2020年8月第36卷第15期

2020年8月第36卷第15期

Accession number:20204109331126
Title:Analysis and evaluation of farmland soil nutrient balance in Heilongjiang Land Reclamation Areas, China
Title of translation:黑龙江垦区农田土壤养分平衡分析与评价
Authors:Chu, Tianshu (1); Wang, Derui (2); Han, Lujia (1); Yang, Zengling (1)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Yantai Institute of China Agricultural University, Yantai; 264670, China
Corresponding author:Yang, Zengling(yangzengling@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:19-27
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Efficient nutrient utilization has become an essential part of green development in modern agriculture. Evaluation on the nutrient balance of farmland soil is therefore critical to the Heilongjiang Land Reclamation Area (HLRA), one of the major grain-producing regions in China. HLRA has produced 410 million tons of grain since 1949, currently serving as a typical representative of agricultural mechanization and modern production. In HLRA, the total grain output was 2 279.64 104 t in 2018, accounting for 3.47% of the national total. Taking the HLRA as the research subject, this study aims to develop a nutrient balance method for the evaluation on the input and output of N, P, and K from farmland soil during the period from 2000 to 2018. The results showed that: 1) From 2000 to 2018, the N, P, and K inputs of farmland soil in HLRA showed a steady-increase-decline trend. In 2018, the N, P and K input decreased to 6.55108, 1.27108 and 5.29108 kg. The N input mainly came from chemical fertilizer, organic fertilizer, and biological nitrogen fixation, whereas, the P and K inputs were mostly from chemical fertilizer and straw returning to field. 2) The N, P, and K outputs of farmland soil in HLRA also showed a steady-increase-decline trend from 2000 to 2018. In 2018, the N, P and K outputs decreased to 6.12108, 7.34107 and 4.88108 kg. The main ways of N and P outputs were for grain and straw, with special emphasis on the N output from ammonia volatilization, whereas the way of K output was mainly for straw. 3) The N, P, and K inputs per area of farmland soil in HLRA also showed a steady-increase-steady trend from 2000 to 2018. The N, P and K inputs per area in 2018 were 228.08, 44.32 and 183.98 kg/hm2. The N, P, and K inputs per value of farmland soil in HLRA showed a decline trend from 2000 to 2018. Specifically, the N, P, and K inputs per value in 2000 were 94.86, 18.43, 76.52 kg/104 yuan. There was an increase in the N and P utilization efficiency of farmland soil in HLRA, but a decrease in that of K, indicating 51.03% for N, 27.98% for P, and 10.04% for K in 2018. There was an increase trend in the N profit and loss of farmland soil in HLRA, and a steady trend for that of P, while a decline trend for that of K. The N profit and at an excellent level, compared with that from Australia, Canada, France, Germany, Japan, UK, and the USA. But a relatively low level occurred for the profit and loss. It infers that the N and K were in a nutrient balance state since 2017, whereas P was in a nutrient surplus condition. The reason can be that the P input into the farmland was easily fixed by soil. At the current stage, the fertilizer input in HLRA was mainly relying on chemical fertilizer, whereas, the amount of organic fertilizer was relatively low. This arrangement can be not conducive to fertilizing soil and slowing down the degradation of black soil. Therefore, a sound recommendation for HLRA can be made to develop various methods, such as subsidies and demonstration, further to gradually promote the application of organic fertilizers. Besides, a long-term monitoring of nutrient balance in the farmland soil was required in the near future, in order to scientifically adjust and optimize management strategies of soil nutrients, and thereby to improve nutrient utilization efficiency and food security in green development of modern agriculture. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Farms
Controlled terms:Agricultural robots - Ammonia - Degradation - Efficiency - Food supply - Grain (agricultural product) - Land reclamation - Land use - Machinery - Nitrogen fertilizers - Nitrogen fixation - Nutrients - Plants (botany) - Profitability - Soils
Uncontrolled terms:Agricultural mechanization - Ammonia volatilization - Analysis and evaluation - Biological nitrogen fixations - Chemical fertilizers - Long term monitoring - Management strategies - Nutrient utilization
Classification code:403 Urban and Regional Planning and Development - 442.2 Land Reclamation - 483.1 Soils and Soil Mechanics - 802.2 Chemical Reactions - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.4 Agricultural Products - 822.3 Food Products - 911.2 Industrial Economics - 913.1 Production Engineering
Numerical data indexing:Percentage 1.00e+01%, Percentage 2.80e+01%, Percentage 3.47e+00%, Percentage 5.10e+01%
DOI:10.11975/j.issn.1002-6819.2020.15.003

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109330561
Title:Preliminary study on interpretation of LF-NMR T<inf>2</inf> inversion spectrum of ginkgo biloba seed during germination process
Title of translation:银杏种子萌发过程低场核磁T<inf>2</inf>反演谱解译初探
Authors:Zhao, Maocheng (1, 2); Gu, Sheng (1); Wang, Xiwei (1); Wang, Guibin (3); Li, Zhong (4)
Author affiliation:(1) College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing; 210037, China; (2) Taizhou University, Taizhou; 225300, China; (3) College of Forestry, Nanjing Forestry University, Nanjing; 210037, China; (4) National-provincial Joint Engineering Research Center of Electromechanical Product Packaging with Biomaterials, Nanjing Forestry University, Nanjing; 210037, China
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:317-324
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:With the development of science and technology, low field nuclear magnetic resonance (LF-NMR) is increasingly used in agriculture. At present, the interpretation of the transverse relaxation time (T<inf>2</inf>) inversion spectrum stays at the level of the water phase state distribution in the measured sample. It is beneficial to connect the T<inf>2</inf> inversion spectrum to the chemical composition of test subject. In order to find such a connection as the substance-oriented interpretation of the T<inf>2</inf> inversion spectrum, LF-NMR was applied for 20 ginkgo biloba (Ginkgo Biloba L.) seeds during germination that divided into 10 groups. Temporal observation of ginkgo biloba seeds over the germination process were carried out in vivo using LF-NMR. Their T<inf>2</inf> inversion spectra were collected and compared with those from the reference samples that made from seed powder or different mixtures of the main ingredients of ginkgo biloba seeds to explore the forming mechanism of the signal peaks of T<inf>2</inf> inversion spectra for a viable interpretation from the perspective of substances. Analysis of the T<inf>2</inf> inversion spectra of ginkgo biloba seeds indicated that water in live ginkgo biloba seed could be divided into 4 phase states according to T<inf>2</inf>, including 2 distinctive bound water of different kinetic activity with transverse relaxation times spiking at T<inf>21</inf> and T<inf>22</inf>, semi-bound water spiking at T<inf>23</inf>, and free water at T<inf>24</inf>. The peak T<inf>21</inf>, T<inf>22</inf>, T<inf>23</inf> of the T<inf>2</inf> inversion spectrum of the starch and protein mixed sample and the peak T<inf>24</inf> of the starch and oil mixed sample were exactly the same as the corresponding signal peaks of the seed powder sample in terms of peak time. When the material composition and the ratio were completely the same, the peak times of the peak T<inf>21</inf>-T<inf>24</inf> of the T<inf>2</inf> inversion spectrum of the seed powder sample were 12.98%, 32.21%, 13.02% and 0% different from those of the fresh seed, respectively. The proportions of peak T<inf>21</inf> and T<inf>22</inf> are 41.72% and 29.33% less than those of fresh species, respectively, the proportion of peak T<inf>23</inf> is 92.26% higher, and the proportion of peak T<inf>24</inf> is 91.71% lower. This showed that the seed tissue structure would affect the relaxation time and phase distribution ratio of its internal water to a certain extent. Only from the perspective of material composition, the water in seed was mainly expressed as relaxation time T<inf>21</inf>, T<inf>22</inf>, T<inf>23</inf> under the influence of starch and protein, and T<inf>24</inf> under the influence of starch and lipid. Therefore, it was believed that peak T<inf>21</inf> and T<inf>22</inf> was the signal of bound water (their phases are different) that mainly adsorbed on starch and protein, peak T<inf>23</inf> is the signal of semi-bound water that mainly fettered by starch and protein, and peak T<inf>24</inf> is mainly the signal of free water in seed (a small amount derived from lipid). Results of the temporal observation over the germination process found an interesting pattern of change regarding the water phase states in live seeds. While the unit mass signal amplitude of semi-bound water on a monotonous rise and the rest phase states fluctuate over time, the relaxation time of all signal peaks showed an increasing trend on the whole, and there was no significant fluctuation except T<inf>21</inf>. What's more, 2 new signal peaks that spiking at T<inf>2a</inf> (around 10 ms) and T<inf>2b</inf> (over 1 000 ms) developed when a seed approaches the stage of seed-split and the spikes continued to grow ever since. We called them the "prophet spikes" for they foretell an important change in the seed and was about to split. The approach supply a new angle to interpret T<inf>2</inf> inversion spectra with chemical and NMR detection principle insights and a new reference for in vivo analysis of chemical composition changes during seed germination based on LF-NMR method. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:34
Main heading:Seed
Controlled terms:Agricultural robots - Chemical detection - Cultivation - Nuclear magnetic resonance - Nuclear magnetic resonance spectroscopy - Proteins - Relaxation time - Spectrum analysis - Starch
Uncontrolled terms:Chemical compositions - Development of science and technologies - Forming mechanism - Ginkgo biloba seeds - Low field nuclear magnetic resonance (LF NMR) - Material compositions - Phase distribution - Transverse relaxation time
Classification code:801 Chemistry - 804.1 Organic Compounds - 821.3 Agricultural Methods - 821.4 Agricultural Products - 931 Classical Physics; Quantum Theory; Relativity
Numerical data indexing:Percentage 0.00e+00%, Percentage 1.30e+01%, Percentage 2.93e+01%, Percentage 3.22e+01%, Percentage 4.17e+01%, Percentage 9.17e+01%, Percentage 9.23e+01%, Time 1.00e+00s, Time 1.00e-02s
DOI:10.11975/j.issn.1002-6819.2020.15.038

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109331867
Title:Spatial variability of soil nutrients in topsoil of cultivated land
Title of translation:农田表层土壤养分空间变异特性研究
Authors:Wang, Jie (1, 2); Niu, Wenquan (1, 3); Zhang, Wenqian (1, 2); Li, Guochun (3); Sun, Jun (1, 2); Wang, Yanbang (1, 2)
Author affiliation:(1) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling; 712100, China; (2) College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling; 712100, China; (3) Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling; 712100, China
Corresponding author:Niu, Wenquan(nwq@nwafu.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:37-46
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Crop productivity depends mostly on water management and soil nutrients in the cultivated land. Taking Caoxinzhuang farmland in Yangling as the study area, this study aims to provide a sound basis for the layout of field nutrient monitoring facilities, in order to investigate farmland soil nutrients. Two sampling places were selected, concurrently named as test field 1 (farmland) and test field 2 (farmland), respectively. Specifically, test field 1 was newly reclaimed wasteland, whereas, test field 2 was the cultivated all year round, mainly wheat-corn rotation. The selected field was divided into 12 m12 m nested 6 m6 m plots, based on the soil nutrient samples collected in the topsoil (0-20 cm) of different fields during the growth stage of winter wheat. Classical statistical analysis and Geostatistics with Kriging method were employed to explore the characteristics of soil nutrient variability. SPSS22.0 software was used for the descriptive statistical analysis and normal distribution test. According to the Cochran optimal sampling quantity calculation formula, the optimum sampling number of each nutrient index in the field soil was determined.GS+ software (version 9.0, Gamma Design Software, USA) was used to perform a spatial semi-variogram analysis of soil nutrients, and further to adjust different model parameters for model fitting, including determination coefficient R2. Kriging interpolation and cross validation were carried out using the Geostatistics Analysis module in ArcGIS10.5. Sufer software (version 13.0, Golden Software, USA) was used to represent the spatial variation of parameters, including the soil organic matter (SOM), available phosphorus (AP), available potassium (AK), total nitrogen (TN), nitrate nitrogen (NO<inf>3</inf>--N), and ammonium nitrogen (NH<inf>4</inf>+-N). The results show that during the heading and ripening stages of winter wheat, the variation coefficient (CV) of total nitrogen (TN) <10% in the surface soil of farmland, indicating a weak variation, while, the CV of soil organic matter (SOM) and available phosphorus (AP) were between 10% and 100%, indicating a moderate variation. There was a strong variation coefficient (CV) >100% in the available potassium (AK) and the ammonium nitrogen (NH<inf>4</inf>+-N). The nitrate nitrogen (NO<inf>3</inf>⁻-N) changed from strong variation to moderate variation during the ripening stages of winter wheat. The optimal spherical model can be achieved in the semi-variable function model of soil nutrients. It infers that there were some differences in the spatial correlation of soil nutrients at different stages of crop growth. The nugget coefficient of soil organic matter (SOM) and the total nitrogen (TN) were less than 25% at two growth stages, indicating a strong spatial correlation that mainly affected by structural factors. There was a relatively large variability in the quick-acting nutrients, including the available phosphorus (AP), the available potassium (AK), the nitrate nitrogen (NO<inf>3</inf>--N), and the ammonium nitrogen (NH<inf>4</inf>+-N), where the nugget coefficient was between 25% and 75% at the heading stages of winter wheat, indicating the significant role of random factors. At the ripening stages, the nugget coefficient of quick-acting nutrients was less than 25%, indicating the enhanced spatial correlation. When the sampling interval was expanded from 6 m × 6 m to 12m × 12m, the degree of variation remained constant, while the variation coefficient difference of each index fluctuated within the range of 0.04%-59.48%, except for available potassium (398%) in the ripening stage of test field 2. In each index, the difference of nugget coefficient fluctuated within the range of 0.065%-34.177%, while the spatial variation distribution remained basically consistent. The 12 m12 m grid can be recommended for the topsoil nutrient sampling. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Nutrients
Controlled terms:Biogeochemistry - Crops - Farms - Interpolation - Land use - Nitrates - Normal distribution - Organic compounds - Phosphorus - Potassium - Software testing - Soils - Stages - Statistical methods - Testing - Water management
Uncontrolled terms:Available phosphorus - Available potassiums - Determination coefficients - Kriging interpolation - Soil organic matters - Spatial correlations - Spatial variability - Variation coefficient
Classification code:402.2 Public Buildings - 403 Urban and Regional Planning and Development - 481.2 Geochemistry - 483.1 Soils and Soil Mechanics - 549.1 Alkali Metals - 723.5 Computer Applications - 804 Chemical Products Generally - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 821.4 Agricultural Products - 921.6 Numerical Methods - 922.1 Probability Theory - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 1.00e+01% to 1.00e+02%, Percentage 2.50e+01%, Percentage 2.50e+01% to 7.50e+01%, Percentage 3.98e+02%, Percentage 4.00e-02% to 5.95e+01%, Percentage 6.50e-02% to 3.42e+01%, Size 0.00e+00m to 2.00e-01m, Size 1.20e+01m
DOI:10.11975/j.issn.1002-6819.2020.15.005

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109332337
Title:Effects of increased nitrogen deposition and anthropogenic perturbation on soil respiration in a semiarid grassland
Title of translation:氮沉降增加和人类干扰对半干旱草地土壤呼吸的影响
Authors:Zhao, Xinxin (1); Li, Yulin (2); Li, Youwen (3); Ju, Tianzhen (4)
Author affiliation:(1) School of Environmental Studies, China University of Geosciences, Wuhan; 430074, China; (2) Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou; 730000, China; (3) College of Chemistry and Environmental Science, Kashgar University, Kashgar; 844000, China; (4) School of Earth and Environmental Sciences, Northwestern Normal University, Lanzhou; 730000, China
Corresponding author:Li, Yulin(liyl@lzb.ac.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:120-127
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Soil respiration is the primary pathway for the global carbon cycle, but the response mechanism of soil respiration to global change and anthropogenic perturbation in semiarid grassland in the context of global change is still unclear. In this study, a field experiment was conducted to explore the effects of nitrogen (N) addition, anthropogenic perturbation, and their interactions on soil respiration throughout the growing season from May to September in 2017, in Horqin sandy land, northern China. In the field research, the nitrogen deposition included no nitrogen and nitrogen addition (10 g/(m2•a)), whereas, anthropogenic perturbation consisted of control, burning, and clipping activities. In soil respiration, the components and environmental factors, including soil temperature, soil moisture and soil microbial biomass carbon, were monitored in the whole plant growing season. The monitoring data was used to identify the effects of single factor, such as nitrogen deposition, burning and clipping, and their interaction on soil respiration, as well as the contributions of microbial and root respiration to soil respiration. The results showed that the soil respiration presented obvious seasonal dynamics, with the highest in July. Both soil temperature and soil moisture can regulate the seasonal variability pattern of soil respiration in the semiarid grassland, while nitrogen deposition, burning or clipping cannot alter that. The contribution ratios of microbial respiration to soil respiration were 64.68%, 54.99%, 69.20%, 57.88%, 50.50% and 57.66% under no nitrogen (N0)+control, N0+burning, N0+clipping, nitrogen addition (N10)+control, N10+burning and N10+clipping, respectively, indicating that the microbial respiration was main contributor to soil respiration in this semiarid grassland. The increased nitrogen deposition can remarkably enhance the root respiration by 42% (P<0.001), resulting in a significant increase in soil respiration by 17% (P<0.001). In the nitrogen addition, there was no significant effect on microbial respiration in this semiarid grassland, due to the nitrogen deposition cannot efficiently change soil microbial biomass. It infers that the decrease of soil carbon sequestration induced by nitrogen addition can mainly stem from the increase in the root respiration under the future global nitrogen deposition addition. The burning significantly increased the soil temperature, and thereby enhanced the root respiration by 25% (P<0.01), but it cannot efficiently increased the soil respiration, due to the reduction of microbial respiration that induced by the decrease of soil microbial biomass. Furthermore, the single nitrogen deposition enhanced the positive effect of single burning on soil respiration, indicating that both the nitrogen deposition and burning can be used to synergistically promote the soil respiration in this semiarid grassland. The clipping process significantly reduced the soil temperature by 7% (P<0.001), inducing the root respiration decreased by 20% (P<0.05). Moreover, the clipping significantly decreased the soil microbial biomass, and thus reduced the microbial respiration by 13% (P<0.001), thereby to effectively inhibit the soil respiration (16%, P<0.001). However, the nitrogen deposition and clipping had no significant interaction on the soil respiration. Different influences of nitrogen deposition, burning and clipping on soil respiration can provide the sound basis for the prediction of the soil carbon cycle, and for the scientific management of natural grassland in sandy grassland under the global climate change. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:36
Main heading:Nitrogen
Controlled terms:Biomass - Carbon - Climate change - Forestry - Soil moisture - Temperature
Uncontrolled terms:Environmental factors - Global climate changes - Microbial respiration - Scientific management - Semi-arid grasslands - Soil carbon sequestration - Soil microbial biomass - Soil microbial biomass carbons
Classification code:443.1 Atmospheric Properties - 483.1 Soils and Soil Mechanics - 641.1 Thermodynamics - 804 Chemical Products Generally
Numerical data indexing:Percentage 1.60e+01%, Percentage 5.05e+01%, Percentage 5.50e+01%, Percentage 5.77e+01%, Percentage 5.79e+01%, Percentage 6.47e+01%, Percentage 6.92e+01%
DOI:10.11975/j.issn.1002-6819.2020.15.015

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109331810
Title:Automatic detection model for pest damage symptoms on rice canopy based on improved RetinaNet
Title of translation:改进RetinaNet的水稻冠层害虫为害状自动检测模型
Authors:Yao, Qing (1); Gu, Jiale (1); Lyu, Jun (1); Guo, Longjun (1); Tang, Jian (2); Yang, Baojun (2); Xu, Weigen (3)
Author affiliation:(1) Department of Information, Zhejiang Sci-Tech University, Hangzhou; 310016, China; (2) China National Rice Research Institute, Hangzhou; 311400, China; (3) Plant Protection, Quarantine and Pesticide Management Station of Zhejiang, Hangzhou; 310020, China
Corresponding author:Tang, Jian(tangjian@caas.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:182-188
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:In China, the current field survey methods of pest damage symptoms on rice canopy mainly rely on the forecasting technicians to estimate the sizes and numbers of damage symptoms by visual inspection for estimating the damage level of pests in paddy fields. The manual survey method is subjective, time-consuming, and labor-intensive. In this study, an improved RetinaNet model was proposed to automatically detect the damage symptom regions of two pests (Cnaphalocrocis medinalis and Chilo suppressalis) on rice canopy. This model was composed of one ResNeXt network, an improved feature pyramid network, and two fully convolutional networks (one was class subnet and the other was regression subnet). In this model, ResNeXt101 and Group Normalization were used as the feature extraction network and the normalization method respectively. The feature pyramid network was improved for achieving a higher detection rate of pest damage symptoms. The focal loss function was adopted in this model. All images were divided into two image sets including a training set and a testing set. The training images were augmented by flipping horizontally, enhancing contrast, and adding Gaussian noise methods to prevent overfitting problems. The damage symptom regions in training images were manually labeled by a labeling tool named LabelImg. 6 RetinaNet models based on VGG16, ResNet101, ResNeXt101, data augmentation, improved feature pyramid network, and different normalization methods respectively were developed and trained on the training set. These models were tested on the same testing set. Precision-Recall curves, average precisions and mean average precisions of six models were calculated to evaluate the detection effects of pest damage symptoms on 6 RetinaNet models. All models were trained and tested under the deep learning framework PyTorch and the operating system Ubuntu16.04. The Precision-Recall curves showed that the improved RetinaNet model could achieve higher precision in the same recall rates than the other 5 models. The mean average precision of the model based on ResNeXt101 was 12.37% higher than the model based on VGG16 and 0.95% higher than the model based on ResNet101. It meant that the ResNeXt101 could effectively extract the features of pest damage symptoms on rice canopy than VGG16 and ResNet101. The average precision of the model based on improved feature pyramid network increased by 4.93% in the detection of C. medinalis damage symptoms and the mean average precision increased by 3.36% in the detection of 2 pest damage symptoms. After data augmentation, the mean average precision of the improved model increased by 9.13%. It meant the data augmentation method could significantly improve the generalization ability of the model. The improved RetinaNet model based on ResNeXt101, improved feature pyramid network, group normalization and data augmentation achieved the average precision of 95.65% in the detection of C. medinalis damage symptoms and the average precision of 91.87% in the detection of C. suppressalis damage symptoms. The mean average precision of the damage symptom detection of 2 pests reached 93.76%. These results showed that the improved RetinaNet model improved the detection accuracy and robustness of pest damage symptoms on rice canopy. It took an average time of 0.56 s to detect one image using the improved RetinaNet model, which could meet the realtime detection task of pest damage symptoms on rice canopy. The improved RetinaNet model and its results would provide the field survey data and forecasting of damage symptoms of Cnaphalocrocis medinalis and Chilo suppressalis on the rice canopy. It could be applied in precision spraying pesticides and pest damage symptom patrol by unmanned aerial vehicles. It would realize the intelligent forecasting and monitoring of rice pests, reduce manpower expense, and improve the efficiency and accuracy of the field survey of pest damage symptoms on rice canopy. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:37
Main heading:Damage detection
Controlled terms:Antennas - Convolutional neural networks - Deep learning - Feature extraction - Forecasting - Gaussian noise (electronic) - Image enhancement - Military vehicles - Surveys
Uncontrolled terms:Automatic Detection - Convolutional networks - Generalization ability - Intelligent forecasting - Learning frameworks - Normalization methods - Over fitting problem - Real-time detection
Classification code:404.1 Military Engineering
Numerical data indexing:Percentage 1.24e+01%, Percentage 3.36e+00%, Percentage 4.93e+00%, Percentage 9.13e+00%, Percentage 9.19e+01%, Percentage 9.38e+01%, Percentage 9.50e-01%, Percentage 9.57e+01%
DOI:10.11975/j.issn.1002-6819.2020.15.023

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109331992
Title:Comprehensive evaluation of environmental comfort in layer poultry house using radar graph
Title of translation:基于雷达图的蛋鸡舍综合环境舒适度评价及应用
Authors:Du, Xinyi (1); Teng, Guanghui (1); Du, Xiaodong (1); Liu, Mulin (1); Wang, Chaoyuan (1, 2)
Author affiliation:(1) College of Water Resources and Civil Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs Facility, Beijing; 100083, China
Corresponding author:Teng, Guanghui(futong@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:202-209
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Housing environment has become a driving factor on hen health, egg quality and efficient production in intensive poultry industry. In large layer poultry housing system, an environmental index is typically used to control air conditions, including air temperature (T), relative humidity (RH) and concentration of carbon dioxide (CO<inf>2</inf>) in different season. In hot seasons, thermal environment of laying hen housing is usually assessed using the temperature humidity index (THI) via the online monitored temperature and relative humidity. However, the simple assessment cannot meet the harsh requirement of indoor air quality in a large layer house, as the stocking density increases in recent years. In this paper, a comprehensive environmental index (CEI) was proposed based on the radar map to integrate the impacts of both thermal and air quality factors, in order to systematically assess the environment comfort of the birds. A LabVIEW software system was selected to illustrate the environmental comfort using the fuzzy mathematics. Five key environmental factors, including temperature, RH, air velocity, concentration of CO<inf>2</inf> and ammonia (NH<inf>3</inf>), were used to evaluate the environmental comfort of laying hen housing, where the their weights were normalized in a radar map. In the visualized system, early warning can be delivered when either a single factor or the CEI was over the prescribed value, and thereby a quick action can be taken to adjust the environmental conditions in the layer housing to a comfort level. A field measurement was carried out at a commercial layer breeder farm in Hebei province from January to February, and from July to August, 2019. The experimental results indicated that a good performance of the CEI can be obtained to assess the comprehensive environment in the tested house via monitoring the five environmental indexes. The specific sensors were properly settled in the henhouse. The data of environmental conditions was collected to verify, and then the maximum of five indexes were selected for later use. During the summer daytime, the CEI agreed well with the THI, indicating the positive impacts of thermal environment on the comfort of the birds, while it performed better to integrate the thermal and air quality at night time. In winter, the increase in the weight of CO<inf>2</inf> concentration can contribute to the CEI for the comfort in layer housing system. The in situ monitoring data in the actual use demonstrated that the CEI can efficiently represent the change of comprehensive environmental comfort in the chicken house, and respond to the interaction between environmental factors in each period, especially when the air quality has an increasing influence on the comprehensive environmental comfort at night in summer and in winter. The findings can provide a promising method to systematically evaluate the housing environment via the five key factors in laying hen housing, further improve the birds comfort via precisely controlling the air conditions in intensive poultry production. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:19
Main heading:Air
Controlled terms:Air quality - Ammonia - Birds - Carbon dioxide - Computer programming languages - Houses - Humidity control - Indoor air pollution - Radar
Uncontrolled terms:Comprehensive evaluation - Environmental comfort - Environmental conditions - Environmental factors - Environmental index - Temperature and relative humidity - Temperature humidity index - Thermal environment
Classification code:402.3 Residences - 451 Air Pollution - 451.2 Air Pollution Control - 716.2 Radar Systems and Equipment - 723.1.1 Computer Programming Languages - 804 Chemical Products Generally - 804.2 Inorganic Compounds
DOI:10.11975/j.issn.1002-6819.2020.15.025

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109331772
Title:Precision fertilization by UAV for rice at tillering stage in cold region based on hyperspectral remote sensing prescription map
Title of translation:基于高光谱遥感处方图的寒地分蘖期水稻无人机精准施肥
Authors:Yu, Fenghua (1, 2); Cao, Yingli (1, 2); Xu, Tongyu (1, 2); Guo, Zhonghui (1); Wang, Dingkang (1)
Author affiliation:(1) College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang; 110866, China; (2) Liaoning Agricultural Information Engineering Technology Research Center, Shenyang; 110866, China
Corresponding author:Xu, Tongyu(xutongyu@syau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:103-110
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:The extra-root topdressing of rice at the tillering stage is one of the key steps in the management of rice production; it is also an important stage of fertiliser demand during the entire cycle of rice growth. The efficiency of extra-root topdressing directly affects the number of rice tillers and their growth in the middle and final stages. Due to the rapid advancement of the UAV technology in recent years, agricultural UAV are used for fertiliser spraying in the fields, which not only increased the rice yield but also reduced labour intensity and labour costs to a large extent, and greatly improved the efficiency of rice field management. In order to explore the use of UAV remote sensing to construct prescription maps to guide agricultural UAV to accurately topdressing rice at the tillering stage, realieze the field-scale nutritional diagnosis and UAV precise spraying, optimize fertilizer consumption, and ensure maximum rice yield, in this research, combining UAV remote sensing diagnosis with precision operation of agricultural UAV, UAV hyperspectral technology was used to establish the prescription maps of fertilization amount in rice tillering stage, combined with the operation parameters of agricultural UAV, grid division of fertilizing plots was carried out to determine the amount of precise fertilization, and precision fertilization was carried out by agricultural UAV. The consistent and desired end-member hyperspectral information of the ground features in the rice field were extracted to retrieve the nitrogen content of riceand a rice tillering stage fertilisation prescription map was established based in this, and the fertilization formula map of rice at tillering stage was established. According to the fertilizer amount prescription map, the operation parameters of agricultural UAV were set, and the plots to be fertilized were divided into grids to determine the spraying amount of each grid topdressing operation, and the precision topdressing was carried out by agricultural UAV. Dajiang spirit 4 RTK UAV was used to obtain the orthophoto image of the test fields with spatial information, the actual position of each topdressing grid was determined, and the variable spraying was realized by controlling the working voltage of the liquid medicine pump by PID algorithm. During the spraying process, droplet test cards were arranged on the ground at the same time to calculate the droplet coverage and other parameters such as droplet coverage rate. The results showed that five hyperspectral characteristic variables of rice were extracted in the 450-950 nm band by the method of feature band selection and feature extraction, the effects of rice nitrogen content inversion model constructed by Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) was better than that of Extreme Learning Machine (ELM), and the coefficient of determination was 0.838 and the root mean square error was 0.466. The rice yield of UAV variable topdressing was basically the same as that of traditional topdressing, but the amount of pure nitrogen decreased by 27.34%.The study results can provide data and model basis for the precision variable topdressing of agricultural UAV in the tillering stage of rice in cold regions. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:29
Main heading:Unmanned aerial vehicles (UAV)
Controlled terms:Agricultural robots - Agriculture - Diagnosis - Drops - Efficiency - Feature extraction - Fertilizers - Knowledge acquisition - Machine learning - Mean square error - Nitrogen - Particle swarm optimization (PSO) - Remote sensing - Wages
Uncontrolled terms:Coefficient of determination - Extreme learning machine - Hyper-spectral characteristics - Hyperspectral information - Hyperspectral remote sensing - Operation parameters - Precision fertilizations - Root mean square errors
Classification code:461.6 Medicine and Pharmacology - 652.1 Aircraft, General - 723 Computer Software, Data Handling and Applications - 723.4 Artificial Intelligence - 804 Chemical Products Generally - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 912.4 Personnel - 913.1 Production Engineering - 922.2 Mathematical Statistics
Numerical data indexing:Percentage 2.73e+01%, Size 4.50e-07m to 9.50e-07m
DOI:10.11975/j.issn.1002-6819.2020.15.013

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109332339
Title:Spatiotemperal evolution of land use pattern in the Yellow River Basin (Henan section) from 1990 to 2018
Title of translation:1990-2018年黄河流域(河南段)土地利用格局时空演变
Authors:Xiao, Dongyang (1); Niu, Haipeng (1, 2); Yan, Hongxuan (3, 4); Fan, Liangxin (1, 2); Zhao, Suxia (1, 2)
Author affiliation:(1) School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo; 454000, China; (2) Institute of Ecological Civilization and High-quality Development of Yellow River, Henan Polytechnic University, Jiaozuo; 454000, China; (3) Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing; 100190, China; (4) Center for Forecasting Science, Chinese Academy of Sciences, Beijing; 100190, China
Corresponding author:Niu, Haipeng(niuhaipeng@126.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:271-281
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Understanding the historical change trajectory of Land Use/Land Cover Change (LUCC) is helpful to analyze the land use trends under the interference of human activities and changes in the natural environment, thereby helping decision makers to eliminate the negative impact of unreasonable land use patterns to the greatest extent. Yellow River Basin is the fifth longest river in the world, its ecological environment continues to deteriorate due to the rapid population growth and urban expansion, which has become one of the regions with the most serious soil erosion in China. Aiming to promote the ecological quality and high-quality development of social economy under the human interference and natural environment change, we analyzed the spatiotemporal dynamic of the LUCC in the Yellow River Basin (Henan section) from the watershed scale and proposed policy recommendations. We introduced the chord diagram visualization model to intuitively show the flow, direction and diversity of land cover changes, which would enrich the visual research method system of the land cover quantity transfer trajectory. The land use change index, chord diagram model and gravity center transfer model were used in this paper to analyze the spatial-temporal evolution trajectory and policy implications of the land use pattern in the Yellow River Basin (Henan section) in the past 30 years based on the land use remote sensing data in 1990, 2000, 2010 and 2018. The results showed that: 1) Ecological conservation land (35%) was mainly concentrated in the mountainous areas with higher elevation in the middle reaches; food security land (55%) and production and living land (10%) were mainly distributed in the central and eastern plains. 2) The outflows and inflows of dry land from 1990 to 2018 showed significant dominance; paddy field, water and grassland were dominated by outflow; urban land, rural settlements and other construction land were dominated by inflow. 3) The land use change in central cities had significant regional driving effects throughout the period. 4) The gravity center of food security land and ecological conservation land moved to the West and the area showed a decreasing trend, which proved that the high-quality cultivated land in the plain area in the lower reaches decreased and the ecosystem service function gradually weakened; The shift of gravity center of urban land to the East indicated that the social and economic development in the lower reaches were gradually active. The shift of gravity center of rural residential and other construction land to the West indicated that the population scale in the middle reaches was gradually increasing. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:42
Main heading:Landforms
Controlled terms:Decision making - Economic and social effects - Economics - Ecosystems - Food supply - Forestry - Land use - Population statistics - Public policy - Remote sensing - Rivers - Trajectories - Urban growth - Watersheds
Uncontrolled terms:Ecological conservation - Ecological environments - Ecosystem service functions - Land use/land cover change - Rapid population growth - Social and economic development - Spatial-temporal evolution - Spatio-temporal dynamics
Classification code:403 Urban and Regional Planning and Development - 403.1 Urban Planning and Development - 444.1 Surface Water - 454.3 Ecology and Ecosystems - 481.1 Geology - 822.3 Food Products - 912.2 Management - 971 Social Sciences
Numerical data indexing:Age 3.00e+01yr, Percentage 1.00e+01%, Percentage 3.50e+01%, Percentage 5.50e+01%
DOI:10.11975/j.issn.1002-6819.2020.15.033

Database:Compendex
Compilation and indexing terms, Copyright 2021 Elsevier Inc.

Accession number:20204109331726
Title:Effects of yellow silage additives on methane production and microbial community dynamics during anaerobic digestion of wheat straw
Title of translation:外源添加剂对黄贮小麦秸秆产甲烷潜力及微生物群落的影响
Authors:Yan, Jing (1); Lu, Bingyuan (1); Xi, Huayue (1); Meng, Xingyao (2); Yuan, Xufeng (1); Zhu, Wanbin (1); Cui, Zongjun (1)
Author affiliation:(1) College of Agronomy and Biotechnology, China Agricultural University, Beijing; 100193, China; (2) Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing; 100048, China
Corresponding author:Cui, Zongjun(acuizj@cau.edu.cn)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:252-260
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Straw-biogas-fertilizer has become one of the promoted utilization modes for the agricultural waste, particularly on large amount of crop straw that produced annually in China. However, a long-term storage of straw has posed a great problem on a straw biogas plant. A commonly used method, the yellow silage, can be expected to preserve nutrient, while reduce the dry matter loss of straw during storage. This study aims to explore the effects of yellow silage additives on methane production and microbial community dynamics during anaerobic digestion of wheat straw. The experiment was divided into two parts: yellow silage and anaerobic digestion. Yellow silage treatments were inoculated additives into the dry yellow wheat straw with a moisture content of 50% for 65 days, including CK group (without additives), ACE group (acetic acid addition of 3), MI1 group (lactic acid bacteria community addition of 3), MI2 group (lactic acid bacteria community addition of 6). The results showed that the pH of four groups below 4.7, indicating excellent fermentation quality. Hemicellulose and cellulose decreased during yellow silage, especially CK group only 22.2% and 31.8%, respectively. In the treatment with additives, the hemicellulose and cellulose content were significantly higher than that in the CK group, indicating that the addition of additives was helpful to preserve available nutrients. In the anaerobic digestion experiment, the raw materials were the yellow silage wheat straw (CK, ACE, MI1, MI2 group) and dry yellow wheat straw (WS group). The batch tests were conducted for up to 20 days at (362)℃. The working volume of each reactor was 300 mL, consisting of 12 g (TS) inoculum and 3 g (TS) substrate, with a TS content of 5 % and the remaining space filled with nitrogen gas. Inoculum without any added feedstock was used as a blank. Triplicate reactors were run for each treatment. The biogas production and methane composition were measured every day, whereas, the pH value, volatile fatty acids (VFA), soluble chemical oxygen demand (s COD) were measured during anaerobic digestion. High-throughput sequencing was used to determine the microbial community structure on the twentieth day of anaerobic digestion, in order to detect the effect of yellow silage pretreatment on the bacteria and archaea community in anaerobic fermentation system. The results from the anaerobic experiment showed that the VFA concentration and s COD increased significantly in yellow silage group at the initial stage, where mainly VFA in the fermentation system were lactic acid and acetic acid. As the fermentation time increased, the VFA concentration and s COD decreased after 2 days fermentation, where the propionic acid was the main component of VFA. The cumulative methane yield of ACE group, MI1 group, MI2 group were 213.7, 202.2, 207.9 mL/g, increased by 10.6%, 4.7% and 7.6%, respectively, compared with WS group (193.2 mL/g), while CK group were 175.8 mL/g, decreased by 9.0% compared with WS group. After anaerobic digestion, the main bacteria were Bacteroidetes, Firmicutes, Proteobacteria, while the main archaea were Methanosaeta, indicating that the yellow silage can affect the microbial structure in the fermentation system. This finding can provide an important theoretical and technical support for energy conversion of crop straw in large-scale biogas production. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Number of references:44
Main heading:Anaerobic digestion
Controlled terms:Acetic acid - Additives - Agricultural robots - Bacteria - Biogas - Cellulose - Chemical oxygen demand - Crops - Energy conversion - Fermentation - Lactic acid - Methane - Nutrients - pH - Propionic acid - Straw - Volatile fatty acids
Uncontrolled terms:Anaerobic fermentation - Fermentation qualities - Fermentation systems - High-throughput sequencing - Lactic acid bacteria - Microbial community dynamics - Microbial community structures - Soluble chemical oxygen demands
Classification code:525.5 Energy Conversion Issues - 801.1 Chemistry, General - 803 Chemical Agents and Basic Industrial Chemicals - 804.1 Organic Compounds - 811.3 Cellulose, Lignin and Derivatives - 821.4 Agricultural Products - 821.5 Agricultural Wastes
Numerical data indexing:Age 1.78e-01yr, Age 5.48e-02yr, Age 5.48e-03yr, Percentage 1.06e+01%, Percentage 2.22e+01%, Percentage 3.18e+01%, Percentage 4.70e+00%, Percentage 5.00e+00%, Percentage 5.00e+01%, Percentage 7.60e+00%, Percentage 9.00e+00%, Specific_Volume 1.76e-01m3/kg, Specific_Volume 1.93e-01m3/kg, Specific_Volume 2.08e-01m3/kg, Volume 3.00e-04m3
DOI:10.11975/j.issn.1002-6819.2020.15.031

Accession number:20204109331665
Title:Experiment on combustion characteristic and densified biomass pellets from maize stalk char mixing typical agricultural wastes
Title of translation:玉米秸秆炭和典型农业废弃物混合成型与燃烧特性试验
Authors:Xie, Teng (1, 2); Wang, Yajun (3); Cong, Hongbin (2); Zhao, Lixin (1, 4); Qiu, Ling (1); Yao, Zonglu (4); Kang, Kang (1); Zhu, Mingqiang (1); Zhang, Tianle (1); Huo, Lili (4); Yuan, Yanwen (2)
Author affiliation:(1) College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling; 712100, China; (2) Chinese Academy of Agricultural Engineering, Key Laboratory of Energy Resource Utilization from Agriculture Residues, Ministry of Agriculture and Rural Affairs, Beijing; 100125, China; (3) Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin; 300191, China; (4) Institute of Agriculture Environment and Sustainable Development, Chinese Academy of Agriculture Science, Beijing; 100081, China
Corresponding author:Zhao, Lixin(zhaolixin5092@163.com)
Source title:Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
Abbreviated source title:Nongye Gongcheng Xuebao
Volume:36
Issue:15
Issue date:August 1, 2020
Publication year:2020
Pages:227-234
Language:Chinese
ISSN:10026819
CODEN:NGOXEO
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Engineering
Abstract:Renewable energy, often referred to as clean energy, become necessary in recent years, as the consumption of fossil energy has worsened the environment and global climate. The rich agriculture resources in China can be expected to achieve the typical renewable energy for the sustainable development. Normally, biochar can be used as adsorbing materials, catalyst carrier and fuel, due to its abundant pore structure. This study aims to investigate the combustion characteristic of a molding fuel that fabricated by maize straw char and agricultural wastes. Four kinds of agriculture wastes were selected as adhesive, including the maize stalk, apple tree branch, biogas residue and mushroom dreg, and then molded with maize stalk char to manufactured by a hydraulic granulating machine. The maize stalk char was produced by the slow pyrolysis at 550℃, with the heating rate of 3℃/min. The pressure of all the pellets was 6 MPa, while the content of agriculture waste was 10%-70% in weight. The drop test machine was used to examine the crush resistance of molding fuel and agriculture waste pellets. The results showed that the combination properties of molding fuel depended strongly on the type and content of agricultural wastes during densification. Furthermore, the mechanical strength of molding fuel increased with the increase of agricultural waste content. The durability of molding fuel particles reached 99.68%, when the content of agriculture waste was 70%, with an emphasis on the content of maize stalk. In addition, a chamber with constant temperature and humidity was used to explore the water absorption characteristics of the densified biomass pellets. The water absorption characteristics of molding fuel can be ranked in order, AB70, MD70, MS70, BR70, lower than four kind of agriculture waste pellets. Scanning electron microscope was used to characterize the binding mechanism of molding fuel. The surface of molding fuel became more smoothly as the combustion proceeded. The lignin was regarded as the major component of binder in the process of molding. The thermogravimetric analyzer was used to analyze the combustion characteristics of the molding fuel. The energy density and bulk density of MS70 improved by 4.25 times and 5.06 times, respectively, compared with other agriculture wastes. It infers that the molding fuel can be beneficial to the storage and transportation of agriculture wastes. The energy density of moldin

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