Water, Harvest Interval, and Applied Nitrogen Effects on Forage Yield of Bermudagrass and Bahiagrass

Overman, A. R.; Neff, C. R.; Wilkinson, S. R.; Martin, F. G.

doi:10.2134/agronj1990.00021962008200050034x

Cited by 33 publications

(15 citation statements)

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“…Due to its excellent active production during the months of May through October, in which most other forage species decrease production activities (Bouraoui et al, 2002), it fits into a unique niche among forages. Extensive efforts have been made to test its nutritional responses and management (Overman et al, 1990; Franzluebbers et al, 2001).…”

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confidence: 99%

Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

et al. 2018

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The evaluation of the forage quality nutritive value and biomass usually takes multiple harvests and is considered time consuming, labor intensive, and expensive. The use of sensors to evaluate different forage traits has been proposed as a method to alleviate this problem. However, most analytical techniques involve the use of traditional linear methods for prediction, but prediction models can still be improved with the use of nonlinear methods. Thus, the objectives of this study are twofold: (i) to evaluate the performance of different prediction methodologies in important forage agronomic traits; and (ii) to evaluate the impact of sensor variables on predictive model performance in the different methods. The nondestructive multisensor system that accommodates spectral, ultrasonic, and laser data was tested on a bermudagrass [Cynodon dactylon (L.) Pers] experiment. Partial least square regression, ridge regression, support vector machine with radial kernel (SVM), and random forest were tested for the prediction performance. The random forest had the best performance, with 0.89 correlation for dry matter yield trait prediction, whereas SVM performed best in the remaining 15 traits, with correlation ranging from 0.72 to 0.95. Besides the prediction performance of statistical models, this study also provided some insight about the importance of variables by removing variables and reevaluating the model performance in dry matter yield. Overall, most of the traits in this study could be reliably predicted by the SVM. The multisensor system can facilitate the measurement of agriculturally important traits in a time‐, labor‐ and cost‐efficient way.

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confidence: 99%

Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

et al. 2018

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“…and 13 g protein kg-1 at an N rate of 1120 kg ha-1 • Overman and Wilkinson (1992) noted that a reasonable first approximation in yield response to applied N requires at least three years to approach a steady state, thus promoting the need for long-term field experiments. Other work by Overman et al (1990) has developed models to relate forage yield to applied N for bermudagrass using water availability and harvest interval in both dry and humid climates.…”

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confidence: 99%

Bermudagrass Response to High Nitrogen Rates, Source, and Season of Application

et al. 1999

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High N rates and source of N have been thoroughly evaluated in bennudagrass [Cynodon dactylon (L.) Pers.] forage production, but less is known concerning season of application and estimated fertilizer N recovery in these systems. Two field studies (Ardmore, OK: Wilson silt loam, Vertic Haplustalfs; Burneyville, OK: Minco fine sandy loam, Udic Haplustolls) were conducted at two locations over two years to evaluate the effects of rate, timing, and source of N on bermuda grass forage yield, total N, N0 3 concentration, and estimated fertilizer N recovery. Nitrogen was applied at rates of 112, 224, 448, 672, and 1344 · kg N ha-1 as NU.N0 3 or urea in early spring (March) and late summer (August). Fertilizer N recovery can be maximized at rates of 112 and 224 kg N ha-t applied in the early spring and late summer, respectively. Even when N rates of 1344 kg N ha-1 were applied annually, bermudagrass forage NO,-N was seldom above 2000 mg kg-1 , which is below published toxic levels (2400-4500 mg kg-1 ) for cattle (Bos taurus) consumption. Early-spring applied N increased yields, N removal, and fertilizer recovery compared with late-summer applied N. Fertilizer N recovery was higher for NH 4 N0 3 than for urea, especially when applied in late summer. Late-summer applications of urea should be avoided, due to increased NH 3 volatilization losses. Nitrogen applied at 112 kg N ha-1 in early spring can result in fertilizer recoveries in excess of 85%. These high recoveries in forage production systems are possibly a result of continuous preanthesis forage harvesting when gaseous plant N losses are small, but which increase following an thesis.

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“…Seasonal yield and nutrient uptake are known to depend on harvest interval, water availability, and applied nutrients. [8] Dependence of production on nutrient input is discussed in part 2 of this series.…”

Section: Discussionmentioning

confidence: 99%

Model Analysis Of An Overland Flow Waste Treatment System. I. Accumulation Of Dry Matter And Plant Nutrients With Time

Overman

Scholtz

2003

Communications in Soil Science and Plant Analysis

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Land application is widely used for treatment of animal waste. One of the methods is overland flow treatment where soil properties favor surface runoff over infiltration. In these systems the land is graded to a uniform slope on which a grass cover is established. The grass is then harvested periodically. In this article an empirical model is used to describe accumulation of dry matter and plant nutrients with time. Data from a field study are used to illustrate the procedures for model parameter evaluation. Treatments of the study included a control, inorganic fertilizer, and three loading rates with effluent from a swine lagoon. The empirical model of crop growth provides a relatively simple means of

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Water, Harvest Interval, and Applied Nitrogen Effects on Forage Yield of Bermudagrass and Bahiagrass

Cited by 33 publications

References 2 publications

Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

Bermudagrass Response to High Nitrogen Rates, Source, and Season of Application

Model Analysis Of An Overland Flow Waste Treatment System. I. Accumulation Of Dry Matter And Plant Nutrients With Time

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