Tropical Grasslands (2005) Volume 39,

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1 Tropical Grasslands (25) Volume 39, Estimation of herbage mass in a bahia grass (Paspalum notatum) and a centipede grass (Eremochloa ophiuroides) pasture using a capacitance probe, a sward stick and a rising plate S. OGURA, Y. NAGATOMO AND M. HIRATA Division of Grassland Science, Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan Abstract The accuracy of 3 non-destructive techniques, an electronic capacitance probe, a sward stick and a rising plate in estimating herbage mass (HM) in a bahia grass (Paspalum notatum, BG) and a centipede grass (Eremochloa ophiuroides, CG) pasture from late spring to late autumn in 2 grazing seasons was determined. Two studies were conducted: in Experiment 1, stratified samplings investigated 1 locations (5 cm 5 cm) whose HM covered the range of HM in each pasture at almost constant intervals; and in Experiment 2, systematic samplings investigated 4 locations along line transects in each pasture. With stratified samples from BG, all techniques consistently resulted in similar, significant linear relationships (r 2 =.86.98, P<.1) between HM and capacitance or height readings. By contrast, for CG, the stick (r 2 =.57.98, P<.5 or.1) and the plate (r 2 =.59.96, P<.1 or.1) tended to be less accurate than the probe (r 2 =.83.98, P<.1). With systematic samples, the 3 techniques also produced significant linear relationships between HM and capacitance or height readings for both BG (r 2 =.47.82, P<.1) and CG (r 2 =.28.74, P<.1), although r 2 values were lower than those from stratified samplings. The probe and the stick showed relatively poor performance for BG and CG, respectively. In both stratified and systematic samplings, HM-capacitance and HM-height relationships for BG in autumn underestimated actual HM in high HM locations, due to high amount of stems and dead materials in these overgrown Correspondence: Dr S. Ogura, Graduate School of Agricultural Science, Tohoku University, Sendai , Japan. s-ogura@bios.tohoku.ac.jp areas. Quadratic regressions were more accurate in predicting actual HM in these situations. It was concluded that, although the rising plate was the most precise technique for estimating HM on the tropical grass pastures across seasons, further work is needed to overcome the bias of high HM locations on BG. Introduction Bahia grass (Paspalum notatum), a sod-forming, warm-season perennial, is one of the most important forage species grown for grazing and hay in the low-altitude regions of south-western Japan (Hirata et al. 23). In recent years, centipede grass (Eremochloa ophiuroides), which is also a sod-forming, warm-season perennial, has been studied as a new, promising forage species for grazing because of its high productivity and quality during grazing seasons (Hirata et al. 22). Since herbage intake and performance of grazing animals are often highly related to herbage mass (HM) (Burns et al. 1989; Humphreys 1991), rapid estimation of HM facilitates both management decisions and studies of grazing systems. The most accurate method for measuring HM of a single sampling location (e.g. 1 m 1 m area) is cutting and weighing above-ground biomass of vegetation. However, this destructive technique requires high inputs of labour and/or equipment if the area or the number of sampling locations is large (Mannetje ). The technique also prevents further monitoring of vegetation in the same sampling location. On the other hand, non-destructive techniques such as an electronic capacitance probe (Vickery et al. 198), a sward stick (Barthram 1986) and a rising plate (Castle 1976; Bransby et al. 1977) are widely used to estimate HM due to their simplicity and rapidity of use (Mannetje ). They also allow the monitoring of HM changes in a number of permanent locations in a pasture (Hirata ).

2 Herbage mass estimation in tropical grass pastures 23 In the non-destructive techniques, however, high accuracy of calibration equations between HM and readings from an instrument is needed to obtain precise estimates of HM. In temperate pastures, many comparative studies have shown the usefulness of the techniques for estimating HM (e.g. Michell and Large 1983; Stockdale and Kelly 1984; Crosbie et al. 1987; Gabriëls and Van Den Berg 1993; Murphy et al. 1995). In contrast, relatively little information is available on comparative performance of the techniques in tropical grass pastures. Whitney (1974) showed the usefulness of a rising plate in estimating HM of kikuyu grass (Pennisetum clandestinum) and pangola digit grass (Digitaria decumbens). Gonzalez et al. (199) also showed the potential for using non-destructive techniques in bermuda grass (Cynodon dactylon) pastures. However, no comparative studies exist on the accuracy of the 3 non-destructive techniques for estimating HM in bahia grass and centipede grass pastures. In this study, the relationships between HM and readings from an electronic capacitance probe, a sward stick and a rising plate were quantified, and the accuracy of the techniques in estimating HM was assessed in the 2 tropical grass pastures during 2 grazing seasons. Materials and methods Site and pasture management The study was conducted in a Pensacola bahia grass and a Common centipede grass pasture (1.1 and.4 ha, respectively) at the Sumiyoshi Livestock Farm (31 59 N, E), Faculty of Agriculture, Miyazaki University, Japan, in Each pasture was dominated by the respective sown grass (usually >8% of herbage DM). These pastures formed part of a rotational grazing system (total pasture area = 6.7 ha) with Japanese Black cows and calves. During the grazing seasons (mid-may early November) of 21 22, each pasture was grazed by cows and 5 14 calves for 6 periods of 3 5 days. Mean air temperature and total rainfall during May November were 21.5 C and 274 mm in 21 and 22.1 C and 1197 mm in 22, respectively. Mean monthly temperature was highest in August (27.1 and 27. C in 21 and 22, respectively) and lowest in November (13. and 11.6 C in 21 and 22, respectively) in both years. Monthly rainfall ranged from 45 (November) to 491 (September) mm in 21 and from 67 (November) to 268 (June) mm in 22. The annual fertilisation rates were 84 kg N (split applications in April May and August), 23 kg P (April May) and 26 kg K (April May) per ha and 88 kg N (split applications in April and August), 24 kg P (April) and 27 kg K (April) per ha, for bahia grass and centipede grass, respectively. Experiment 1. Stratified samplings Stratified samplings were conducted 6 times in each pasture at approximately monthly intervals from May (late spring) to November (late autumn) (Table 1). The sampling dates were at least 14 days after the end of the previous grazing period in the pasture, to obtain a relatively wide range of HM. On each sampling occasion, 1 or 2 line transects (9 1 m each) were set to cross the pasture. Corrected meter reading (CMR) was measured by an electronic capacitance probe (PastureProbe TM, Mosaic Systems Ltd, New Zealand) at locations (5 cm 5 cm) spaced at 1-m intervals along the transect(s), to estimate an approximate range of CMR in the pasture. Then, 1 locations (5 cm 5 cm), whose maximum and minimum CMR covered the range of CMR in the pasture, were selected at constant intervals of CMR, and the reading in each location was recorded, to obtain herbage samples at approximately constant intervals of HM (i.e., stratification). Sward surface height (SSH) and rising plate height (RPH) in these 1 locations were measured using an automated sward stick (Jenquip, New Zealand; maximal measuring height = 27 cm) and a rising plate (Filips Folding Pasture Meter, Jenquip, New Zealand; a 3 g aluminium disc 35 cm in diameter, maximal measuring height = 4 cm), respectively. The rising plate was used last because of its potential effect of sward compaction. CMR and SSH were measured at 5 points (4 corners and the centre) within each location, and the mean value was recorded. HM of the locations was measured by cutting the sward at a height of 3 cm with scissors. All herbage samples were dried at 85 C for 72 h to determine final HM. Experiment 2. Systematic samplings Systematic samplings were conducted in September (early autumn) 21, June (early summer)

3 24 S. Ogura, Y. Nagatomo and M. Hirata Table 1. Details of samplings and herbage mass (HM) (range and mean) of samples. Pasture Experiment Sampling date Sample number HM Range Mean Bahia grass 1 May 24, Jun 24, Jul 19, Aug 14, Sep 19, Oct 24, Sep 17, Jun 7, Jul 3, Centipede grass 1 Jun 1, Jul 6, Aug 1, Sep 1, Oct 4, Nov 1, Sep 11, Jun 14, Aug 5, and July August (mid- to late summer) 22 in the pastures (Table 1). The samplings were conducted 2 or 3 days before the beginning of a grazing period. On each sampling occasion, 4 parallel line transects (1 m each) were set in the pasture at constant intervals. The CMR, SSH, RPH and HM were systematically measured at 4 locations spaced at 1-m intervals along the transects according to the method described earlier. Regression analysis Relationships between HM (Y) and readings by the 3 instruments (X) were examined by fitting linear (Y = ax + b) and quadratic (Y = cx 2 + d) equations to data from each sampling occasion in each pasture. Coefficient of determination (r 2 ), residual standard deviation (RSD, kg/ha DM) and coefficient of variation (CV; the proportion of RSD to the mean value of Y) were calculated to assess the predictive ability and variation associated with each regression equation. Results HM of samples ranged from kg/ha DM and kg/ha DM for bahia grass and centipede grass, respectively (Table 1). Centipede grass usually showed higher values for both mean and maximum HM than bahia grass on similar dates in each year. Experiment 1. Stratified samplings In the bahia grass pasture, SSH was not measured at 3 locations with high HM in July September (Table 2) because the heights exceeded the upper limit of the applicable range (27 cm) of the stick. Linear regressions always produced significant r 2 values (P<.5) for HM-CMR and HM-height relationships on both pastures (Tables 2 and 3). For bahia grass, the 3 techniques showed similar predictive accuracies, with RSD values of kg/ha DM, kg/ha DM and kg/ha DM and CV values of , and for CMR, SSH and RPH, respectively (Table 2). In contrast, for centipede grass, CMR (RSD = kg/ha DM; CV = ) tended to show higher predictive abilities than SSH (RSD = kg/ha DM; CV = ) and RPH (RSD = kg/ha DM; CV = ) (Table 3). Predictive accuracies with SSH and RPH were particularly low in August and November. Quadratic regressions improved the predictive accuracies only for CMR on bahia grass (RSD = kg/ha DM; CV = ) (Table 2). The highest predictive accuracy was obtained in August (r 2 =.99; RSD = 133 kg/ha DM; CV =.57).

4 Herbage mass estimation in tropical grass pastures 25 Table 2. Linear (HM = ax + b) and quadratic (HM = cx 2 + d) equations relating herbage mass (HM, kg/ha DM) to corrected meter reading (CMR), sward surface height (SSH, cm) and rising plate height (RPH, cm) in a bahia grass pasture (Experiment 1). Linear relationship Quadratic relationship X Month d.f. a b r 2 P 1 RSD CV (proportion) c d r 2 P RSD CV (proportion) CMR May *** Jun *** Jul *** Aug *** Sep *** Oct *** *** 264 *** *** *** *** *** SSH May *** *** Jun *** *** Jul *** *** Aug *** *** Sep *** *** Oct *** *** RPH May *** *** Jun *** *** Jul *** *** Aug *** *** Sep *** *** Oct *** *** Significant at P<.1.

5 26 S. Ogura, Y. Nagatomo and M. Hirata Table 3. Linear (HM=aX+b) and quadratic (HM = cx 2 + d) equations relating herbage mass (HM, kg/ha DM) to corrected meter reading (CMR), sward surface height (SSH, cm) and rising plate height (RPH, cm) in a centipede grass pasture (Experiment 1). Linear relationship Quadratic relationship X Month d.f. a b r 2 P 1 RSD CV (proportion) c d r 2 P RSD CV (proportion) CMR Jun *** *** Jul *** *** Aug *** *** Sep *** *** Oct *** *** Nov *** *** SSH Jun *** *** Jul *** *** Aug *** *** Sep *** *** Oct *** *** Nov * * RPH Jun *** *** Jul *** *** Aug *** ** Sep *** *** Oct *** *** Nov ** * *, ** and *** indicate significance at P<.5, P<.1 and P<.1, respectively.

6 Herbage mass estimation in tropical grass pastures 27 Experiment 2. Systematic samplings On bahia grass (Figure 1), 4 locations with high HM showed a considerable bias in the HM-CMR and HM-height relationships for all techniques in September. The 4 locations with high HM were a part of tall patches and contained significant quantities of stems and dead materials. Among these locations, SSH of one location was not measured as its height exceeded the measurable limit (>27 cm). For the full data set, quadratic regressions produced higher predictive accuracies (RSD = kg/ha DM; CV = ) than linear regressions (RSD = kg/ha DM; CV =.2.212). However, when data sets from the 4 or 3 locations were excluded, linear regressions showed markedly improved Herbage mass (HM, kg/ha DM) September 21 d.f. = HM = 13CMR 1138 r 2 =.55*** RSD = 33 kg/ha DM CV = June 22 July 22 HM = 1CMR r 2 =.53*** RSD = 363 kg/ha DM CV = Corrected meter reading (CMR) HM = 18CMR 246 r 2 =.64*** RSD = 556 kg/ha DM CV = d.f. = 34 d.f. = 34 7 HM = 156SSH 653 HM = 16SSH 5 HM = 17SSH 455 r 2 =.63*** r 2 =.7*** r 2 =.67*** RSD = 277 kg/ha DM CV =.145 RSD = 291 kg/ha DM CV =.126 RSD = 49 kg/ha DM CV = Sward surface height (SSH, cm) 7 d.f. = 34 HM = 162RPH HM = 187RPH + 23 HM = 241RPH r 2 =.47*** r 2 =.66*** r 2 =.82*** RSD = 331 kg/ha DM RSD = 39 kg/ha DM RSD = 392 kg/ha DM CV =.174 CV =.134 CV = Rising plate height (RPH, cm) Figure 1. Relationships between herbage mass (HM) and corrected meter reading (CMR), sward surface height (SSH) and rising plate height (RPH) in a bahia grass pasture (Experiment 2). Open symbols show high HM locations with senescent and dead materials (excluded from regressions). RSD and CV indicate residual standard deviation and coefficient of variation, respectively. *** denotes significance at P<.1.

7 28 S. Ogura, Y. Nagatomo and M. Hirata predictive accuracies for all techniques (RSD = kg/ha DM; CV = ). In June and July, all regression equations were highly significant (P<.1, r 2 =.53.82; RSD = kg/ha DM; CV = ) and SSH and RPH gave more accurate estimations than CMR, although SSH was not measured at the tallest 4 locations in July. On centipede grass (Figure 2), all regression equations were highly significant (P<.1). For all techniques, predictive accuracy was lowest in September (RSD = kg/ha DM; CV = ), and highest in June (RSD = kg/ha DM; CV = ). RPH tended to give most accurate estimations and SSH least accurate estimations throughout. Herbage mass (HM, kg/ha DM) September 21 HM = 23CMR 233 r 2 =.56*** RSD = 874 kg/ha DM CV = RSD = 477 kg/ha DM CV =.169 HM = 272SSH HM = 164SSH HM = 254SSH r 2 =.43*** r 2 =.28*** r 2 =.58*** 4 1 RSD = 993 kg/ha DM CV =.281 RSD = 733 kg/ha DM CV = Sward surface height (SSH, cm) HM = 461RPH 772 r 2 =.7*** RSD = 721 kg/ha DM CV =.24 June 22 August 22 HM = 13CMR 274 r 2 =.43*** RSD = 425 kg/ha DM CV = Corrected meter reading (CMR) HM = 223RPH r 2 =.49*** RSD = 43 kg/ha DM CV = Rising plate height (RPH, cm) HM = 22CMR 21 r 2 =.74*** RSD = 572 kg/ha DM CV = HM = 338RPH 346 r 2 =.73*** RSD = 59 kg/ha DM CV =.183 Figure 2. Relationships between herbage mass (HM) and corrected meter reading (CMR), sward surface height (SSH) and rising plate height (RPH) in a centipede grass pasture (Experiment 2). RSD and CV indicate residual standard deviation and coefficient of variation, respectively. *** denotes significance at P<.1.

8 Herbage mass estimation in tropical grass pastures 29 Discussion A major finding from the present study was the failure of locations with high HM to fit the standard HM-CMR and HM-height relationships in the autumn bahia grass pasture. This was clearly shown in systematic samples as deviation of some data sets in September; i.e., actual HMs in the high HM locations were higher than those predicted by the relationships for locations with low-intermediate HMs (Figure 1). The bias was also detected in stratified samples, as the increased slope values in the regression equations in October (Table 2). This was attributable to the fact that high HM areas in autumn were mainly tall patches and included a large amount of stems and dead material as a result of previous rejection by grazing animals (Ogura et al. 22). A capacitance probe is less sensitive to stems and dead material than leaves, because CMR depends on the moisture content of plant material (Jones et al. 1977). The divergent points in Figure 1 also indicate high bulk density in such locations, suggesting that neither SSH nor RPH is useful in predicting HMs with different densities at the same heights. Similar observations have been reported on temperate pastures (Michell and Large 1983; Douglas and Crawford 1994). Our results thus show that linear regression equations derived from samples covering the whole range of HM are not appropriate for estimating HM of bahia grass in autumn. Quadratic regression increased the precision of calibration equations on some occasions, especially for CMR on bahia grass in all seasons. These results indicate that calibration with a quadratic equation can be used to decrease the error caused by samples with high HM when a capacitance probe is used (Hirata ). It is reported that variation in botanical composition among quadrats can cause errors in HM estimation (Jones and Hargreaves 1979). However, the pastures used in the present study were dominated by the respective sown grasses and other species accounted for a small or nil proportion in any sampling locations. This indicates that there was little effect of botanical composition on the errors or the biases in HM estimation. Another major finding from the present study was the poor performance of the sward stick in the centipede grass pasture (Table 3; Figure 2). In this pasture, there was no evidence of the bias caused by high HM locations (Figure 2), and the slopes of the regression equations were usually steeper than in the bahia grass pasture (Table 3; Figure 2). These results indicate that centipede grass did not include significant amounts of stems or dead material and maintained higher bulk density than bahia grass throughout the grazing seasons. This supports the findings of Hirata et al. (22) that a centipede grass pasture shows higher proportions of green leaves ( on a DM basis) and higher bulk densities ( kg/ha/cm DM) than a bahia grass pasture ( and kg/ha/cm DM, respectively). Our results agree with a previous finding that height is less useful in predicting HM of a denser sward (Whitney 1974). SSH was not measured at some locations in the bahia grass pasture in summer autumn (Table 2; Figure 1), because the height exceeded the upper limit of the applicable range of the sward stick (27 cm). This does not necessarily mean that SSH is inappropriate for estimation of HM because a sward stick with a higher upper limit can be used. However, the relatively low accuracy of SSH on the centipede grass pasture confirms that the sward stick is inappropriate for estimation of HM. More measurements (>5 points) within each location may be needed to improve the precision of the sward stick as a tool for predicting HM. In contrast to the sward stick, the capacitance probe showed high predictive accuracies except for the bias in the high HM locations in the bahia grass pasture (Table 2; Figure 1), although Jones et al. (1977) concluded that the capacitance probe was not likely to be successful in measuring pasture dry matter yield in grazed tropical pastures. This was probably due to the higher proportion of green material (i.e., highly detectable materials by a capacitance probe) in DM yields in the pasture (Higashiyama and Hirata 1995; Hirata et al. 22) resulting from much higher rainfall during the grazing seasons in our study than that of Jones et al. (1977). In addition to the 3 techniques, visual estimation is another option for non-destructive and rapid estimation of HM in pastures. It is known that this technique is less complicated for monospecific swards and accurate when estimates are calibrated to dry matter yield of cut standards (Mannetje ). Furthermore, because visual estimation enables an observer to estimate HM in a quadrat based on many vegetation attributes such as height, mass and density of plants and

9 3 S. Ogura, Y. Nagatomo and M. Hirata proportion of stems and dead materials, there is a potential for this technique to overcome the bias which a capacitance probe, a sward stick and a rising plate showed for high HM locations in the autumn bahia grass pasture. In conclusion, the rising plate proved a rapid and reliable technique for estimating HM on the tropical pastures across seasons, with slightly higher accuracy than the capacitance probe for systematic sampling. However, on bahia grass pasture with high HM and significant proportions of stems and dead material, none of the techniques was particularly accurate. This finding may be the case for both stratified and systematic samplings. Further studies are warranted to determine how the bias of high HM locations in a bahia grass pasture may be overcome. Acknowledgements We thank Dr K. Fukuyama, the Sumiyoshi Livestock Farm, for management of the pastures, and Miss K. Adachi, Miss R. Fujii, Miss M. Furuya, Miss E. Harada, Mr K. 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