Inventory of ammonia emissions from UK agriculture 2009

The National Ammonia Reduction Strategy Evaluation System (NARSES) model (spreadsheet version) was used to estimate ammonia (NH3) emissions from UK agriculture for the year 2009. Year-specific livestock numbers and fertiliser N use were added for 2009 and revised for previous years. The estimate for 2009 was 231.8 kt NH3, representing a 2.3 kt increase from the previously submitted estimate for 2008. Backward and forward projections using the 2009 model structure gave estimates of 317, 245 and 244 kt NH3 for the years 1990, 2010 and 2020, respectively. This inventory reports emission from livestock agriculture and from nitrogen fertilisers applied to agricultural land. There are a number of other minor sources reported as ‘agriculture’ in the total UK emission inventory, including horses not kept on agricultural holdings, emissions from composting and domestic fertiliser use

Coordination variability associated with attendance to a longitudinal reducing biofeedback schedule

The aim of this paper was to assess skill exploration via coordinated variability (CoordVar) during attendance to a longitudinal, reducing biofeedback (BFb) intervention. Novices (n=15 BFb; n=15 Control) were introduced to a lunge touch task. Visual BFb were given on the timing and magnitude of rear leg kinematics. A modified CI2 method (CI2area) was used to quantify CoordVar for rear leg joint couplings. Coefficient of variability was used to quantify CoM horizontal velocity as performance variability (PerfVar). Linear regression 95% confidence intervals were compared between groups to assess changes over time. The BFb group demonstrated increasing CoordVar as a response to the BFb, with all participants showing no change in PerfVar. This highlights the potential for CoordVar to identify the effectiveness of BFb provision by practitioners

Soft n-Ary Subgroups

AbstractSoft set theory plays a vital role in solving many complicated problems with inherited uncertainty. An n-ary algebraic systems is a generalization of algebraic structures and it is the most natural way for the further development, deeper understanding of their properties. In this paper, we apply soft set theory to an n-ary algebraic systems and introduce the notions of soft n-ary groups and soft n-ary subgroups. Further, some operations on soft sets are extended to the former. Finally, we provide the characterization of soft n-ary subgroups over an n-ary group (G,f) and study their related properties

Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture

The use of in-situ sensors capable of real-time monitoring of soil nitrogen (N) may facilitate improvements in agricultural N-use efficiency (NUE) through better fertiliser management. The optimal design of such sensor networks, consisting of clusters of sensors each attached to a data logger, depends upon the spatial variation of soil N and the relative cost of the data loggers and sensors. The primary objective of this study was to demonstrate how in-situ networks of N sensors could be optimally designed to enable the cost-efficient monitoring of soil N within a grassland field (1.9 ha). In the summer of 2014, two nested sampling campaigns (June & July) were undertaken to assess spatial variation in soil amino acids, ammonium (NH4+) and nitrate (NO3−) at a range of scales that represented the within (less than 2 m) and between (greater than 2 m) data logger/sensor cluster variability. Variance at short range (less than 2 m) was found to be dominant for all N forms. Variation at larger scales (greater than 2 m) was not as large but was still considered an important spatial component for all N forms, especially NO3−. The variance components derived from the nested sampling were used to inform the efficient design of theoretical in-situ networks of NH4+ and NO3− sensors based on the costs of a commercially available data logger and ion-selective electrodes (ISEs). Based on the spatial variance observed in the June nested sampling, and given a budget of £5000, the NO3− field mean could be estimated with a 95% confidence interval width of 1.70 μg N g−1 using 2 randomly positioned data loggers each with 5 sensors. Further investigation into “aggregate-scale” (less than 1 cm) spatial variance revealed further large variation at the sub 1-cm scale for all N forms. Sensors, for which the measurement represents an integration over a sensor-soil contact area of diameter less than 1 cm, would be subject to this aggregate-scale variability. As such, local replication at scales less than 1 cm would be needed to maintain the precision of the resulting field mean estimation. Adoption of in-situ sensor networks will depend upon the development of suitable low‐cost sensors, demonstration of the cost-benefit and the construction of a decision support system that utilises the generated data to improve the NUE of fertiliser N management