Response of Coastal Bermudagrass and Pensacola Bahiagrass to Applied Nitrogen and Seasonal Rainfall #

Transcription

1 COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS Vol. 34, Nos. 7 & 8, pp , 2003 Response of Coastal Bermudagrass and Pensacola Bahiagrass to Applied Nitrogen and Seasonal Rainfall # A. R. Overman, 1, * R. V. Scholtz III, 1 and C. G. Chambliss 2 1 Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida, USA 2 Agronomy Department, University of Florida, Gainesville, Florida, USA ABSTRACT Applied nutrients and seasonal rainfall are both important inputs for forage production. A mathematical model is needed to link these factors. Since the logistic model provides a quantitative description of seasonal dry matter response to applied nutrients, the role of water availability should be identified. Previous analysis showed that the linear parameter A of the logistic model can incorporate this factor. In this article the relationship of A to seasonal rainfall, W, is shown to follow an exponential equation for Coastal bermudagrass (Cynodon dactylon L.) and Pensacola bahiagrass (Paspalum notatum Flügge). This agrees with previous results for corn (Zea mays L.). # Florida Agricultural Experiment Station Journal Series No. R *Correspondence: A. R. Overman, Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL , USA; aoverman@agen.ufl.edu DOI: /CSS Copyright q 2003 by Marcel Dekker, Inc (Print); (Online)

2 1098 Overman, Scholtz, and Chambliss INTRODUCTION There have been several attempts to link the logistic model for response of dry matter yields to applied N to water availability. Overman and Evers [1] used data from Evers [2,3] for bermudagrass and bahiagrass in Texas to establish linear dependence of the model parameter A to seasonal rainfall. Similar results were obtained by Overman et al. [4] from studies of Sanderson et al. [5] with bermudagrass and lovegrass (Panicum coloratum L.) in Texas. Reck and Overman [6] performed a similar analysis for corn in Georgia [7] and Florida [8] and also found a linear relationship. More recently Overman and Scholtz [9] used data of Tolk et al. [10] for corn in Texas to establish an exponential model. A similar procedure is used in this analysis for Coastal bermudagrass and Pensacola bahiagrass. by MATHEMATICAL MODEL The logistic model for seasonal yield response to applied nitrogen is given Y ¼ A 1 þ expðb 2 cnþ where N ¼ applied nitrogen, kg ha 21 ; Y ¼ dry matter yield, Mg ha 21 ; A ¼ maximum yield at high N, Mgha 21 ; b ¼ intercept parameter; c ¼ response coefficient for applied N, ha kg 21. Now Overman and Scholtz [9] showed that coupling between yield and evapotranspiration, ET, followed an exponential equation for corn under irrigation. In this analysis we assume that parameter A of the logistic model is related to seasonal rainfall, W, by a similar equation A ¼ A w ½1 2 expðb 0 2 c 0 WÞŠ where A w ¼ maximum value of parameter A at high W; b 0 ¼ intercept parameter; c 0 ¼ response parameter for rainfall. ð1þ ð2þ DATA ANALYSIS Data for this analysis are taken from a field study with Coastal bermudagrass (CB) and Pensacola bahiagrass (PB) on Greenville fine sandy loam (fine, kaolinitic, thermic Rhodic Kandiudults) by Evans et al. [11] The

3 Coastal Bermudagrass and Pensacola Bahiagrass 1099 Table 1. Response of dry matter yield (Y) to applied nitrogen (N) and irrigation for Coastal bermudagrass and Pensacola bahiagrass grown at Thorsby, AL ( ) a. Y (Mg ha 21 ) A/Y 2 1 Y* (Mg ha 21 ) Grass N (kg ha 21 ) NI I NI I NI I Bermuda Avg A (Mg ha 21 ) b c (ha kg 21 ) r Bahia Avg A (Mg ha 21 ) b c (ha kg 21 ) r a Yield data adapted from Evans et al. (Table 1). [11] study covered the four year period , and included both irrigated (I) and non-irrigated (NI) treatments. We first use average yields to obtain parameter estimates for Eq. (1). These data are listed in Table 1. Results are shown in Fig. 1, where the curves are drawn from A Y ¼ ð3þ 1 þ expð1:40 2 0:0070NÞ where A (CB, NI) ¼ 19.5 Mg ha 21, A (CB, I) ¼ 22.7 Mg ha 21, A (PB, NI) ¼ 17.5 Mg ha 21, A (PB, I) ¼ 20.8 Mg ha 21. The model appears to provide excellent description of the data. The first step in the procedure is to use the linearized form of Eq. (1) ln A Y 2 1 ¼ b 2 cn ð4þ Parameter A is estimated graphically from Fig. 1 and then used to perform linear regression on lnða=y 2 1) vs. N to estimate parameters b and c. This

4 1100 Overman, Scholtz, and Chambliss Figure 1. Dependence of seasonal yield (Y) on applied nitrogen (N) with irrigation (I) and non-irrigation (NI) for Coastal bermudagrass and Pensacola bahiagrass grown at Thorsby, AL ( ). Data adapted from Evans et al. (Table 1). [11] Curves drawn from Eq. (3). leads to high correlation coefficients, r, as shown in Table 1. Average values of b ¼ 1:40 and c ¼ 0:0070 ha kg 21 are then selected. The next step is to calculate standardized yield values, Y*, for each applied N in Table 1 from Y* ¼ Y½1 þ expð1:402 0:0070NÞŠ ð5þ which are averaged to obtain best estimates of A. Yield data for individual years are listed in Table 2 for non-irrigated treatments. It is assumed that all the variability from year to year can be

5 Coastal Bermudagrass and Pensacola Bahiagrass 1101 Table 2. Response of dry matter yield (Y) to applied nitrogen (N) without irrigation (NI) for Coastal bermudagrass and Pensacola bahiagrass grown at Thorsby, AL ( ) a. Y (Mg ha 21 ) Y* (Mgha 21 ) Grass N (kg ha 21 ) Bermuda Avg Bahia Avg a Data adapted from Evans et al. (Table 1). [11] accounted for in the linear parameter A. Standardized yield values are again calculated from Eq. (5). These are then averaged to obtain best estimates of A for each year. A summary of the parameters for both grasses is given in Table 3 and shown in Fig. 2. A trial-and-error process is used to estimate parameters in Table 3. Dependence of logistic model parameter A on seasonal rainfall (W) for Coastal bermudagrass and Pensacola bahiagrass without irrigation (NI) at Thorsby, AL. Year Rainfall A (Mg ha 21 ) (cm) Bermuda Bahia Avg

6 1102 Overman, Scholtz, and Chambliss Figure 2. Dependence of logistic model parameter (A) on seasonal rainfall (W) for Coastal bermudagrass and Pensacola bahiagrass grown at Thorsby, AL without irrigation (NI). Data from Table 3. Curve drawn from Eq. (6). Eq. (2) to obtain A ¼ 32:0½1 2 expð1:35 2 0:0333WÞŠ ¼ 32:0 12 exp 2 W 2 40:5 30:0 The curve in Fig. 2 is drawn from Eq. (6). ð6þ DISCUSSION The logistic model describes response of yields to applied N quite well with common parameters b and c for the two grasses. Effect of irrigation vs. natural rainfall is accounted for in the linear parameter A. Analysis of data without irrigation for individual years shows that parameter A can be related to seasonal rainfall through Eq. (6). At W ¼ 80 cm; A ¼ 23:5Mgha 21 ; or 73% of projected maximum. On the other hand, at W ¼ 50 cm we estimate A ¼ 8:5Mgha 21 ; or 27% of projected maximum. Based on Eq. (6) and Fig. 2, it would require 30 cm of irrigation to increase production from 8.5 to 23.5 Mg ha 21. These results show the great sensitivity of forage production to available water.

7 Coastal Bermudagrass and Pensacola Bahiagrass 1103 From this analysis, it was concluded that yield response follows a multiplicative relationship to applied N and water for forage grass as well as for corn. REFERENCES 1. Overman, A.R.; Evers, G.W. Estimation of yield and nitrogen removal by bermudagrass and bahiagrass. Trans. Am. Soc. Agr. Eng. 1992, 35, Evers, G.W. Effect of Nitrogen Fertilizer, Clovers, and Weed Control on Coastal Bermudagrass and Pensacola Bahiagrass in Southeast Texas; Texas Agric. Exp. Sta. Bull. MP-1546; Texas A&M University: College Station, TX, Evers, G.W. Forage and nitrogen contributions of arrowleaf and subterranean clovers overseeded on bermudagrass and bahiagrass. Agron. J. 1985, 77, Overman, A.R.; Sanderson, M.A.; Jones, R.M. Logistic response of bermudagrass and bunchgrass cultivars to applied nitrogen. Agron. J. 1993, 85, Sanderson, M.A.; Jones, R.M.; Newman, J.S. Productivity of 10 Warm- Season Perennial Grasses Over Several Years in Central Texas; Texas Agric. Exp. Stn. Bull. B-1695; Texas A&M University: College Station, TX, Reck, W.R.; Overman, A.R. Estimation of corn response to water and applied nitrogen. J. Plant Nutr. 1996, 19, Carreker, J.R.; Wilkinson, S.R.; Barnett, A.P.; Box, J.E. Soil and Water Management Systems for Sloping Land; ARS-S-160 U.S. Department of Agriculture: Washington, DC, Rhoads, F.M. Scheduling irrigation and fertilization for maximum yield and minimum environmental pollution in the Southeast. In Proc. Environmentally Sound Water and Soil Management; Kruse, E.C., Burdick, C.R., Yousef, Y.A., Eds.; American Society of Civil Engineers: New York, 1982; Overman, A.R.; Scholtz, R.V. Corn response to irrigation and applied nitrogen. Commun. Soil Sci. Plant Anal. 2002, 33, Tolk, J.A.; Howell, T.A.; Evett, S.R. Evapotranspiration and yield of corn grown on three high plains soils. Agron. J. 1998, 90, Evans, E.M.; Ensminger, L.E.; Doss, B.D.; Bennett, O.L. Nitrogen and Moisture Requirements of Coastal Bermuda and Pensacola Bahia; Ala. Agric. Exp. Sta. Bull. Alabama Agricultural Experiment Station, Auburn University: Auburn, AL, 1961; 337.