Fertilization regulates global thresholds in soil bacteria.

Global patterns in soil microbiomes are driven by non-linear environmental thresholds. Fertilization is known to shape the soil microbiome of terrestrial ecosystems worldwide. Yet, whether fertilization influences global thresholds in soil microbiomes remains virtually unknown. Here, utilizing optimized machine learning models with Shapley additive explanations on a dataset of 10,907 soil samples from 24 countries, we discovered that the microbial community response to fertilization is highly dependent on environmental contexts. Furthermore, the interactions among nitrogen (N) addition, pH, and mean annual temperature contribute to non-linear patterns in soil bacterial diversity. Specifically, we observed positive responses within a soil pH range of 5.2-6.6, with the influence of higher temperature (>15°C) on bacterial diversity being positive within this pH range but reversed in more acidic or alkaline soils. Additionally, we revealed the threshold effect of soil organic carbon and total nitrogen, demonstrating how temperature and N addition amount interacted with microbial communities within specific edaphic concentration ranges. Our findings underscore how complex environmental interactions control soil bacterial diversity under fertilization.

Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?

Agricultural soils play a dual role in regulating the Earth's climate by releasing or sequestering carbon dioxide (CO2 ) in soil organic carbon (SOC) and emitting non-CO2 greenhouse gases (GHGs) such as nitrous oxide (N2 O) and methane (CH4 ). To understand how agricultural soils can play a role in climate solutions requires a comprehensive assessment of net soil GHG balance (i.e., sum of SOC-sequestered CO2 and non-CO2 GHG emissions) and the underlying controls. Herein, we used a model-data integration approach to understand and quantify how natural and anthropogenic factors have affected the magnitude and spatiotemporal variations of the net soil GHG balance in U.S. croplands during 1960-2018. Specifically, we used the dynamic land ecosystem model for regional simulations and used field observations of SOC sequestration rates and N2 O and CH4 emissions to calibrate, validate, and corroborate model simulations. Results show that U.S. agricultural soils sequestered 13.2 ± 1.16 $$ 13.2\pm 1.16 $$ Tg CO2 -C year-1 in SOC (at a depth of 3.5 m) during 1960-2018 and emitted 0.39 ± 0.02 $$ 0.39\pm 0.02 $$ Tg N2 O-N year-1 and 0.21 ± 0.01 $$ 0.21\pm 0.01 $$ Tg CH4 -C year-1 , respectively. Based on the GWP100 metric (global warming potential on a 100-year time horizon), the estimated national net GHG emission rate from agricultural soils was 122.3 ± 11.46 $$ 122.3\pm 11.46 $$ Tg CO2 -eq year-1 , with the largest contribution from N2 O emissions. The sequestered SOC offset ~28% of the climate-warming effects resulting from non-CO2 GHG emissions, and this offsetting effect increased over time. Increased nitrogen fertilizer use was the dominant factor contributing to the increase in net GHG emissions during 1960-2018, explaining ~47% of total changes. In contrast, reduced cropland area, the adoption of agricultural conservation practices (e.g., reduced tillage), and rising atmospheric CO2 levels attenuated net GHG emissions from U.S. croplands. Improving management practices to mitigate N2 O emissions represents the biggest opportunity for achieving net-zero emissions in U.S. croplands. Our study highlights the importance of concurrently quantifying SOC-sequestered CO2 and non-CO2 GHG emissions for developing effective agricultural climate change mitigation measures.

Mollisol Erosion-Driven Efflux of Energetic Organic Carbon and Microflora Increases Greenhouse Gas Emissions from Cold-Region Rivers.

Massive soil erosion occurs in the world's Mollisol regions due to land use change and climate warming. The migration of Mollisol organic matter to river systems and subsequent changes in carbon biogeochemical flow and greenhouse gas fluxes are of global importance but little understood. By employing comparative mesocosm experiments simulating varying erosion intensity in Mollisol regions of northeastern China, this research highlights that erosion-driven export and biomineralization of terrestrial organic matter facilitates CO2 and CH4 emission from receiving rivers. Stronger Mollisol erosion, as represented by a higher soil-to-water ratio in suspensions, increased CO2 efflux, particularly for the paddy Mollisols. This is mechanistically attributable to increased bioavailability of soluble organic carbon in river water that is sourced back to destabilized organic matter, especially from the cultivated Mollisols. Concurrent changes in microbial community structure have enhanced both aerobic and anaerobic processes as reflected by the coemission of CO2 and CH4. Higher greenhouse gas effluxes from paddy Mollisol suspensions suggest that agricultural land use by supplying more nitrogen-containing, higher-free-energy organic components may have enhanced microbial respiration. These new findings highlight that Mollisol erosion is a hidden significant contributor to greenhouse gas emissions from river water, given that the world's four major Mollisol belts are all experiencing intensive cultivation.

The effect of environmental regulation and green subsidies on agricultural low-carbon production behavior: A survey of new agricultural management entities in Guangdong Province.

Agricultural low-carbon production emerges as a pivotal function for achieving sustainable green development. However, there remains insufficient empirical evidence regarding the effect of environmental regulations and green subsidies upon the low-carbon production behavior of new agricultural entities. In this study, a questionnaire survey was administered to 268 respondents representing new agricultural entities in Guangdong Province, P.R.C. Subsequently, a structural equation model had been employed for validation analysis. This study's findings demonstrate that in general, environmental regulations positively and significantly affect the behavior of agricultural low-carbon production. Conversely, the influence of green subsidies is not statistically significant. In addition, differences are observed across different sectors, with environmental regulations significantly affecting low-carbon production behavior in the plantation sector, but not in the livestock sector. Conversely, green subsidies significantly impact low-carbon production behavior in the livestock sector, but not in the plantation sector. These findings highlight the promotional role of government-enforced environmental regulations and green subsidies in fostering low-carbon agricultural practices. Therefore, new agricultural entities should strive to augment green production technology capacities to realize sustainable green development.

Using stable isotopes to inform water resource management in forested and agricultural ecosystems.

Present and future climatic trends are expected to markedly alter water fluxes and stores in the hydrologic cycle. In addition, water demand continues to grow due to increased human use and a growing population. Sustainably managing water resources requires a thorough understanding of water storage and flow in natural, agricultural, and urban ecosystems. Measurements of stable isotopes of water (hydrogen and oxygen) in the water cycle (atmosphere, soils, plants, surface water, and groundwater) can provide information on the transport pathways, sourcing, dynamics, ages, and storage pools of water that is difficult to obtain with other techniques. However, the potential of these techniques for practical questions has not been fully exploited yet. Here, we outline the benefits and limitations of potential applications of stable isotope methods useful to water managers, farmers, and other stakeholders. We also describe several case studies demonstrating how stable isotopes of water can support water management decision-making. Finally, we propose a workflow that guides users through a sequence of decisions required to apply stable isotope methods to examples of water management issues. We call for ongoing dialogue and a stronger connection between water management stakeholders and water stable isotope practitioners to identify the most pressing issues and develop best-practice guidelines to apply these techniques.

A spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination in sub-watersheds.

Best management practices (BMPs) have been extensively employed in effective watershed management for non-point source pollution. The weights of objective functions and the restrictive conditions of combined BMPs are the vital requirements for BMPs allocation. Therefore, it is more beneficial to explore that a spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination. Here is the novel framework based on Soil and Water Assessment Tool (SWAT) and the Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ), which considers multiple objectives in deriving watershed-scale pollution control practices by considering BMPs cost and combined reduction rates of total nitrogen (TN) and total phosphorus (TP). The framework also integrates combined Entropy Weight method (EWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the weights of TN and TP, and considers the attributes of the sub-basin itself, which is more local suitability. Four categories of BMPs, tillage management, nutrient management, vegetative filter strips, and landscape management, were evaluated in the Jing River Basin (JRB) and resulted in reduction rates of 9.77%, 10.53%, 16.40%, and 14.27% averagely, respectively. BMP allocation schemes, derived from multi-objective optimization, are stratified into three financial scenarios. Low-cost scenario, costing up to 2 billion RMB, primarily targets the grain for green program in 28.81% of sub-basins. Medium-cost scenario, between 2 and 6 billion RMB, predominantly utilizes the grain for green in areas with a slope greater than 15°, accounting for 20.00% of sub-basins. High-cost scenario exceeds 6 billion RMB, mainly due to the implementation of multiple combination measures. The three configuration scenarios can provide decision-makers with a trade-off between measure costs and reduction efficiency. Overall, the innovative framework not only facilitates cost-effective implementation but provides a beneficial methodology for selecting cost-effective conservation practices in other regions.

Validating Farmland Biodiversity Life Cycle Assessment at the Landscape Scale.

Life cycle assessment (LCA) aims at providing standardized evaluations of processes involving resource use, human health, and environmental consequences. Currently, spatial dependencies are most often neglected, though they are essential for impact categories like biodiversity. The "Swiss Agricultural Life Cycle Assessment for Biodiversity (SALCA-BD)" evaluates the impact of agricultural field management on 11 indicator species groups. We tested if its performance can be improved by accounting for the spatial context of the individual fields. We used high-resolution bird/butterfly point observations in two agricultural regions in Switzerland and built linear mixed models to compare SALCA-BD scores to the observed species richness at the field/landscape scale. We calculated a set of landscape metrics, tested their relationship with the landscape-model prediction errors, and then added all significant metrics as additional predictors to the landscape models. Our results show that field-scale SALCA-BD scores were significantly related to the observed field-scale richness for both indicator groups. However, the performance decreased when aggregated to the landscape scale, with high variability between regions. Adding specific landscape metrics improved the landscape model for birds but not for butterflies. Integrating the spatial context to LCA biodiversity assessments could provide moderate benefits, while its usefulness depends on the conditions of the respective assessment.

Effects of conservation tillage on soil enzyme activities of global cultivated land: A meta-analysis.

The negative impacts of conventional agriculture and the imperative to adopt conservation tillage garnered significant attention. However, the effects of conservation tillage on soil enzyme activities still lack comprehensive cognition. Here, we collected 14,308 pairwise observations from 369 publications worldwide to systematically evaluate the effects of different conservation tillage practices (reduced tillage (T), reduced tillage with straw return (TS), reduced tillage with straw mulch return (TSO), no-tillage (NT), no-tillage with straw return (NTS), and no-tillage with straw mulch return (NTSO)) on the activities of 35 enzymes in soil. The results showed that: (1) the effect of conservation tillage on soil enzyme activity varied by enzyme type, except for peroxidase (-12.34%), which showed an overall significant positive effect (10.28-89.76%); (2) the NTS and TS demonstrated strong potential to improve soil enzyme activities by increasing a wide variety of soil enzyme activities (12-15) and efficacy (9.76-75.56%) than other conservation tillage (8.60-68.68%); (3) in addition, the effect of conservation tillage on soil enzyme activity was regulated by soil depth, crop type, years of conservation tillage, climate (mean annual precipitation and temperature), and soil physicochemical properties (e.g., pH, bulk density, electrical conductivity, organic matter, ammonium nitrogen, total phosphorus, available phosphorus, total potassium, available potassium, etc.). Overall, our quantitative analysis clearly suggests that conservation tillage is an effective measure for improving soil enzyme activity on global croplands, where combination of reduced tillage or no-till with straw return are considered to have great potential and promise. The results contribute to better comprehend the effects of conservation tillage on soil activity and provide a valuable insight for agricultural management.

Evaluating the impacts of sustainable land management practices on water quality in an agricultural catchment in Lower Austria using SWAT.

Managing agricultural watersheds in an environmentally friendly manner necessitate the strategic implementation of well-targeted sustainable land management (SLM) practices that limit soil and nonpoint source pollution losses and translocation. Watershed-scale SLM-scenario modeling has the potential to identify efficient and effective management strategies from the field to the integrated landscape level. In a case study targeting a 66-hectare watershed in Petzenkirchen, Lower Austria, the Soil and Water Assessment Tool (SWAT) was utilized to evaluate a variety of locally adoptable SLM practices. SWAT was calibrated and validated (monthly) at the catchment outlet for flow, sediment, nitrate-nitrogen (NO3-N), ammonium nitrogen (NH4-N), and mineralized phosphorus (PO4-P) using SWATplusR. Considering the locally existing agricultural practices and socioeconomic and environmental factors of the research area, four conservation practices were evaluated: baseline scenario, contour farming (CF), winter cover crops (CC), and a combination of no-till and cover crops (NT + CC). The NT + CC SLM practice was found to be the most effective soil conservation practice in reducing soil loss by around 80%, whereas CF obtained the best results for decreasing the nutrient loads of NO3-N and PO4-P by 11% and 35%, respectively. The findings of this study imply that the setup SWAT model can serve the context-specific performance assessment and eventual promotion of SLM interventions that mitigate on-site land degradation and the consequential off-site environmental pollution resulting from agricultural nonpoint sources.

Deceleration of Cropland-N2O Emissions in China and Future Mitigation Potentials.

Agricultural soils are the largest anthropogenic emission source of nitrous oxide (N2O). National agricultural policies have been implemented to increase crop yield and reduce nitrogen (N) losses to the environment. However, it is difficult to effectively quantify crop-specific and regional N2O mitigation priorities driven by policies, due to lack of long-term, high-resolution crop-specific activity data, and oversimplified models. Here, we quantify the spatiotemporal changes and key drivers of crop-specific cropland-N2O emissions from China between 1980 and 2017, and future N2O mitigation potentials, using a linear mixed-effect model and survey-based data set of agricultural management measures. Cropland-N2O emissions from China tripled from 102.5 to 315.0 Gg N yr-1 between 1980 and 2017, and decelerated since 1998 mainly driven by country-wide deceleration and decrease in N rate and the changes in sowing structure. About 63% of N2O emissions could be reduced in 2050, primarily in the North China Plain and Northeast China Plain; 83% of which is from the production of maize (33%), vegetables (27%), and fruits (23%). The deceleration of N2O emissions highlights that policy interventions and agronomy practices (i.e., optimizing N rate and sowing structure) are potential pathways for further ambitious N2O mitigation in China and other developing countries.

Rapid Detection of Fusarium oxysporum Using Insulated Isothermal PCR and a Rapid, Simple DNA Preparation Protocol.

We developed an insulated isothermal PCR (iiPCR) method for the efficient and rapid detection of Fusarium oxysporum (Fo), which is a fungus that infects various hosts and causes severe crop losses. The Fo iiPCR method was sensitive enough to detect up to 100 copies of standard DNA template and 10 fg of Fo genomic DNA. In addition, it could directly detect 1 pg of mycelium and 10 spores of Fo without DNA extraction. Our study compared the performance of Fo iiPCR to that of three published in planta molecular detection methods-conventional PCR, SYBR green-based real-time PCR, and hydrolysis probe-based real-time PCR-in field detection of Fo. All diseased field samples yielded positive detection results with high reproducibility when subjected to an Fo iiPCR test combined with a rapid DNA extraction protocol compared to Fo iiPCR with an automated magnetic bead-based DNA extraction protocol. Intraday and interday assays were performed to ensure the stability of this new rapid detection method. The results of detection of Fo in diseased banana pseudostem samples demonstrated that this new rapid detection method was suitable for field diagnosis of Fusarium wilt and had high F1 scores for detection (the harmonic mean of precision and recall of detection) for all asymptomatic and symptomatic Fo-infected banana samples. In addition, banana samples at four growth stages (seedling, vegetative, flowering and fruiting, and harvesting) with mild symptoms also showed positive detection results. These results indicate that this new rapid detection method is a potentially efficient procedure for on-site detection of Fo.

Decoupling between ammonia emission and crop production in China due to policy interventions.

Cropland ammonia (NH3 ) emission is a critical driver triggering haze pollution. Many agricultural policies were enforced in past four decades to improve nitrogen (N) use efficiency while maintaining crop yield. Inadvertent reductions of NH3 emissions, which may be induced by such policies, are not well evaluated. Here, we quantify the China's cropland-NH3 emission change from 1980 to 2050 and its response to policy interventions, using a data-driven model and a survey-based dataset of the fertilization scheme. Cropland-NH3 emission in China doubled from 1.93 to 4.02 Tg NH3 -N in period 1980-1996, and then decreased to 3.50 Tg NH3 -N in 2017. The prevalence of four agricultural policies may avoid ~3.0 Tg NH3 -N in 2017, mainly located in highly fertilized areas. Optimization of fertilizer management and food consumption could mitigate three-quarters of NH3 emission in 2050 and lower NH3 emission intensity (emission divided by crop production) close to the European Union and the United States. Our findings provide an evidence on the decoupling of cropland-NH3 from crop production in China and suggest the need to achieve cropland-NH3  mitigation while sustaining crop yields in other developing economies.

Achievable agricultural soil carbon sequestration across Europe from country-specific estimates.

The role of soils in the global carbon cycle and in reducing GHG emissions from agriculture has been increasingly acknowledged. The '4 per 1000' (4p1000) initiative has become a prominent action plan for climate change mitigation and achieve food security through an annual increase in soil organic carbon (SOC) stocks by 0.4%, (i.e. 4‰ per year). However, the feasibility of the 4p1000 scenario and, more generally, the capacity of individual countries to implement soil carbon sequestration (SCS) measures remain highly uncertain. Here, we evaluated country-specific SCS potentials of agricultural land for 24 countries in Europe. Based on a detailed survey of available literature, we estimate that between 0.1% and 27% of the agricultural greenhouse gas (GHG) emissions can potentially be compensated by SCS annually within the next decades. Measures varied widely across countries, indicating differences in country-specific environmental conditions and agricultural practices. None of the countries' SCS potential reached the aspirational goal of the 4p1000 initiative, suggesting that in order to achieve this goal, a wider range of measures and implementation pathways need to be explored. Yet, SCS potentials exceeded those from previous pan-European modelling scenarios, underpinning the general need to include national/regional knowledge and expertise to improve estimates of SCS potentials. The complexity of the chosen SCS measurement approaches between countries ranked from tier 1 to tier 3 and included the effect of different controlling factors, suggesting that methodological improvements and standardization of SCS accounting are urgently required. Standardization should include the assessment of key controlling factors such as realistic areas, technical and practical feasibility, trade-offs with other GHG and climate change. Our analysis suggests that country-specific knowledge and SCS estimates together with improved data sharing and harmonization are crucial to better quantify the role of soils in offsetting anthropogenic GHG emissions at global level.

Effects of Pesticides on the Diversity of Endophytic Fungi in Tea Plants.

We examined the effects of agrochemicals on the endophytic fungal community associated with tea plants. Endophytic fungi were isolated from four different tea plant tissues (bark, xylem, old leaves, new leaves) collected from pesticide-treated and untreated plots. In pesticide-treated plot, the acaricides, fungicides, and insecticides are typically applied 3 times each year. The infection rate was slightly lower in the pesticide-treated plot, but the difference between plots was not statistically significant. Colletotrichum camelliae, Phyllosticta capitalensis, and Pleosporales sp. were common endophytes in both plots. Among a total of 41 fungal species, only 21 were considered common endophytes. Colletotrichum pseudomajus was the predominant endophyte in the bark tissue in the untreated plot, whereas C. camelliae was predominant in the pesticide-treated plot. Paraphaeosphaeria neglecta and Phoma bellendis were predominant in the xylem tissues of samples from the untreated and treated plots, respectively. Colletotrichum camelliae was the most commonly found species in leaf tissues in both plots, but the colonization frequency was significantly lower in the pesticide-treated plot. Species richness was not affected by pesticide treatment. The community structure of endophytic fungi in stem tissues (bark and xylem) differed significantly between plots, but leaf tissue endophytic fungal community structure was not significantly influenced by pesticide treatment.

Agricultural practices modulate the bacterial communities, and nitrogen cycling bacterial guild in rhizosphere: field experiment with soybean.

BACKGROUND: Modern agricultural management approaches are often dependent on the application of chemicals, resulting in adverse impacts on human and environmental health. Therefore, for sustainable agriculture, there is a need to implement integrated agriculture practices that can maintain natural soil microbiome and enhance crop production. Various agricultural approaches influence crop production by impacting the functional bacterial community entailed in biogeochemical cycles, for example, nitrogen (N) cycle. This study aimed to assess the rhizospheric N cycling community of soybean under three agricultural practices, namely, conservation agriculture (CA), conventional treatment (CT), and organic agriculture (OA) for two consecutive years (2017 and 2018). RESULTS: A field experiment was designed under soybean-wheat cropping system employing CA, CT, and OA modules that included different practices of tillage, crop bedding pattern, crop residue retention, and nutrient application. Assessment of bacterial communities contributing to N transformation was performed with quantitative polymerase chain reaction (qPCR) of important markers (nifH, amoA, narG, and nirK). CONCLUSION: Results concluded that the practice of conservation agriculture comprising of raised bed, zero tillage, crop residue retention, and application of NPK (nitrogen, phosphorus, potassium) nutrients favorably affected the plant attributes and the abundance of N cycling bacterial community over the two consecutive years. The outcome revealed the mechanistic principle behind enhanced plant growth under conservation agriculture, and opened up the possibility of regulating the N cycling bacterial community to develop sustainable and productive agro-ecosystems. © 2020 Society of Chemical Industry.

Stemflow generation as influenced by sugarcane canopy development.

Rainfall is generally partitioned into throughfall, stemflow, and interception in ecosystems. Stemflow variability can affect the hydrology, ecology, and soil chemistry patterns. However, the influence of canopy structure and rainfall characteristics on stemflow production in sugarcane plantations which are important for renewable energy production remain poorly understood. By using funnels attached to the sugarcane stems, the present study determined the stemflow amount during the period of sugarcane growth and its relationship with plant development. Approximately, 14% of gross rainfall reached the soil as stemflow, and the funneling ratios was 60. In general, it was observed a positive relationship between stemflow rates with both leaf area index and plant height. This was attributed to an increasing number of acute branching angles of the sugarcane leaves as well as high stem tillering and density. However, at the end of growth cycle, stemflow rate was lower than in previous periods which can be attributed to changes in sugarcane canopy such as stems inclination and lodging, reducing the effectiveness of water conveyance along the stem. Our study showed the need to include stemflow to better understand the hydrology of sugarcane plantations.

Boundary line models for soil nutrient concentrations and wheat yield in national-scale datasets.

In boundary line analysis a biological response (e.g., crop yield) is assumed to be a function of a variable (e.g., soil nutrient concentration), which limits the response in only some subset of observations because other limiting factors also apply. The response function is therefore expressed by an upper boundary of the plot of the response against the variable. This model has been used in various branches of soil science. In this paper we apply it to the analysis of some large datasets, originating from commercial farms in England and Wales, on the recorded yield of wheat and measured concentrations of soil nutrients in within-field soil management zones. We considered boundary line models for the effects of potassium (K), phosphorus (P) and magnesium (Mg) on yield, comparing the model with a simple bivariate normal distribution or a bivariate normal censored at a constant maximum yield. We were able to show, using likelihood-based methods, that the boundary line model was preferable in most cases. The boundary line model suggested that the standard RB209 soil nutrient index values (Agriculture and Horticulture Development Board, nutrient management guide (RB209), 2017) are robust and apply at the within-field scale. However, there was evidence that wheat yield could respond to additional Mg at concentrations above index 0, contrary to RB209 guidelines. Furthermore, there was evidence that the boundary line model for yield and P differs between soils at different pH and depth intervals, suggesting that shallow soils with larger pH require a larger target P index than others. HIGHLIGHTS: Boundary line analysis is one way to examine how soil variables influence crop yield in large datasets.We showed that boundary line models could be applied to large datasets on soil nutrients and crop yield.The resulting models are consistent with current practice for P and K, but not for Mg.Models suggest that more refined recommendations for P requirement could be based on soil pH and depth.

Modeling the artificial lake-surface area change in arid agro-ecosystem: A case study in the newly reclaimed area, Egypt.

Land reclamation is critically required to overcome the environmental and anthropogenic challenges in arid lands. The Western Nile Delta region, Egypt, is experiencing rapid reclamation processes for agriculture expansion. West Nubaria (781.92 km2) is one of the newly reclaimed areas in the Western Nile Delta. Due to extensive agricultural practices and poor management, an artificial saline lake formed in this area. Two primary goals of this research; 1) monitoring the annual change in the lake surface area between 2013 and 2017. 2) Predicting the areal extent of the lake surface in 2020, 2030, and 2040 based on two management scenarios. The maximum likelihood classifier (MLC) was applied to distinguish the LULC classes in 2017. Additionally, the annual modified normalized difference water index (MNDWI) calculated between 2013 and 2017. Then, the land change modeler (LCM) was utilized to predict the 2017 free water area based on the resulted MNDWI maps of 2013 and 2016 using two scenarios. With the high agreement between the actual and predicting free water area of 2017 (Kappa index = 0.93), the LCM was applied to predict the future surface water expansion in 2020, 2030, and 2040. Three land use/land cover (LULC) distinguished in 2017; agricultural land, uncultivated land, and free water class based on MLC. The MNDWI results reveal that there was an increase in the surface water area from 593 to 883 ha between 2013 and 2017, respectively. The LCM results indicate that expected increases in the surface water areas of 1068, 1711, and 2267 ha in 2020, 2030 and 2040, respectively (scenario 1) and 1065, 1726, and 2343 ha in the respective dates (scenario 2). These extend will exist over the agricultural and uncultivated lands surrounding the lake causing land degradation. Two solutions were suggested to combat the waterlogging and land degradation in this area by evacuating the artificial saline lake.

Effects of Agricultural Management on Rhizosphere Microbial Structure and Function in Processing Tomato Plants.

Agricultural management practices affect bulk soil microbial communities and the functions they carry out, but it remains unclear how these effects extend to the rhizosphere in different agroecosystem contexts. Given close linkages between rhizosphere processes and plant nutrition and productivity, understanding how management practices impact this critical zone is of great importance to optimize plant-soil interactions for agricultural sustainability. A comparison of six paired conventional-organic processing tomato farms was conducted to investigate relationships between management, soil physicochemical parameters, and rhizosphere microbial community composition and functions. Organically managed fields were higher in soil total N and NO3-N, total and labile C, plant Ca, S, and Cu, and other essential nutrients, while soil pH was higher in conventionally managed fields. Differential abundance, indicator species, and random forest analyses of rhizosphere communities revealed compositional differences between organic and conventional systems and identified management-specific microbial taxa. Phylogeny-based trait prediction showed that these differences translated into more abundant pathogenesis-related gene functions in conventional systems. Structural equation modeling revealed a greater effect of soil biological communities than physicochemical parameters on plant outcomes. These results highlight the importance of rhizosphere-specific studies, as plant selection likely interacts with management in regulating microbial communities and functions that impact agricultural productivity.IMPORTANCE Agriculture relies, in part, on close linkages between plants and the microorganisms that live in association with plant roots. These rhizosphere bacteria and fungi are distinct from microbial communities found in the rest of the soil and are even more important to plant nutrient uptake and health. Evidence from field studies shows that agricultural management practices such as fertilization and tillage shape microbial communities in bulk soil, but little is known about how these practices affect the rhizosphere. We investigated how agricultural management affects plant-soil-microbe interactions by comparing soil physical and chemical properties, plant nutrients, and rhizosphere microbial communities from paired fields under organic and conventional management. Our results show that human management effects extend even to microorganisms living in close association with plant roots and highlight the importance of these bacteria and fungi to crop nutrition and productivity.

Weakened growth of cropland-N2 O emissions in China associated with nationwide policy interventions.

China has experienced rapid agricultural development over recent decades, accompanied by increased fertilizer consumption in croplands; yet, the trend and drivers of the associated nitrous oxide (N2 O) emissions remain uncertain. The primary sources of this uncertainty are the coarse spatial variation of activity data and the incomplete model representation of N2 O emissions in response to agricultural management. Here, we provide new data-driven estimates of cropland-N2 O emissions across China in 1990-2014, compiled using a global cropland-N2 O flux observation dataset, nationwide survey-based reconstruction of N-fertilization and irrigation, and an updated nonlinear model. In addition, we have evaluated the drivers behind changing cropland-N2 O patterns using an index decomposition analysis approach. We find that China's annual cropland-N2 O emissions increased on average by 11.2 Gg N/year2 (p < .001) from 1990 to 2003, after which emissions plateaued until 2014 (2.8 Gg N/year2 , p = .02), consistent with the output from an ensemble of process-based terrestrial biosphere models. The slowdown of the increase in cropland-N2 O emissions after 2003 was pervasive across two thirds of China's sowing areas. This change was mainly driven by the nationwide reduction in N-fertilizer applied per area, partially due to the prevalence of nationwide technological adoptions. This reduction has almost offset the N2 O emissions induced by policy-driven expansion of sowing areas, particularly in the Northeast Plain and the lower Yangtze River Basin. Our results underline the importance of high-resolution activity data and adoption of nonlinear model of N2 O emission for capturing cropland-N2 O emission changes. Improving the representation of policy interventions is also recommended for future projections.