South Dakota Farmland Market Trends: 1991-2000

Agricultural land values and cash rental rates in South Dakota, by region and by state, are the primary topics of this report, which is written for farmers and ranchers, landowners, agricultural professionals (lenders, rural appraisers, professional farm managers, Extension agents, and educators), and policy makers interested in agricultural land market trends. This report contains the results of the 2000 SDSU South Dakota Farm Real Estate Market Survey, the tenth annual SDSU survey developed to estimate agricultural land values and cash rental rates by land use in different regions of South Dakota

An Efficient Sampling Protocol for Sagebrush/Grassland Monitoring

Rangeland scientists and quantitative ecologists have developed numerous methods and monitoring techniques that can be used for vegetation sampling (Barbour et al. 1987). The methods used to position samples (transects, quadrats, lines, and points) vary and can be classed as selective, capricious, systematic, or random. One of the prerequisites for valid statistical inference is that samples are taken randomly. A random sampling procedure implies that all elements or units of the population being studied have an equal chance of being represented in the sample. It also implies that selection of an element or unit does not influence the chance of other units being sampled. Data that is collected using randomsampling procedures can be used to compare attributes of different populations or sites such as vegetative cover, density, production, growth rates, etc. This paper suggests a random sampling protocol that can be easily applied in the field for sagebrush/grassland monitoring

Wolves: A Primer for Ranchers

Ranch management has become more complex since wolves were reintroduced into Idaho and Wyoming in 1996. In wolf areas, livestock have experienced increased death loss and greater stress. Increased livestock aggressiveness has been observed, especially toward working dogs, making handling livestock more difficult. Additionally ranchers have reported a loss of body condition, lower conception rates, increased time and expense for management. Our study was designed to investigate the effect of wolf presence on cattle behavior, landscape use patterns, and resource selection by comparing high wolf density areas against low wolf density areas. This study also generated baseline information on cattle spatial behavior before wolves were on the landscape. A Before-After/Control-Impact Paired (BACIP) experimental design was used. Control study areas in Idaho (3) have high wolf presence while Impact study areas in Oregon (3) started with no wolf presence, and are shifting to elevated wolf presence. Paired Idaho and Oregon areas have similar topography, vegetation composition, wild ungulate prey bases, and livestock management. Cows are tracked at 5-minute intervals using GPS collars (10 per area) throughout the grazing season. Wolf presence is monitored by GPS, trail cameras, and scat surveys. Ten GPS-collared cattle in an Idaho study area encountered a GPS-collared wolf 783 times at less than 500 meters during 137 days in the 2009 grazing season. At 100 meters there were 53 encounters; 52 at night. Tests of naïve and experienced cattle exposed to a simulated wolf encounter found increased excitability and fear-related physiological stress responses in cows previously exposed to wolves. This was shown through increased cortisol levels, body temperature, and temperament scores. Cattle presence near occupied houses doesn’t offer protection from wolves. Data shows wolves within 500m of occupied houses 588 times during 198 days of tracking. Many confirmed depredations on this site were also close to houses

Genetic analyses of longitudinal phenotype data: a comparison of univariate methods and a multivariate approach

BACKGROUND: We explored three approaches to heritability and linkage analyses of longitudinal total cholesterol levels (CHOL) in the Genetic Analysis Workshop 13 simulated data without knowing the answers. The first two were univariate approaches and used 1) baseline measure at exam one or 2) summary measures such as mean and slope from multiple exams. The third method was a multivariate approach that directly models multiple measurements on a subject. A variance components model (SOLAR) was employed in the univariate approaches. A mixed regression model with polynomials was employed in the multivariate approach and implemented in SAS/IML. RESULTS: Using the baseline measure at exam 1, we detected all baseline or slope genes contributing a substantial amount (0.08) of variance (LOD > 3). Compared to the baseline measure, the mean measures yielded slightly higher LOD at the slope genes, and a lower LOD at the baseline genes. The slope measure produced a somewhat lower LOD for the slope gene than did the mean measure. Descriptive information on the pattern of changes in gene effects with age was estimated for three linked loci by the third approach. CONCLUSION: We found simple univariate methods may be effective to detect genes affecting longitudinal phenotypes but may not fully reveal temporal trends in gene effects. The relative efficiency of the univariate methods to detect genes depends heavily on the underlying model. Compared with the univariate approaches, the multivariate approach provided more information on temporal trends in gene effects at the cost of more complicated modelling and more intense computations

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