Weed infestation and factors affecting weed incidence in spring cereals in Finland - a multivariate approach Jukka Salonen Salonen, J. 1993. Weed infestation and factors affecting weed incidence in spring cereals in Finland - a multivariate approach. Agric. Sci. Finl. 2: 525-536. (Agric. Res. Centre of Finland, Inst. PI. Prot., F1N31600 Jokioinen, Finland.) Weed vegetation of spring cereal fields in southern and central Finland was analyzed by ordination methods to provide a community level description of weed populations. Attention was paid particularly to the relative importance of environmental factors affecting weed incidence such as crop management, soil properties and weather condi- tions. A data set of 33 weed taxa from 252 fields was subjected to both indirect and direct gradient analysis. Indirect ordination was obtained with correspondence analysis (CA), and direct gradient analyses were performed with redundancy analysis (RDA) and withcanonical correspondence analysis (CCA) relating environmental factors to the occurrence of weeds. Among several management factors, continuous herbicide use explained best the variation in the species composition of weed flora. Weed vegetation was also associated with soil type, moisture conditions and soil pHh 2o- Ordination diagrams visualized the species-environment interactions and detected characteristic weed species for different geographical regions. In addition to ordination analyses of weed flora, the level and structure of weed infestation are described. The density of weeds averaged 170 plants m 2(median=l24) and the air-dry weight of weeds 320 kg ha I (median=lB3). The average weed density was the same in different soil types, but the weed biomass was lower in clay soils than in coarse mineral and organic soils. Key words: broad-leaved weeds, ordination, barley, oats, wheat, canonical correspond- ence analysis, CA, CCA, RDA, CANOCO Introduction Arable fields are continuously subjected to differ- ent agricultural measures particularly in annual crops. Although many weed species are adapted to the prevailing conditions, the constantly changing habitat selectively affects weed communities and, consequently, changes the weed flora (Rade- MACHER et al. 1970, Reuss 1981, Mahn 1984, Chancellor 1985,Légére et al. 1993). Weed flora in spring cereals was investigated during 1982-84 in Finland (Ervio and SALONEN 1987). Attention was paid particularly to the changes in weed infestation by comparing the data with the previous study from the 1960 s (Mukula et al. 1969). The occurrence of individual weed species was related to several explanatory variables by the analysis of variance and regression tech- niques. These methods are appropriate if detailed responses of particular weed species to explanatory factors are studied. The problem was, however, to give a summary of the relative importance of fac- tors affecting the weed incidence. Therefore, the data from weed survey was subjected to ordination 525 Agric. Sei. Fin!. 2 (1993) https://www.c-info.fi/en/info/?token=ksunEcZl3CLrT3So.Icwa8rEJMmKpoZ0amxLmhQ.hLLccopUSbnNJKJXfJFkJeVRtdeU86U41XBa4XWOW54TusR5O8J-JR3bmid4ksLW8v-EBC58Ds3PoivRyfBZzsClFKSPzKxRzKwJmy4cdAub7ZxcyGzn4bR1dq6iksBFxQA4CBnEcNyMIN8JARqcsllzZ8qs2thAq2FmYP5Hv3WSYN1hlKFmxnDLGlBr3nyk7pvAQmcGENrx6OPylvKR-KntgEUzkF54LqLVVZ9fuwLC--siKPY7lrb0cVtSETiTWQ1BtQ analyses which have proved to be appropriate for community level description of weed vegetation (Ter Braak 1987a). Multivariate analysis of community data is fre- quently applied in ecological studies to summarize the information in samples-by-species data matri- ces (Gauch 1982). In weed science, the multivari- ate approach is feasible to describe and predict the response of weed vegetation to farming practices (POST 1988). Multivariate methods in ecology can be divided into three groups (JONGMAN et al. 1987): direct gradient analysis (regression), indirect gradi- ent analysis (ordination) and classification (cluster analysis). Indirect methods analyze the species data only, whereas species-environment interactions can be analyzed simultaneously by direct methods. In this paper, the weed survey data from 1982- 1984 was subjected to ordination analyses to give a community level description ofweed flora in spring cereal fields. The objective was to find charac- teristic weed species in different geographical re- gions and to illustrateresponses of weed vegetation to environmental factors. Furthermore, the level of weed infestation, proportion of the most abundant weed species and the occurrence ofweeds in differ- ent soil types are reported. Material and methods A total of 267 spring cereal fields (barley, oats or wheat) in southern and centralFinland were studied during 1982-1984. In each field there were 4 to 5 sample plots of 0.25 m in size from which the above-ground occurrence of 33 weed species (Table 1)or, in fact, weed taxa was assessed in late July by counting the number and weighing the air- dry biomass of weeds. The sample plots were not sprayed with herbicides. Frequency of weeds (Table 1) denotes the proportion of the fields where the particular weed species was observed out of the all fields studied. Detailed information of the sur- vey and the occurrence of weed species has been given by Erviö and Salonen (1987). Data on factors involved in each field was col- lected either by observing, measuring or by inter- viewing the farmer. Twelve factors describing either the current crop, crop rotation, soil properties or climate (Table 2) were used as environmental variables in the CCA. The factors were chosen from among the 21 factors studied in the regression ana- lysis and considered the most important (Erviö and Salonen 1987). The survey localities were grouped into three regions based on their geo- graphical locations: South-western Finland (SW), eastern part of central Finland (CE) and western part of central Finland (CW). Features of regression analysis and ordination are integrated in canonical ordination techniques (Jongman et al. 1987). These techniques provide a direct analysis of species-envir- onment interactions which was earlier possible only by re- gression methods. ’Canonical correspondence analysis’ (CCA) by Ter Braak (1986) is probably the most common canonical ordination technique currently applied in various ecological studies (Birks and Austin 1992). CCA and the related indirect technique ’correspondence analysis’ (CA) (Gauch 1982) have been applied also in agricultural research (Jukola-Sulonen 1983,Wentworth et al, 1984,Post 1986, Siepel et al. 1989,PvSek and LepS 1991, Dale et al. 1992). CA and CCA fit the unimodal curve to the species-environ- ment data, whereas a linear response model between species data and environmental variables can be fitted by the ’redundancy analysis’ (RDA). The ordination techniques mentioned above are all available in the computer program CANOCO (Ter Braak 1987b). Environmental variables were either qualitative (nominal scale) or quantitative (interval scale) (Table 2). The crop rotation was considered cereal dominant if a cereal crop had been grown at least for three years of theprevious four years. Otherwise it was classified as mixed rotation. The use of herbicides indicates only the intensity of chemical weed control, not the type of herbicides applied during the last nine years. The soil pHh20 was measured from the top 0-20 cm layer. The soil type of fields was classified into three categories: clay (clay content >30%), organic (>20% organic mat- ter) and coarse mineral soils. The subjective assess- ment of soil moisture was primarily based on the soil type and the drainage of the field. Nominal type environmental factors were transformed into binary dummy variables. Due to missing values of ex- planatory factors, some sample fields had to be excluded, since missing data are not accepted in the CANOCO run. Thus, a final data set consisted of 526 Agric. Sei. Fint. 2 (1993) 527 Table 1. Frequency, the effective number of occurrences (N 2) and average biomass production of the 33 weed species studied in 252 spring cereal fields. Frequency denotes the proportion of the fields where the species was found. The N 2 value obtained from the CANOCO run is based on the weighted averages of weed densities and it indicates the number of fields where the species was abundant. Air-dry biomass indic- ates the average infestation of the species in those fields it was found. Weed taxa Code" Frequency N 2 Biomass % g m 2 Chenopodium album L. CHEAL 87 163 5.0 Galeopsis spp. L. GAESS 85 166 6.1 Viola arvensis MURRAY VIOAR 85 146 1.0 Stellaria media (L.) VILL. STEME 81 155 2.8 Fallopia convolvulus (L.) A. LOVE POLCO 61 112 1.3 Erysimum cheiranthoides L. ERYCH 58 95 1.5 Lapsana communis L. LAPCO 54 94 4.0 Polygonum aviculare L. POLAV 52 71 0.5 Myosotis arvensis (L.) HILL MYOAR 52 66 0.5 Elymus repens (L.) GOULD AGRRE 51 92 13.0 Spergula arvensis L. SPRAR 46 68 2.9 Fumaria officinalis L. FUMOF 43 ?< 1.4 Galium spp. L. GALSS 35 57 1.0 Tripleurospermum inodorum SCHULTZ BIP. MATIN 32 34 0.7 Polygonum lapathifolium L. POLLA 30 45 1.7 Sonchus arvensis L. SONAR 27 43 2.8 Lamium spp. L. LAMSS 25 39 1.9 Matricaria matricarioides (LESS.) PORTER MATMT 18 23 1.9 Gnaphalium uliginosum L. GNAUL 18 15 0.1 Capsella bursa-pastoris (L.) MEDIK. CAPBP 17 23 0.3 Ranunculus repens L. RANRE 17 13 0.2 Thlaspi arvense L. THLAR 16 21 0.8 Equiselum spp. L. EQUSS 13 26 2.1 Brassica rapa L. ssp. oleifera DC. (volunt.) BRSRO 13 25 4.0 Poa annua L. POAAN 13 14 0.5 Brassica spp. L. BRSSS 12 15 4.2 Rumex spp. L. (Sorrels) RUMSS 12 14 0.7 Achillea spp. L. ACHSS 5 11 2.1 Cirsium arvense (L.) SCOP. CIRAR 5 7 2.0 Sonchus spp. L. (S. asper, S. oleraceus) SONSS 4 5 9.4 Urlica spp. L. URTSS 2 1 0.5 Avenafalua L. AVEFA 1 2 9.4 Stachys palustris L. STAPA 1 1 3.0 11 Weed codes are according to the BAYER standard (BAYER 1992). 252 fields. The geographical regions were used as ordination axes. Only the central area of the dia- environmental variables in RDA, and as covari- gram is shown to improve the visibility of species ables in partial CCA. near the origin. Consequently, some species and Ordination analyses were performed with the environmental variables lie outside the drawn area CANOCO program (Ter Braak 1987b) applying (Figs. 4 and 5). CA, CCA and RDA. Ordination diagrams (species- Due to the skewed distribution of the response environment biplots) were drawn with the CANO- values (weed density and weed biomass) the weed DRAW program (Smilauer 1990). The relation- data was log-transformed (ln(y+l)) in the ship between the weed communities and environ- CANOCO run. Species diversity was described by mental variables is displayed with the first two the N 2 value from the CANOCO output. The N2 Table 2. Environmental variables subjected to the canonical correspondence analysis (CCA). Variable (scale) Code Range or No. of fields CROP VARIABLES Cover, % (interval) COVER 13-100 Yield, kg ha-' (interval) YIELD 520-7300 MANAGEMENT VARIABLES Cereal dominance (nominal) Cereals CER 144 Mixed rotation MIX 108 Herbicide use during 9 previous years (interval) HERB 0 years 6 1 8 2 14 3 26 4 20 5 30 6 17 7 14 8 20 9 97 SOIL VARIABLES Soil type (nominal) Coarse COARSE 110 Clay CLAY 112 Organic ORGANIC 30 Moisture type (nominal) Dry DRY 40 Normal NORMAL 199 Wet WET 13 Soil pH H2o (interval) PH 4.85-7.65 ( 1 IMA I K VARIABLES (between sowing and sampling) Effective temperature sum, DD (base 5°C) (interval) ETS 281-857 Precipitation, mm (interval) PREC 40-222 where X is Simpson’s diversity index, nj is the number of ith species in the population and N is the total number of all S species in the population. Results Occurrence of weeds 2The weed density averaged 170plants m (SE= 10, median=l24) and the biomass production 320 kg ha' 1 (SE=23, 183). The total weed biomass correlated weakly (r=0.54, P<0.01) with the total weed density. A typical weed density was 50-150 weeds m’2 , whereas the biomass production was distributed more evenly into different classes (Fig. 1). Weed densities in different soil types were at the same level, but the biomass production of weeds was on average lower in clay soils than in coarse mineral or organic soils (Fig. 2). Only ten weed species occurred in more than half of the fields studied (Table 1). The ranking order based on the N 2 value was slightly different from the frequency order. The N 2 value for samples averaged 6.9 (range 2.1-13.4), i.e. on average there were seven relatively abundant weed species in each field. Moreover, in order to emphasize therelative im- portance of different weed species, they were ranked according to their average biomass produc- tion (Table 1), and also with regard to their propor- tion of the total density and biomass ofweeds (Fig. 3). The nine most dominant weeds constituted two- thirds of the total weed infestation. value is analogous to Hill’s N 2 diversity number (Hill 1973). For samples, N 2 is the inverse of Simpson’s diversity index (Ludwig and Rey- nolds 1988): N 2 = IA where s X = 2, i= 1,2,3 S = 1 Species-environment relationships CCA and RDA were applied to the species ordina- tion in the three geographical regions. Both tech- niques characterized the typical weed species of different regions illustrated here by the RDA dia- gram (Fig. 4) which provided a slightly better sep- aration of samples and weed species than the CCA diagram. The CCA ordination diagrams for the density and biomass data were very much alike. Thus, only 528 Agric. Sei. Fint. 2 (1993) the diagram for biomass data is shown, since it provided somewhat higher eigenvalues (Table 3, Fig. 5). Eigenvalue denotes the dispersion of the species scores along the ordination axis, and is thus a measure of importance of the ordination axis (JONGMAN et al. 1987). The first canonical axis ("x-axis") extracted by CCA was closely related to the management prac- tices, as indicated by long vectors and nearby cen- troids of nominal factors (Fig. 5) and by high inter- set correlations with the axis (Table 4). Continuous herbicide application proved to be the most "effective" factor explaining the composition of weed flora. The second axis ("y-axis") was associated with soil variables, particularly pH, and with climatic factors, precipitation and effective temperature sum between sowing and sampling. Galeopsis spp. and Polygonum spp. occurred frequently in moist or- ganic soils, whereas Sonchus spp., Poa annua and Lapsana communis thrived in coarse soils and warm and humid weather conditions which were typical of the eastern region of the survey. Although the eigenvalues obtained by CCA were low, the first two canonical axes from the con- strained ordination accounted for 49% of the total species-environment variation. In the analysis of weed density, the corresponding value was 53%. Partial CCA with regions as covariables slightly reduced the explained variance. The first canonical axis was statistically significant (P=o.ol, Monte Carlo permutation test) in all analyses. Fig. 1. Distribution of spring cereal fields into weed infestation classes according to a) weed density and b) air-dry biomass Assessment was made from unsprayed sample plots in July. Fig. 2. Weed infestation in different soil types. The mean weed density (left bar) and air-dry biomass (right bar) in unsprayed fields. Vertical line indicates the standard error of the mean. 529 Agric. Sei. Fin!. 2 (1993) Fig. 3. Mean proportion (%) of the most abundant weed species of (a) the total weed density and (b) weed biomass in unsprayed spring cereal fields. Assessment was made in July. Fig. 4. Ordination diagram based on redundancy analysis (RDA) of weed densities de- scribing indicator species for southwest (SW), central-east (CE) and central-west (CW) regions of Finland. Centroids of all regions lie outside the range of the diagram. Some species near the origin are not shown because of their over- lapping position. 530 Agric. Sei. Fint. 2(1993) Key to abbreviations Weed species: ACHSS = Achillea spp., AGRRE = Elymus repens, AVEFA = Avena falua, BRSSS = Brassica spp., BRSRO = Brassica rapa ssp. oleifera, CAPBP = Capsella bursapastoris, CHEAL = Chenopodium album, CIRAR = Cirsium arvense, EQUSS =Equisetum spp., ERYCH = Erysimum cheiranlhoides, FUMOF =Fumaria officinalis, GAESS = Galeopsis spp, GALSS = Galium spp., GNAUL = Gnaphalium uliginosum, LAMSS =Lamium spp., LAPCO =Lapsana communis, MATIN = Tripleurospermum inodorum, MATMT = Matricaria matricarioides, MYOAR = Myosotis arvensis, POAAN = Poa annua, POLAV = Polygonum aviculare, POLCO = Fallopio convolvulus, POLLA = Polygonum lapalhifolium, RANRE = Ranunculus repens, RUMSS =Rumex spp., SONAR =Sonchus arvensis, SONSS= Sonchus spp., SPRAR = Spergula arvensis, STAPA = Slachys paluslris, STEME = Slellaria media, THLAR = Thlaspi arvense, URTSS = Urtica spp., VIOAR = Viola arvensis. Weed codes are according to the BAYER standard (BAYER 1992). Explanatory factors: CER = Cereal-dominated rotation, CLAY =Clay soil, COARSE = Coarse soil, COVER = Crop cover, DRY = Dry soil, ETS = Effectice temperature sum between sowing and sampling, HERB = Duration of herbicide use, MIX = Mixed crop rotation, NORMAL = Normal soil moisture, ORGANIC = Organic soil, PH = Soil pHH 20, PREC = Precipitation sum between sowing and sampling, WET = Wet soil, YIELD =Crop yield. Fig. 5. Ordination diagram based on canonical correpondence analysis (CCA) of weed biomass data from 252 spring cereal fields. The end of dotted vectors lies outside the range of the diagram. Two species (STAPA, URTSS) near the origin are not shown because of their overlapping position with other species. 531 Agric. Sei. Fin!. 2 (1993) 3 Table 3. Eigenvalues (XM) corresponding to the first four ordination axes from Correspondence Analysis (CA) and Canonical Correspondence Analysis (CCA). Environmental values for CCA are given in Table 2. Partial analyses were performed with regions as covariables. Weed infestation values from 252 spring cereal fields were transformed with ln(y + I). Eigenvalues Ordination method X, X, X, X 4 Weed density data CA 0.253 0.209 0.194 0.181 CCA 0.122 0.073 0.048 0.030 Partial CCA 0.076 0.056 0.037 0.030 Weed biomass data CA 0.315 0.281 0.275 0.263 CCA 0.142 0.097 0.065 0.046 Partial CCA 0.101 0.066 0.063 0.046 Discussion The average weed density in spring cereal fields was relatively low, as in 58% of the fields the weed density remained below 150 plants nf , and the . 2median weed density was only 124 plants m . Since the density values showed a skewed distribu- tion, the median value is a more appropriate meas- ure to indicate the level of weed infestation in spring cereal fields. The results correspond to the present weed infestation levels found in field ex- periments in the Nordic countries (Hallgren 1993,Salonen 1993). The weed flora was dominated by rather few species (Table 1, Fig. 3) which is a common phe- nomenon in intensified farming systems (Neu- rurer 1965, Callauch 1981, Albrecht and Bachthaler 1988). The low number of abundant species makes e.g. the choice of herbicides easier. Table 4. Inter-set correlations of environmental variables with the first four ordination axes from CCA for the weed biomass data. The two highest values of each axis are underlined. Factor group VARIABLE Axes 12 3 4 X, = 0.142 X 2 = 0.097 X 3 = 0.065 X 4 = 0.046 Crop & Management COVER (of crop) YIELD -0.12 0.23 -0.15 -0.01 -0.21 0.14 -0.18 -0.04 CER(eal dominance) MlX(ed rotation) HERB(icide use) -0.43 -0.01 0.09 -0.19 0.43 0.01 -0.09 0.19 -0.56 0.01 0.17 0.08 Soil Soil type COARSE CLAY 0.35 -0.31 -0.07 -0.23 -0.47 0.08 0.04 0.15 ORGANIC 0.18 0.38 0.18 0.14 Soil moisture DRY 0.02 -0.11 0.10 0.24 NORMAL WET 0.01 0.03 0.18 0.19 0.01 0.23 0.15 -0.04 PH (soil) -0.22 -0.19 0.09-0.45 Climate ETS 0.18 -0.28 -0,33 0.09 -0.29 0.06PREC(ipitation) 0.16 0.26 532 Agric. Sei. Finl. 2 (1993) The mean proportion of individual weed species out of the total weed density and biomass in each field (Fig. 3) indicated that Chenopodium album, Stellaria media and Viola arvensis are the most dominantspecies in terms ofweed density whereas, Galeopsis spp., C. album and Elymus repens were the most dominant species in terms of biomass production. Furthermore, Stellaria media and Viola arvensis had a higherproportion in densities than in biomass. The most aggressive weed species such as Galeopsis spp. and volunteer turnip rape (BRSRO) were detectedboth by theirproportion of total weed biomass (Fig. 3) and along the crop cover vector (COVER) in the ordination diagram (Fig. 5). Differences in weed abundances and weed biomass production between soil types (Fig. 2) infer both to growth conditions and species compo- sition. Apparently, differences in weed growth be- tween soil types are also reflected in yield re- sponses of the crop. Indeed, yield responses of cereals have been found to be the lowest in clay soils (Jensen 1985,Hallgren 1989). Ordination analysis provided easily interpretable results which were in agreement with the conclu- sions based on the regression analysis (Erviö and Salonen 1987) with regard to the most important factors affecting the occurrence of weeds. How- ever, the relative importance of different factors was more clearly and easily pointed out by the ordination analysis than by theregression analysis. A particular advantage in applying ordination ana- lyses is that the CCA ordination diagrams are not in any way hampered by high correlations between weed species or between environmental variables (Ter Braak 1987c). The most frequent species like Chenopodium album and Viola arvensis located near the origin of the ordination diagram (Fig. 5). These species were found in all field types indicating that they are well adapted to agricultural ecosystem. Each geographical regions had its characteristic weed species as was also concluded earlier with the analysis of variance (Erviö and Salonen 1987). Some less frequent species like Achillea spp., and Galium spp. were particularly associated with cer- tain regions (Fig. 4). The results achieved withRDA (Fig. 4) and CCA (Fig. 5) can be combined. Typical weed species for cereal-dominated rotations with frequent use of herbicides were Lamium spp., Galium spp., Fu- maria officinalis, Tripleurospermum inodorumand volunteer turnip rape. In addition, Lamium spp. and Galium spp. thrived in clay soils as reported also by mukula et al. (1969) and Andreasen et al. (1991). Indeed, cereal-dominated crop rotations were common in south-western Finland (SW) where most of the fields were clay soils and herbi- cides were frequently used. In this region, turnip rape and winter cereals are common crops in rota- tion, thus promoting the occurrence of volunteer oilseed rape and Tripleurospermum inodorum which is a common species in wintercereals (Raa- tikainen et al. 1978). Long-term use of herbicides has evidently se- lected the weed populations in south-western Fin- land towards the more tolerant species like Galium spp., Lamium spp. and Tripleurospermum ino- dorum. Indicator species for geographical regions hopefully help e.g. advisory services to direct gen- eral control recommendations to differentregions. Some weed species were typical of central Fin- land. Rumex spp. (R. acetosa and K. acetosella) and Ranunculus repens are associated with grassland (Raatikainen and Raatikainen 1975) which is a common crop in rotations in central Finland. Poly- gonum spp. were characteristically in organic soils which were most often found in central Finland. Both management practices and soil properties affected the weed vegetation. However, the effects of individual environmental variables should be interpreted with caution due to the confounded na- ture ofseveral factors associated with crop rotation and crop management. Similar conclusions were drawn from the regression analysis (Erviö and Sa- lonen 1987) and in other weed surveys (Streibig et al. 1984, Andreasen et al. 1991, Dale et al. 1992). In general, the possibilities to draw definite con- clusions from thiskind ofweed survey data seem to be limited due to the complex cropping history in each field as discussed also by Cousens et al. (1988). Relatively high eigenvalues of the third and fourth axes (Table 4) indicated the complex nature 533 Agric. Sei. Fin!. 2 (1993) of weed communities which was impossible to ex- plain with the few environmental variables chosen. Obviously, more detailed information on the crop- ping history, like crop rotation, type of herbicides used and their control efficacy, is needed to de- scribe better theresponses ofweed flora to environ- mental conditions. Forward selection of environ- mental variables in CANOCO appeared to be rather liberal in judging the variables statistically signific- ant. This is a typical shortcoming of stepwise selec- tion of variables because the overall size of the test is not controlled (Ter Braak 1990). Low eigenvalues of ordination axes reveal either short environmental gradients in cereal fields or the plasticity of most weed species to grow in diverse conditions. Low eigenvalues are also the con- sequence of few abundant species which occurred in most fields. As the number of observed weeds was limited to 33 taxa, the study probably ignored some weed species thatmight have had an explana- tory power in the ordination analysis. Nevertheless, ordination diagrams illustrated some clear patterns in weed communities and related environmental factors and agricultural practices to the weed data. Ordination techniques detected groups of weed species similar to those by the TWINSPAN classi- fication of the same data (Salonen 1990). In conclusion, weed flora showed a response particularly to management practices. Herbicide use affected most the composition of weed flora. This should be considered in planning the crop rotations and long-term weed control measures. The ordination analysis detected also an evident relationship between the weed composition and soil properties in the field. Some of these properties, e.g. soil pH, can be manipulated if neccessary. Simultaneous analysis of several weed species with environmental factors and plotting of data gave a better community level description on weed incidence than regression analysis (Erviö and SA- LONEN 1987) which rather provided a deeper but narrower insight into weed vegetation and individ- ual weed species. Still, the ordination analysis should be regarded both as a hypothesis provoking and explaining approach. Nevertheless, it is appro- priate to start the analysis of data from weed sur- veys with ordination analysis and to continue, if necessary, with regression analysis to solve more detailedhypotheses. Acknowledgements. I am grateful to Drs Ben J. 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Gradient analysis and numerical classification of apple orchard weed vegetation. Agric. Ecosyst. Envir. 11: 239-251. Manuscript received June 1993 Jukka Salonen AgriculturalResearch Centre of Finland Institute of Plant Protection FIN - 31600 Jokioinen,Finland 535 Agric. Sei. Fin!. 2 (1993) SELOSTUS Kevätviljapeltojen rikkakasvillisuus ja rikkakasvien esiintymiseen vaikuttavien tekijöiden tarkastelu ordinaatioanalyysillä Jukka Salonen Maatalouden tutkimuskeskus Kevätviljapeltojen rikkakasvillisuutta analysoitiin ordinaatio- menetelmillä ja kuvattiin koordinaatistokuvilla. CANOCO- ohjelman monimuuttujamenetelmillä pystyttiin erottelemaan eri maantieteellisille alueille tyypillisiä rikkakasvilajeja janii- den esiintymiseen vaikuttavia tekijöitä. Viljelytekniset toimet kuten yksipuolinen viljanviljely ja jatkuva herbisidien käyttö suosivat mm. mataroiden, peippi- en, pihatähtimön ja peltoemäkin esiintymistä. Nämä lajit esiintyivät yleisimmin Lounais-Suomessa. Keski-Suomen tyypillisiä lajeja olivat mm. linnunkaali, juolavehnä ja kärsä- möt. Maan happamuus ja maalaji olivat tärkeimpiä kasvualus- taan liittyviä tekijöitä, jotka vaikuttivat rikkakasvuston koos- tumukseen. Jatkuva herbisidien käyttö oli merkittävin yksit- täinen rikkakasvillisuuden koostumusta selittävä tekijä, joskin eri ympäristötekijöiden vaikutuksia rikkakasvilajeihin ei pys- tytty täysin erottamaan toisistaan. Rikkakasvien keskimääräinen kasvutiheys oli 170 kpl/m 2 (mediaani 124kpl/m2 ). Rikkakasvien tuottamamaanpäällinen ilmakuiva biomassa oli keskimäärin 320 kg/ha (mediaani 183 kg/ha). Rikkakasvien keskimääräinen kasvutiheys ei vaihdel- lut eri maalajeilla, mutta kasvit tuottivat enemmän biomassaa karkeilla kivennäismailla ja turvemailla kuin savimailla. Te- hokkaimpia biomassan tuottajia olivat pillikkeet, jauhosavik- kaja juolavehnä. Rikkakasvikartoituksen lajisto koostui 33 ennalta valitusta lajista. Tyypillisesti kunkin pellon rikkakasvillisuus koostui muutamasta lajista. Yksittäisten lajien suhteellista runsautta kuvaavan N2-diversiteetin perusteella pelloilla esiintyi keski- määrin seitsemän kasvutiheydeltään (kpl/m 2 ) merkittävää lajia. Lajisto jakautui lähes kaikilla viljelyksillä esiintyviin lajeihin (jauhosavikka, pillikkeet, orvokki) ja eri alueille tyy- pillisiin lajeihin. Ordinaatiokuvat antoivat selkeämmän koko- naiskuvan rikkakasvien esiintymisestä ja ympäristötekijöiden vaikutuksestakuin aiemmin samastaaineistosta julkaistureg- ressiotarkastelu. 536 Agric. Sei. Finl. 2 (1993)