Models can be used to estimate yield response of grasses to applied N. This analysis was conducted to show variation of model parameters among grasses at the same location and differences among locations. The logistic equation was used to relate annual dry matter yield to applied N for four locations (Blairsville, GA; Fayetteville, AR; Thorsby, AL; Jay, FL) and five perennial grasses [bermudagrass (Cynodon dactylon L., Pers.), bahiagrass (Paspalum notatum Flugge), tall fescue (Festuca arundinacea Schreb.), dallisgrass (Paspalum dilatatum Poir.), and orchardgrass (Dactylis glomerata L.)]. The model contained three parameters (A, b, c). It was shown by analysis of variance that all grasses exhibited common exponetial coefficients (b, c) for each location, with variation among grasses and with water availability assigned to the linear coefficient (A). Applied N, N1/2, to reach one‐half maximum yield appeared to be inversely related to clay content of the soil. Maximum efficiency of conversion of applied N to dry matter at N = N1/2 was highest for Coastal bermudagrass (approximately 40). The logistic equation provides an excellent model for yield response to applied N.
A logistic equation was used to relate forage yield to applied N for bermudagrass [Cynodon dactylon (L.) Pers.] and bahiagrass [Paspalum notatum Flugge]. It was shown that the equation could be factored into a triple product–one term each for water availability, harvest interval and applied N. The equation provides high correlation (R ≥ 0.98) between yield and applied N for all levels of water availability and harvest interval. Appropriate statistical procedures for nonlinear regression were utilized in the analysis. These results are useful for estimating effects of management factors on forage production.
A simple model is needed that relates forage grass production (yield and N removal) to management factors (applied N, harvest interval, and water availability). The objective of this analysis was to extend a previous model to include quantitative coupling between yield and N removal in response to applied N for perennial grasses. The extended model was developed from three postulates: (1) annual dry matter yield follows logistic response to applied N, (2) annual plant N removal follows logistic response to applied N, and (3) the N response coefficients are the same for both. Three additional consequences derive from these postulates: (i) plant N concentration response to applied N follows a ratio of logistic functions, (ii) annual dry matter yield and plant N removal are related by a hyperbolic equation, and (iii) plant N concentration and plant N removal follow a linear relationship. Data from a field study in Louisiana with dallisgrass [Paspalum dilatation Poir.] grown on Olivier silt loam (fine‐silty, mixed, thermic Aquic Fragiudalf) were used to demonstrate applicability of the model and to illustrate procedures. Analysis of variance supported Postulate 3 for these data, with an overall correlation coefficient of 0.9990. Plant N concentration (Nc for this study was bounded by 11.8
Models provide a quantitative means to evaluate yield response of forage grasses to applied N and water availability. The objective of this analysis was to estimate model parameters (A, b, c) for bermu· dagrasses and bunchgrasses grown at the same location and during the same time period. The logistic equation was used to relate annual dry matter production to applied N for three bermudagrasses [C)· nodon dactylon L., 'Coastal', 'Alicia', and 'Coastcross-1'] and four bunchgrasses [Eragrostis curvuw (Schrad.) Nees, 'Morpa' and 'Ren· ner' lovegrass; Panicum coloratum L., 'Selection-75' kleingrass, and Cenchrus ciliDris L., 'Strain 18-35' buffelgrass]. Data were from 4 yr of field experiments at Stephenville, TX. Analysis of variance was used to show that the N coefficients b and c were independent of year and climate for each grass, in agreement with recently published results. Applied N required to provide one-half of maximum yield was approximately 150 kg ha -I for bermudagrass and 50 kg ha -I for lovegrass. Variation among years was accounted for in the linear coefficient A, which was then related to rainfall. Bunchgrasses typically had lower A and b, but similar c values compared with bermudagrasses.The logistic model allows estimates of yields in terms of applied N and rainfall, and quantifies the interaction between these two factors. Bermudagrass yielded approximately 45% more dry matter than lovegrass for the same rainfall. Correlation coefficients exceeded 0.95 throughout the analysis.
Accumulation of dry matter by warm-season annuals depends upon time of season, including planting time. A mathematical model has been developed to simulate the growth process. The model contains a Gaussian environmental function and a linear-exponential intrinsic growth function. Previous work has shown the applicability of the model to data for the perennials bahiagrass (Paspalum notatum) and bermudagrass (Cynodon dactylon). This article applies the model to field data for the annual corn (Zea mays) from four locations. Only two of the five parameters are varied for the different studies to match dry matter simulation with data. A hyperbolic relationship between plant nutrient accumulation [nitrogen (N), phosphorus (P), or potassium (K)] and dry matter accumulation has been included. Parameters for the hyperbolic equation for plant N agree closely for the three locations where plant N was measured. Results for P and K varied. Since the total plant dry matter accumulates at a faster rate than plant nutrients, plant nutrient concentrations for N, P, and K all decrease rapidly with age.
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