Farm-gate phosphorus balances and soil phosphorus concentrations on intensive dairy farms in the south-west of Ireland

peer-reviewedThis project was part funded by the European Research and Development Fund under INTERREG IIIB: Green Dairy Project Number 100 and partly by the Dairy Levy. Financial support for post-graduate students involved in this study was provided by the Teagasc Walsh Fellowship Scheme.Phosphorus (P) loss to water is a significant threat to water quality in Ireland. Agriculture is an important source of this P. There is concern about balancing agronomic requirements and environmental protection in regulations prescribing P management on farms. This study examined farm-gate (P) balances and soil test P (STP) concentrations on 21 dairy farms in the south west of Ireland over four years, from 2003 to 2006 inclusive. Stocking density on the farms averaged 2.4 (s.d. = 0.4) livestock units (LU) per ha. Annual mean import of P onto farms was 21.6 (1.9) kg P/ha. Fertilizer P accounted for 47% (0.041), concentrates 35% (0.060) and organic manures 18% (0.034) of imported P. The mean annual P balance per farm was 9.4 (1.2) kg/ha, ranging from –3 to 47 kg/ha and mean P use efficiency was 0.71 (0.05) ranging from 0.24 to 1.37. The mean STP per farm following extraction using Morgan’s solution was 8.15 (2.9) mg/L of soil and ranged from 4.4 (2.2) to 14.7 (6.4) mg/L. There was a positive relationship (R2 = 0.34; P < 0.01) between STP and P balance; farms with a deficit of P tended to have agronomically sub-optimal STP and vice versa. The high between- and withinfarm variation in STP indicates that farmers were either unaware or were not making efficient use of STP results, and consequently there was agronomically sub-optimal soil P status in some fields and potentially environmentally damaging excesses on others (often within one farm). There was considerable potential to improve P management practices on these farms with clear agronomic and environmental benefits.European UnionTeagasc Walsh Fellowship ProgrammeDairy Levy Fun

An Empirical Examination of a Causal Model of User Information Satisfaction

Users\u27 satisfaction with their information systems is generally recognized as one of the most important indicators of success in designing and implementing computer-based systms. User information satisfaction (UIS) is defined as the extent to which users believe the information systems available to them meets their information requirements. [Ives, Olson, and Baroudi, 1983]The construct is often used as a surrogate for relative information system value because it is more accurately and easily measurable. [Nolan and Seward, 1974] Powers and Dickson [1973] concluded that user satisfaction is the most critical criterion for measuring computer systems success or failure. User information satisfaction has been measured in several ways. Different instruments have been created by Larcker and Lessig [1980], Jenkins and Ricketts [1979], and Gallagher [ 1974]. Recently, Bailey and Pearson [1983] have developed an instrument for UIS that represents an important achievement in the evolution of this construct. Their instrument is broadly based and includes measures of both the system and support quality perceived by the user. Some evidence of the reliability and validity of the instrument has been provided. 77:e Bailey-Pearson U/S Instrument Bailey and Pearson (BP) reviewed 22 MIS studies for factors affecting computer user satisfaction and identified 36 constructs. Three further constructs were added to the list and it was tested for completeness using taped interviews with 32 middle managers. The researchers concluded that there was a 0.99 probability that a mentioned (construct) was on the list (of 39) [Bailey and Pearson, 1983, p. 532]. The thirty-nine constructs are listed in Table I. For each construct, four semantic differential seven point scales were developed and a fifth scale, common to all, asked about the importance of the construct to the respondent. A measure of the response to a construct by a respondent was estimated as the average of responses to the four semantic differential scales for the construct. The general measure of user information satisfaction for a respondent was estimated as the average of the construct estimates weighted by the importance measure of that construct, after having eliminated those for which the response on each of the four semantic differential scales were neutral: Using the instrument, data was gathered from 29 of the 32 managers interviewed during the development of constructs. Analysis of the data led the authors to conclude that the instrument was reasonably reliable and valid. The test sample was quite small and potentially biased since respondents had previously participated in developing the instrument. 1ves, Olson, and Baroudi [ 1983] chose to further study the empirical properties of the Bailey. Pearson (B-P) instrument with an additional objective to develop a shorter version of the instrument. 71:e /ves, Olson, and Baroudi Short Instrument: Ives, Olson, and Baroudi (IOB) collected data from 200 production managers (25 % response rate) in U. S. manufacturing organizations using the Bailey-Pearson instrument and a separate 4-scale instrument of information satisfaction. They performed an exploratory factor analysis on construct scores estimated from the BP data and obtained four significant factors or dimensions, as shown in Table I. Any construct that did not load on one or more factors at the 0.5 level or above was eliminated from a shortened version of the Bailey-Pearson instrument. As well, vendor support which loaded on its own factor was eliminated. Twenty-one of the original 39 constructs passed this test and were included in the shortened instrument. For each of these, two semantic differential scales were retained as well as the importance scale. The results of the factor analysis suggest that a multidimensional structure may underly the user information satisfaction construct. IOB discovered a three factor model of UIS (putting aside vendor support). The factors are: (1) EDP staff and services; (2) the quality of the information product; and (3) the knowledge and involvement levels of the user. Their instrument contains twenty-one questions, each with two semantic differential scales, that measure the three independent factors, but no general measures of user information satisfaction. Their method of arriving at this questionnaire eliminated any general measures of UIS since these measures, by definition, load across all factors. Since this was an exploratory analysis, no evidence was provided as to whether the measures of the underlying dimensional factors achieved convergent or discriminant validity. Evidence was provided that the shortened instrument is a reliable and valid measure ofuser information satisfaction. The 2-scale reliability scores were approximately equal to the 4-scale scores, supporting the claim of high reliability. The general measure of UIS estimated using 21 constructs and the measure using all 39 constructs had a correlation across respondents of 0.9. The correlations of each of these measures with the measure estimated using the separate 4-scale instrument were 0.54 and 0.55 respectively. This evidence supports the claim that the shortened instrument is measuring approximately the same thing as the complete Bailey-Pearson instrument. There are several problems with the evidence provided to support the reliability and validity of the shortened instrument. The reliability estimates obtained are unusually high for this type of instrument, ranging between 0.81 and 0.97 respectively. This evidence supports the claim that the shortened instrument is measuring approximately the same thing as the complete Bailey-Pearson instrument. There are several problems with the evidence provided to support the reliability and validity of the shortened instrument. The reliability estimates obtained are unusually high for this type of instrument, ranging between 0.81 and 0.97. This may be explained in part by a mechanical methods bias introduced by placing all four semantic differential scales on the same page and scored in the same direction. Thus, the analysis of 4-scale, versus 2- scale reliabilities may not be accurate. As well, no estimate was provided of the reliability with which the 21 questions measure the underlying UIS variable. The 0.9 correlation between the 39-construct instrument and the 21-construct instrument may partly be explained by the fact that the two instruments have 21 constructs in common. Just as this correlation is significantly different from zero, it is also significantly different from 1.0 (significant at the 0.001 level). This may be a better test of whether the shortened instrument is significantly different from the Bailey-Pearson instrument. Evidence tha tthe two instruments correlate equally with a third 4-scale instrument is of little help. There is no evidence that the third instrument is either a reliable or valid measure ofuser information satisfaction. Central to the process by which the researchers eliminated constructs from the instrument was an exploratory factor analysis. As they observed, the ratio of sample size to number of scales in the study (7:1) must also be regarded with some caution. . [Ives, Olson, Baroudi, 1983, p. 789]. Thus, one place to begin an analysis of the shortened version of the Bailey-Pearson instrument is by employing the identified factor structure to test the measurement qualities of the instrument. Successful results on this test will support the hypothesized multi-dimensional structure of user information satisfaction. The factor structure obtained by Ives, Olson, and Baroudi is significant not only because they employed it to make decisions about which constructs to eliminate from the shortened instrument. Perhaps more significantly, the factor analysis has yielded underlying dimensions to user information satisfaction that point toward a causal model of UIS. Such a model coutdbe usedas an importantdiagnostictool for information systems builders. Such an important potential development deserves careful study

Toward a Behaviorally Grounded Theory of Inforrmation Value

Economic models of information value have had little impact on the theory and practice of MIS. This is due in part to difficulties in operationalizing these models, but more importantly, it is due to problems in the theory that stem from descriptively invalid assumptions. This paper examines those assumptions and reviews five major areas for modification: the decision process, human judgment under uncertainty, the choice of actions, multiple information signal resolution, and multiple decisions over time. Incorporation of valid descriptive assumptions in the economic theory wi I I move the field toward a behaviorally grounded theory of information value

THE IMPACT OF INFORMATION TECHNOLOGY ON CONTROL: A LEADERSHIP THEORY PERSPECTIVE

In this paper, we explore two models of the impact of Information Technology (IT) on control based on the perspective of leadership theories. The objective is to explain how IT can enhance the control mechanisms in a work group. Review of leadership literature suggests two relevant theories: leadership behavior theory and leadership substitute theory. TO explore these conceptual models, data was collected from 136 managers and professionals who use well-established information systems. The data provides support that ITs impact on control can be explained through its effects on the control factors identified from the two leadership theories. The two models are equally powerful in explaining the criterion variance of control, and the two sets of independent variables from the models are highly correlated, suggesting that either model is good for the study of Irs impact on control. In addition, the data provides support that the impact of IT on control is stronger in dynamic environments. The data also shows that the innovative ability of a work group is positively related to ITs impact on control

Vitamin C for preventing and treating the common cold

Peer reviewe

Characterization of defect structures in nanocrystalline materials by X-ray line profile analysis

X-ray line profile analysis is a powerful alternative tool for determining dislocation densities, dislocation type, crystallite and subgrain size and size-distributions, and planar defects, especially the frequency of twin boundaries and stacking faults. The method is especially useful in the case of submicron grain size or nanocrystalline materials, where X-ray line broadening is a well pronounced effect, and the observation of defects with very large density is often not easy by transmission electron microscopy. The fundamentals of X-ray line broadening are summarized in terms of the different qualitative breadth methods, and the more sophisticated and more quantitative whole pattern fitting procedures. The efficiency and practical use of X-ray line profile analysis is shown by discussing its applications to metallic, ceramic, diamond-like and polymer nanomaterials