Mutual feedback between algal blooming and global warming

Global warming and algal blooms have been two of the most pressing problems faced by the world today.In recent decades,numerous studies indicated that global warming promoted the expansion of algal blooms.However,research on how algal blooms respond to global warming is scant.Global warming coupled with eutrophication promoted the rapid growth of phytoplankton,which resulted in an expansion of algal blooms.Algal blooms are affected by the combined effects of global warming,including increases in temperatures,CO_(2)concentration,and nutrient input to aquatic systems by extreme weather events.Since the growth of phytoplankton requires CO_(2),they appear to act as a carbon sink.Unfortunately,algal blooms will release CH4,CO_(2),and inorganic nitrogen when they die and decompose.As substrate nitrogen increases from decompose algal biomass,more N2O will be released by nitrification and denitrification.In comparison to CO_(2),CH4has 28-fold and N2O has 265-fold greenhouse effect.Moreover,algal blooms in the polar regions may contribute to melting glaciers and sea ice(will release greenhouse gas,which contribute to global warming)by reducing surface albedo,which consequently would accelerate global warming.Thus,algal blooms and global warming could form feedback loops which prevent human survival and development.Future researches shall examine the mechanism,trend,strength,and control strategies involved in this mutual feedback.Additionally,it will promote global projects of environmental protection combining governance greenhouse gas emissions and algal blooms,to form a geoengineering for regulating the cycles of carbon,nitrogen,and phosphorus.

Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Animal Safety Test of Bacillus thuringiensis BT Protein

[Objectives]To determine the biological safety of BT protein from Bacillus thuringiensis(Bt)fermentation broth to mammals at high doses.[Methods]Healthy mice were randomly divided into 4 groups with 10 mice in each group.The experimental groups were fed with Bt fermentation supernatant at 0.2,0.6 and 1.0 mL/kg,respectively,once a day for 7 consecutive days.The blank control group was fed normally without intragastric administration.[Results]There was no significant difference in blood routine and blood biochemical analysis between the experimental group and the control group.After intragastric administration,the mice were dissected,and no obvious pathological changes were found;the heart,liver,spleen,lung and kidney were taken to make tissue sections,and no pathological changes were found by microscopic observation.[Conclusions]Mice can tolerate high doses of BT protein from B.thuringiensis fermentation broth.

AVIRIS高光谱数据空-谱特征在植被分类中的对比分析

植被分类是高光谱影像分类中的特定应用问题,光谱特征和空间特征是植被分类中常用的两类特征,比较这两类特征的性能,对实际植被分类应用中选择合适的特征类型或两者的有效结合具有指导意义。用主成分分析(PCA)提取光谱特征时,常选择前几个主成分(PCs)作为光谱特征,虽然它们包含较大的信息量但并不能保证较高的类别可分性和分类正确率,针对这一问题本研究提出了一种混合特征提取方法,对高光谱影像在PCA的基础上用改进的基于分散矩阵的特征选择方法选出具有较高类别可分性的PCs用于后续分类。利用一景AVIRIS高光谱植被影像,从分类精度的角度,首先比较了所提出的混合特征提取方法和原始PCA、独立主成分分析(ICA)及线性判别分析(LDA) 3种常用子空间特征提取方法在高光谱影像植被分类中的性能。试验结果表明所提出的混合特征提取方法在研究中数据集1和2上均获得了最高的总体分类正确率,分别为82.7%和86.5%。与原始PCA相比,本研究提出的混合特征提取方法的总体分类正确率,在数据集1和2上分别提高了1.5%和2.5%。由此阐明了所提出的混合特征提取方法在高光谱植被分类中的有效性。对光谱特征和空间特征在高光谱影像植被分类性能的比较中,总体上空间特征获得的分类正确率比光谱特征高,特别是Gabor特征,在两个数据集上均获得了最高的总体分类正确率分别为95.5%和96.7%。由此表明空间特征较光谱特征在高光谱影像植被分类中更具优势。本研究结果为后续改进空-谱特征方法及其两者有效结合,进一步提高植被分类正确率提供了参考。

Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

Visualization of Chlorophyll Content Distribution in Apple Leaves Based on Hyperspectral Imaging Technology

We took distribution visualization of chlorophyll content in apple leaves to estimate the nutrient content and growth levels of apple leaves. 130 mature and non-destructive apple leaves were collected, and imaging spectroscopy data were collected by SOC710VP hyperspectral imager. The chlorophyll content of the leaves was determined on the spectral information of the leaves. After pre-processing, we took linear wavelength stepwise regression method to choose the sensitive wavelength of chlorophyll content. And then we established partial least squares, principal component analysis and stepwise regression model. Finally, the chlorophyll content distribution visualization was realized. The results showed that the sensitive wavelengths of the chlorophyll content were 712.50 nm, 509.95 nm, 561.22 nm, 840.62 nm, 696.67 nm and 987.91 nm. The R2, RMSE, RE of the optical chlorophyll content estimation model, and the principal component analysis regression model, were 0.800, 0.319 and 26.4%. The chlorophyll content of each pixel on the hyperspectral image of apple leaves was calculated by the best estimation model and we completed the visualization distribution of chlorophyll content, which provided a technical support for the rapid detection of nutrient distribution.

Evaluation and Exploitation of Retrieval Algorithms for Estimating Biophysical Crop Variables Using Sentinel-2,Venus,and PRISMA Satellite Data

This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite(e.g.,PRISMA,EnMAP,and GF-5).Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index(LAI)and Leaf chlorophyll content(Cab)were evaluated:non-kernel-based and kernel-based Machine Learning Regression Algorithms(MLRA);Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season.Results show that for Sentinel-2 data,Gaussian Process Regression(GPR)was the best performing algorithm for both LAI(R 2=0.89 and RMSE=0.59)and Cab(R 2=0.70 and RMSE=8.31).Whereas,for PRISMA simulated data the Kernel Ridge Regression(KRR)was the best performing algorithm among all the other MLRA(R 2=0.91 and RMSE=0.51)for LAI and(R 2=0.83 and RMSE=6.09)for Cab,respectively.Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands,which are used as tie-points in the PROSAIL inversion,are extremely useful for an accurate retrieving of crop biophysical parameters.

Simulated Reflectance of Apple Trees in Canopy Level Based on the PROSAIL Model and HJ-1A-HSI Data

Using the PROSAIL radiation transfer model and HJ-1A-HSI data to simulate the canopy reflectivity of apple trees, this study lays the foundation for the inversion of canopy parameters. Taking Qixia City of Yantai City, Shandong Province as the research area, the apple tree was taken as the research object, and the hyperspectral reflectance, LAI and sample GPS of apple canopy were measured in the field. The parameters required for the PROSAIL model were obtained by experimental methods. The model simulates the reflectivity;the HSI image data is preprocessed, and the canopy reflectivity is extracted by GPS coordinates. The PROSAIL model and the HSI image simulated reflectance were fitted to the measured apple canopy reflectivity. The decisive factor (R2) of the simulated reflectance and the measured reflectance of the PROSAIL model was 0.9944, and the relative error (RE%)was 0.1845. The HSI data simulated reflectance and measured reflectance. The coefficient of determination is 0.9714 and the relative error is 0.6202. Both have achieved good fitting effects and can be used for inversion studies of apple canopy parameters.

Inversion of Canopy Nitrogen Content in Apple Orchard Based on GF-1 Satellite Image

The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74– 41.978^*NPCI-10.78^*NDVI. The R^2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries.Currently,there is growing research interest in this plant.For the tea industry,it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment.This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants.The topics covered include leaf,canopy,and field-level assessments,as well as statistical analysis techniques and characteristics specific to the research.Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases,monitoring quality,and nutrients,determining tenderness and grade,identifying tea plant varieties,automatically detecting,and identifying tea buds,monitoring tea plant growth,and extracting tea garden areas through remote sensing.It also helps to evaluate planting suitability,assess disasters,and estimate yields.Additionally,the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants.It can assist researchers and producers in comprehensively understanding the tea environment,quality characteristics,and growth process,thereby enhancing tea production quality,and fostering tea industry development.

贺兰山甲虫物种丰富度分布格局及其环境解释

理解山地物种丰富度分布格局及其成因对于山地生物多样性保护具有重要意义。本文基于贺兰山地区甲虫31科252属469种的分布信息,结合相关气候与生境异质性数据,系统地探讨了贺兰山地区甲虫及6个优势科物种丰富度地理格局及其影响因素。结果表明,甲虫物种丰富度及科属区系分化强度以贺兰山中段最高,南段比北段高,西坡比东坡高。基于183个栅格内物种分布的二元数据聚类分析,贺兰山甲虫分布可分为北段强旱生景观甲虫地理群、中西段半湿生景观甲虫地理群、中东段及南段半旱生景观甲虫地理群3个地理群。冗余分析(RDA)表明年均温和年均降水量是影响最显著的因子。方差分解结果显示,水分与能量因子共同解释了全部甲虫物种丰富度57.1%的空间变异,单独解释率分别为5.9%和7.1%。生境异质性解释了全部甲虫物种丰富度35.2%的变异,单独解释率仅为1.8%。气候因素与生境异质性对不同优势科物种丰富度的相对影响并不一致。在贺兰山的南段和北段,生境异质性和水分因子对甲虫物种丰富度影响作用明显。水分和能量因子是贺兰山地区甲虫物种丰富度空间分布格局的主导因子,生境异质性有助于提高甲虫物种丰富度。从未解释的比例来分析,地形和土壤因素可能对贺兰山甲虫物种丰富度存在重要影响。

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Lodging is one of the main factors affecting the quality and yield of crops.Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses.The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle(UAV)for determination of the maize lodging grade.A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making.Indeed,a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition.The images obtained constitute a highly effective tool for analyzing plant health.Maize samples with different lodging grades were obtained by visual interpretation,and the spectral reflectance,texture feature parameters,and vegetation indices of the training samples were extracted.Different feature transformations were performed,texture features and vegetation indices were combined,and various feature images were classified by maximum likelihood classification(MLC)to extract four lodging grades.Classification accuracy was evaluated using a confusion matrix based on the verification samples,and the features suitable for monitoring the maize lodging grade were screened.The results showed that compared with a multispectral image,the principal components,texture features,and combination of texture features and vegetation indices were improved by varying degrees.The overall accuracy of the combination of texture features and vegetation indices is 86.61%,and the Kappa coefficient is 0.8327,which is higher than that of other features.Therefore,the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

Unbiased Quasi-regression

Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.

A proposed framework for accelerating technology trajectories in agriculture:a case study in China

Precision agriculture(PA) technologies have great potential for promoting sustainable intensification of food production, ensuring targeted delivery of agricultural inputs, and hence food security and environmental protection. The benefits of PA technologies are applicable across a broad range of agronomic, environmental and rural socio-economic contexts globally. However, farmer and land-manager adoption in low to middle income countries has typically been slower than that observed in more affluent countries. China is currently engaged in the process of agricultural modernisation to ensure food security for its 1.4 billion population and has developed a portfolio of policies designed to improve food security,while simultaneously promoting environmental protection.Particular attention has been paid to the reduction of agricultural inputs such as fertilisers and pesticides. The widespread adoption of PA technologies across the Chinese agricultural landscape is central to the success of these policies. However, socio-economic and cultural barriers, farm scale,(in particular the prevalence of smaller family farms) and demographic changes in the rural population,(for example, the movement of younger people to the cities) represent barriers to PA adoption across China. A framework for ensuring an acceptable and accelerated PA technology trajectory is proposed which combines systematic understanding of farmer and end-user priorities and preferences for technology design throughout the technology development process, and subsequent end-user requirements for implementation(including demonstration of economic and agronomic benefits, andknowledge transfer). Future research will validate the framework against qualitative and quantitative socioeconomic, cultural and agronomic indicators of successful,or otherwise, PA implementation. The results will provide the evidence upon which to develop further policies regarding how to secure sustainable food production and how best to implement PA in China, as well as practical recommendations for optimising end-user uptake.

A comparative review of the state and advancement of Site-Specific Crop Management in the UK and China

Precision agriculture, and more specifically Site-Specific Crop Management(SSCM), has been implemented in some form across nearly all agricultural production systems over the past 25 years. Adoption has been greatest in developed agricultural countries. In this review article, the current situation of SSCM adoption and application is investigated from the perspective of a developed(UK) and developing(China) agricultural economy. The current state-of-the art is reviewed with an emphasis on developments in position system technology and satellite-based remote sensing. This is augmented with observations on the differences between the use of SSCM technologies and methodologies in the UK and China and discussion of the opportunities for(and limitations to)increasing SSCM adoption in developing agricultural economies. A particular emphasis is given to the role of socio-demographic factors and the application of responsible research and innovation(RRI) in translating agritechnologies into China and other developing agricultural economies. Several key research and development areas are identified that need to be addressed to facilitate the delivery of SSCM as a holistic service into areas with low precision agriculture(PA) adoption. This has implications for developed as well as developing agricultural economies.

Effect and economic benefit of precision seeding and laser land leveling for winter wheat in the middle of China

Rapid socio-economic changes in China,such as land conversion and urbanization,are creating new scopes for the application of precision agriculture(PA).An experiment to assess the economic benefits of two precision agriculture methods was applied for one year–precision seeding and precision seeding with land leveling.Whilst the results for this were positive,of itself it did not provide evidence of longer terms gains.The costs of land leveling are accrued in a single year but the benefits could carry over into subsequent years.Thus,in this case if the PA method provides carry over benefits to future years,the economic assessment would incorrectly assign all the costs to a single year of benefits i.e.the benefit-cost ratio would be underestimated.To gauge whether there was carry over benefits in future years we looked at NDVI and GUI as proxies for future year benefits.For the single year experiment,our results showed that:(1)Winter wheat yield was increased 23.2%through the integration of precision seeding and laser leveling technologies.(2)Both the single technology and the integrated technologies significant reduced the concentration of soil ammonium nitrogen at the depths of 60 cm;(3)The benefit/cost ratio's of the treatments exceeded that of the baseline by approximately 10%which translated to an increase of several hundred US$per hectare.The NDVI analysis showed that the effect of laser land leveling could last to the next two years.When considering the multi-year impact of land leveling,the benefit/cost ratio of PSLL will increase to 23.5%and 22.9%with and without laser land leveling subsidies.Making clear the eco-nomic benefits of using PA technologies will likely promote application of the technologies in the region.

Agri-BIGDATA:A smart pathway for crop nitrogen inputs

Big data provide a pathway to lower crop nitrogen inputs from genetic breeding to field production.Moreover,multidisciplinary efforts from plant health sensing,deep machine learning and cloud computing can integrate multi-source data to form information and knowledge.So big data analysis as a prospective optimal method,will make leaps towards addressing future issues of sustainable agriculture.