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In this paper we present effects of four paired agricultural management practices (organic matter (OM) addition versus no organic matter input, no-tillage (NT) versus conventional tillage, crop rotation versus monoculture, and organic agriculture versus conventional agriculture) on five key soil quality indicators, i.e., soil organic matter (SOM) content, pH, aggregate stability, earthworms (numbers) and crop yield. We have considered organic matter addition, no-tillage, crop rotation and organic agriculture as “promising practices”; no organic matter input, conventional tillage, monoculture and conventional farming were taken as the respective references or “standard practice” (baseline). Relative effects were analysed through indicator response ratio (RR) under each paired practice. For this we considered data of 30 long-term experiments collected from 13 case study sites in Europe and China as collated in the framework of the EU-China funded iSQAPER project. These were complemented with data from 42 long-term experiments across China and 402 observations of long-term trials published in the literature. Out of these, we only considered experiments covering at least five years. The results show that OM addition favourably affected all the indicators under consideration. The most favourable effect was reported on earthworm numbers, followed by yield, SOM content and soil aggregate stability. For pH, effects depended on soil type; OM input favourably affected the pH of acidic soils, whereas no clear trend was observed under NT. NT generally led to increased aggregate stability and greater SOM content in upper soil horizons. However, the magnitude of the relative effects varied, e.g. with soil texture. No-tillage practices enhanced earthworm populations, but not where herbicides or pesticides were applied to combat weeds and pests. Overall, in this review, yield slightly decreased under NT. Crop rotation had a positive effect on SOM content and yield; rotation with ley very positively influenced earthworms’ numbers. Overall, crop rotation had little impact on soil pH and aggregate stability − depending on the type of intercrop; alternatively, rotation of arable crops only resulted in adverse effects. A clear positive trend was observed for earthworm abundance under organic agriculture. Further, organic agriculture generally resulted in increased aggregate stability and greater SOM content. Overall, no clear trend was found for pH; a decrease in yield was observed under organic agriculture in this review.

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and static, whereas SOC is dynamic and SOC dynamics are of particular interest to carbon sequestration and land degradation studies. Thus, there is a clear need to extend spatial SOC mapping to space–time SOC mapping. temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large. Fil: Heuvelink, Gerard B.M. ISRIC - World soil information; Holanda. Wageningen University. Soil Geography and Landscape Group; Holanda Fil: Angelici, Marcos E. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina Fil: Poggio, Laura ISRIC - World soil information, Wageningen; Holanda Fil: Bai, Zhanguo ISRIC - World soil information, Wageningen, The Netherlands Fil: Batjes, Niels H. ISRIC - World soil information, Wageningen, The Netherlands Fil: an den Bosch, Rik ISRIC - World soil information, Wageningen, The Netherlands Fil: Bossio, Deborah The Nature Conservancy; Estados Unidos Fil: Estella, Sergio Vizzuality; España Fil: Lehmann, Jhoannes. Cornell University. Soil and Crop Sciences; Estados Unidos Fil: Olmedo, Guillermo F. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentina Fil: Sandermann, Jonathan. Woods Hole Research Center; Estados Unidos

With increasing discrepancies between population growth and food production in China, the monitoring of crop yield is essential to support food security policies. However, current studies about spatio-temporal variation of yield mainly focus on the influence of climatic factors on grain crops, and do not explore the contributions of agricultural, environmental and economic factors on crop yield in China. In this study, a large yield dataset, covering 31 provinces and a 38-year period from 1978 to 2015, and related explanatory variables were collected for analyzing the spatio-temporal variation of different yield aggregations using stepwise multiple linear regression. At the national scale, the average aggregate yield increased from 3.04 Mg ha−1 in 1978 to 10.04 Mg ha−1 in 2015. Overall, the average aggregate yield increased in all provinces but the average annual growth rates varied: it was smaller than 2.5 % in Heilongjiang, Guizhou, Beijing, Qinghai and Jilin, more than 4.0 % in Hainan, Guangxi, Ningxia, Hebei and Shaanxi, and between 2.5 % and 4.0 % in other provinces. The spatial patterns of the average yield from 1978 to 2015 were different for different crop aggregations. Most of the regression models explained more than 60 % of the yield variance, except for rice, potato and cotton models. Agricultural management practices, soil and economic covariates were important explanatory variables in all models. Topography and climatic covariates were also important for some of the crop models. The regression model of the aggregate yield for all crops explained 95 % of the yield variance, which was mainly explained by planting area index of vegetables (20 %), followed by farmer income (14 %), planting area index of other crops (orchards 11 %, melons 8 %, sugar 6 %, cereals 6 %), and density of agricultural diesel engines (5 %). Although the regression residual of the aggregate yield model was zero on average, the trends were different in different provinces: most provinces demonstrated a small negative or positive residual; the yield was substantially lower (< -0.20 Mg ha−1 y−1) than predicted by the regression model in three provinces in central China (Hebei, Shaanxi and Anhui) and substantially higher (> 0.20 Mg ha−1 y−1) in four provinces (Shanxi, Shandong, Sichuan and Guangdong). These systematic over- and underpredictions may be caused by other factors, such as plagues, pests, natural hazards, market structures (such as competition for labor or impediments to market access) and farmer's management skills. With the increasing population and limited agricultural land resources, enhancing economic growth might be an adequate solution to meet the growing demand for food. It can also promote agricultural efficiency in China, certainly when combined with better management practices, crop composition, breeding and planting technologies.

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate mitigation through better land management. In this work we report on the development, implementation and application of a data-driven, statistical space-time method for mapping SOC stocks, using Argentina as a pilot area. We used the Quantile Regression Forest machine-learning algorithm to predict SOC stock at 0-30 cm depth at 250 m resolution for Argentina between 1982 and 2017, on an annual basis. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. Most covariates were static and could only explain the spatial SOC distribution. SOC change over time was modelled using time series maps of the AVHRR NDVI vegetation index. These NDVI time series maps were pre-processed using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Spatial patterns of SOC stock predictions were persistent over time and comparable to baseline SOC stock maps of Argentina. Predictions had modest temporal variation with an average decrease for the entire country from 2.55 kg C m‑2 to 2.48 kg C m‑2 over the 36-year period (equivalent to a decline of 210.7 Gg C, 3.0% of the total 0‑30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 kg C m‑2 to 4.34 kg C m‑2 (5.9%) during the same period. For the 2001-2015 period, predicted temporal variation was 7-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and the United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a Mean Error of 0.03 kg C m-2 and a Root Mean Squared Error of 2.04 kg C m-2. The model explained 45% of the SOC stock variation. In spite of the large uncertainties, this work showed that machine learning methods can be used for space-time SOC mapping and may yield valuable information to land managers and policy makers, provided that SOC observation density in space and time is sufficiently large.

Soil information is fundamental for many global applications, such as food security, land degradation, water resources, hydrology, climate change and ecological conservation. To address these diverse needs, it is important to provide free, consistent, easily accessible and standardized soil information. SoilGrids meets these requirements being a global product supporting global modelling and providing complementary information for the development of regional and national products in data-poor areas. This presentation will focus on the methodological aspects for modelling and mapping of global soil information. We describe the selection of models for global mapping using quantile random forest and recursive feature elimination to obtain a parsimonious model. We also use a refined cross-validation procedure to account for bias caused by spatial differences in sampling density at different depths. SoilGrids also quantifies location-specific uncertainty at global level by computing 90% prediction interval limits.

Nitrogen use efficiency (NUE) is crucial to establish efficient fertilizer application guidelines that balance crop yield, economic return and environmental sustainability. Although there are quite a few researches about the spatial and temporal variation of NUE, little work has been done on modelling NUE through deriving empirical relationships with explanatory environmental variables and exploring their relative importance quantitatively. The space-time patterns of NUE indicators (i.e., the Partial Factor Productivity of nitrogen, PFPN, and the Partial Nutrient Balance of nitrogen, PNBN) at provincial scale in China were derived and related to environmental covariates using stepwise multiple linear regression. PFPN was higher in east and south China than in central and west China and was smaller than 30 kg kg−1 yr−1 in most provinces, while PNBN was moderate in most provinces (0.41–0.50 kg kg−1 yr−1) and low (< 0.40 kg kg−1 yr−1) in south China. The national PFPN declined slightly from 32 kg kg−1 in 1978 to 27 kg kg−1 in 1995 and went up gradually to reach 38 kg kg−1 in 2015. The national PNBN decreased from 0.53 to 0.36 kg kg−1 from 1978 to 2003, thereafter stabilizing at around 0.40 kg kg−1 yr−1 between 2004 and 2015. The multiple linear regression models explained 74 % of the variance of PFPN and PNBN. The main explanatory variables of PFPN were planting area index of sugar crop (32 % of the R-square), followed by Arenosols (12 %), planting area index of oil crop (8 %), planting area index of vegetables (5 %), silt content (5 %) and total potassium (5 %). For PNBN, the variation was mainly attributed to mean annual daytime surface temperature (28 % of the R-square), planting area index of crops (beans 20 %, orchards 10 % and vegetables 9 %) and wet day frequency (5 %). The results of this study indicate that crop types, temperature and soil properties are important variables that determine NUE. These should be considered by policy makers when agricultural land development decisions are made in order to balance NUE and productivity (i.e., agronomy and environment).

Sampling and analysis or visual examination of soil to assess its status and use potential is widely practiced from plot to national scales. However, the choice of relevant soil attributes and interpretation of measurements are not straightforward, because of the complexity and site-specificity of soils, legacy effects of previous land use, and trade-offs between ecosystem services. Here we review soil quality and related concepts, in terms of definition, assessment approaches, and indicator selection and interpretation. We identify the most frequently used soil quality indicators under agricultural land use. We find that explicit evaluation of soil quality with respect to specific soil threats, soil functions and ecosystem services has rarely been implemented, and few approaches provide clear interpretation schemes of measured indicator values. This limits their adoption by land managers as well as policy. We also consider novel indicators that address currently neglected though important soil properties and processes, and we list the crucial steps in the development of a soil quality assessment procedure that is scientifically sound and supports management and policy decisions that account for the multi-functionality of soil. This requires the involvement of the pertinent actors, stakeholders and end-users to a much larger degree than practiced to date.

We report on the first investigation of the triple-layer coupling pattern formation in a dielectric barrier discharge system. The pattern basically consists of one discharge subpattern ignited in the gas gap and two surface-charge subpatterns deposited on the dielectric surfaces. The coupling of the three subpatterns (layers) is presented by analyzing the time-resolved discharge sequence of a white-eye hexagonal super lattice pattern (WEHSP). A triple-layer coupling reaction-diffusion regime is also established to conduct simulations and the simulated WEHSP agrees well with the experiment. This paper will provide a deeper understanding for the layer coupling mechanism in pattern formation.

This study compares the results of two nationwide land degradation assessments for Senegal. The first approach is based on expert judgments, the second on trend analyses of Rainfall Use Efficiency (RUE). The comparison yields some interpretable results for extreme negative RUE slope values with maximum degrees of land degradation as indicated by experts, yet, overall, the correlation between both approaches proves to be low and without clear sign. As RUE trend analysis suffers from several inherent methodological problems and expert judgments are found to be consistent we consider expert opinions as the preferable option for a nation-wide land degradation assessment in Senegal. Yet, we argue that methodological improvements would increase the explanatory power of the expert approach and consolidate its position in a policy making framework.

We report on the collective vibration of discharge current filaments in a self-organized superlattice pattern within an air-argon dielectric barrier discharge system. The period of collective vibration decreases as the air content increases. An antisymmetric normal vibration mode of discharge current filaments is observed. The measurements of the spatiotemporal behavior of the superlattice pattern show that the discharge of the vibrating dot (filament) is asynchronous with that of its eight neighboring dots, including four big dots and four small dots. The forces exerted on the vibrating dot are analyzed, and a motion equation is set up in each half cycle of the applied voltage. It is found that the motion of the small dot is not an ideal vibration but is a vibration with fluctuating amplitude, which is in good agreement with experiments.

Ventricular fibrillation (VF) is the major cause of sudden cardiac death, the leading cause of death in the industrialized world. However, the mechanisms for its onset are still not well understood. Recent experiments indicate that VF is induced by transitions of cardiac electric propagationg waves from a single spiral wave to multiple waves. To further understand the underlying mechanism of VF, we investigated the interaction between two waves in a two-dimensional excitable media. Three types of multiple spirals including multi-arm spirals have been found depending on the rotation direction and the distance among spiral waves.

Radial structures of atmospheric pressure glow discharges in dielectric barrier discharge with multiple current pulses are investigated by a self-consistent two-dimensional fluid model. The simulation results show that the discharge current densities at different radial positions do not reach their peaks simultaneously, indicating that there exists a radial non-uniform structure. The time delay between the plasma ignitions in different positions become larger and larger in the successive current pulses. It is argued that the radial non-uniformity originates from the edge effects in the first current pulse, and grows in the successive current pulses by the activation-inhibition mechanism.