

83.3 a 16.7 b

Origin

Exotics Natives Cosmopolitans

70.8 a 16.4 d 12.8 d

58.0 b 30.7 c 11.3 d

Emergence season

Autumn–winter

64.8 a

49.4 b

Spring–summer

35.2 c

50.6 b

Means followed by different letters significantly differ (P < 0.05) within each main functional group (morphotype, life cycle, origin, emergence season).

S.L. Poggio et al. / Agriculture, Ecosystems and Environment 103 (2004) 225–235

Differences between the functional compositions of pea and wheat crops, tested with MRPP, were statistically significant, even if expressed as the combination of morphotype, life cycle and origin (P < 0.001), or if the emergence seasons of species were used in the combination instead of their origin (P < 0.001). Annual dicotyledonous species, exotics as well as natives, were the most abundant functional groups in both pea and wheat crops. Annual dicotyledons, belonging both to exotic and natives, and exotic annual monocotyledons were the most abundant functional groups in wheat but did not differ significantly. Proportion of weeds from exotic annual monocotyledons was significantly greater in wheat than in pea, exotic perennial dicotyledonous being more abundant in pea. Annual dicotyledons that emerged in autumn–winter were the most abundant functional group in both crops, its abundance being significantly greater in pea (Table 2). Proportion of annuals with spring–summer emergence in both monocotyledons and dicotyledons, was greater in wheat. Seven floristic groups were defined based on 43 species with constancy >10% (Table 3). The weed community in pea was mostly composed of floristic groups I, II, IV, V and VI. These groups had species with maximum indicator values in pea or wheat (Table 3). Floristic group I was dominated by A. cristata and D. sanguinalis which had maximum indicator values in wheat only (Table 3). This community included mainly species from floristic groups IV and VI with maximum indicator values in pea crops, such as S. oleraceus and C. australis, the wheat community being formed by floristic groups I, III, V and VII.

Table 3 Indicator values and constancy of weed species in each floristic group of pea and wheat crops Group Species

Indicator value

Constancy (%)

Total

Pea Wheat Pea Wheat I

C. album A. cristata D. sanguinalis S. halepense P. aviculare S. media E. crusgalli V. persica P. convolvulus B. catharticus

34 16 19 40 45a 18 23 23 5 13

40 55a 49a 21 9 29 17 15 34a 6

76 68 76 68 68 41 41 38 24 22

73 73 65 51 27 51 35 35 43 14

74 70 70 59 47 46 39 38 34 19

II

L. multiflorum L. amplexicaule P. annua G. max

16 31a 20 1

24 5 6 34a

49 51 43 14

32 14 11 38

42 32 27 26

III

O. chrysantha T. minuta A. philoxeroides A. leptophyllum P. oleracea B. rapa

6 4 7 9 1 2

39a 35a 17 13 14 10

38 30 22 19 8 8

46 41 24 24 16 14

42 35 23 22 12 11

IV

G. pensylvanica C. bonariensis T. aestivum A. cotula

21 42a 43a 11

8 0 0 3

41 43 43 16

16 3 0 8

28 23 22 12

V

C. acanthoides C. vulgare C. didymus D. feroz B. incana T. officinale T. repens Z. mays

38 23 29 13 22 11 20 13

21 32 19 27 14 16 5 1

81 68 51 32 46 30 38 22

41 46 41 46 27 24 11 3

61 58 47 39 36 27 24 12

VI

S. oleraceus C. australis R. crispus C. dactylon A. annua S. magadascariensis C. arvensis

71a 67a 41a 43a 14 32a 20a

1 0 2 1 6 0 0

76 68 49 51 32 32 22

16 3 11 5 11 0 5

46 35 30 28 22 16 14

VII

A. fatua G. parviflora V. arvensis X. strumarium

0 0 10 2

27 16a 5 12

0 5 24 14

27 16 8 14

14 11 16 14

3.1. Weed community and crop yield Wheat and pea crops differed noticeably in both management and yield (Table 4). Yield was significantly greater in wheat than in pea, while averaged ground-cover of weeds was greater in pea. Wheat received a greater amount of nitrogen, while more phosphorus was applied to pea. There was no difference in mechanical weed control before sowing nor in tillage practices. However, crops differed in the type of product used in the weed chemical control. Broadleaf herbicides were applied to both pea and wheat crops,

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a Values with significantly highest indicator values evaluated by the Monte Carlo method (P < 0.05).

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Table 4 Agronomic variables of pea and wheat crops for 39 fields surveyed in 1999 and 2000 Pea

Wheat

Grain yield (kg ha−1 ) Glucose yield (kg ha−1 ) Weed cover (%)

1736 ± 127.9 b† 2699 ± 198.9 b† 9.4 ± 1.21 a‡

3568 ± 118.7 a 4723 ± 157.1 a 6.4 ± 0.67 b

15.5 ± 0.59 b‡ 39.7 ± 1.50 a‡

39.1 ± 6.60 a 19.1 ± 3.49 b

Fertilization (kg ha−1 ) Nitrogen Phosphorus

Weed control (% of the fields) Mechanicala 100 Chemical 100 5 Presowingb Post-emergence 100 Grassesc 5 21 Broadleafd 100 Grasses-broadleafe

89 100 0 100 0 100 0

Previous crop (% of the fields) Maize 79 Soybean 0 21 Wheat–soybeanf Wheat 0 Sunflower 0

20 60 10 5 5

Number of fields

20

19

Values are means and standard errors. Significant differences (P < 0.05) between crops are indicated with different letters corresponding to mean comparison tests. a Mechanical weed control was performed by regular tillage practices before sowing. b Presowing herbicides: glyphosate (360 g a.i. l−1 , mean dose: 2.5 l ha−1 ); roundup, Monsanto + Metribuzin (0.75 g a.i. g−1 , mean dose: 400 g ha−1 ); Sencorex 75 WG, Bayer AG. c Grass herbicide applied to pea: Propaquizafop (100 ml a.i. l−1 , mean dose: 0.7 l ha−1 ); Agil, Ciba. d Broadleaf herbicide applied to pea: MCPA (280 ml a.i. l−1 , mean dose: 0.5 l ha−1 ); MCPA, DowElanco. Broadleaf herbicides applied to wheat: Dicamba (480 ml a.i. l−1 , mean dose: 0.1 l ha−1 ) + Metsulphuron Methil A (0.6 g a.i. g−1 , mean dose: 5 g ha−1 ); Misil, Du Pont. e Grass-broadleaf herbicide applied to pea: Imazethapyr (100 ml a.i. l−1 , mean dose: 0.8 l ha−1 ); Pivot, Cyanamid. f Soybean relay crop sown immediately after wheat harvest without fallow period. † Student’s t-test (unpaired). ‡ Student’s t-test with Welch’s correction.

being the only type of chemical product used in the latter. Grass herbicides and products to control both broadleaf and grass weeds were only applied to pea (Table 4).

Axis 2 (Eigenvalue = 0.284)

Agronomic variables

CONAR

CYNDA

POLAV

RUMCR LAMAM SONOL

B-G

P

TRIAX SENMA CULAU

ERIBO

B

TAGMI DIGSA GLYMX POLCO N ANVCR

Y

OXACH GASPA

AVEFA

Axis 1 (Eigenvalue = 0.452) Fig. 1. Ordination diagrams from canonical correspondence analysis corresponding to pea and wheat fields. ANVCR: Anoda cristata; AVEFA: Avena fatua; CONAR: Convolvulus arvensis; CULAU: Cotula australis; CYNDA: Cynodon dactylon; ERIBO: Conyza bonariensis; GASPA: Galinsoga parviflora; GLYMX: Glycine max; LAMAM: Lamium amplexicaule; OXACH: Oxalis chrysantha; POLAV: Polygonum aviculare; POLCO: Polygonum convolvulus; RUMCR: Rumex crispus; SENMA: Senecio madagascariensis; SONOL: Sonchus oleraceus; TAGMI: Tagetes minuta; TRIAX: Triticum aestivum (Y: glucose yield; N: nitrogen fertilization; P: phosphorus fertilization; B: broadleaf herbicides; B-G: broadleaf-grass herbicides; ( ): species belonging to pea; and (䊏): to wheat weed communities).

CCA ordered the weed communities of pea and wheat crops according to the most important environmental variables. Vector lengths and nearby centroids of nominal factors (Fig. 1) and high intraset correlations with each axis indicated the importance of environmental variables in the ordination of the community. Main variables related to axis 1, were grain yield (Y), broadleaf (B) and grass-broadleaf weed controls (G–B), nitrogen (N), and phosphorus (P) fertilization, and previous crop. When pea and wheat crops were analysed separately with CCA, there was no relationship between weed species and the environmental variables in both crops, since eigenvalues and Pearson correlations between matrices did not differ significantly.

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4. Discussion and conclusions Wheat was more dominant than pea, and this was reflected in the structure of their weed communities. As a consequence of the difference in crop dominance, weed ground-cover was higher in pea than in wheat fields. Yield was closely related to total biomass production, and may be used as an indicator of the amount of resources captured by a crop (Spitters, 1983). Weeds took advantage of the slow growth rate and prostrate habit of pea. Weed species diversity in pea fields was greater than in wheat at both regional and field scales, mainly because of the presence of species with low constancy. If the regional pool of weeds was the same for pea and wheat, environmental constraints on species invasion in pea would be weaker than in wheat. As a crop grows, both the availability of open sites for the establishment of weedy species, and the amount of available resources are restricted. It may therefore be assumed that there were more available sites for species arrival, establishment and further growth in pea canopies than in wheat. Moreover, higher yields of wheat may indicate that wheat intercepted a higher proportion of the income radiation. The reduction of solar radiation reaching low canopy strata, may reduce establishment and growth, and increase mortality (Goldberg and Miller, 1990). Moreover, most of the pea cultivars sown in the region are semi-leafless and intercept less radiation than conventional cultivars (Heath and Hebblethwaite, 1985). A dominant crop not only restricts the availability of resources but may also modify other components of the abiotic environment (Tilman and Pacala, 1993; Benech-Arnold et al., 2000). The inclusion of pea in a crop rotation could provide refuges for weed species that are otherwise suppressed by the dominating cereal crops thus reducing the extinction risk. The degree to which a crop reduces both species diversity, abundance, and the amount of propagules produced by the survivor weeds during its growing period would be reflected in the weed community structure of the following crop. Open sites and resources available in pea may result in larger population of weeds, and probably greater species richness, than in wheat. Data did not allow to accurately separate the effect of crop dominance and herbicides on weed community. However, it can be accepted that except for

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a few herbicides with long-lasting residual effects, most products have a short duration in the soil. Once the herbicide effects have diminished, or disappeared, new weed seedling cohorts of both susceptible and non-susceptible species may establish. At least temporally, herbicides may be considered as being less important to weeds than crop dominance. Exotic annual dicotyledons were the most abundant group of weeds in pea crops, in agreement with observations in soybean and maize crops (de la Fuente et al., 1999; Suárez et al., 2001). Broadleaf herbicides applied to wheat apparently reduced the abundance of exotic annual dicotyledons. Herbicide applications also affected the proportion of autumn–winter species which was significantly higher than spring–summer species in pea, while there was no difference in wheat. The denser wheat canopy complemented the effects of the herbicides, curtailing both growth of the survivor individuals and establishment of new cohorts of autumn–winter species. In pea fields, the greater proportion of species belonging to that group would also be due to the lower ground-cover of pea crops. When the canopy decay started at the end of the crop cycle, spring emerging weeds, such as A. cristata and D. sanguinalis, occupied the open sites in senescent wheat. In pea, autumn–winter weeds, still green at the end of the crop cycle, prevented the establishment of spring emerging species. The abundance of native species in wheat fields was promoted by an increase in the availability of open sites during the beginning of crop senescence. Species entrance into cropped land is mainly governed by factors operating at a broader scale, such as the dynamic between habitats that function as source of species and those that act as species sinks (Williamson, 1996). Differences in fertilization and previous crop between crops could also explain some differences in the weed communities. Significantly more nitrogen was applied to wheat than to pea, which could increase the dominance of wheat, and decrease weed occurrence. Soybean and maize as the most frequent previous crop of wheat and pea crops, respectively, were present as volunteer plants. Species number of both dicotyledons and monocotyledons was greater in the weed community of pea but there was no difference between crops when abundance was expressed in relative terms. Dicotyledons were the main weedy species in the original grassland

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of the Rolling Pampa (Parodi, 1930), and remained the most invasive species to cropped land (Ghersa and León, 1999). Environmental constraints in a crop, for instance climatic factors operating at regional scale, may also explain the differences observed between crops in the numbers of dicotyledons and monocotyledons.

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