No abstract

No abstract

No abstract

Estimates of age, growth, and mortality of spotted seatrout Cynoscion nehulosus were made by analyzing fish from commercial and recreational catches in 1986-1988 in three estuarine areas of Florida: Apalachicola Bay, Charlotte Harbor, and Indian River Lagoon. Thin sections of sagittae were used to determine age: annulus formation occurred in November-May. The maximum observed age differed among areas, ranging from 5 to 9 years for males and from 6 to 8 years for females. Spotted seatrout reached 301-337 mm total length at the end of their first year. Females were generally larger at age than males, although growth was highly variable. After age 1, male growth slowed to an average of 34-51 mm/year and was modeled best by area-specific linear growth equations. Females showed asymptotic growth that slowed from 87-107 mm at ages 1-2 to 46-60 mm at ages 4-5 and that was modeled best by area-specific Gompertz growth equations. Males and females from Indian River Lagoon and Apalachicola Bay were generally larger than those from Charlotte Harbor. Males were heavier than females of the same length. Estimates of total annual mortality were 48-76% in 1986-1988 and seemed highest in Apalachicola Bay. Patterns of growth did not reflect those previously used to support hypothesized divisions of spotted seatrout into separate subpopulations in Florida estuaries. Differences in growth and age composition observed among estuaries may reflect differences in environmental and fishing effects rather than genetic differentiation among estuaries. MethodsSpotted seatrout were sampled monthly from commercial and recreational catches during

No abstract

No abstract

The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤ 12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%)=8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%)=12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%)=10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions.

No abstract