A Natural Value Unit - Econophysics as Arbiter between Finance and Economics

Foreign exchange markets show that currency units (= accounting or nominal price units) are variables. Technical and economic progress evidence that the consumer baskets (= purchasing power units or real price units) are also variables. In contrast, all physical measurement units are constants and either defined in the SI (= metric) convention or based upon natural constants (= "natural" or Planck units). Econophysics can identify a constant natural value scale or vaue unit (natural numaraire) based upon Planck energy. In honour of the economist L. Walras, this "Planck value" could be called walras (Wal), thereby using the SI naming convention. One Wal can be shown to have a physiological and an economic interpretation in that it is equal to the annual minimal real cost of physiological life of a reference person at minimal activity. The price of one Wal in terms of any currency can be estimated by hedonic regression techniques used in inflation measurement (axiometry). This pilot research uses official disaggregated Swiss Producer and Consumer Price Index data and estimates the hedonic walras price (HWP), quoted in Swiss francs in 2003, and its inverse, the physical purchasing power (PhPP) of the Swiss franc in 2003.Comment: 19 pages, 1 table, Appendix with 2 tables, RevTex4, APFA5 200

New approach for sensor simulation for Hardware In the Loop test systems

International audienceAutomatic test systems can evaluate the functionality of measurement and/or control hardware by simulating real world signals and verifying the expected response. Simulation takes the dynamics of real-world environments and models them using software to test the performance of critical system hardware components. Sensor simulation is the process of providing realistic sensor signals to the inputs of a device under test and evaluating how a piece of equipment will respond across a broad range of operating conditions. This paper will discuss the benefits of sensor simulation, and specifically reference the additional advantages to using an FPGA-based implementation

Pessimistic outcome expectancy does not explain ambiguity aversion in decision-making under uncertainty.

When faced with a decision, most people like to know the odds and prefer to avoid ambiguity. It has been suggested that this aversion to ambiguity is linked to people's assumption of worst possible outcomes. We used two closely linked behavioural tasks in 78 healthy participants to investigate whether such pessimistic prior beliefs can explain ambiguity aversion. In the risk-taking task, participants had to decide whether or not they place a bet, while in the beliefs task, participants were asked what they believed would be the outcome. Unexpectedly, we found that in the beliefs task, participants were not overly pessimistic about the outcome in the ambiguity condition and in fact closer to optimal levels of decision-making than in the risk conditions. While individual differences in pessimism could explain outcome expectancy, they had no effect on ambiguity aversion. Consequently, ambiguity aversion is more likely caused by general caution than by expectation of negative outcomes despite pessimism-dependent subjective weighting of probabilities

Modeling Approaches to Assess Soil Erosion by Water at the Field Scale with Special Emphasis on Heterogeneity of Soils and Crops

Information on soil erosion and related sedimentation processes are very important for natural resource management and sustainable farming. Plenty of models are available for studying soil erosion but only a few are suitable for dynamic soil erosion assessments at the field-scale. To date, there are no field-scale dynamic models available considering complex agricultural systems for the simulation of soil erosion. We conducted a review of 51 different models evaluated based on their representation of the processes of soil erosion by water. Secondly, we consider their suitability for assessing soil erosion for more complex field designs, such as patch cropping, strip cropping and agroforestry (alley-cropping systems) and other land management practices. Several models allow daily soil erosion assessments at the sub-field scale, such as EPIC, PERFECT, GUEST, EPM, TCRP, SLEMSA, APSIM, RillGrow, WaNuLCAS, SCUAF, and CREAMS. However, further model development is needed with respect to the interaction of components, i.e., rainfall intensity, overland flow, crop cover, and their scaling limitations. A particular shortcoming of most of the existing field scale models is their one-dimensional nature. We further suggest that platforms with modular structure, such as SIMPLACE and APSIM, offer the possibility to integrate soil erosion as a separate module/component and link to GIS capabilities, and are more flexible to simulate fluxes of matter in the 2D/3D dimensions. Since models operating at daily scales often do not consider a horizontal transfer of matter, such modeling platforms can link erosion components with other environmental components to provide robust estimations of the three-dimensional fluxes and sedimentation processes occurring during soil erosion events.Peer reviewe

Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa

Climate change impacts on maize production in South Africa, i.e., interannual yield variabilities, are still not well understood. This study is based on a recently released reanalysis of climate observations (AgERA5), i.e., temperature, precipitation, solar radiation, and wind speed data. The study assesses climate change effects by quantifying the trend of agrometeorological indicators, their correlation with maize yield, and analyzing their spatiotemporal patterns using Empirical Orthogonal Function. Thereby, the main agrometeorological factors that affected yield variability for the last 31 years (1990/91-2020/21 growing season) in major maize production provinces, namely Free State, KwaZulu-Natal, Mpumalanga, and North West are identified. Results show that there was a significant positive trend in temperature that averages 0.03-0.04 degrees C per year and 0.02-0.04 degrees C per growing season. There was a decreasing trend in precipitation in Free State with 0.01 mm per year. Solar radiation did not show a significant trend. Wind speed in Free State increased at a rate of 0.01 ms(-1) per growing season. Yield variabilities in Free State, Mpumalanga, and North West show a significant positive correlation (r > 0.43) with agrometeorological variables. Yield in KwaZulu-Natal is not influenced by climate factors. The leading mode (50-80% of total variance) of each agrometeorological variable indicates spatially homogenous pattern across the regions. The dipole patterns of the second and the third mode suggest the variabilities of agrometeorological indicators are linked to South Indian high pressure and the warm Agulhas current. The corresponding principal components were mainly associated with strong climate anomalies which are identified as El Nino and La Nina events.Peer reviewe

Anaerobic Digestate from Biogas Plants—Nuisance Waste or Valuable Product?

Biogas production in waste-to-energy plants will support the decarbonization of the energy sector and enhance the EU’s energy transformation efforts. Digestates (DG) formed during the anaerobic digestion of organic wastes contain large amounts of nutrients. Their use for plant fertilization allows for diversifying and increasing the economic efficiency of farming activities. However, to avoid regional production surpluses, processing technologies allowing the acquisition of products that can be transported over long distances are required. This study therefore aimed at determining the effect of applied methods of DG treatment on the chemical composition of the resulting products and their effect on the yields and chemical composition of plants. The following digestate-based products (DGBPs) were tested: two different digestates (DGs), their liquid (LF) and solid fractions (SF) and pellets from DGs (PDG), and pellets form SFs (PSF). Results from the experiment show that during SF/LF separation of DGs, >80% of nitrogen and 87% of potassium flows to LFs, whereas >60% of phosphorus and 70% of magnesium flows to SFs. The highest yields were obtained using untreated DGs and LFs. The application of DGs and LFs was not associated with a leaching of nutrients to the environment (apparent nutrients recovery from these products exceeded 100%). Pelletized DG and SF forms can be used as slow-release fertilizer, although their production leads to significant nitrogen losses (>95%) by ammonia volatilization

Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980-2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070-2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070-2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where "no severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. In contrast, underestimation of situations where "severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios