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Soil nutrients vary greatly and information regarding their spatial-temporal variability is critical for achieving site-specific nutrient management. However, the problem is that the high cost of soil sampling and laboratory analysis limits the number of samples current research budgets can afford. Besides, large sampling intervals sometimes fail to capture the variability of typical soil properties. Consequently, determining a reasonable yet feasible sampling interval to characterize the spatial variation of soil properties is greatly needed to delineate soil nutrient management zones (MZs). For this purpose, this study used four sampling intervals, 25 m × 25 m, 50 m × 50 m, 75 m × 75 m, and 100 m × 100 m by resampling the raw data acquired from seven adjacent fields located in Quzhou County to estimate the optimal design of soil sampling. Five soil properties (pH, electrical conductivity, extractable phosphorus, extractable potassium, and soil organic matter) were selected to delineate MZs using principal component analysis and fuzzy c-means clustering algorithm. The results indicated that indigenous P and K varied significantly, with the coefficients of variation of 34.33% and 24.01 %, respectively. The findings suggested that the optimal soil sampling interval was 50 m to accurately delineate MZs in medium- and small-scale farmlands. Besides, findings also demonstrated that site-specific P and K management could save approximately 21 kg P ha−1 and 30 kg K ha−1 as compared to conventional farming practices while ensuring target yield. The results can help make precision P and K management decisions in medium- and small-scale farming systems.

Ag and Co co-doped La1-xAgxMn1-yCoyO3-δ perovskites were prepared by the citric acid sol–gel method. The maximum solubility of Ag+ in LaMnO3 decreased with the increase of Co doped in B-site, which lead to the exsolution of excess Ag+ from A-site of perovskite. The exsolution progress is more favorable to the formation of uniform and stable Ag nanoparticles on the perovskite substrate. The non-thermal plasma (NTP) assisted La1-xAgxMn1-yCoyO3-δ for soot catalytic combustion had been investigated under diesel exhaust gas conditions. When La1-xAgxMn1-yCoyO3-δ was utilized as the catalyst without NTP, the soot could hardly be oxidized at 200 °C. However, when La1-xAgxMn1-yCoyO3-δ was combined with NTP, the efficiency of soot removal was dramatically improved. The NTP-La0.5Ag0.5Mn0.8Co0.2O3-δ hybrid had the highest soot removal efficiency and CO2 selectivity. The combination of NTP and La1-xAgxMn1-yCoyO3-δ could form a synergistic catalytic effect, which significantly promoted the combustion of soot at low temperature. The results of characterization and experiments indicated that the synergistic catalytic performance depended on the coordination of short-living species in plasma, adsorbed oxygen and Ag nanoparticles on surface of La1-xAgxMn1-yCoyO3-δ. Based on these results, the reaction mechanism of plasma-catalyst hybrid for soot catalytic combustion was proposed.

In the development of internet technology innovation, the quantity of big data information continues to rise, showing the characteristics of dynamic, heterogeneous, massive, and so on. How to explore valuable and potential knowledge from the network system is the main topic of research and scholars. Based on the understanding of web data extraction and web mining technology, this paper makes a systematic study of web text mining, proposes the most commonly used hidden Markov model in probability distribution, and constructs the corresponding text mining method. The hidden Markov model is a statistical model, which is mainly used to represent a Markov process with unknown parameters. The difficulty in practical application lies in how to define the hidden parameters of the process from observable parameters and then use these parameters to conduct in-depth research. Finally, the practical results show that the hidden Markov model can not only obtain the information of different regions but also deeply analyze the data set, which proves the feasibility of this research technology.

Maximum genetically possible crop growth and yield are inhibited by abiotic and or biotic stresses. Biotic stresses may include pests, insects or disease infestations whereas abiotic stress includes nutritional deficiencies. Sensors have been developed and are being developed to detect stresses and to assist in crop stress management. Decision support systems are being developed to help manage structured and unstructured crop stress issues. Weather prediction of disease and insect outbreaks help to assess the risk of these stresses over an area, but do not address the spatial variation of stress outbreak within a field. Most commercially developed sensors are remote sensing sensor technologies that use some form of normalized difference vegetative index (NDVI) to address nitrogen (N) sufficiency or deficiency in crops. Sensors for detection of disease and insect infestations are still under development, and include ‘nose’ sensors to detect stress-specific volatile organic compounds (VOCs), digital imagery of individual leaves, fluorescence imagery and thermography. Effective remotely sensed strategies need to be easy to implement for general adoption by farmers and crop consultants.

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30&deg, 51&prime, 55&Prime, N, 115&deg, 02&prime, 06&Prime, E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha&minus, 1) with three planting densities (55,000, 70,000, and 85,000 plant ha&minus, 1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha&minus, 1) than in aeolian sandy soil (186 kg ha&minus, 1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha&minus, 1, 176 $ ha&minus, 1, and 163 $ ha&minus, 1, and improve N use efficiency (NUE) by 37&ndash, 42%, 52%, and 67&ndash, 71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7&ndash, 8%, 16&ndash, 19% and 28&ndash, 34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year&rsquo, s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.

Accurate leaf area simulation is critical for the performance of crop growth models. Area of fully expanded individual leaves of maize hybrids released before 1995 (defined as old hybrids) has been simulated using a bell-shaped function (BSF) and the relationship between its parameters and total leaf number (TLNO). However, modern high-yielding maize hybrids show different canopy architectures. The function parameters calibrated for old hybrids will not accurately represent modern hybrids. In this study, we evaluated these functions using a dataset including old and modern hybrids that have been widely planted in China in recent years. Maximum individual leaf area (Y0) and corresponding leaf position (X0) were not predicted well by TLNO (R2 = 0.56 and R2 = 0.70) for modern hybrids. Using recalibrated shape parameters a and b with values of Y0 and X0 for modern hybrids, the BSF accurately predicted individual leaf area (R2 = 0.95–0.99) and total leaf area of modern hybrids (R2 = 0.98). The results show that the BSF is still a robust way to predict the fully expanded leaf area of maize when parameters a and b are modified and Y0 and X0 are fitted. Breeding programs have led to increases in TLNO of maize but have not altered Y0 and X0, reducing the correlation between Y0, X0, and TLNO. For modern hybrids, the values of Y0 and X0 are hybrid-specific. Modern hybrids tend to have less-negative values of parameter a and more-positive values of parameter b in the leaf profile. Growth conditions, such as plant density and environmental conditions, also affect the fully expanded leaf area but were not considered in the original published equations. Thus, further research is needed to accurately estimate values of Y0 and X0 of individual modern hybrids to improve simulation of maize leaf area in crop growth models. Keywords: Hybrids, Leaf area, Leaf number, Plant density, Environmental conditions

In-season diagnosis of crop nitrogen (N) status is crucial for precision N management. Critical N dilution curve and N nutrition index (NNI) have been proposed as effective methods for diagnosing N status of different crops. Critical N dilution curves have been developed for Indica rice in the tropical and temperate zones and Japonica rice in the subtropical-temperate zone, but they have not been evaluated for short-season Japonica rice in Northeast China. The objective of this study was to evaluate the previously developed critical N dilution curves for rice in Northeast China, and develop a more suitable critical N dilution curve in this region. A total of 17 N rate experiments were conducted in Jiansanjiang, Heilongjiang province in Northeast China from 2008 to 2013. The results indicated that none of the two previously developed critical N dilution curves was suitable for diagnosing N status of the short season Japonica rice in Northeast China. A new critical N dilution curve was developed and can be described by the equation N c = 27.7W −0.34 (aboveground biomass ≥ 1 Mg DM ha −1 ) or N c = 27.7 g kg −1 DM (aboveground biomass −1 ). This new curve was lower than those previous curves. It was validated using a separate dataset, and it could discriminate non-limiting and limiting N nutritional conditions. More studies are needed to further evaluate it for diagnosing N status of different rice cultivars in Northeast China and develop efficient non-destructive methods to estimate NNI for practical applications.

PurposeThe purpose of this paper is to study the effect of doping, annealing temperature and visible optical excitation on CuO-ZnO nanocomposites’ acetone sensing properties and introduce an attractive candidate for acetone detection at about room temperature.Design/methodology/approachZnO nanoparticles doped with CuO were prepared by sol-gel method, and the structure and morphology were characterized via X-ray diffraction, scanning electron microscope, energy dispersive spectroscopy and Brunauer-Emmett-Teller. The photoelectric responses of CuO-ZnO nanocomposites to cetone under the irradiation of visible light were investigated at about 30°C. The photoelectric response mechanism was also discussed with the model of double Schottky.FindingsThe doping of CuO enhanced performance of ZnO nanoparticles in terms of the photoelectric responses and the gas response and selectivity to acetone of ZnO nanoparticles, in addition, decreasing the operating temperature to about 30ºC. The optimum performance was obtained by 4.17% CuO-ZnO nanocomposites. Even at the operating temperature, about 30ºC, the response to 1,000 ppm acetone was significantly increased to 579.24 under the visible light irradiation.Practical implicationsThe sensor fabricated by 4.17% CuO-ZnO nanocomposites exhibited excellent acetone-sensing characteristics at about 30ºC. It is promising to be applied in low power and miniature acetone gas sensors.Originality/valueIn the present research, a new nanocomposite material of CuO-ZnO was prepared by Sol-gel method. The optimum gas sensing properties to acetone were obtained by 4.17% CuO-ZnO nanocomposites at about 30ºC operating temperature when it was irradiated by visible light with the wavelength more than 420 nm.

Ganoderma lucidum is a valuable basidiomycete with numerous pharmacological compounds, which is widely consumed throughout China. We previously found that the polysaccharide content of Ganoderma lucidum fruiting bodies could be significantly improved by 45.63% with treatment of 42 °C heat stress (HS) for 2 h. To further investigate genes involved in HS response and explore the mechanisms of HS regulating the carbohydrate metabolism in Ganoderma lucidum, high-throughput RNA-Seq was conducted to analyse the difference between control and heat–treated mycelia at transcriptome level. We sequenced six cDNA libraries with three from control group (mycelia cultivated at 28 °C) and three from heat-treated group (mycelia subjected to 42 °C for 2 h). A total of 99,899 transcripts were generated using Trinity method and 59,136 unigenes were annotated by seven public databases. Among them, 2790 genes were identified to be differential expressed genes (DEGs) under HS condition, which included 1991 up-regulated and 799 down-regulated. 176 DEGs were then manually classified into five main responsive-related categories according to their putative functions and possible metabolic pathways. These groups include stress resistance-related factors; protein assembly, transportation and degradation; signal transduction; carbohydrate metabolism and energy provision-related process; other related functions, suggesting that a series of metabolic pathways in Ganoderma lucidum are activated by HS and the response mechanism involves a complex molecular network which needs further study. Remarkably, 48 DEGs were found to regulate carbohydrate metabolism, both in carbohydrate hydrolysis for energy provision and polysaccharide synthesis. In summary, this comprehensive transcriptome analysis will provide enlarged resource for further investigation into the molecular mechanisms of basidiomycete under HS condition.

The material of Ag–TiO2 supported by artificial zeolite (Ag–TiO2/Z) was synthesized by the sol–gel method. Tetrabutyl titanate was used as the precursor, AgNO3 as the Ag source, and artificial zeolite as a carrier. The samples were analyzed by XRD, SEM, EDS, XPS, UV–Vis DRS and N2 adsorption–desorption isotherms. The results indicated that Ag–TiO2/Z had an anatase crystal phase, the dopant Ag was in the form of Ag0 and Ag2O nanocrystalline, and the existence of Ag2O can introduce impurity level and reduce the band gap of TiO2, which can lead to the absorption spectrum of the sample shift into the region of visible light, with 2.75 eV band gap energy. In addition, the photocatalytic properties of the samples were investigated by the degradation of methylene blue (MB) under visible light irradiation. The degradation rate of 3 at% Ag–TiO2/Z reached 93% within 90 min, which exhibited the best photocatalytic activity among all the samples. Meanwhile, with reused photocatalyst, the stability of 3 at% Ag–TiO2/Z was evaluated by the degradation of MB, whose efficiency stayed at around 89% after 5 times degradation. This study will be conducive to the development of visible-light activity of TiO2 and the application of aqueous pollutants.

One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.

Nitrogen (N) management needs to be significantly improved to address the triple challenge of global food security, environmental pollution and climate change. In addition to being site-specific, dynamic in-season management is needed to respond to temporal variability in soil N supply and crop N demand. Active canopy sensor-based precision N management (CS-PNM) aims to match N supply with crop N demand in both space and time. Studies that systematically compare this strategy with other N management strategies are limited, especially in intensively farmed regions of developing countries. The objective of this study was to compare CS-PNM strategy in terms of agronomic and environmental impacts in comparison with farmer’s N practice, regional optimum N management, modified Green Window-based N Management and soil test-based in-season root zone N management for an intensive winter wheat (Triticum aestivum L.) and summer maize (Zea mays L.) rotation system in North China Plain. A field experiment was conducted from 2008 to 2012 in Quzhou, Hebei Province of China to evaluate these systems. The CS-PNM strategy was consistently better for both crops than the other tested strategies. In comparison with farmer’s practice and regional optimum N management, the CS-PNM strategy reduced N fertilizer applications by 62% and 36%, increased N use efficiencies by 68–123% and 20–61%, decreased apparent total N losses by 81% and 57%, and lowered intensities of total N2O emission, greenhouse gas emission and reactive N losses by 54–68% and 20–42%, respectively. Here we demonstrate that relative to current N management strategies, the CS-PNM strategy has significant potential to improve N use efficiencies and mitigate environmental degradation for sustainable intensification of agriculture in developing countries.

RapidSCAN is a new portable active crop canopy sensor with three wavebands in red, red-edge, and near infrared spectral regions. The objective of this study was to determine the potential and practical approaches of using this sensor for non-destructive diagnosis of rice nitrogen (N) status. Sixteen plot experiments and ten on-farm experiments were conducted from 2014 to 2016 in Jiansanjiang Experiment Station of the China Agricultural University and Qixing Farm in Northeast China. Two mechanistic and three semi-empirical approaches using the sensor’s default vegetation indices, normalized difference vegetation index and normalized difference red edge, were evaluated in comparison with the top performing vegetation indices selected from 51 tested indices. The results indicated that the most practical and stable method of using the RapidSCAN sensor for rice N status diagnosis is to calculate N sufficiency index with the default vegetation indices and then to estimate N nutrition index non-destructively (R2 = 0.50–0.59). This semi-empirical approach achieved a diagnosis accuracy rate of 59–76%. The findings of this study will facilitate the application of the RapidSCAN active sensor for rice N status diagnosis across growth stages, cultivars and site-years, and thus contributing to precision N management for sustainable intensification of agriculture.

The main emphasis of exploration and development is shifting from simple structural reservoirs to complex reservoirs, which all have the characteristics of complex structure, thin reservoir thickness and large buried depth. Faced with these complex geological features, hydrocarbon detection technology is a direct indication of changes in hydrocarbon reservoirs and a good approach for delimiting the distribution of underground reservoirs. It is common to utilize the time–frequency (TF) features of seismic data in detecting hydrocarbon reservoirs. Therefore, we research the complex domain-matching pursuit (CDMP) method and propose some improvements. First is the introduction of a scale parameter, which corrects the defect that atomic waveforms only change with the frequency parameter. Its introduction not only decomposes seismic signal with high accuracy and high efficiency but also reduces iterations. We also integrate jumping search with ergodic search to improve computational efficiency while maintaining the reasonable accuracy. Then we combine the improved CDMP with the Wigner–Ville distribution to obtain a high-resolution TF spectrum. A one-dimensional modeling experiment has proved the validity of our method. Basing on the low-frequency domain reflection coefficient in fluid-saturated porous media, we finally get an approximation formula for the mobility attributes of reservoir fluid. This approximation formula is used as a hydrocarbon identification factor to predict deep-water gas-bearing sand of the M oil field in the South China Sea. The results are consistent with the actual well test results and our method can help inform the future exploration of deep-water gas reservoirs.