Substitution of manure for chemical fertilizer affects soil microbial community diversity, structure and function in greenhouse vegetable production systems

Soil microbial communities and enzyme activities together affect various ecosystem functions of soils. Fertilization, an important agricultural management practice, is known to modify soil microbial characteristics; however, inconsistent results have been reported. The aim of this research was to make a comparative study of the effects of different nitrogen (N) fertilizer rates and types (organic and inorganic) on soil physicochemical properties, enzyme activities and microbial attributes in a greenhouse vegetable production (GVP) system of Tianjin, China. Results showed that manure substitution of chemical fertilizer, especially at a higher substitution rate, improved soil physicochemical properties (higher soil organic C (SOC) and nutrient (available N and P) contents; lower bulk densities), promoted microbial growth (higher total phospholipid fatty acids and microbial biomass C contents) and activity (higher soil hydrolase activities). Manure application induced a higher fungi/bacteria ratio due to a lower response in bacterial than fungal growth. Also, manure application greatly increased bacterial stress indices, as well as microbial communities and functional diversity. The principal component analysis showed that the impact of manure on microbial communities and enzyme activities were more significant than those of chemical fertilizer. Furthermore, redundancy analysis indicated that SOC and total N strongly influenced the microbial composition, while SOC and ammonium-N strongly influenced the microbial activity. In conclusion, manure substitution of inorganic fertilizer, especially at a higher substitution rate, was more efficient for improving soil quality and biological functions.</p

BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas

BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas. View Full-Tex

Assessing the impact of bridge construction on the land use/cover and socio-economic indicator time series: A case study of Hangzhou Bay Bridge

Construction of transportation infrastructure is a vital step in boosting economic and societal opportunities and often results in land use changes. In this study, we focus on the land use dynamics of the urban agglomeration around Hangzhou Bay, where the Qiantang River flows into the East China Sea. The Hangzhou Bay Bridge crosses the bay since 2008. We used Interrupted Time Series Analysis (ITSA) to analyze the influence of the bridge on the land use and land cover (LULC) time series of the surrounding areas and on socio-economic indicators. We applied the Random Forest method to classify Landsat imagery from 2000 to 2017, thus enabling us to quantify LULC changes before and after the construction of the Hangzhou Bay Bridge. Google Earth Engine (GEE) was used for data acquisition, pre-processing, and classification. The results showed that during the period from 2000 to 2017, impervious surface areas expanded rapidly at the expense of agricultural land, and this transformation continued even more rapidly after 2008. ITSA showed that the driver behind the impervious surface area expansion switched from residential and industrial area growth in 2000–2008, to exclusively infrastructure area growth in 2008–2017. The construction of the bridge accelerated the expansion of impervious surface in the joint area of the bridge-connected cities of Ningbo and Jiaxing. With the Hangzhou Bay Bridge connection, various socio-economic factors, including tourism, GDP, tertiary industry, real estate investment, and highway freight, increased rapidly. The outcomes of this research could contribute to policymaking and impact assessments for sustainable urban development and land management. The methods used in this study are universal and therefore can also be used to assess the effect of any notable event that may impact LULC change

Diurnal variation of sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAV

Unmanned Aerial Vehicle (UAV)-based measurements allow studying sun-induced chlorophyll fluorescence (SIF) at the field scale and can potentially upscale results from ground to airborne/satellite level. The objective of this paper is to present the FluorSpec system providing SIF measurements at the field level onboard a UAV, and to evaluate the potential of this system for understanding diurnal SIF patterns for different arable crops. The core components of FluorSpec are a point spectrometer configured to measure in the O2 absorption bands at sub-nanometer resolution, bifurcated fibre optics to switch between the downwelling irradiance and upwelling radiance measurements, and a laser range finder allowing accurate atmospheric correction. The processing chain is explained and the capability of the novel Spectral Shape Assumption Fraunhofer Line Discrimination (SSA-FLD) method to retrieve SIF was tested. To test the reliability of FluorSpec diurnal SIF measurements, near-canopy diurnal SIF was monitored during the growing season over potato and sugar beet plants with a ground-based setup. The two crops exhibited a clear diurnal SIF pattern, which positively correlated with the photosynthetically active radiation (PAR). The divergence in diurnal patterns between SIF and PAR indicated that the crops might be suffering from heat stress. A significant correlation between SIF and the Photosystem II Quantum Yield was obtained. By mounting the FluorSpec on a UAV, SIF measurements were obtained over the same crops during a clear day. UAV-based SIF also exhibited a pronounced diurnal pattern similar to the ground-based measurements and it showed clear spatial variation within different crop fields. The obtained results demonstrate the ability of the FluorSpec system to reliably measure plant fluorescence at ground and field level, and the possibility of the UAV-based FluorSpec to bridge the scale gap between different levels of SIF observations

Space-time statistical analysis and modelling of nitrogen use efficiency indicators at provincial scale in China

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)

Organic amendment increases soil respiration in a greenhouse vegetable production system through decreasing soil organic carbon recalcitrance and increasing carbon-degrading microbial activity

Purpose: Recent works have shown that fertilization has an important influence on soil respiration (Rs); however, the underlying mechanisms involved in regulating Rs in greenhouse vegetable production (GVP) systems remain unclear.Materials and methods: Samples from six kinds of soils that were amended with different fertilization patterns (8 years) were incubated for 36 days to determine soil microbial community (PLFA), enzyme activities, soil organic C (SOC) quality (13C NMR), and Rs in a GVP system in Tianjin, China. Treatments included 100% chemical N (CN) and different substitution rates of CN with manure-N and/or straw-N.Results and discussion: Compared with 100%CN treatment, organic amendment strongly promoted microbial (e.g., fungi, bacteria, and actinomycetes) growth, enhanced the majority of C-degrading enzyme activities, affected SOC chemical composition with increasing O-alkyl (labile) C and reducing aromatic (stable) C, decreased SOC recalcitrance, and enhanced Rs. Redundancy analysis indicated that variations in microbial community and SOC chemical composition were closely linked to light fraction organic C (LFC) and readily oxidizable C (ROC), respectively. Further, structural equation modeling and linear regression analysis revealed that SOC recalcitrance (negative effects) and C-degrading enzyme activities (positive effects) together mediate Rs rates; meanwhile, microbial community can indirect affect Rs rates through altering C-degrading enzyme activities. Conclusions: Agricultural soil abiotic properties (mainly labile C fractions, i.e., LFC and ROC) are altered by adding organic resources (i.e., manure and straw), the changes of which can promote soil microbial growth, enhance C-degrading microbial activity, and reduce SOC recalcitrance, and in turn accelerate Rs in GVP systems

Space-time statistical analysis and modelling of nitrogen use efficiency indicators at provincial scale in China

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)

Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops

Sun-induced chlorophyll fluorescence (SIF) is a promising proxy of the dynamic photosynthetic process. Unmanned Aerial Vehicles (UAVs) are flexible and cost-effective for acquiring SIF data at high temporal and spatial resolution. The UAV-based point spectrometer FluorSpec was designed to measure SIF within agricultural fields. To correctly understand SIF values and further photosynthetic research, the ability of the UAV-based FluorSpec to provide reliable SIF information within agricultural fields needs evaluation. In this paper, the UAV-based FluorSpec was compared with the high-performance airborne imaging spectrometer HyPlant using diurnal far-red SIF measurements over different crop types (i.e. two varieties of winter wheat, two varieties of spring barley, bean, and maize), which were acquired by almost simultaneous airborne and UAV flights during a clear sky day in 2019. After improving the footprint geolocation of FluorSpec measurements using concurrent red-green-blue (RGB) images, we compared the FluorSpec and HyPlant SIF measurements, their diurnal developments, and spatial distributions for different crop types. The results from both systems show consistent, clear diurnal patterns that are positively correlated with photosynthetically active radiation (PAR) over most crop types. Similar SIF spatial patterns were shown within crop fields as well. UAV-based FluorSpec SIF showed a good linear correlation with HyPlant SIF with an R2 up to 0.76. The good agreement confirms that the UAV-based FluorSpec system is able to measure meaningful SIF values at the field scale and thus stimulates SIF applications in agriculture. The systematic errors up to 0.3 mW m−2 sr−1 nm−1 from the linear regression between the two systems indicate that the UAV-based FluorSpec system should be improved by considering the main sources of uncertainty discussed in this paper. Future studies with dedicated experiments are recommended to assess the systematic uncertainties of UAV-based FluorSpec derived SIF information

Diurnal UAV-based sun-induced chlorophyll fluorescence over potato and sugar beet fields

UAV-based sun-induced chlorophyll fluorescence was acquired over potato and sugar beet fields during clear sky conditions on August 2. 2018.  Four flights following a diurnal cycle were carried out at 9:45, 11:38, 14:15, and 16:28.  A Parrot Sequoia + collected multispectral measurements over the two crop fields around 11:00 on the same day.  </p