The seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as ‘non-flatness’ throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

Tornquist input quantity indices were used to derive total and partial factor productivity measures for U.S. cotton across time, region, and scale. Total factor productivity for U.S. cotton increased .2 percent per year between 1974 and 1982. Partial productivity measures revealed that yield growth was about .6 percent and input use grew about .4 percent per year. Cotton enterprises in Alabama and Mississippi gained and those in the Texas High Plains lost competitive advantage relative to California. In 1982, very large (1750-5900 acres) and large (950-1749 acres) cotton enterprises were 2 percent more productive than medium-size enterprises (570-949 acres).

Glyphosate and paraquat herbicide drift injury to crops may substantially reduce growth or yield. Determining the type and degree of injury is of importance to a producer. This research was conducted to determine whether remote sensing could be used to identify and quantify herbicide injury to crops. Soybean and corn plants were grown in 3.8-L pots to the five- to seven-leaf stage, at which time, applications of nonselective herbicides were made. Visual injury estimates were made, and hyperspectral reflectance data were recorded 1, 4, and 7 d after application (DAA). Several analysis techniques including multiple indices, signature amplitude (SA) with spectral bands as features, and wavelet analysis were used to distinguish between herbicide-treated and nontreated plants. Classification accuracy using SA analysis of paraquat injury on soybean was better than 75% for both 1/2- and 1/8× rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2× rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. Applications of a 1/2× rate of glyphosate produced 55 to 81% soybean injury and 20 to 50% corn injury 4 and 7 DAA, respectively. However, using SA analysis, the moderately injured plants were indistinguishable from the uninjured controls, as represented by the low classification accuracies at the 1/8-, 1/32-, and 1/64× rates. The most promising technique for identifying drift injury was wavelet analysis, which successfully distinguished between corn plants treated with either the 1/8- or the 1/2× rates of paraquat compared with the nontreated corn plants better than 92% 1, 4, and 7 DAA. These analysis techniques, once tested and validated on field scale data, may help determine the extent and the degree of herbicide drift for making appropriate and, more importantly, timely management decisions.

Two experiments, one focusing on preemergence (PRE) herbicides and the other on postemergence (POST) herbicides, were conducted and repeated in time to examine the utility of hyperspectral remote sensing data for discriminating common cocklebur, hemp sesbania, pitted morningglory, sicklepod, and soybean after PRE and POST herbicide application. Discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an overall average classification accuracy of 65% for all species. These data suggest that these models are relatively robust and could potentially be used across a wide range of herbicide applications in field scenarios. From the data set pooled across time and experiment types, a single discriminant model was created with multiple indices that discriminated soybean from weeds 88%, on average, regardless of herbicide, rate, or species. Signature amplitudes, an additional classification technique, produced variable results with respect to discriminating soybean from weeds after herbicide application and discriminating between controls and plants to which herbicides were applied; thus, this was not an adequate classification technique.

Previous studies of historical risk have used either nominal or real data to calculate risk measures for agricultural prices and income. However, the effects of using nominal and real data have not been evaluated. This study utilizes theoretical variance approximation relationships to examine variances from detrended real and nominal time series. The relationships between variances are derived for quarterly U.S. farm milk prices for 1960-72, 1973-80, and 1981-90. Contrary to common intuitive arguments, results indicate that variances of real time series can be larger than variances of nominal series. While definitive conclusions are not possible, several reasons for using nominal data in risk analysis are given.

Legislative professionalism has played a prominent role in state politics research for decades. Despite the attention paid to its causes and consequences, recent research has largely set aside questions about professionalism's conceptualization and operationalization. Usually measuring it as an aggregate index, scholars theoretically and empirically treat professionalism as a unidimensional concept. In this article, we argue that exclusive use of aggregate indices can limit state politics research. Using a new dataset with almost 40 years of data on state legislative resources, salary, and session length, we reconsider the validity of using an index to study professionalism across the states. We evaluate the internal consistency of professionalism components over time, the relationship between components and the Squire Index, and the degree to which professionalism components are unidimensional using classical multidimensional scaling. We find enough commonality and enough variation between professionalism components to support a range of measurement strategies like the use of unidimensional indices (such as the Squire Index), disaggregating the components and analyzing their effects individually, or formulating multidimensional measures. Scholars should take care to choose the appropriate measure of the concept that best fits the causal relationships under examination.

During the latest decade, empirical research on the causes and consequences of the rule of law has expanded and, in the process, become extremely influential. However, we show that a number of widely used indices of the rule of law are not interchangeable. This lack of interchangeability is reflected in the fact that they are based on different defining attributes, to some extent cover distinct empirical scopes, do not correlate highly with each other, and support different explanatory factors. Until a consensus has been established with respect to the conceptualization of the rule of law, scholars are thus not free to opt for the measure that fits their data requirements best regarding spatial and/or temporal scope. Instead, they must carefully assess the content validity vis-à-vis their stipulated definition of the rule of law. Given the amount of money and time poured into the rule of law agenda, the problems identified reflect the lack of maturity of ‘good governance’ research.

The purpose of this study has been to evaluate the applicability of discriminant function analysis to determine gender dimorphism in Hucul horses, based on morphological indices obtained in different stages of life. A total of 243 horses, divided into six age groups, have been examined in its course. For each horse we have measured 12 metric traits, which were then used to calculate 13 biometric indices commonly used in horse breeding in Poland. These have become the basis for defining functions classifying the animals by gender in each of the six age groups. This study answers the question of what parameters play the greatest role in the course of shaping of body proportions of male and female horses in post-foetal development. The following indices have been found to significantly contribute in discriminant models: boniness, smaller trunk length, height at the croup, pelvis width and width of chest.

We investigate the asymptotic behaviour of the entropy numbers of the compact embedding $B^{s_1}_{p_1,q_1}(\mathbb{R}^d,w_1)\hookrightarrow B^{s_2}_{p_2,q_2}(\mathbb{R}^d,w_2)$. Here $B^s_{p,q}(\mathbb{R}^d,w)$ denotes a weighted Besov space. We present a general approach which allows us to work with a large class of weights.

We discuss meromorphic 1-forms on compact complex manifolds and on complete intersections with isolated singularities. We give a Poincaré–Hopf type formula for them, i.e. we express the Euler characteristic in terms of singularities of a meromorphic 1-form. For that we introduce a suitable notion of an index of a germ of a meromorphic 1-form (with an additional structure) on an isolated complete intersection singularity. For a meromorphic 1-form on a complete intersection V with isolated singularities the indices of the singular points sum up to (plus–minus) the Euler characteristic of a smoothing of V.

Reductions in red meat and increases in cereals in the diet may compromise the intake and bioavailability of Zn. In this cross-sectional study of 330 premenopausal New Zealand women aged 18–40 years, we have assessed the inter-relationships among dietary intakes (via computer-administered food-frequency questionnaire), biochemical Zn status, and anthropometric indices, and compared our results with earlier data. Fasting serum (12·00 (SD 1·36) ΜMOL/L) AND HAIR ZN (2·71 (sd 0·36) μmol/g) were lower than those for young Dunedin, New Zealand, women in 1973 (non-fasting serum Zn 18·6 (sd 4·6) μmol/l, hair Zn 2·99 (sd 0·35) μmol/g). Further, our mean serum Zn was at the 25th percentile of the US National Health and Nutrition Examination Survey (NHANES) (1976–1980) reference sample for women aged 20–44 years. Meat–poultry–fish contributed only 28 % total Zn in the present study, a level comparable with that from cereals–nuts–legumes (27 %), compared to about 40 % in 1989. Significant negative correlations existed between serum Zn and dietary [phytate]:[Zn] molar ratios (r -0·163, P<0·01); 35 % had diets with [phytate]:[Zn] >15, a level said to compromise Zn status. Mean serum Zn of a subgroup of non-oral contraceptive users free of infection was higher in the red-meat eaters (n 149) compared with non-red-meat eaters (n 48) (12·2 v. 11·8 μmol/g, P<0·05). In contrast, serum Zn was lower in those with dietary [phytate]:[Zn] ratios >15 v. <15 (i.e. 11·9 v. 12·3 μmol/l, P=0·04). We postulate that the lower biochemical Zn status of these New Zealand women may be associated in part with changes in food selection patterns, which have led to a reduction in the bioavailability of dietary Zn.