Tracing the Sources and Fate of NO3- in the Vadose Zone-Groundwater System of a Thousand-Year-Cultivated Region.

Excess nitrate (NO3-) loading in terrestrial and aquatic ecosystems can result in critical environmental and health issues. NO3--rich groundwater has been recorded in the Guanzhong Plain in the Yellow River Basin of China for over 1000 years. To assess the sources and fate of NO3- in the vadose zone and groundwater, numerous samples were collected via borehole drilling and field surveys, followed by analysis and stable NO3- isotope quantification. The results demonstrated that the NO3- concentration in 38% of the groundwater samples exceeded the limit set by the World Health Organization. The total NO3- stock in the 0-10 m soil profile of the orchards was 3.7 times higher than that of the croplands, suggesting that the cropland-to-orchard transition aggravated NO3- accumulation in the deep vadose zone. Based on a Bayesian mixing model applied to stable NO3- isotopes (δ15N and δ18O), NO3- accumulation in the vadose zone was predominantly from manure and sewage N (MN, 27-54%), soil N (SN, 0-64%), and chemical N fertilizer (FN, 4-46%). MN was, by far, the greatest contributor to groundwater NO3- (58-82%). The results also indicated that groundwater NO3- was mainly associated with the soil and hydrogeochemical characteristics, whereas no relationship with modern agricultural activities was observed, likely due to the time delay in the thick vadose zone. The estimated residence time of NO3- in the vadose zone varied from decades to centuries; however, NO3- might reach the aquifer in the near future in areas with recent FN loading, especially those under cropland-to-orchard transition or where the vadose zone is relatively thin. This study suggests that future agricultural land-use transitions from croplands to orchards should be promoted with caution in areas with shallow vadose zones and coarse soil texture.

Impacts of nitrogen practices on yield, grain quality, and nitrogen-use efficiency of crops and soil fertility in three paddy-upland cropping systems.

BACKGROUND: Global food security faces a number of challenges due to increasing population, climate change, and urbanization, while excessive use of nitrogen fertilizers has become a major challenge for sustainable, intensive agriculture. Assessing the impact of agronomic management practices on seed yield, grain quality, and soil fertility is a critical step in understanding nutrientuse efficiency. RESULT: The comprehensive evaluation index had good fitness to that of single attribute (i.e. seed yield, crop quality and soil fertility), indicating that the comprehensive evaluation index was reliable. Applying controlled-release urea (rice in wheat and oilseed rape field: 150 kg N ha-1 , other crops: 120 kg N ha-1 ) plus common urea (30 kg N ha-1 ) incorporating straw from the previous season across the growing season for cereal and oilseed crops showed a slight improvement in seed productivity and Nuse efficiency among three cropping systems in the traditional evaluation method. Compared with local farm practice (applying common urea of 150 kg N ha-1 ), applying these practices in combination based on the outcome of the comprehensive evaluation index method decreased the seed yield by -1.27 ~ 29.8% but improved quality and soil fertility for the paddy-upland cropping system, respectively. CONCLUSION: Properly managing N application by applying partial and fully controlled release of urea with or without straw incorporation for a specific crop system has the potential to provide a better compromise among yield, grain quality, and soil fertility in southern China. © 2020 Society of Chemical Industry.

Soil C and N dynamics and hydrological processes in a maize-wheat rotation field subjected to different tillage and straw management practices.

The impact of farmland nutrient losses on environment security is of serious concern. Conservation tillage led to reduced water and soil losses and increased grain yield, and is therefore one potential solution, but this approach requires an understanding of the complex adaptive traits for environment conditions. In this study, a 4-year field experiment was conducted to quantify the crop yield, runoff and soil water, organic C and N content dynamics in summer maize-winter wheat rations subjected to different tillage and straw management practices. Based on these, the effects of different tillage and straw management regimes on water, C and N balances of the soil-plant system was evaluated with a 11-year model prediction using the SPACSYS model. The treatments used in this study included conventional tillage (CT) with straw removal, conventional tillage with straw returning (CTSR), reduced tillage (RT) with straw removal and reduced tillage with straw returning (RTSR). The results showed that maize yield was remarkably affected by straw returning while there was no significant tillage effect. By contrast, wheat yield showed a high inter-annual variability, but was not significantly influenced by tillage and straw management practices. The soil water balance analysis demonstrated that the treatments with straw returning improved water use efficiency by increasing transpiration while reducing water losses through evaporation and runoff, compared to the straw-removal treatments. The simulations for all of the treatments showed that the soils acted as C and N sinks in the present study. Furthermore, plots that included straw returning amassed more C and N in the soil than the that with straw removal. Our work demonstrates that in maize-wheat rotation slopping land reduced tillage with straw returning is a win-win practice for the equilibrium between agricultural productivity and low soil water, C and N losses.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.

Modelling field scale spatial variation in water run-off, soil moisture, N2O emissions and herbage biomass of a grazed pasture using the SPACSYS model.

In this study, we evaluated the ability of the SPACSYS model to simulate water run-off, soil moisture, N2O fluxes and grass growth using data generated from a field of the North Wyke Farm Platform. The field-scale model is adapted via a linked and grid-based approach (grid-to-grid) to account for not only temporal dynamics but also the within-field spatial variation in these key ecosystem indicators. Spatial variability in nutrient and water presence at the field-scale is a key source of uncertainty when quantifying nutrient cycling and water movement in an agricultural system. Results demonstrated that the new spatially distributed version of SPACSYS provided a worthy improvement in accuracy over the standard (single-point) version for biomass productivity. No difference in model prediction performance was observed for water run-off, reflecting the closed-system nature of this variable. Similarly, no difference in model prediction performance was found for N2O fluxes, but here the N2O predictions were noticeably poor in both cases. Further developmental work, informed by this study's findings, is proposed to improve model predictions for N2O. Soil moisture results with the spatially distributed version appeared promising but this promise could not be objectively verified.

Higher yields and lower methane emissions with new rice cultivars.

Breeding high-yielding rice cultivars through increasing biomass is a key strategy to meet rising global food demands. Yet, increasing rice growth can stimulate methane (CH4 ) emissions, exacerbating global climate change, as rice cultivation is a major source of this powerful greenhouse gas. Here, we show in a series of experiments that high-yielding rice cultivars actually reduce CH4 emissions from typical paddy soils. Averaged across 33 rice cultivars, a biomass increase of 10% resulted in a 10.3% decrease in CH4 emissions in a soil with a high carbon (C) content. Compared to a low-yielding cultivar, a high-yielding cultivar significantly increased root porosity and the abundance of methane-consuming microorganisms, suggesting that the larger and more porous root systems of high-yielding cultivars facilitated CH4 oxidation by promoting O2 transport to soils. Our results were further supported by a meta-analysis, showing that high-yielding rice cultivars strongly decrease CH4 emissions from paddy soils with high organic C contents. Based on our results, increasing rice biomass by 10% could reduce annual CH4 emissions from Chinese rice agriculture by 7.1%. Our findings suggest that modern rice breeding strategies for high-yielding cultivars can substantially mitigate paddy CH4 emission in China and other rice growing regions.

Greenhouse gas emissions and stocks of soil carbon and nitrogen from a 20-year fertilised wheat-maize intercropping system: A model approach.

Accurate modelling of agricultural management impacts on greenhouse gas emissions and the cycling of carbon and nitrogen is complicated due to interactions between various processes and the disturbance caused by field management. In this study, a process-based model, the Soil-Plant-Atmosphere Continuum System (SPACSYS), was used to simulate the effects of different fertilisation regimes on crop yields, the dynamics of soil organic carbon (SOC) and total nitrogen (SN) stocks from 1990 to 2010, and soil CO2 (2007-2010) and N2O (2007-2008) emissions based on a long-term fertilisation experiment with a winter-wheat (Triticum Aestivum L.) and summer-maize (Zea mays L.) intercropping system in Eutric Cambisol (FAO) soil in southern China. Three fertilisation treatments were 1) unfertilised (Control), 2) chemical nitrogen, phosphorus and potassium (NPK), and 3) NPK plus pig manure (NPKM). Statistical analyses indicated that the SPACSYS model can reasonably simulate the yields of wheat and maize, the evolution of SOC and SN stocks and soil CO2 and N2O emissions. The simulations showed that the NPKM treatment had the highest values of crop yields, SOC and SN stocks, and soil CO2 and N2O emissions were the lowest from the Control treatment. Furthermore, the simulated results showed that manure amendment along with chemical fertiliser applications led to both C (1017 ± 470 kg C ha(-1) yr(-1)) and N gains (91.7 ± 15.1 kg N ha(-1) yr(-1)) in the plant-soil system, while the Control treatment caused a slight loss in C and N. In conclusion, the SPACSYS model can accurately simulate the processes of C and N as affected by various fertilisation treatments in the red soil. Furthermore, application of chemical fertilisers plus manure could be a suitable management for ensuring crop yield and sustaining soil fertility in the red soil region, but the ratio of chemical fertilisers to manure should be optimized to reduce C and N losses to the environment.

Simulation dataset of annual crop growth, GHG emission and SOC stock dynamics under current and projected climate conditions for major crops with current and reduced fertiliser inputs in Southwest, England.

For mitigating the unintended environmental impacts associated with intensive farming across the world, it is crucial to understand the complex impacts of potential reductions in fertiliser use on multiple ecosystem services, including crop production, GHG emissions and changes in soil organic carbon (SOC) stocks. Using site specific spatial data and information, a novel integrated modelling approach using established agroecosystem models (SPACSYS and RothC) was implemented to evaluate the impacts of various fertiliser reductions (10 %, 30 % and 50 %) under current / baseline and projected (RCP2.6, RCP4.5 and RCP8.5) climate scenarios in a study catchment in southwest England. 48 unique combinations of soil types, climate conditions and fertiliser inputs were evaluated for five major arable crops (winter wheat, maize, winter barley, spring barley, winter oilseed rape) plus ryegrass. Modelled annual estimates of crop yields and biomass, emissions of gases with warming potentials (nitrous oxide, methane, carbon) and SOC stocks in the topsoil (0-30 cm) were tabulated for all combinations considered. These simulated data series could be further analysed to evaluate inter-annual variations and their implications for climate resilience and combined with additional data to quantify nutrient use efficiency and undertake cost- benefit analysis, and to contribute to inter-regional comparisons of fertiliser management at broad scale.

Grazing decreases net ecosystem carbon exchange by decreasing shrub and semi-shrub biomass in a desert steppe.

Livestock grazing can strongly determine how grasslands function and their role in the carbon cycle. However, how ecosystem carbon exchange responds to grazing and the underlying mechanisms remain unclear. We measured ecosystem carbon fluxes to explore the changes in carbon exchange and their driving mechanisms under different grazing intensities (CK, control; HG, heavy grazing; LG, light grazing; MG, moderate grazing) based on a 16-year long-term grazing experimental platform in a desert steppe. We found that grazing intensity influenced aboveground biomass during the peak growing season, primarily by decreasing shrubs and semi-shrubs and perennial forbs. Furthermore, grazing decreased net ecosystem carbon exchange by decreasing aboveground biomass, especially the functional group of shrubs and semi-shrubs. At the same time, we found that belowground biomass and soil ammonium nitrogen were the driving factors of soil respiration in grazed systems. Our study indicates that shrubs and semi-shrubs are important factors in regulating ecosystem carbon exchange under grazing disturbance in the desert steppe, whereas belowground biomass and soil available nitrogen are important factors regulating soil respiration under grazing disturbance in the desert steppe; this results provide deeper insights for understanding how grazing moderates the relationships between soil nutrients, plant biomass, and ecosystem CO2 exchange, which provide a theoretical basis for further grazing management.

Moderate grazing increased carbon, nitrogen and phosphorus storage in plants and soil in the Eurasian meadow steppe ecosystem.

The effects of grazing on the cycling of carbon (C), nitrogen (N) and phosphorus (P) in grassland ecosystems are complex. Uncertainty still exists as regards the allocation of C, N and P storage amounts in grazed ecosystems in Inner Mongolia, situated at the eastern end of the Eurasian dryland. Based on the long-term cattle grazing experimental platform in the Hulun Buir meadow steppe of Inner Mongolia, a 3-year (2019-2021) field control experiment was conducted to assess how the grazing intensity influenced the quantities of C, N and P stored in canopy biomass, root, litter and soil compartments. We examined the relationships between the different pools and their regulatory pathways at the ecosystem level across six grazing intensities. In general, grazing increased the aboveground N and P contents but decreased the aboveground biomass C content and nutrient storage amounts in aboveground biomass, roots and litter. The grazing intensity of 0.34 AU ha-1 increased soil organic carbon, total nitrogen and total phosphorus storage amounts, with the soil accounting for 98 % of total reserves on average. Grazing affected soil pH， nutrient contents, above- and belowground biomass and soil environmental factors such as soil bulk density, which in turn affected C, N and P storage in the ecosystem according to the results of the structural equation model; therefore, grazing intensity can be an important factor regulating the input and output of nutrients in the ecosystem. In the future, for adaptive management of grasslands, moderate grazing could effectively increase C, N and P storage in meadow steppe ecosystems and ensure the nutrient balance and long-term sustainable development.

On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies.

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize varieties with different nitrogen efficiencies. These findings were consistent with the performance of yield, dry matter mass, and leaf nitrogen content and were also found highest under both medium and high nitrogen conditions in the double-high variety QL368. The correlations of dry matter quality, leaf nitrogen content, yield, and vegetation indices (NDVI, GNDVI, RVI, and GOSAVI) at the filling stage of different N-efficient maize varieties were all highly significant and positive. In this relationship, the best effect was found at the filling stages, with correlation coefficients reaching 0.772-0.942, 0.774-0.970, 0754-0.960, and 0.800-0.960. The results showed that the yield, dry matter weight, and leaf nitrogen content of maize varieties with different nitrogen efficiencies increased first and then stabilized with the increase in the nitrogen application level in different periods, and the highest nitrogen application level of maize yield should be between 270 and 360 kg/hm2. At the filling stage, canopy vegetation index of maize varieties with different nitrogen efficiencies was positively correlated with yield, dry matter weight, and leaf nitrogen content, especially GNDVI and GOSAVI on the leaf nitrogen content. It can be used as a means to predict its growth index.

Responses of crop productivity and reactive nitrogen losses to the application of animal manure to China's main crops: A meta-analysis.

The effective utilization of manure in cropland systems is essential to sustain yields and reduce reactive nitrogen (Nr) losses. However, there are still uncertainties regarding the substitution of mineral nitrogen (N) fertilizer with manure in terms of its effects on crop yield and Nr losses. We conducted a comprehensive meta-analysis of wheat, maize, and rice applications in China and discovered that substituting mineral N fertilizer with manure increased wheat and maize yields by 4.9 and 5.5 %, respectively, but decreased rice yield by 1.7 %. The increase of yield is larger at low N application and low mineral N substitution rates ((SR) ≤30 %) for silt soils, warm regions, and acidic soils. High SR (>70 %) decreased rice yield as well as the N use efficiency of wheat and maize. Substitution of mineral N fertilizer with manure resulted in lower NH3 volatilization for wheat (48.7 %), lower N2O and NH3 emissions, and N runoff for maize (12.8, 49.6, and 66.7 %, respectively), and lower total Nr losses for rice (11.3-26.5 %). The loss of Nr was significantly and negatively correlated with soil organic carbon content. The rate of N application, soil properties, and climate were critical factors influencing N2O and NH3 emissions and N leaching, whereas climate or soil properties were the dominant factors influencing response in N runoff. We concluded that in silt soils, warm regions, and neutral soils, a ≤ 50 % substitution of mineral N fertilizer with manure can sustain crop yields while mitigating Nr losses.

Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK.

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha-1) but reduced soil C (-4.32 t C ha-1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.

Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning.

BACKGROUND: The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning. METHODS: The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost). RESULTS: Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship. CONCLUSION: Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.

Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform.

Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model's output with: a) a semi-parametric conditional extreme model and b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, United Kingdom. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.

Quantifying the spatio-temporal drivers of planned vegetation restoration on ecosystem services at a regional scale.

Vegetation restoration often has a significant effect on the supply of an ecosystem service (ES). Assessment of this effect is crucial for informed decision-making in sustainable ecosystem management. In this respect, this study analyses three regulating, two provisioning, and a single cultural ES over a 30-year period (1985 to 2015, with 15 years pre-restoration and 15 years post-restoration) in the Loess Plateau, China, using data from a combination of modelling and statistical yearbooks. On applying a suite of standard statistical tools, results indicate: (1) regional scale restoration promotes the increase of vegetation cover as the coverage increased faster between 2000 and 2015 than between 1985 and 2000; (2) vegetation restoration changes the temporal trend of regulating ESs, and enhances the supply of provisioning and cultural ESs; (3) the 40 municipalities of the Loess Plateau can be divided into four ES categories where areas with poor ES delivery account for about 30% of the Loess Plateau; (4) vegetation restoration changes the interaction among ESs, resulting in synergistic relationships between provisioning and regulating ESs; (5) precipitation has a significant impact on regulating ESs, while population density is critical for provisioning and cultural ESs. This study demonstrates that ESs, their interactions and their groupings can change across both time and space following the implementation of a vegetation restoration programme, which makes understanding ES dynamics complicated. Recommendations are provided for improved and coherent ecosystem management.

Permanent dry soil layer a critical control on soil desiccation on China's Loess Plateau.

The wide spread of dry soil layers (DSL) in China's Loess Plateau region has negative effects on the ecosystem, including soil degradation and vegetation failure. To understand the temporal persistence of DSL, a ca. 860 km south-north transect was established and soil water content of the 0-5 m depth soil layer repeatedly measured for a period of four years. The results indicated that DSL varied with time and had a distribution area over 21.5-47.0% in the 860 km transect during the study period. The DSL could be divided into temporary and permanent types based on the length of period for which the soil remains dry. While temporary DSL is recoverable, permanent DSL (which existed in 47 out of 86 sites) was apparently unrecoverable as it persisted throughout the observation period. Permanent DSL was characterized by high temporal persistence, severe soil desiccation and thick dry layers; all of which suggested severe negative effect on the ecosystem. Non-climatic factors, rather than climate factors, contributed more to the formation of permanent DSL in the study area. Thus, it was suggested that policies and measures should be enacted to control especially permanent DSL formation in the region.

When multi-functional landscape meets Critical Zone science: advancing multi-disciplinary research for sustainable human well-being.

Environmental degradation has become one of the major obstacles to sustainable development and human well-being internationally. Scientific efforts are being made to understand the mechanism of environmental degradation and sustainability. Critical Zone (CZ) science and research on the multi-functional landscape are emerging fields in Earth science that can contribute to such scientific efforts. This paper reviews the progress, similarities and current status of these two scientific research fields, and identifies a number of opportunities for their synergistic integration through functional and multi-functional approaches, process-based monitoring, mechanistic analyses and dynamic modeling, global long-term and networked monitoring and systematic modeling supported by scaling and deep coupling. These approaches proposed in this paper have the potential to support sustainable human well-being by strengthening a functional orientation that consolidates multi-functional landscape research and CZ science. This is a key challenge for sustainable development and human well-being in the twenty-first century.

Simulation of Phosphorus Chemistry, Uptake and Utilisation by Winter Wheat.

The phosphorus (P) supply from soils is crucial to crop production. Given the complexity involved in P-cycling, a model that can simulate the major P-cycling processes and link with other nutrients and environmental factors, e.g., soil temperature and moisture, would be a useful tool. The aim of this study was to describe a process-based P module added to the SPACSYS (Soil Plant and Atmosphere Continuum System) model and to evaluate its predictive capability on the dynamics of P content in crops and the impact of soil P status on crop growth. A P-cycling module was developed and linked to other modules included in the SPACSYS model. We used a winter wheat (Triticum aestivum, cv Xi-19) field experiment at Rothamsted Research in Harpenden to calibrate and validate the model. Model performance statistics show that the model simulated aboveground dry matter, P accumulation and soil moisture dynamics reasonably well. Simulated dynamics of soil nitrate and ammonium were close to the observed data when P fertiliser was applied. However, there are large discrepancies in fields without P fertiliser. This study demonstrated that the SPACSYS model was able to investigate the interactions between carbon, nitrogen, P and water in a single process-based model after the tested P module was implemented.

Deep soil water storage varies with vegetation type and rainfall amount in the Loess Plateau of China.

Soil-water storage in a deep soil layer (SWSD), defined as the layer where soil water is not sensitive to daily evapotranspiration and regular rainfall events, functions as a soil reservoir in China's Loess Plateau (LP). We investigated spatial variations and factors that influence the SWSD in the 100-500 cm layers across the entire plateau. SWSD generally decreased from southeast to northwest following precipitation gradient, with a mean value of 587 mm. The spatial variation in the SWSD in grassland was the highest, followed by protection forests, production forests and cropland. Variation in the >550 mm rainfall zone was much lower than that in the <550 mm zone. The significant influencing variables explained 22.3-65.2% of the spatial variation in SWSD. The joint effect of local and climatic variables accounted for most of the explained spatial variation of SWSD for each vegetation type and the <450 mm rainfall zone. Spatial variation of SWSD, however, was dominantly controlled by the local variables in the 450-550 and the >550 mm rainfall zones. Therefore, regional models of SWSD for a specific vegetation need to incorporate climatic, soil and topographic variables, while for a rainfall zone, land use should not be ignored.