Rice is a staple food for more than three billion people and accounts for up to 11% of the global methane (CH 4 ) emissions from anthropogenic sources. With increasing populations, particularly in less developed countries where rice is a major cereal crop, production continues to increase to meet demand. Implementing site-specific mitigation measures to reduce greenhouse gas emissions from rice is important to minimise climate change. Measuring greenhouse gases is costly and time-consuming; therefore, many farmers, supply chains, and scientists rely on greenhouse gas accounting tools or internationally acceptable methodologies (e.g., Intergovernmental Panel on Climate Change) to estimate emissions and explore mitigation options. In this paper, existing empirical models that are widely used have been evaluated against measured CH 4 emission data. CH 4 emission data and management information were collected from 70 peer-reviewed scientific papers. Model input variables such as soil organic carbon (SOC), pH, water management during crop season and pre-season, and organic amendment application were collected and used for estimation of CH 4 emission. The performance of the models was evaluated by comparing the predicted emission values against measured emissions with the result showing that the models capture the impact of different management on emissions, but either under- or overestimate the emission value, and therefore are unable to capture the magnitude of emissions. Estimated emission values are much lower than observed for most of the rice-producing countries, with R correlation coefficient values varying from −0.49 to 0.87 across the models. In conclusion, current models are adequate for predicting emission trends and the directional effects of management, but are not adequate for estimating the magnitude of emissions. The existing models do not consider key site-specific variables such as soil texture, planting method, cultivar type, or growing season, which all influence emissions, and thus, the models lack sensitivity to key site variables to reliably predict emissions.
The international pharmaceutical regulatory academic and industrial toxicology communities are collaborating to improve the efficiency and effectivenes s of cancer hazard identification based on dramatic improvements in our understanding of the cancer process. Guidelines emanating from the International Conference on Harmonization provide for use of in vivo alternatives. Standard practices utilizing lifetime rat and mouse studies are recognized as seriously flawed with over 80% false positive rates. Furthermore, tobacco, the most important human carcinogen commercialized by industry, is negative in these traditional lifetime studies. The lifetime mouse bioassay is generally recognized in pharmaceutical development as not adding value in safety assessment. An international consortium under the aegis of ILSI has recently completed an evaluation of alternative mouse cancer models. Transgenic models are less expensive, use fewer animals and take less time than traditional lifetime bioassays. These alternative models have now been sufficiently evaluated to be considered useful in the safety assessment plan for pharmaceuticals in development. Specifically for example, the rasH2 appears useful in detecting nongenotoxic as well as genotoxic rodent tumorigens with improved concordance with human response. The p53 +/- heterozygous mouse apparently identifies hormonal carcinogenic mechanisms, immunosuppressive carcinogens, and genotoxic carcinogens. The TG:AC predicts for rodent tumorigens applied topically. Recent experiences at FDA, CPMP, and MHW indicate that with good planning and agency interactions, regulatory acceptability can be anticipated.
Abstract When planning sampling in an experiment where soil organic carbon (SOC) content is expected to change, it is necessary to know how many samples will need to be taken to demonstrate a change in SOC and after how long this change will be detectable. Much has been published on the number of samples required to demonstrate the minimum detectable difference in SOC, but less on how long it takes for this change to be detectable. In this paper, a model of SOC dynamics is used to estimate the minimum time taken for a change in total SOC content to become measurable under different carbon inputs, land uses and soil types. For free air carbon dioxide enrichment (FACE), and other experiments in which SOC is expected to increase, relationships between the percentage change in C inputs and the time taken to measure a change in SOC are presented, for two levels of sampling intensity corresponding to the maximum that is practically possible in most experiments (∼100 samples) and that used regularly in field experiments (10–20 samples). In FACE experiments, where C inputs increase by a maximum of about 20–25%, SOC change could be detected with 90% confidence after about 6–10 years if a sampling regime allowing 3% change in background SOC level (probably requiring a very large number of samples) were used, but could not be detected at all if a sampling regime were used that allowed only a 15% change in background SOC to be detected. If increases in C inputs are much below 15%, it might not be possible to detect a change in soil C without an enormous number of samples. Relationships between the change in C inputs and the time taken to measure a change in SOC are robust over a range of soil types and land uses. The results demonstrate how models of SOC dynamics can be used to complement statistical power analyses for planning when, and how intensively, to sample soils during experiments. An advantage of the modelling approach demonstrated here is that estimates of the minimum time taken for a change in soil carbon to become detectable can be made, even before any detailed soil samples are taken, simply from estimates of the likely increase in carbon inputs to the soil (via expected changes in net primary production).
The world's soils contain approximately 1500 Pg (1 Pg = 1 Gt = 1015 g) of organic carbon (Batjes, 1996), roughly three times the amount of carbon in vegetation and twice the amount in the atmosphere (IPCC, 2001; Denman et al., 2007). The annual fluxes of CO2 from atmosphere to land (global net primary productivity, NPP) and land to atmosphere (respiration and fire) are of the order of 60 Pg C y−1 (IPCC, 2000b). During the 1990s, fossil fuel combustion and cement production emitted 6.4 ± 1.3 Pg C y−1 to the atmosphere, while land-use change emitted 1.6 ± 0.8 Pg C y−1. Atmospheric carbon increased at a rate of 3.2 ± 0.1 Pg C y−1, the oceans absorbed 2.3 ± 0.8 Pg C y−1 and there was an estimated terrestrial sink of 2.6 ± 1.3 Pg C y−1 (Schimel et al., 2001; Denman et al., 2007). The amount of carbon stored in soils globally is therefore large compared to gross and net annual fluxes of carbon to and from the terrestrial biosphere, and the pools of carbon in the atmosphere and vegetation. Because of this, increasing the size of the global soil carbon pool by even a small proportion has the potential to sequester large amounts of carbon, and thus soils have an important role to play in mitigating climate change.
Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils. The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks.
<p>Reducing greenhouse gas (GHG) emissions in to the atmosphere to limit global warming is the big challenge of the coming decades. The focus lies on negative emission technologies to remove GHGs from the atmosphere from different sectors. Agriculture produces around a quarter of all the anthropogenic GHGs globally (including land use change and afforestation). Reducing these net emissions can be achieved through techniques that increase the soil organic carbon (SOC) stocks. These techniques include improved management practices in agriculture and grassland systems, which increase the organic carbon (C) input or reduce soil disturbances. The C sequestration potential differs among soils depending on climate, soil properties and management, with the highest potential for poor soils (SOC stock farthest from saturation).</p><p>Modelling can be used to estimate the technical potential to sequester C of agricultural land under different mitigation practices for the next decades under different climate scenarios. The ECOSSE model was developed to simulate soil C dynamics and GHG emissions in mineral and organic soils. A spatial version of the model (GlobalECOSSE) was adapted to simulate agricultural soils around the world to calculate the SOC change under changing management and climate.</p><p>Practices like different tillage management, crop rotations and residue incorporation showed regional differences and the importance of adapting mitigation practices under an increased changing climate. A fast adoption of practices that increase SOC has its own challenges, as the potential to sequester C is high until the soil reached a new C equilibrium. Therefore, the potential to use soil C sequestration to reduce overall GHG emissions is limited. The results showed a high potential to sequester C until 2050 but much lower rates in the second half of the century, highlighting the importance of using soil C sequestration in the coming decades to reach net zero by 2050.</p>
