The role of soil organic matter in maintaining the productivity and yield stability of cereals in China

The role of soil organic matter (SOM) in agricultural systems has been widely studied in conjunction with the potential for greenhouse gas mitigation. However, the link between SOM accumulation in croplands, crop productivity and yield stability has not yet been clearly established. In this paper, we collected data on provincial yearly crop productivity (yields, total cropland area) during 1949–1998 and the average SOM contents in croplands sampled and determined from the National Soil Survey in 1979–1982 of mainland China. The cereal productivity was assessed both with an overall mean of 1949–1998 and with the mean values for different time periods within this overall time, respectively. The yield variability within a single stabilizing stage, and between the fluctuating years, was calculated as a negative measure of yield stability. The correlation between SOM and cereal productivity was very significant for most provinces, but the relationship has become less significant as we approach the present. Moreover, the average yield variability was very significantly and negatively correlated with the cropland SOM level. The findings support our previous hypothesis from case studies, that C sequestration in China's croplands may provide win–win benefits, by enhancing crop productivity and stabilizing yield. This offers a sound basis as a greenhouse gas mitigation strategy by promoting C sequestration in croplands, and enhancing food security in China's agriculture.     

Soil C storage as affected by tillage and straw management: An assessment using field measurements and model predictions

Soil tillage and straw management are both known to affect soil organic matter dynamics. However, it is still unclear whether, or how, these two practices interact to affect soil C storage, and data from long term studies are scarce. Soil C models may help to overcome some of these problems. Here we compare direct measurements of soil C contents from a 9 year old tillage experiment to predictions made by RothC and a cohort model. Soil samples were collected from plots in an Irish winter wheat field that were exposed to either conventional (CT) or shallow non-inversion tillage (RT). Crop residue was removed from half of the RT and CT plots after harvest, allowing us to test for interactive effects between tillage practices and straw management. Within the 0–30 cm layer, soil C contents were significantly increased both by straw retention and by RT. Tillage and straw management did not interact to determine the total amount of soil C in this layer. The highest average soil C contents (68.9 ± 2.8 Mg C ha^?1) were found for the combination of RT with straw incorporation, whereas the lowest average soil C contents (57.3 ± 2.3 Mg C ha^?1) were found for CT with straw removal. We found no significant treatment effects on soil C contents at lower depths. Both models suggest that at our site, RT stimulates soil C storage largely by decreasing the decomposition of old soil C. Extrapolating our findings to the rest of Ireland, we estimate that RT will lead to C mitigation ranging from 0.18 to 1.0 Mg C ha^?1 y^?1 relative to CT, with the mitigation rate depending on the initial SOC level. However, on-farm assessments are still needed to determine whether RT management practices can be adopted under Irish conditions without detrimental effects on crop yield.     

Estimating net primary production and annual plant carbon inputs, and modelling future changes in soil carbon stocks in arable farmlands of northern Japan

Soil C sequestration in croplands is deemed to be one of the most promising greenhouse gas mitigation options for Japan's agriculture. In this context, changes in soil C stocks in northern Japan's arable farming area over the period of 1971-2010, specifically in the region's typical Andosol (volcanic ash-derived) and non-Andosol soils, were simulated using soil-type-specific versions of the Rothamsted carbon model (RothC). The models were then used to predict the effects, over the period of 2011-2050, of three potential management scenarios: (i) baseline: maintenance of present crop residue returns and green manure crops, as well as composted cattle manure C inputs (24-34 Mg ha⁻¹ yr⁻¹ applied on 3-55% of arable land according to crop), (ii) cattle manure: all arable fields receive 20 Mg ha⁻¹ yr⁻¹ of composted cattle manure, increased C inputs from crop residues and present C inputs from green manure are assumed, and (iii) minimum input: all above-ground crop residues removed, no green manure crop, no cattle manure applied. Above- and below-ground residue biomass C inputs contributed by 8 major crops, and oats employed as a green manure crop, were drawn from yield statistics recorded at the township level and crop-specific allometric relationships (e.g. ratio of above-ground residue biomass to harvested biomass on a dry weight basis). Estimated crop net primary production (NPP) ranged from 1.60 Mg C ha⁻¹ yr⁻¹ for adzuki bean to 8.75 Mg C ha⁻¹ yr⁻¹ for silage corn. For the whole region (143 × 10³ ha), overall NPP was estimated at 952 ± 60 Gg C yr⁻¹ (6.66 ± 0.42 Mg C ha⁻¹ yr⁻¹). Plant C inputs to the soil also varied widely amongst the crops, ranging from 0.50 Mg C ha⁻¹ yr⁻¹ for potato to 3.26 Mg C ha⁻¹ yr⁻¹ for winter wheat. Annual plant C inputs to the soil were estimated at 360 ± 45 Gg C yr⁻¹ (2.52 ± 0.32 Mg C ha⁻¹ yr⁻¹), representing 38% of the cropland NPP. The RothC simulations suggest that the region's soil C stock (0-30 cm horizon), across all soils, has decreased from 13.96 Tg C (107.5 Mg C ha⁻¹ yr⁻¹) in 1970 to 12.46 Tg C (96.0 Mg C ha⁻¹ yr⁻¹) in 2010. For the baseline, cattle manure and minimum input scenarios, soil C stocks of 12.13, 13.27 and 9.82 Tg C, respectively, were projected for 2050. Over the period of 2011-2050, compared to the baseline scenario, soil C was sequestered (+0.219 Mg C ha⁻¹ yr⁻¹) by enhanced cattle manure application, but was lost (−0.445 Mg C ha⁻¹ yr⁻¹) under the minimum input scenario. The effect of variations of input data (monthly mean temperature, monthly precipitation, plant C inputs and cattle manure C inputs) on the uncertainty of model outputs for each scenario was assessed using a Monte Carlo approach. Taking into account the uncertainty (standard deviation as % of the mean) for the model's outputs for 2050 (5.1-6.1%), it is clear that the minimum input scenario would lead to a rapid decrease in soil C stocks for arable farmlands in northern Japan.     

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.