Vol. 4:397^05. Predicting herbage mass of Phleum pratense L. pastures with a disk meter Perttu Virkajärvi and Kaisa Matilainen Agricultural Research Centre ofFinland, East Finland Research Unit, Karelia Research Station, FIN-82600 Tohmajärvi, Finland A simple disk meter was calibrated for predicting the herbage mass (HM) of rotationally grazed pastures dominated by Phleum pratense L. in 1991-1992. A total of 696 paired observations were made on disk height (DH) and HM > 4 cm. The samples were classified for three different statuses: spring growth, post grazing and aftermath. In 1991 three different disk weights (c. 3.5, 5.0 and 6.5 kg/m 2 ) were compared and the lightest disk was chosen for further studies in 1992. The variation in HM was adequately explained by linear models. There were only small differences between the pre- dictive ability of different disk weights, the lightest disk having the highest r 2 values. Post grazing and aftermath samples could be pooled, whilst spring growth samples needed a separate model. Year had no significant effect on the parameters of any model. The models chosen were: spring growth HM = -406.7 + 113.4(DH); r 2 = 0.95, post grazing and aftermath HM = -629.1 + 122.1(DH); r 2 = 0.88. The disk meter is a potential tool for predicting the HM of rotationally grazed timothy pastures. Key words: biomass estimation, rising plate, double sampling methods, grassland management, graz- ing ntroduction Herbage mass (HM; kg ha-1 ) or an expression reflecting it is an essential factor in interpreting the animal response in grazing experiments. HM is also needed for understanding grassland re- sponses to different management practices. Due to the considerable variability within a grazed sward, a large number of direct samples are of- ten needed to get reliable HM estimates. To over- come the need for large sample numbers and to find nondestructive methods for HM estimation several procedures have been developed, e.g. visual estimation, determination of sward height with a ruler or a disk meter, measurements with a capacitance meter and spectral analysis (Frame 1981, Burns et al. 1990). The disk meter is cheap and both simple and quick to use. A number of studies have indicated good relationship between HM and disk meter readings (e.g. Powell 1974, Castle 1976, Bransby et al. 1977, Griggs and Stringer 1988, Mould 1990). Since the disk me- ter is easy to construct (e.g. Castle 1976), its © Agricultural Science in Finland Manuscript received December 1994 397 AGRICULTURAL SCIENCE IN FINLAND https://www.c-info.fi/en/info/?token=Uac00ey1-0Qi1lkB.FbusfsPIoXi0TDRBHmFsHA.31Fce6tcJs47OsO9F-Ymh2YxlPoDXQTzVKX75_sSWx6mj7BZXdYedpWpjPBhhBOzG6DbLyQx0SlTqqbdj8LLncHFVPSOux74ErAjRxPer1SoA-_xPYrhzrwHfxtrHyBRyFSe-QHmxpvYOP-qadTmXpFxLwFYOoYYMfUnmQAumeTO094QmeZvbVL0Vs4Q0B7B-prkxrjI2q3sgRe0OTrd7FiGZ8JaPIj6-2SEjqYgUsI-MYGT9kfZxp1ecmvc1270I5S79Al9zaN36Lmdn2tQ9ZIKPATzLV-WfMmkCM8vDQ Virkajärvi, P. & Matilainen, K.: Predicting herbage mass ofPhleum pratense L. .. price will depend on the components available; costs are minimal in any case. According to Castle (1976) and Bransby et al. (1977), 50 read- ings can be taken in 10-15 minutes from a pad- dock 1.6-2.5 ha in size. Even including the time for calibration, the disk meter is still preferred to the clipping method when estimating the HM of large areas (e.g. Vartha and Matches 1977, Earle and McGowan 1979, Griggs and Stringer 1988). Most studies on non-destructive sampling methods have been conducted on swards consist- ing of perennial ryegrass (Lolium perenne L.) with or without white clover (Trifolium repens L.) (e.g. Powell 1974, Castle 1976, Michell and Large 1983, Piggot 1989, Gabriels and van der Berg 1992). Several reports of HM estimation of tall fescue swards (Festuca arundinaceae Schreb.) have also been published (Bransby et al. 1977, Vartha and Matches 1977, Bryan et al. 1989). Finnish pastures differ from those reported in previous disk meter experiments in terms of species composition, sward structure and man- agement practices. The most common species are timothy (Phleum pratense L.) and meadow fes- cue (F. pratensis Huds.); cocksfoot (Dactylis glomerata L.) is used to some extent. Due to species composition, rapid spring growth, rapid generative development during the long days of summer and the rotational grazing system, tiller number remains low, from 2000-2500 (Huoku- na 1964) to 8000 tiller nr2 , with large variations within a single paddock. The leaf area index (LAI) also remains relatively low (Virkajärvi, manuscript). The height of the stand of pregrazed swards is usually higher than that reported in most previous disk meter studies. Thus, it was necessary to clarify the prediction ability of the disk meter on rotationally grazed pastures dom- inated by timothy, Phleum pratense L., under Nordic conditions. First, different disk weights were compared to establish the most suitable downward pressure for timothy. The stability of calibration in the course of the grazing season and between grazing seasons was studied in the following year. Material and methods The disk meter was calibrated by developing re- gression equations between the meter readings (cm) and herbage mass (kg ha~‘ dm > 4.0 cm) at the Karelia Research Station in 1991 and 1992. The pastures, which were rotationally grazed by suckler cows, contained 80-90% timothy and 0- 20% meadow fescue. The amount of dicotyle- donous weeds was insignificant. The pastures were fertilized with 170-180 N, 20-40 P and 140-210 K depending on the soil properties. Climatic data were recorded at the Karelia Re- search Station. In all, 18 series of calibrations were made between 30th May 1991 and 15th September 1992. The calibration included spring growth, post-grazed swards and aftermath, later referred to as field status. Comparison of three disk weights with accuracy of the method The disk meter was constructed as described in Castle (1976). It consisted of a plastic rod (1100 mm long, 22 mm diameter) and two free- ly sliding aluminium plates (1 mm thick) joined together. The diameter of the lower aluminium disk was 300 mm. Additional brass weights were used to give weight of 247, 354 and 458 g to correspond to weights ofc. 3.5, 5.0 and 6.5 kg/m2 . The rod was graduated in 1 cm intervals. For measurements of HM, the meter was held up- right and then pushed into the vegetation. The horizontal plate was raised by the vegetation, and the settling height was read from the rod with 0.5 cm accuracy. The available HM was determined by cutting 0.225 m 2 quadrats to a height of 4.0 cm with elec- tric garden shares. Before the cutting, three disk meter readings (DH) were taken ofeach quadrat at each disk weight and the mean was pooled against HM. In addition, extended height (SH) was measured with a ruler, and sward density (DEN) was determined visually as percentages of full tiller density. The dry matter content (DM) 398 AGRICULTURAL SCIENCE IN FINLAND Vol. 4: 397-^O5. Table 1. Monthly mean temperatures and precipitation during growing seasons 1991 and 1992 and average long-term values. Mean temperature, °C Precipitation, mm 1991 1992 1961-1990 1991 1992 1961-1990 May 7.6 9.5 8.6 41 18 36 June 13.3 14.5 14.0 117 40 57 July 16.1 14.8 15.9 124 54 70 August 14.5 13.3 13.5 130 105 80 September 8.0 11.3 8.3 80 46 65 was determined by force-drying samples at 100°C for 20 hours. In order to describe the ma- terial more precisely, a representative sample was collected on each occasion, and crude protein (CP) and crude fibre (CF) were determined at the Central Laboratory, Agricultural Research Centre, Jokioinen, according to standard proce- dures. The data were split according to Neter et al. 1989, i.e. about two-thirds of the observations were used to develop estimation equations (esti- mation data set) and the remaining third were used for validation (validation data set). For the comparison of disk weights made in 1991, HM prediction equations were developed from the data of calibrations 1,3, 4,5, 7 and 9. The re- maining calibrations (2, 6 and 8), one of each field status, were used for validation. The HM prediction models were developed by SAS GLM and REG procedures (SAS Insti- tute 1985) on the basis of residual diagnostics (Henderson and Velleman 1981),r 2 and residual standard deviation (RSD). For the comparison of disk meter weights, the original HM model had field status (ST), calibration (CAL) and DH and all possible interactions of these as varia- bles. The terms which were not significant in the regression model at the 0.05 level were exclud- ed stepwise. Quadratic models (Y = DH + DH 2) were studied to test curvilinearity. Logarithmic (log (Y) = DH) models were also studied to sat- isfy the equal variance assumption. The prediction equations developed were applied to the validation data, i.e. the remaining third of observations. The accuracy of predic- tion models was studied by comparing the pre- dicted values of HM with their corresponding clipped HM values in terms of r 2, and the stand- ard error of validation (SEV). SEV is defined as: [X(Y. -'£) 2/n] l/2 (Griggs and Stringer 1988). Accuracy of light disk in 1991-1992 To determine the accuracy of the disk meter over the years, measurements were continued in 1992 with the lightest disk only. To ensure the repre- sentativeness of the estimation data set, the sam- ples were classified in the field into weak, aver- age and strong vegetation, including extreme sites in estimation data set (Bransby and Clarke 1988). The proportions of each class in the esti- mation data set were approximately the same. The original HM model had year (YR), ST, CAL and DH and all possible interactions of these as variables. The procedure continued as described previously. Results The weather conditions are presented in Table 1. There were marked differences in growing sea- sons: 1991 was extremely wet but in 1992 the pastures suffered from drought. HM and SH varied a great deal during the 399 AGRICULTURAL SCIENCE IN FINLAND Virkajärvi, P. & Matilainen, K.: Predicting herbage mass ofPhleum pratense L. .. Table 2. Date, number of paired observations, field status, yields ofclipped samples, extended sward height and crude fibre content for 18 series of calibrations. Calibration Field Herbage mass > 4cm Sward height Crude fibre status' kg/ha DM cm % No. Date n Mean Range Mean Mean 1991 1 30.5 30 SPGR 705 271-1151 20 16 2 4.6 25 SPGR 1400 507-2413 29 22 3 11.6 25 SPGR 2251 996-4187 40 23 4 2.7 22 POSTG 1387 18-3240 26 33 5 23.7 33 AFT 999 62-1938 29 24 6 30.7 26 POSTG 728 156-2040 15 26 7 26.8 33 POSTG 194 18- 840 8 24 8 27.8 28 AFT 508 22-2280 17 23 9 3.9 30 AFT 385 58- 889 16 21 1992 10 3.6 50 SPGR 715 89-1631 19 17 11 9.6 50 SPGR 1404 396-2649 22 17 12 23.6 49 SPGR 4436 2627-7107 53 28 13 2.7 48 POSTG 1203 9-3747 19 27 14 15.7 49 AFT 750 187-1782 17 21 15 21.7 50 POSTG 622 13-1773 12 23 16 31.8 49 AFT 510 18-2169 14 21 17 14.9 49 AFT 1026 40-3098 20 22 18 15.9 50 POSTG 733 40-1880 13 23 Total 696 ' Field status: SPGR = spring growth, POSTG = post grazing, AFT = aftermath. experimental period. MaximumHM, 7107 kg ha 1 DM, was obtained on 23rd June, 1992.The mean sward height of 11th June, 1991,40cm, and that of 23rd June, 1992, 53 cm, are both clearly higher than recommended as a grazing stage inFinland. CF content was moderate, exceeding 26% on only three occasions (Table 2). DFI readings correlated better with SH (r = 0.92, p < 0.0001) than with DEN (r = 0.14, p < 0.0001). HM correlated slightly better with DH (r = 0.97, p < 0.0001) than SH alone (r = 0.93, p < 0.0001). The correlation between HM and DEN was weak (r = 0.17, p < 0.0001), being stronger in spring growth (r = 0.36 p < 0.0001) than in aftermath samples (r = 0.16, p < 0.01). In post grazing samples the correlation was weak and statistically not significant (r = 0.08, p > 0.05). Effect of disk weight on accuracy of the method The variation in HM was adequately explained by linear models. The data on after grazing and aftermath samples could be pooled, but those on first-growth samples had to be analysed sepa- rately. The results of the comparison were pub- lished as a poster at European Grassland Feder- ation’s XIV General Meeting 1992 (Virkajärvi et al. 1992). The differences in accuracy of the 400 AGRICULTURAL SCIENCE IN FINLAND Vol. 4: 397-405. Table 3. Selected available HM prediction models' for three disk meters, light (L), medium (M) and heavy (H). Calibration Validation Disk n Mean Y a ± SE b ± SE R 2 RSD n R 2 SEV type kg/ha kg/ha kg/ha kg/ha kg/ha Spring growth samples: L 55 1408 -868.2 ±93.7 140.0 ±5.4 0.93 257.0 25 0.90 165.6 M -814.5 ±96.5 150.6±6.1 0.92 269.4 0.87 197.1 H -727.8 ±90.7 157.7± 6.2 0.92 262.3 0.85 206.6 Post grazing and aftermath samples; L 118 690 -502.8 ±37.6 103.5 ± 2.8 0.92 202.3 54 0.64 303.8 M -469.2 ± 37.2 109.8 ±3.0 0.92 204.1 0.63 309,0 H -484.8 ±37.6 122.2 ±3.4 0.92 204.6 0.63 309.9 Combined calibrations: L 173 918 -627.6 ± 42.0 118.6 ± 2.8 0.91 258.1 79 0.84 251.6 M -589.0 ±43.0 126.8 ±3.2 0.90 269.1 0.83 261.8 H -588.1 ±40.8 138.7 ± 3.3 0.91 256.5 0.82 268.0 'Model terms: HM = herbage mass (dry matter kg/ha > 4 cm), DH = disk height (cm), n = number of observations used for calibration and validation, SE = standard error of estimate, RSD = residual standard deviation, SEV = standard error of validation (defined as: [Z(Y -Y.) 2 /n] l/2 (Griggs and Stringer 1988). method between three disk weights were small (Table 3). The effect of disk weight was strong- est in spring growth. The light disk (L) was slightly the most reliable in terms ofr 2 and SEV on all occasions and was therefore chosen for sampling in 1992. od was good in spring growth and slightly poor- er in post grazing and aftermath. Discussion Accuracy of light disk in 1991-1992 The original HM model had YR, ST, CAL and DH and all possible interactions of these as var- iables. Year and calibration or their interactions were not significant (p > 0.05) terms in the mod- el. As in the case of different disk weights, the variation in HM was adequately explained by linear models. Use of quadratic instead of the linear models did not markedly improve r 2 val- ues (from 0.948 to 0.952). The data on after graz- ing and aftermath samples could be pooled, but the first growth samples had to be analysed sep- arately. The HM estimation models chosen are presented in Table 4. The accuracy of the meth- Effect of disk weight on accuracy of the method The downward pressures used, 3.5-6.5 kg/m 2 , were rather average when compared with those reported in other studies, in whichpressures have varied from 2.9 (Castle 1976, Mould 1992) to 15 kg nr2 (Bransby et al. 1977). Downward pres- sure did not affect the accuracy of the method but only the slope of the regression line. Here, timothy pasture resembles tall fescue (Bransby et al. 1977).The light disk gave a slightly more accurate result than the other disks. Thus it would seem sensible to study lighter rather than heavi- er disks, as also suggested by Mould (1992). 401 AGRICULTURAL SCIENCE IN FINLAND Virkajärvi, P. & Matilainen, K.: Predicting herbage mass ofPhleum pratense L. .. Table 4. Selected HM prediction models' for the light disk 1991-1992. Calibration Validation n MeanY a±SE b±SE R 2 RSD n R 2 SEV kg/ha kg/ha kg/ha kg/ha kg/ha SPGR 152 1894 ±48.2 113.4 ±2.0 0.95329.5 77 0.95345.4 POSTG 149 794 -587.2 ± 45.5120.3 ±3.5 0.88258.3 80 0.87287.4 AFT 157 710 -692.2 ±47.3 126.8 ±4.0 0.87202.5 81 0.90177.0 POSTG+AFT 306 751 -629.1 ±32.1 122.1 ±2.6 0.88231.7 161 0.88239.5 ALL 458 1130 -546.1 ±22.5 117.6 ± 1.30.94 273.0 238 0.94287.4 'Model terms: HM = herbage mass (dry matter kg/ha > 4 cm), DH = disk height (cm), n =number of observation, SE = stadard error of estimate, RSD = residual standard deviation, SEV = standard error of validation (defined as: [I(Y-Y) 2/n] l/2 (Griggs & Stringer 1988). Accuracy of the light disk meter In terms of accuracy of the method or residual deviation, the variation in HM was adequately described as a linear function of DH, although the residuals tended to increase as DH increased (Fig. 1). Logarithmic models were not suitable. Although there was a slight tendency to curvi- linearity in the validation set of spring growth samples (Fig. la.), the quadratic model did not markedly improve the r 2 value (less than 1% unit). This is probably due to the robustness of the linear model and the sensitivity of the quad- ratic model to differences between the estima- tion and the validation data set. The relationship between DH and HM remained linear even though the height of the stand was above the normal range for pastures. From the literature it can be concluded that linear functions have been used when the DH values have been relatively low, but at high DH values (> 40 cm) a polyno- mial function has been more accurate (Bransby et al. 1977, Baker et al. 1981). It is clear that when the average height increases, the risk of lodging also increases, thus causing considera- ble error. The method may not, therefore, be suit- able for estimating, say, silage yield if lodging is abundant. The accuracy of the method is also strongly impaired if the grass is trampled or if the soil surface is uneven. The r 2 values found here were relatively high, which may be attributed to the uniform botani- cal composition (up to 90% of timothy). SEV, too, was low, 345 and 240kg ha-1 DM (Table 4), but because oflow HM, the coefficient of varia- tion was high. This may restrict the usefulness of the method, especially when measuring HM after grazing. However, the cutting height, 4 cm, lowered the HM values observed compared with those for clipping to ground level. Density correlated only very weakly with HM, This could be partly due to the fact it was estimated visually, but the correlation was low with more accurate methods, too (Urioste 1984, Griggs and Stringer 1988). It is interesting to note that DH correlated clearly better with SH (r = 0.92, p < 0.0001) than with DEN (r = 0.14, p<0.0001). The inclusion of density in the HM prediction models did not improve the accuracy of prediction. According to several researchers, the disk meter should be calibrated several times during the growing season and again in different years (e.g. Bransby 1977 et al., Vartha and Matches 1977, Bryan et al. 1989, Mould 1990) because of changes in the botanical composition of the sward and differences in vegetative and genera- tive growth type. Here, the parameters of the spring growth model differed from those of post grazing and aftermath. Other differences in the course of the growing season were not found, possibly because botanical composition was uni- 402 AGRICULTURAL SCIENCE IN FINLAND Vol. 4: 397-405. form throughout the growing season. Moreover, the cutting height of4 cm may have affected the results,as herbage often accumulates under the grazing horizon in the course of the grazing sea- son. The nearer the sampling height and the low- er limit of the actual grazing horizon are to each other, the less effect this accumulation should have. Uniform botanical composition is the most probable reason for therebeing no statistical dif- ferences between years, even though 1991 was extremely moist whilst 1992 was dry, and swards clearly suffered from drought. Thus turgor pres- sure of the leaves or plants appears to have had only a slight effect on the parameters of the re- gression line. Many researhers have concluded that there is no uniform calibration equation (Griggs and Stringer 1988, Bryan et al. 1989, Gonzalez et al. 1990). Although in this study sta- bility was good under very different moisture conditions, the disk meter should still be cali- brated for each occasion if the results are to be expressed as HM. Depending on the purpose of the determinations, the calibration could be done according to Bransby and Clarke (1988) or us- ing the simplified version described by Stock- dale (1984). Conclusions The disk meter is accurate enough for predict- ing the HM of timothy pastures of uniform bo- tanical composition for specific purposes when large paddocks are to be evaluated. Such pur- poses might be estimation of the effects of over- wintering damage on growth in different man- agement practices or estimation ofsward growth Fig. 1. Relationship between disk height (DH) and dry matter herbage mass (HM) samples for 1991-1992. Solid line = estimated HM. a) Spring growth b) post grazing and aftermath. 403 AGRICULTURAL SCIENCE IN FINLAND Virkajärvi, P. & Matilainen, K.: Predicting herbage mass ofPhleum pratense L. . rate; it can also be used as a tool for manage- ment decisions in grazing trials. The disk meter cannot be used to predict the HM of a sward in the event of disturbances in canopy height, e.g. lodging or the presence of old stubble or tram- pled grass. Sward density is ofminor importance to meter readings. Acknowledgements. The authors would like to thank E. Ketoja, Agricultural Research Centre, Data and Infor- mation Services, for statistical advice concerning the ef- fect of different disk weights. We also thank the technical staff of the Karelia Research Station for assistance with measurements and the Central Laboratory of the Agricul- tural Research Centre for the chemical analysis. References Baker, 8.5., Eynden,T.V. & Boggess, N. 1981. Hay yield determinations of mixed swards using a disk meter. Agronomy Journal 73: 67-69. Bransby, D.l. & Clarke, G.P. 1988. Biological, practical and statistical considerations associated with measuring forage availability in grazing trials. Proceedings of the Southern Pasture and Forage Improvement Conference, p. 59-63. Bransby, D. 1., Matches, A.G. & Krause, G.F. 1977. Disk meter for rapid estimation of herbage yield in grazing tri- als. Agronomy Journal 69: 393-396. Bryan, W.8., Thayne, W.V. & Prigge, E.C. 1989. Use of a disk meter to evaluate continuously grazed pastures. Journal of Agronomy and Crop Science, 163: 44-48. Burns, J.C., Lippke, H. & Fisher, D.S. 1990. The rela- tionship of herbage mass and characteristics to animal responses in grazing experiments. In : Grazing research, design, methodology and analysis. CSSA special publi- cation No 16. Madison, USA. p. 7-19. Castle, M.E. 1976. A simple disc instrument for estimat- ing herbage yield. Journal of the British Grassland Soci- ety 31: 37-40. Earle, D.F. & McGowan, A.A. 1979. Evaluation and cal- ibration of an automated rising plate meter for estimating dry matter yield of pasture. Australian Journal of Experi- mental Agriculture and Animal Husbandry 19: 337-343. Frame, J. 1981. Herbage mass. In: Hodgson, J. et al. (eds.). Sward measurement handbook. British Grassland Society. Maidenhead, UK. p. 39-69. Gabriels, P.C.J. &Van Den Berg, J.V. 1993. Calibration of two techniques for estimating herbage mass. Grass and Forage Science 48: 329-335. Gonzalez, M.A., Hussey, M.A. & Conrad, B.E. 1990. Plant height, disk and capacitance meters used to esti- mate bermudagras herbage mass. Agronomy Journal82: 861-864. Griggs T.C. & Stringer W.C. 1988. Prediction of alfalfa herbage mass using sward height, ground cover and disk technique. Agronomy Journal 80: 204-208. Henderson, H.V. & Velleman, P.F. 1981. Building multi- ple regression models interactively. Biometrics 37: 391- 411. Huokuna, E. 1964. The effect frequency and height of cutting on cocksfoot swards. Annales Agriculturae Fen- niaa. 3: Suppl. no. 4. 83 p. Michell, P. & Large, R.V. 1983. The estimation of herb- age mass of perennial ryegrass swards; a comparative evaluation of a rising plate meter and a single probe ca- pacitance meter calibrated at and above ground level. Grass and Forage Science 38: 295-299. Mould, F.L. 1990. A note on the use of a rising-plate meter to estimate herbage yield. Norwegian Journal of Agricultural Sciences 4: 111-117. - 1992. Use of a modified rising-plate meter to estimate herbage yield, Norwegian Journal of Agricultural Scienc- es 4: 111-117. Neter, J.,Wasserman, W. & Kutner, M.M. 1989. Applied linear regression models. 2nd ed. 667 p. Boston. Piggot, G.J. 1989. A comparison of four methods for estimating herbage yield of temperate dairy pastures. New Zealand Journal of Agricultural Research 32: 121- 123. Powell,T.L. 1974. Evaluation of weighted disc meter for pasture yield estimation on intensively stocked dairy pas- ture. New Zealand Journal of Experimental Agriculture 2: 237-241. SAS Institute 1985. SAS/STAT guide for Personal Com- puters, 6th ed. SAS Institute Inc., Cary, NC, USA. 378 p. Stockdale, C.R. 1984. Evaluation of techniques for esti- mating the yield of irrigated pastures intensively grazed by dairy cow. 2. The rising plate meter. Australian Jour- nal of Experimental Agriculture and Animal Husbandry 24: 305-311. Urioste, J. 1984. Utveckling av metoder för skattning av betets avkastning som hjälpmedel för foderstyrning till mjölkkor på bete. Examenarbete i Husdjurens Utfodring och Vård. 36 p. Sveriges Lantbruksuniversitet. Uppsala. Vartha, E.W. & Matches, A.G. 1977. Use of a weighted- disk measure as an aid in sampling the herbage yield on tall fescue pastures grazed by cattle. Agronomy Journal 69: 888-890. Virkajärvi, P., Karvonen, K. & Ketoja, E. 1992.Calibra- tion of a disk meter for predicting herbage mass of Phle- um pretense pastures. Proceedings of the 14th General Meeting of the European Grassland Federation. Lahti, Finland, p. 556-557. 404 AGRICULTURAL SCIENCE IN FINLAND Vol. 4: 397-405. SELOSTUS Timoteivaltaisen laidunnurmen massan määrittäminen mittalautasen avulla Perttu Virkajärvi jaKaisa Matilainen Maatalouden tutkimuskeskus Laidunkaudella 1991 ja 1992 tutkittiin Maatalouden tutkimuskeskuksen Karjalan tutkimusasemalla mitta- lautasen käyttökelpoisuutta timoteivaltaisen laidun- nurmen kuiva-ainemassan määrityksessä. Käytetty mittalautanen koostui alumiinisesta lautasesta (hal- kaisija 30 cm), joka liikkui pystysuuntaisesti pitkin muovista mittakeppiä. Tutkimuksessa havainnoitiin mittalautasen lukemien ja nurmen massan välisen riippuvuuden luonnetta sekä miten tämä riippuvuus muuttuu kasvukauden aikana ja eri vuosina. Aineis- to koostui yhteensä 696 havaintoparista. Lautasen painon vaikutusta menetelmän tarkkuu- teen tutkittiin laidunkaudella 1991. Eri painoisten lautasten kasvustoon kohdistamat paineet olivat 3,5 kg/m 2 , 5 kg/m 2 ja 6,5 kg/m 2 . Mittalautasen paino ei juurikaan vaikuttanut estimointiyhtälöiden tarkkuu- teen. Kevein lautanen osoittautui hienokseltaan tar- kimmaksi. Lineaarinen regressio kuvasi parhaiten eri muut- tujien välisiä riippuvuuksia. Vaikka vuodet poikke- sivat etenkin sademääriltään toisistaan, ei vuodella tai kalibraatiokerralla ollut vaikutusta parametrien arvoi- hin vaan aineistot voitiin yhdistää. Yhdistetyssä ai- neistossa 1991-1992 (kevein lautanen) nurmen kui- va-ainemassa pystyttiin arvioimaan melko tarkasti mittalautasen lukemien perusteella, parhaiten kevät- kasvustossa. Estimointiyhtälöiden samankaltaisuuden vuoksi laiduntamisen jälkeiset ja odelmahavainnot voitiin yhdistää. Nurmen tiheys ja kuiva-ainepitoisuus korreloivat heikosti sadon kanssa, eivätkä ne parantaneet mitta- lautaslukemiin perustuvaa satomallia oleellisesti. Tut- kimuksen perusteella mittalautanen oli riittävän luo- tettava timoteivaltaisen laidunnurmen massan kuvaa- jana. Kevätkasvustossa lautanen oli luotettavampi kuin laiduntamisen jälkeen tai odelmassa. Kasvusto ei saa olla lakoontunut eikä tallattu. Menetelmä vaa- tii lisätutkimusta eri nurmikasvilajeilla ja seosnurmis- sa, jotta mittalautasta voitaisiin käyttää yleisesti lai- duntutkimuksissa. 405 AGRICULTURAL SCIENCE IN FINLAND