The Diverse Structure and Organization of U.S. Beef Cow-Calf Farms

Beef cow-calf production in the United States is widespread, occurring in every State. Nearly 765,000 farms, about 35 percent of the 2.2 million farms in the United States, had a beef cow inventory in 2007. Most of these were small, part-time operations. About a third of farms that raise beef animals had a beef cow inventory of less than 10 cows, more than half had fewer than 20 cows, and nearly 80 percent had fewer than 50 cows. In this study, ERS uses data from USDA’s 2008 Agricultural Resource Management Survey for U.S. beef cow-calf operations to examine the structure, costs, and characteristics of beef cow-calf producers. Many small operations are “rural residence farms” that specialize in beef cow-calf production, but their income from off-farm sources exceeds that from the farm. Most beef cow-calf production occurs on large farms, but cow-calf production is not the primary enterprise on many of these farms. Findings suggest that operators of beef cow-calf farms have a diverse set of goals for the cattle enterprise.Beef cow-calf production, farm income, animal traceability, Agricultural Resource Management Survey (ARMS), National Animal Identification System (NAIS), Agribusiness, Agricultural and Food Policy, Farm Management, Livestock Production/Industries,

Beef Cow-Calf Producer Participation in the National Animal Identification System

Farm Management, Livestock Production/Industries, Production Economics,

Spectral leakage suppression properties of linear and quadratic windowing

Journal ArticleAbstract-It is shown that the leakage suppression properties of segment averaging spectrum estimation methods using hear windows and equivalent quadratic windows are asymptotically the same, under the assumption that segments relatively far apart are uncorrelated. Thus, for large data lengths, one can effectively replace linear windowing by its equivalent quadratic windowing and hope to get similar leakage suppression at a substantially reduced computational cost. A simulation example that supports this conclusion is presented

An efficient algorithm for lattice filter/predictor

Journal ArticleABSTRACT An efficient method for updating the lattice filter/predictor coefficients using the sign algorithm is introduced. The pertinent coefficients are updated using only the signs of the estimation errors at each stage. This method requires less number of multiplications than other adaptive lattice filter algorithms. The performance of this method is compared with that of the lattice filter using the LMS algorithm for the problems of spectrum estimation and adaptive interference cancellation

On using the short-time unbiased spectrum estimation algorithm for estimating time delays and magnitude squared coherence functions

Journal ArticleThis paper introduces novel applications of the short-time unbiased sprectral estimation (STUSE) algorithm, which adds biased estimates to yield unbiased spectral estimates. It is shown that STUSE algorithm is an effective tool for estimating time delays and magnitude-squared coherence (MSC) functions between two stationary signals received at spatially separated sensors, especially when one of the signals is a delayed version of the other. Computer simulation results are presented to compare the performances of the STUSE algorithm and the conventional weighted overlapped segment averaging (WOSA) method for spectrum estimation

Adaptive nonlinear digital filter with sequential regression algorithm

Journal ArticleThe purpose of this paper is to introduce an adaptive nonlinear digital filtering algorithm which use the sequential regression (SER) method to update the second order Volterra filter coefficients in a recursive way. Conventionally, the SER method has been used to invert large matrices which result from direct application of the Wiener filter large matrices which result from direct application of the Wiener filter theory to the Volterra filter. However, the algorithm proposed in this paper adopts the simplified least squares solution that results when the input signals are Gaussian, and hence the size of the matrix to be inverted in smaller than that of the conventional approaches. Simulation results are also included to demonstrate the performance of the proposed algorithm

Adaptive realizations of the phase transform for time delay estimation

Journal ArticleABSTRACT This paper introduces two recursive realizations of the Phase Transforn (PHAT) processor for tine delay estimation (TOE), using a simple one-pole lowpass filter and the least mean square (LMS) adaptive filter, respectively. It is shown that these adaptive methods are very effective in reducing the effect of interfering tonals. The performances of these methods are compared with those of other existing adaptive TOE algorithms via computer simulations

Adaptive realizations of the maximum likelihood processor for time delay estimation

Journal ArticleAbstract-This correspondence introduces an adaptive realization of the maximum likelihood (ML) processor for time delay estimation (TDE). Also presented is a modified ML processor, which requires less computations but still performs better than the other when implemented in an adaptive way. Widrow's least mean square (LMS) adaptive filter algorithm is used to implement the two methods. Simulation results comparing these processors with other existing adaptive TDE algorithms are also presented

CONTROLLING WILDLIFE AND LIVESTOCK DISEASE WITH ENDOGENOUS ON-FARM BIOSECURITY

The spread of infectious disease among and between wild and domesticated animals has become a major problem worldwide. We analyze the socially optimal management of wildlife and livestock, including choices involving environmental habitat variables and on-farm biosecurity controls, when wildlife and livestock can spread an infectious disease to each other. The model is applied to the problem of bovine tuberculosis among Michigan white-tailed deer. The optimum is a cycle in which the disease remains endemic in the wildlife, but in which the cattle herd is depleted when the prevalence rate in deer grows too large.Research Methods/ Statistical Methods,