Gravitation Interaction and Electromagnetic Interaction in the Relativistic Universe with Total Zero and Local Non-Zero Energy

In the model of flat expansive homogeneous and isotropic relativistic universe with total zero and local non-zero energy the gravitation energy of bodies and the elecromagnetic energy of charged bodies can be localised.Comment: LaTeX, 10 pages, 1 figur

The effects of cropping intensity and cropland expansion of Brazilian soybean production on green water flows

As land use change alters how green water is appropriated, cropland expansion is instrumental in re-allocating green water towards agriculture. Alongside cropland expansion, agricultural intensification practices modify crop water use and land and water productivity. Particularly, one form of agricultural intensification known as multi-cropping (the cultivation of a piece of land sequentially more than once a year) can result in greater agricultural output per unit of land, as well as more productive use of the available water throughout the annual rainfall cycle. We assess the influence of these two processes, cropland expansion and agricultural intensification, in agricultural green water use in Brazilian agriculture. We applied the biophysical crop model Environmental Policy Integrated Climate (EPIC) to estimate green water use for single and double cropping of soybean (Glycine max) and maize (Zea mays) in Brazil. The first part of our study analyses changes in soybean green water use and virtual water content nationwide between 1990 and 2013, and in a second part we look into the effect of double-cropping on water use for soybean and maize in the Brazilian states of Paraná and Mato Grosso between 2003 and 2013. The results show that cropland expansion plays a more prominent effect in green water use for production of soybean than intensification, and harvested area increase was responsible for the appropriation of an additional 95 km3 of green water in 2013 when compared to 1990, an increase of 155%. We estimate that an additional green water use of around 26 km3 related to second season maize was appropriated through increase of cropping frequency, and without expansion of cropland, in 2013 in the selected states. We discuss the importance of considering multi cropping practices when assessing green water sustainability, and the importance of differentiating green water appropriation through expansion and through cropping frequency changes

The impact of water erosion on global maize and wheat productivity

Water erosion removes soil nutrients, soil carbon, and in extreme cases can remove topsoil altogether. Previous studies have quantified crop yield losses from water erosion using a range of methods, applied mostly to single plots or fields, and cannot be systematically compared. This study assesses the worldwide impact of water erosion on maize and wheat production using a global gridded modeling approach for the first time. The EPIC crop model is used to simulate the global impact of water erosion on maize and wheat yields, from 1980 to 2010, for a range of field management strategies. Maize and wheat yields were reduced by a median of 3% annually in grid cells affected by water erosion, which represent approximately half of global maize and wheat cultivation areas. Water erosion reduces the annual global production of maize and wheat by 8.9 million tonnes and 5.6 million tonnes, with a value of $3.3bn globally. Nitrogen fertilizer necessary to reduce losses is valued at$0.9bn. As cropland most affected by water erosion is outside major maize and wheat production regions, the production losses account for less than 1% of the annual global production by volume. Countries with heavy rainfall, hilly agricultural regions and low fertilizer use are most vulnerable to water erosion. These characteristics are most common in South and Southeast Asia, sub-Saharan Africa and South and Central America. Notable uncertainties remain around large-scale water erosion estimates that will need to be addressed by better integration of models and observations. Yet, an integrated bio-physical modeling framework – considering plant growth, soil processes and input requirements – as presented herein can provide a link between robust water erosion estimates, economics and policy-making so far lacking in global agricultural assessments

Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model

Water erosion on arable land can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in maize and wheat fields between the years 1980 and 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. By using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we obtained the global median water erosion rates of 7 t ha−1 a−1 in maize fields and 5 t ha−1 a−1 in wheat fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide, the varying water erosion estimates from different equations and the complex distribution of cropland in mountainous regions add uncertainty to the simulation results. To reduce the uncertainties in global water erosion estimates, it is necessary to gather more data on global farming techniques to reduce the uncertainty in global land-use maps and to collect more data on soil erosion rates representing the diversity of environmental conditions where crops are grown

Extrapolation of the LTE data for regional prediction of crop production and agro-environmental impacts in the Czech Republic with the EPIC-based modelling system

The long-term crop trials (LTE) provide valuable insights into functioning of the crop systems under variety of crop management strategies. In particular, those field operations which in long run affect the soil organic carbon balance might be of an importance for the climate change impacts oriented research. Bonded strongly to the local site conditions, LTEs provide spatially limited information, not fully reflecting the needs of the large-scale inventories covering countries or big regions. Representing LTEs with a process-based model via locally calibrated model parameters and data, and subsequent upscaling of the model with regional data on climate, terrain, soil, and land use, provides a possible way for LTEs extrapolation to wider geographical domains. As a follow-up to the earlier work on formalising LTE records from several sites in Czechia with the EPIC model, the simulation infrastructure (EPIC-IIASA (CZ)) has been created for regional predictions of crop production and its agro-environmental impacts over the whole territory of Czech Republic (CZ). Conceptually, the EPIC-IIASA (CZ) has been designed based on the EPIC-IIASA global gridded crop modelling system. A set of 977 spatial simulation units (or typical fields, > 1 ha each), which represent a unique combination of an administrative unit (level LAU1), climate region, and soil region, has been compiled using CZ national data. Each simulation unit has been used for linking spatially explicit input data on i) climate, ii) site, iii) soil properties, and iv) crop management to the process-based model EPIC. As an output, various agro-environmental variables may be acquired and visualized geographically. Initially, the spatial infrastructure worked with fixed sowing and harvesting dates across all CZ regions. In order to get the full potential of the EPIC-IIASA (CZ), a calibration with regional planting scenarios was done. Agronomically relevant planting-harvesting windows scenarios were assessed based on the published data (MOCA report), this specifically for traditional production areas in CZ (CZ_R01: Maize growing; CZ_R02: Potato growing; CZ_R03: Cereal growing; CZ_R04: Forage growing; CZ_R05: Sugar beet growing). Since there was not any yield data available for the LAU1 level administrative regions, published LAU1 estimates of the potential yields were used for validation of the EPIC-IIASA (CZ) simulated rainfed and nutrient-unlimited yields. Both absolute simulated yields and the percentage of reported potential yields were displayed geographically and spatial pattern of the simulated values evaluated. Furthermore, longterm average and inter-annual variability of simulated yields were compared to the available statistical data at the NUTS3 administrative level. To date, calibration and validation of two crops, spring barley and winter wheat were successfully performed. Other crops will be calibrated in the next step, so that representative crop rotations could be constructed and used in EPIC-IIASA (CZ) setup to properly approximate the prevailing regional cropping systems in the simulations. Such a completely calibrated and validated crop modelling system could serve as a powerful tool for extrapolating impacts of different crop management strategies, well explored with LTEs, over the larger areas, and hence, provide valuable evidence-based inputs for decision-making support at regional and national levels in CZ

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions

Dynamic soil functions assessment employing land use and climate scenarios at regional scale

Soils as key component of terrestrial ecosystems are under increasing pressures. As an advance to current static assessments, we present a dynamic soil functions assessment (SFA) to evaluate the current and future state of soils regarding their nutrient storage, water regulation, productivity, habitat and carbon sequestration functions for the case-study region in the Lower Austrian Mostviertel. Carbon response functions simulating the development of regional soil organic carbon (SOC) stocks until 2100 are used to couple established indicator-based SFA methodology with two climate and three land use scenarios, i.e. land sparing (LSP), land sharing (LSH), and balanced land use (LBA). Results reveal a dominant impact of land use scenarios on soil functions compared to the impact from climate scenarios and highlight the close link between SOC development and the quality of investigated soil functions, i.e. soil functionality. The soil habitat and soil carbon sequestration functions on investigated agricultural land are positively affected by maintenance of grassland under LSH (20 of the case-study region), where SOC stocks show a steady and continuous increase. By 2100 however, total regional SOC stocks are higher under LSP compared to LSH or LBA, due to extensive afforestation. The presented approach may improve integrative decision-making in land use planning processes. It bridges superordinate goals of sustainable development, such as climate change mitigation, with land use actions taken at local or regional scales. The dynamic SFA broadens the debate on LSH and LSP and can reduce trade-offs between soil functions through land use planning processes

The contribution of Citizens’ Observatories to validation of satellite‐retrieved soil moisture products

The GROW Observatory (GROW) will create a sustainable citizen platform and community to generate, share and utilise information on land, soil and water resources at a resolution hitherto not previously considered. The European Space Agency’s Sentinel‐1 is the first mission capable of providing high‐resolution soil moisture information, but a proper validation of Sentinel data remains a challenge given the scarcity of available in situ reference measurements. Establishment of a dense network of in situ measurement can bridge the gap in spatial resolution between in situ and satellite‐based soil moisture measurements enabling validation and calibration of ground and remotely measured soil moisture observations. The potential exists to answer scientific questions including the validity of satellite data, the impact of climate change on land management thus supporting the needs of growers and integrating citizen and scientific research to be more directly applicable and relevant

Regional topsoil organic carbon content in the agricultural soils of Slovakia and its drivers, as revealed by the most recent national soil monitoring data

Soil organic carbon (SOC) is a primary constituent of soil organic matter and plays an important role in the regulation of many soil processes, including greenhouse gas emissions. Recently, SOC also became an indicator for monitoring climate change mitigation policies in the agricultural sector. The availability of up-to-date SOC inventories is thus crucial in terms of supporting SOC–related actions at country or sub-country scales. Currently, the National Monitoring System of the Agricultural Soils of Slovakia (CMS-P), whose network of 318 monitoring sites was last surveyed in 2018, is the only available source of up-to-date topsoil SOC data for agricultural land in Slovakia. Although very useful at the national scale, the number of CMS-P observations it contains is too limited for much needed sub-national SOC inventories. We hypothesized that with the aid of well-chosen macro-scale drivers of topsoil SOC accumulation in agricultural land in Slovakia, and by mapping those drivers geographically, we could upscale the CMS-P observations and produce a regional estimate of topsoil SOC. Altitude, land cover, topsoil texture, and soil type were assumed to be the key factors controlling topsoil SOC accumulation in Slovakia, and based on these, the country was classified into 14 macro-scale geographical regions. Typical ranges and mid-class values of 0–30cm topsoil SOC concentrations (%) and stocks (t ha−1) were calculated for each macro-scale region from CMS-P data. The average topsoil SOC content in agricultural land was estimated to be 2.13% (72.9 t ha−1). The highest topsoil SOC stock (> 90 t ha−1) was estimated for the lowlands of Slovakia, and the lowest ( 65 t ha−1) being in LAU1 regions in the south-west, south-east, and north of Slovakia where arable land is most prevalent. Total SOC storage in 0–30cm topsoil of agricultural land in Slovakia was estimated at 118.39 Mt, with two-thirds of this amount stored in arable soils in 33 south-west, south-east, and south LAU1 administrative regions. As there is no alternative and up-to-date dataset on topsoil SOC content in Slovakia, the upscaling algorithm presented in this study is an important step toward utilizing CMS-P data for sub-national SOC inventories. It may also offer a new way of providing inputs to help predict future or alternative regional topsoil SOC accumulation trajectories in Slovakian agricultural land using process-based or statistical models