Chinese Academy of Agricultural Engineering

Abstract Climate warming is considered to be among the most serious of anthropogenic stresses to the environment, because it not only has direct effects on biodiversity, but it also exacerbates the harmful effects of other human‐mediated threats. The associated consequences are potentially severe, particularly in terms of threats to species preservation, as well as in the preservation of an array of ecosystem services provided by biodiversity. Among the most affected groups of animals are insects—central components of many ecosystems—for which climate change has pervasive effects from individuals to communities. In this contribution to the scientists' warning series, we summarize the effect of the gradual global surface temperature increase on insects, in terms of physiology, behavior, phenology, distribution, and species interactions, as well as the effect of increased frequency and duration of extreme events such as hot and cold spells, fires, droughts, and floods on these parameters. We warn that, if no action is taken to better understand and reduce the action of climate change on insects, we will drastically reduce our ability to build a sustainable future based on healthy, functional ecosystems. We discuss perspectives on relevant ways to conserve insects in the face of climate change, and we offer several key recommendations on management approaches that can be adopted, on policies that should be pursued, and on the involvement of the general public in the protection effort.

Efficient storage and conversion of renewable energies is of critical importance to the sustainable growth of human society. With its distinguishing features of high hydrogen content, high energy density, facile storage/transportation, and zero-carbon emission, ammonia has been recently considered as a promising energy carrier for long-term and large-scale energy storage. Under this scenario, the synthesis, storage, and utilization of ammonia are key components for the implementation of ammonia-mediated energy system. Being different from fossil fuels, renewable energies normally have intermittent and variable nature, and thus pose demands on the improvement of existing technologies and simultaneously the development of alternative methods and materials for ammonia synthesis and storage. The energy release from ammonia in an efficient manner, on the other hand, is vital to achieve a sustainable energy supply and complete the nitrogen circle. Herein, recent advances in the thermal-, electro-, plasma-, and photocatalytic ammonia synthesis, ammonia storage or separation, ammonia thermal/electrochemical decomposition and conversion are summarized with the emphasis on the latest developments of new methods and materials (catalysts, electrodes, and sorbents) for these processes. The challenges and potential solutions are discussed.

Abstract Recently, researchers have been debating whether children exhibit a universal “noun bias” when learning a first language. The present study compares the proportions of nouns and verbs in the early vocabularies of 24 English- and 24 Mandarin-speaking toddlers (M age = 20 months) and their mothers. Three different methods were used to measure the proportion of noun types, relative to verb types: controlled observations in three contexts (book reading, mechanical toy play, regular toy play), identical across languages; a vocabulary checklist (MacArthur Communicative Development Inventory); and mothers' reporting of their children's “first words.” Across all measures, Mandarin-speaking children were found to have relatively fewer nouns and more verbs than English-speaking children. However, context itself played an important role in the proportions of nouns found in children's vocabularies, such that, regardless of the language spoken, children's vocabularies appeared dominated by nouns when they were engaged in book reading, but not when they were playing with toys. Mothers' speech to children showed the same language differences (relatively more verbs in Mandarin), although both Mandarin- and English-speaking mothers produced relatively more verbs than their children. In sum, whether or not language-learning toddlers demonstrate a “noun bias” depends on a variety of factors, including the methods by which their vocabularies are sampled and the contexts in which observations occur.

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.

Genetic map is a linear arrangement of the relative positions of sites in the chromosome or genome based on the recombination frequency between genetic markers. It is the important basis for genetic analysis. Several kinds of software have been designed for genetic mapping, but all these tools require users to write or edit code, making it time-costing and difficult for researchers without programming skills to handle with. Here, MG2C, a new online tool was designed, based on PERL and SVG languages.Users can get a standard genetic map, only by providing the location of genes (or quantitative trait loci) and the length of the chromosome, without writing additional code. The operation interface of MG2C contains three sections: data input, data output and parameters. There are 33 attribute parameters in MG2C, which are further divided into 8 modules. Values of the parameters can be changed according to the users' requirements. The information submitted by users will be transformed into the genetic map in SVG file, which can be further modified by other image processing tools.MG2C is a user-friendly and time-saving online tool for drawing genetic maps, especially for those without programming skills. The tool has been running smoothly since 2015, and updated to version 2.1. It significantly lowers the technical barriers for the users, and provides great convenience for the researchers.

Most high-yielding rice cultivars developed for irrigated conditions, including the widely grown lowland variety IR64, are highly susceptible to drought stress. This limits their adoption in rainfed rice environments where there is a risk of water shortage during the growing season. Mapping studies using lowland-by-upland rice populations have provided limited information about the genetic basis of variation in yield under drought. One approach to simultaneously improve and understand rice drought tolerance is to generate backcross populations, select superior lines in managed stress environments, and then evaluate which features of the selected lines differ from the recurrent parent. This approach was been taken with IR64, using a range of tolerant and susceptible cultivars as donor parents. Yields of the selected lines measured across 13 widely contracting water environments were generally greater than IR64, but genotype-by-environment effects were large. Traits expected to vary between IR64 and selected lines are plant height, because many donors were not semi-dwarf types, and maturity, because selection in a terminal stress environment is expected to favour earliness. In these experiments it was found that some lines that performed better under upland drought were indeed taller than IR64, but that shorter lines with good yield under drought could also be identified. In trials where drought stress developed in previously flooded (lowland) fields, height was not associated with performance. There was little change in maturity with selection. Other notable differences between IR64 and the selected backcross lines were in their responses to applied ABA and ethylene in greenhouse experiments at the vegetative stage and in leaf rolling observed under chronic upland stress in the field. These observations are consistent with the hypothesis that adaptive responses to drought can effectively allow for improved performance across a broad range of water environments. The results indicate that the yield of IR64 under drought can be significantly improved by backcrossing with selection under stress. In target environments where drought is infrequent but significant in certain years, improved IR64 with greater drought tolerance would be a valuable option for farmers.

Current biofuel production relies on limited arable lands on the earth, and is impossible to meet the biofuel demands.  Oil producing algae are alternative biofuel feedstock with potential to meet the world's ambitious goal to replace fossil fuels.  This review provides an overview of the biological and engineering aspects in the production and processing technologies and recent advances in research and development in the algae to fuels approach.  The article covers biology, selection and genetic modification of algae species and strains, production systems design, culture media and light management, harvest and dewatering, downstream processing, and environment and economic assessment.  Despite the many advances made over several decades, commercialization of algal fuels remains challenging chiefly because of the techno-economic constraints.  Technological breakthroughs in all major aspects must take place before commercial production of algal fuels becomes economically viable. Keywords: algae, microalgae, open pond, enclosed photobioreactor, light, harvest, dewatering, extraction, hydrothermal liquefaction, gasification, pyrolysis, fermentation DOI: 10.3965/j.issn.1934-6344.2009.04.001-030 Citation: Paul Chen, Min Min, Yifeng Chen, Liang Wang, Yecong Li, Qin Chen, et al.  Review of the biological and engineering aspects of algae to fuels approach.  Int J Agric & Biol Eng, 2009; 2(4): 1

Stimulating the development of soil suppressiveness against certain pathogens represents a sustainable solution toward reducing pesticide use in agriculture. However, understanding the dynamics of suppressiveness and the mechanisms leading to pathogen control remain largely elusive. Here, we investigated the mechanisms used by the rhizosphere microbiome induces bacterial wilt disease suppression in a long-term field experiment where continuous application of bio-organic fertilizers (BFs) triggered disease suppressiveness when compared to chemical fertilizer application. We further demonstrated in a glasshouse experiment that the suppressiveness of the rhizosphere bacterial communities was triggered mainly by changes in community composition rather than only by the abundance of the introduced biocontrol strain. Metagenomics approaches revealed that members of the families Sphingomonadaceae and Xanthomonadaceae with the ability to produce secondary metabolites were enriched in the BF plant rhizosphere but only upon pathogen invasion. We experimentally validated this observation by inoculating bacterial isolates belonging to the families Sphingomonadaceae and Xanthomonadaceae into conducive soil, which led to a significant reduction in pathogen abundance and increase in nonribosomal peptide synthetase gene abundance. We conclude that priming of the soil microbiome with BF amendment fostered reactive bacterial communities in the rhizosphere of tomato plants in response to biotic disturbance.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

This paper studies the relationship among population, poverty, and the environmental factors, and the impact they have had on China's land, water, forests and pastures. It does so by examining the extent of environmental degradation and China's success in controlling its environmental problems is reviewed; by investigating how the leadership has tried to develop a legal framework and series of institutions to carry out environmental policy; and by providing empirical evidence demonstrating the determinants of the successes that China has achieved in surmounting (or slowing) some of its environmental problems. Five of China's rural resource concerns are surveyed in this paper: water pollution, deforestation, destruction of the grasslands, soil erosion, and salinization. The paper finds that government policy has not been effective in controlling rural resource degradation primarily because it has limited fiscal resources and poorly trained personnel, and under these constraints the government has delegated responsibility for environmental and resource protection to the ministries of agriculture and forestry, two institutions that have an incentive to favot pro-production policies. Instead, China's efforts to alleviate policy, integrate markets, and control population appear to have helped mitigate a number of adverse environmental consequences of China's development effort of the last 40 years.

Abstract Bacteria, as the key component of soil ecosystems, participate in nutrient cycling and organic matter decomposition. However, how fertilization regime affects the rhizospheric bacterial community of reddish paddy soil remains unclear. Here, a long-term fertilization experiment initiated in 1982 was employed to explore the impacts of different fertilization regimes on physicochemical properties and bacterial communities of reddish paddy rhizospheric soil in Central South China by sequencing the 16S rRNA gene. The results showed that long-term fertilization improved the soil nutrient status and shaped the distinct rhizospheric bacterial communities. Particularly, chemical NPK fertilizers application significantly declined the richness of the bacterial community by 7.32%, whereas the application of manure alone or combined with chemical NPK fertilizers significantly increased the biodiversity of the bacterial community by 1.45%, 1.87% compared with no fertilization, respectively. Moreover, LEfSe indicated that application of chemical NPK fertilizers significantly enhanced the abundances of Verrucomicrobia and Nitrospiraceae , while manure significantly increased the abundances of Deltaproteobacteria and Myxococcales , but the most abundant Actinobacteria and Planctomycetes were detected in the treatment that combined application of manure and chemical NPK fertilizers. Furthermore, canonical correspondence analysis (CCA) and the Mantel test clarified that exchangeable Mg 2+ (E-Mg 2+ ), soil organic carbon (SOC) and alkali-hydrolyzable nitrogen (AN) are the key driving factors for shaping bacterial communities in the rhizosphere. Our results suggested that long-term balanced using of manure and chemical fertilizers not only increased organic material pools and nutrient availability but also enhanced the biodiversity of the rhizospheric bacterial community and the abundance of Actinobacteria , which contribute to the sustainable development of agro-ecosystems.

Abstract A study of a new amino acid analysis method using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate as a precolumn derivatization reagent for the analysis of food and feed is described. All amino acids, including methionine sulfone and cysteic acid, were well separated on a liquid chromatographic system using the optimized chromatographic conditions. Salts in food and feed interfered very slightly with the derivatization yields of all amino acids. Several typical agricultural products and animal feeds, including 2 AOAC test samples, were analyzed with the method. The results agreed well with the data generated by using the classical postcolumn method with ion-exchange chromatography. The average relative standard deviations for corn and broiler starter feed were 0.74 and 0.70%, respectively. Good recoveries of all amino acids were demonstrated (average, 101%), even for a sample with a very complex matrix.

Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

Abstract 1D semiconductor nanomaterials have generated a high interest in heterogeneous photocatalysis. However, most 1D photocatalysts still suffer from poor charge separation and severe charge recombination. Herein, a unique approach via surface doping of phosphorus (P) atoms into 1D Cd 0.5 Zn 0.5 S (CZS) nanorods is demonstrated, leading to an imbalanced charge distribution and a localized built‐in electric field, verified by characterizations including photoluminescence and transient absorption spectra. The CZS‐P nanorods exhibit more than two orders of magnitude enhancement in photocatalytic H 2 production activity relative to pristine CZS under visible light. Further construction of spatially separated dual‐cocatalysts (Pt and PdS) on the tip and lateral surface of the CZS‐P nanorods enables a significant improvement in the photocatalytic activity, which results in an apparent quantum efficiency exceeding 89% at 420 nm. Such efficient photocatalytic hydrogen production is attributed to the synergistic effect of tuning the intrinsic built‐in electric field for spatial charge separation and simultaneously accelerating the reduction and oxidation reaction rates utilizing photogenerated charges. The idea of integrating spatial charge separation via morphology tailoring, additional built‐in electric field, and spatial separation of dual‐cocatalysts provides a pathway for rationally designing artificial photocatalysts for solar energy conversion.

Although production of biodiesels from microalgae is proved to be technically feasible, a commercially viable system has yet to emerge. High-cell-density fermentation of microalgae can be coupled with photoautotrophic cultivation to produce oils. In this study, by optimizing culturing conditions and employing a sophisticated substrate feed control strategy, ultrahigh-cell-density of 286 and 283.5 g/L was achieved for the unicellular alga Scenedesmus acuminatus grown in 7.5-L bench-scale and 1,000-L pilot-scale fermenters, respectively. The outdoor scale-up experiments indicated that heterotrophically grown S. acuminatus cells are more productive in terms of both biomass and lipid accumulation when they are inoculated in photobioreactors for lipid production as compared to the cells originally grown under photoautotrophic conditions. Technoeconomic analysis based on the pilot-scale data indicated that the cost of heterotrophic cultivation of microalgae for biomass production is comparable with that of the open-pond system and much lower than that of tubular PBR, if the biomass yield was higher than 200 g/L. This study demonstrated the economic viability of heterotrophic cultivation on large-scale microalgal inocula production, but ultrahigh-productivity fermentation is a prerequisite. Moreover, the advantages of the combined heterotrophic and photoautotrophic cultivation of microalgae for biofuels production were also verified in the pilot-scale.

Highly efficient OER electrocatalyst based on metal-rich amorphous Co–Fe phosphide was fabricated<italic>via</italic>a non-toxic solvothermal method.

Abstract In response to increasing concerns about domestic food safety issues, establishing tracking systems in the food industry is mandatorily required under newly launched food safety laws. However, the kinds of monitoring and certification systems that should be set up to ensure practical adoption and the effectiveness of the regulation remain unclear. This study aims to analyze consumers’ preferences for milk traceability, with particular interest in investigating how consumers’ preferences could be affected by monitoring and certification systems of the regarding system. Survey data from a choice‐based conjoint (CBC) experiment are used to achieve this objective. In the experiment, milk is defined by a set of attributes in which we assume that milk traceability can be certified by three entities: the government, an industrial association, and a third party. The CBC data are then analyzed by using the alternative‐specific form of a conditional Logit (McFadden's Choice) model. We found that urban Chinese consumers have a strong desire for traceable milk, but their preference for traceable milk is significantly related to the associated certificate issuers. Currently, the highest willingness‐to‐pay goes to government certificated traceable milk, followed by industrial association certificated and third‐party certificated milks. In the future, however, consumers are likely to give more credit to third‐party certification with rising income and knowledge.

Abstract. Agricultural intensification has contributed greatly to the sustained food supply of China's population of 1.3 billion over the 30-year period from 1982 to 2011. Intensification has several and widely recognized negative environmental impacts including depletion of water resources, pollution of water bodies, greenhouse gas emissions and soil acidification. However, there have been few studies over this period on the impacts of intensification on soil organic carbon (SOC) at the regional level. The present study was conducted in Huantai County, a typical intensive farming region in northern China, to analyze the temporal dynamics of SOC influenced by climate and farming practices. The results indicate that from 1982 to 2011, SOC content and density in the 0–20 cm layer of the cropland increased from 7.8 ± 1.6 to 11.0 ± 2.3 g kg−1 (41%) and from 21.4 ± 4.3 to 33.0 ± 7.0 Mg ha−1 (54%), respectively. The SOC stock (0–20 cm) of the farmland for the entire county increased from 0.75 to 1.2 Tg (59%). Correlation analysis revealed that incorporation of crop residues significantly increased SOC, while an increase in the mean annual temperature decreased the SOC level. Therefore, agricultural intensification has increased crop productivity and contributed to SOC sequestration in northern China. In the near future, more appropriate technologies and practices must be developed and implemented for a maintenance or enhancement of SOC in this region and elsewhere in northern China, which also reduce non-CO2 greenhouse gas emissions, since the climate benefit from the additional SOC storage is estimated to be smaller than the negative climate impacts of N2O from N fertilizer additions.

Timely flowering is essential for optimum crop reproduction and yield. To determine the best flowering-time genes (FTGs) relevant to local adaptation and breeding, it is essential to compare the interspecific genetic architecture of flowering in response to light and temperature, the two most important environmental cues in crop breeding. However, the conservation and variations of FTGs across species lack systematic dissection. This review summarizes current knowledge on the genetic architectures underlying light and temperature-mediated flowering initiation in Arabidopsis, rice, and temperate cereals. Extensive comparative analyses show that most FTGs are conserved, whereas functional variations in FTGs may be species specific and confer local adaptation in different species. To explore evolutionary dynamics underpinning the conservation and variations in FTGs, domestication and selection of some key FTGs are further dissected. Based on our analyses of genetic control of flowering time, a number of key issues are highlighted. Strategies for modulation of flowering behavior in crop breeding are also discussed. The resultant resources provide a wealth of reference information to uncover molecular mechanisms of flowering in plants and achieve genetic improvement in crops.

Paddy rice agriculture in Southern China, especially Hunan Province, has been suffered from soil contamination. Several policies including rice fallow and decreasing cropping intensity have been implemented for food safety here. It is thus important to monitor rice planting area and cropping intensity to understand the effectiveness of those land-use policies. However, it is challenging to map rice planting areas due to the complex cropping systems (mixed single- and double-cropping), persistent cloud covers, small crop fields, let alone cropping intensity. Here we used all the available Sentinel-2 and all-weather Sentinel-1 imagery to generate a time series data cube to extract paddy rice planting areas and the rice cropping intensity in the Changsha, Zhuzhou, and Xiangtan areas, which is a traditional rice-growing region with small farms in China. Specifically, we investigated the performances of different features (i.e., spectral, seasonal, polarization backscatter) by comparing five scenarios with different combinations of sensors and features, and identified the most suitable features for certain rice types (early, middle, and late rice). The random forest classifier was used for the classification in the Google Earth Engine (GEE) platform, and a reference map in 2017 based on visual interpretation on the GaoFen-2 images were used for collecting the training and validation samples. The results showed the combined data from Sentinel-1/2 generally outperformed classifications using only a single sensor (Sentinel-1/2), but the contribution of different sensors to certain rice types varied. The early, middle and late rice with the highest accuracies within the five scenarios had the overall accuracies of 85%, 95%, and 95%, respectively (F1 = 0.55, 0.85, and 0.85). The compositing of different types of rice allowed us to generate the rice cropping intensity map with an overall accuracy of 81%, which to our limited knowledge is the first effort to map cropping intensity at 10-m resolution in such a fragmented subtropical region. The result showed the single cropping dominated the rice cropping system in the study area 88%, which used to be a typical area with double cropping of rice. Our study demonstrates the potential of mapping rice cropping intensity in a cloudy and highly fragmented region in South China using all the available Sentinel-1/2 data, which would advance our understanding of regional rice production and mitigation of soil contamination.