Background
Nitrogen (N) as a key input for crop production has adverse effects on the environment through emissions of reactive nitrogen. Less than 20% of the fertiliser nitrogen applied to ...agricultural land is actually consumed by humans in meat. Given this situation, nitrogen budgets have been introduced to quantify potential losses into the environment, to raise awareness in nutrient management, and to enforce and monitor nutrient mitigation measures. The surplus of the N soil surface budget has been used for many years for the assessment of potentially water pollution with nitrate from agriculture.
Results
For the 402 districts in Germany, nitrogen soil surface budgets were calculated for the time series 1995 to 2017. For the first time, biogas production in agriculture and the transfer of manure between districts were included in the budget. Averaged for all districts, the recent N supply to the utilised agricultural area (UAA) totals 227 kg N ha
−1
UAA (mean 2015–2017), among them 104 kg N ha
−1
UAA mineral fertiliser, 59 kg N ha
−1
UAA manure, 33 kg N ha
−1
UAA digestate, 14 kg N ha
−1
UAA from gross atmospheric deposition, 13 kg N ha
−1
UAA biological N fixation, and 1 kg N ha
−1
UAA from seed and planting material. The withdrawal with harvested products accounts for 149 kg N ha
−1
UAA, resulting in an N soil surface budget surplus of 77 kg N ha
−1
UAA. The N surpluses per district (mean 2015–2017) vary considerably between 26 and 162 kg N ha
−1
UAA and the nitrogen use efficiency of crop production ranges from 0.53 to 0.79 in the districts. The N surplus in Germany as a whole has remained nearly constant since 1995, but the regional distribution has changed significantly. The N surplus has decreased in the arable farming regions, but increased in the districts with high livestock density. Some of this surplus, however, is relocated to other districts through the transfer of manure.
Conclusions
The 23-year time series forms a reliable basis for further interpretation of N soil surface surplus in Germany. Agri-environmental programmes such as the limitation of the N surplus through the Fertiliser Ordinance and the promotion of biogas production have a clear effect on the N surplus in Germany as a whole and its regional distribution.
Model-based predictions of the impact of land management practices on nutrient loading require measured nutrient flux data for model calibration and evaluation. Consequently, uncertainties in the ...monitoring data resulting from sample collection and load estimation methods influence the calibration, and thus, the parameter settings that affect the modeling results. To investigate this influence, we compared three different time-based sampling strategies and four different load estimation methods for model calibration and compared the results. For our study, we used the river basin model Soil and Water Assessment Tool on the intensively managed loess-dominated Parthe watershed (315 km²) in Central Germany. The results show that nitrate-N load estimations differ considerably depending on sampling strategy, load estimation method, and period of interest. Within our study period, the annual nitrate-N load estimation values for the daily composite data set have the lowest ranges (between 9.8% and 15.7% maximum deviations related to the mean value of all applied methods). By contrast, annual estimation results for the submonthly and the monthly data set vary in greater ranges (between 24.9% and 67.7%). To show differences between the sampling strategies, we calculated the percentage deviation of mean load estimations of submonthly and monthly data sets as related to the mean estimation value of the composite data set. For nitrate-N, the maximum deviation is 64.5% for the submonthly data set in the year 2000. We used average monthly nitrate-N loads of the daily composite data set to calibrate the model to achieve satisfactory simulation results Nash-Sutcliffe efficiency (NSE) 0.52. Using the same parameter settings with submonthly and monthly data set, the NSE dropped to 0.42 and 0.31, respectively. Considering the different results from the monitoring strategy and the load estimation method, we recommend both the implementation of optimized monitoring programs and the use of multiple load estimation methods to improve water quality characterization and provide appropriate model calibration and evaluation data.
This is the first study to analyze the association of accelerometer-measured patterns of habitual physical activity (PA) and sedentary behavior (SB) with serum BDNF in individuals with coronary heart ...disease. A total of 30 individuals (M = 69.5 years; 80% men) participated in this pre-post study that aimed to test a multi-behavioral intervention. All participants underwent standardized measurement of anthropometric variables, blood collection, self-administered survey, and accelerometer-based measurement of PA and SB over seven days. Serum BDNF concentrations were measured using enzyme-linked immunosorbent assay kit. We applied separate multiple linear regression analysis to estimate the associations of baseline SB pattern measures, light and moderate-to-vigorous PA with serum BDNF (n = 29). Participants spent 508.7 ± 76.5 min/d in SB, 258.5 ± 71.2 min/d in light PA, and 21.2 ± 15.2 min/d in moderate-to-vigorous PA. Per day, individuals had 15.5 ± 3.2 numbers of 10-to-30 min bouts of SB (average length: 22.2 ± 2.1 min) and 3.4 ± 1.2 numbers of > 30 min bouts of SB (average length: 43.8 ± 2.4 min). Regression analysis revealed no significant associations between any of the accelerometer-based measures and serum BDNF. The findings of this study did not reveal an association of accelerometer-measured PA and SB pattern variables with serum BDNF in individuals with coronary heart disease. In addition, our data revealed a considerable variation of PA and SB which should be considered in future studies.
Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on ...associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults.
In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m
) were measured in a standardized way. Separate multilevel mixed-effects linear regression analyses were used to investigate associations of sedentary bouts (1 to 10 min, >10 to 30 min, and >30 min) with WC and BMI. Models were adjusted for potential confounders including sex, age, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use.
Participants (66% females) were on average 57.1 (standard deviation, SD 8.5) years old and 36% had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m
(SD 3.8). The daily number of 1-to-10-minute bouts was inversely associated with BMI (b = -0.027; p = 0.047) and the daily number of >30-minute bouts was positively associated with WC (b = 0.330; p = 0.001). All other associations were not statistically significant.
The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods.
Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237).
The aim of this study was to conduct a comprehensive investigation of the association between different types of leisure-time sedentary behavior (watching television, using a computer, reading and ...socializing) and clustered cardiometabolic risk in apparently healthy adults aged 40 to 65 years.
One hundred seventy-three participants from the general population (64% women; mean age = 54.4 years) consented to attend a cardiovascular examination program and to complete a questionnaire on leisure-time sedentary behaviors. Waist circumference, blood pressure, glucose, triglycerides, and high-density lipoprotein cholesterol of non-fasting blood samples were assessed, and a clustered cardiometabolic risk score CMRS was calculated. Data were collected between February and July 2015. Associations between leisure-time sedentary behaviors and CMRS were analyzed using linear and quantile regression, adjusted for socio-demographic variables and other types of leisure-time sedentary behavior (model 1) and additionally, adjusted for leisure-time physical activity and traveling in motor vehicles (model 2).
Linear regression revealed that there was a positive association between watching television and CMRS (model 1: b = 0.27 CI: 0.03; 0.52; model 2: b = 0.30 CI: 0.05; 0.56). In addition, quantile regression analysis revealed that using a computer was negatively associated with the 50th (model 1: b = - 0.43 CI: -0.79; - 0.07) and the 75th percentiles (model 1: b = - 0.71 CI: -1.27; - 0.14) of CMRS. Reading and socializing were not associated with CMRS.
Watching television was positively associated with a clustered cardiometabolic risk score, while time spent using a computer revealed inconsistent findings. Our results give reason to consider different types of behaviors in which individuals are sedentary and the associations between these behaviors and cardiometabolic risk, supporting the need for behavior-specific assessments as well as public health recommendations to maintain or enhance adults' health.
Clinical trial registration number: NCT02990039 , Retrospectively registered (December 12, 2016).
Zusammenfassung
Hintergrund
Über den Einsatz direkter Messmethoden zur Erfassung körperlicher Aktivität bei Erwachsenen in Deutschland ist bislang wenig bekannt.
Ziel
Ziel der Studie ist die ...Beschreibung der Adhärenz und die Analyse individueller Determinanten, ein Akzelerometer über 7 Tage zu tragen.
Material und Methoden
In einem Einkaufszentrum wurden 365 Personen (40 bis 75 Jahre) für eine 7‑tägige Aufzeichnung ihrer körperlichen Aktivität gewonnen. Von diesen hatten 231 (63,3 %) das Gerät täglich mindestens 10 h getragen.
Ergebnisse
Teilnehmende in Partnerschaft trugen das Akzelerometer mit höherer Wahrscheinlichkeit entsprechend der Anweisung verglichen mit jenen ohne Partnerschaft. Schlechtere subjektive Gesundheit war mit einer geringeren Einhaltung der Anweisungen das Akzelerometer zu tragen assoziiert.
Diskussion
Um möglichst valide Daten über Umfang und Intensität körperlicher Aktivität zu erhalten, sollten Menschen ohne Partner und solche mit schlechterer subjektiver Gesundheit über den Tragezeitraum des Gerätes hinweg motiviert werden, es regelmäßig zu tragen.
Participation in an assessment may change health behavior. This "mere-measurement effect" may be used for prevention purposes. However, little is known about whether individuals' characteristics ...moderate the effect. The objective was to explore whether changes of physical activity (PA) and sedentary time (ST) after a cardiovascular assessment depend on sociodemographic variables and cardiometabolic risk factors.
A sample of n = 175 adults aged 40 to 65 received baseline assessment including self-administered PA and ST questionnaires and standardized measurement of blood pressure, waist circumference, and blood parameters. After 5 weeks, participants again reported PA and ST without any prior treatment or intervention. Linear regression models were used to analyze the dependence of five-week changes in PA and ST on baseline sociodemographic and cardiometabolic variables.
Men increased transport-related PA more than women (b = 9.3 MET-hours/week, P = .031). Men with higher triglycerides increased transport-related PA less than men with lower triglycerides (b = - 5.6 MET-hours/week, P = .043). Men with higher systolic blood pressure reduced ST more than those with lower systolic blood pressure (b = - 35.7 min/week, P = .028). However, this linear association ceased to exist at a level of approximately 145 mmHg (b of squared association = 1.0, P = .080). A similar relationship was found for glycated hemoglobin and ST.
The findings suggest that sex and cardiometabolic risk factors moderate mere-measurement effects on PA and ST. Researchers and practitioners using mere measurement for prevention purposes may address PA and ST according to these individual characteristics.
ClinicalTrials.govNCT02990039. Registered 7 December 2016. Retrospectively registered.
Measuring physical activity (PA) and sedentary time (ST) by self-report or device as well as assessing related health factors may alter those behaviors. Thus, in intervention trials assessments may ...bias intervention effects. The aim of our study was to examine whether leisure-time PA, transport-related PA, and overall ST measured via self-report vary after assessments and whether a brief tailored letter intervention has an additional effect.
Among a sample of subjects with no history of myocardial infarction, stroke, or vascular intervention, a number of 175 individuals participated in a study comprising multiple repeated assessments. Of those, 153 were analyzed (mean age 54.5 years, standard deviation = 6.2; 64% women). At baseline, participants attended a cardiovascular examination (standardized measurement of blood pressure and waist circumference, blood sample taking) and wore an accelerometer for seven days. At baseline and after 1, 6, and 12 months, participants completed the International Physical Activity Questionnaire. A random subsample received a tailored counseling letter intervention at month 1, 3, and 4. Changes in PA and ST from baseline to 12-month follow-up were analyzed using random-effects modelling.
From baseline to 1-month assessment, leisure-time PA did not change (Incidence rate ratio = 1.13,
= .432), transport-related PA increased (Incidence rate ratio = 1.45,
= .023), and overall ST tended to decrease (b = - 1.96,
= .060). Further, overall ST decreased from month 6 to month 12 (b = - 0.52,
= .037). Time trends of the intervention group did not differ significantly from those of the assessment-only group.
Results suggest an effect of measurements on PA and ST. Data of random-effects modelling results revealed an increase of transport-related PA after baseline to 1-month assessment. Decreases in overall ST may result from repeated assessments. A brief tailored letter intervention seemed to have no additional effect. Thus, measurement effects should be considered when planning intervention studies and interpreting intervention effects.
ClinicalTrials.gov NCT02990039. Registered 7 December 2016. Retrospectively registered.
Alternative land management practices such as conservation or no-tillage, contour farming, terraces, and buffer strips are increasingly used to reduce nonpoint source and water pollution resulting ...from agricultural activities. Models are useful tools to investigate effects of such management practice alternatives on the watershed level. However, there is a lack of knowledge about the sensitivity of such models to parameters used to represent these conservation practices. Knowledge about the sensitivity to these parameters would help models better simulate the effects of land management. Hence, this paper presents in the first step a sensitivity analysis for conservation management parameters (specifically tillage depth, mechanical soil mixing efficiency, biological soil mixing efficiency, curve number, Manning's roughness coefficient for overland flow, USLE support practice factor, and filter strip width) in the Soil and Water Assessment Tool (SWAT). With this analysis we aimed to improve model parameterisation and calibration efficiency. In contrast to less sensitive parameters such as tillage depth and mixing efficiency we parameterised sensitive parameters such as curve number values in detail.
In the second step the analysis consisted of varying management practices (conventional tillage, conservation tillage, and no-tillage) for different crops (spring barley, winter barley, and sugar beet) and varying operation dates. Results showed that the model is very sensitive to applied crop rotations and in some cases even to small variations of management practices. But the different settings do not have the same sensitivity. Duration of vegetation period and soil cover over time was most sensitive followed by soil cover characteristics of applied crops.
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