2019 Vol. 18 No. 2 Previous Issue    Next Issue

    Special focus: Digital mapping in agriculture and environment
    Crop Science
    Animal Science · Veterinary Medicine
    Agricultural Economics and Management

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    Special focus: Digital mapping in agriculture and environment
    Editorial- Digital mapping in agriculture and environment
    SHI Zhou, ZHANG Wei-li, TENG Hong-fen
    2019, 18(2): 249-250.  DOI: 10.1016/S2095-3119(19)62580-3
    Abstract ( )   PDF in ScienceDirect  
    Global demand for soil data and information for maintaining and improving agricultural productivity and environmental health is soaring.  The accurate and rapid digital maps of soil characteristics are of key importance for evaluation of soil fertility, precision management of crop inputs, estimation of carbon stocks, and modeling ecological responses as well as environmental threats.  

    The progress in digital soil mapping (DSM) over the last decade provided an improved choice to monitor and map soil characteristics in space and time.  Previous reviews have discussed the history (McBratney et al. 2003; Hartemink et al. 2013; Minasny and McBratney 2016) and the progress in DSM in general (Grunwald et al. 2011; Zhang et al. 2017).  However, the field of DSM has been moving at an accelerated pace and the progress has been observed in all aspects including data organization and quality, soil sampling, environmental covariates, predictive models, and map validation.  In this special issue, the selected eight papers document some of the scopes, developments and progresses in digital mapping in agriculture and environment.

    First four papers documented the progress and developments in predictive models.  Teng et al. (2019) used new methodologies including Collocated CoKriging (ColCOK), geographically weighted regression (GWR) and Random Forest (RF) regression to integrate satellite images, field samples, and ground observations to map the soil loss potential in China.  Cheng et al. (2019) proposed a method of mining soil–environmental relationships from individual soil polygons to update conventional soil maps of the Raffelson watershed in La Crosse County, Wisconsin, United States.  Gao et al. (2019) predicted the spatial variability of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) using geostatistical analysis and regression analysis.  Li et al. (2019) evaluated the spatial variability of soil bulk density and its controlling factors at different soil layers in Southwest China’s agricultural intensive area.

    The following three papers documented the progress in environmental covariate selection, processing and utilization.  Lu et al. (2019) proposed a framework integrating Pearson correlation analysis, generalized additive models (GAMs), and Random Forest (RF) to select environmental covariates for predictive soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.  Wu et al. (2019) used the combination of surface albedo computed from moderate resolution imaging spectroradiometer (MODIS) reflectance products and the actual measured soil moisture data to map an albedo/vegetation coverage trapezoid feature space.  Wang et al. (2019) applied natural language processing (NLP) and rule-based techniques to automatically extract and structure information from soil survey reports regarding soil–environment relationships.

    The last paper talked about soil sampling.  Guo et al. (2019) employed EM38 data to estimate the spatio-temporal variation of soil salinity in different site-specific management zones.  Fuzzy-k means algorithm was used to divide the site-specific management zones and to help sampling design.  

    We believe that the reader both in China and abroad will be interested in these articles and be inspired with the finding of the papers for developing future research on digital mapping in agriculture and environment.  We want to express our deepest appreciation to all the authors for their high-quality contributions and efforts to make this special issue a great success.
    Modelling and mapping soil erosion potential in China
    TENG Hong-fen, HU Jie, ZHOU Yue, ZHOU Lian-qing, SHI Zhou
    2019, 18(2): 251-264.  DOI: 10.1016/S2095-3119(18)62045-3
    Abstract ( )   PDF (22325KB) ( )  
    Soil erosion is an important environmental threat in China.  However, quantitative estimates of soil erosion in China have rarely been reported in the literature.  In this study, soil loss potential in China was estimated by integrating satellite images, field samples, and ground observations based on the Revised Universal Soil Loss Equation (RUSLE).  The rainfall erosivity factor was estimated from merged rainfall data using Collocated CoKriging (ColCOK) and downscaled by geographically weighted regression (GWR).  The Random Forest (RF) regression approach was used as a tool for understanding and predicting the relationship between the soil erodibility factor and a set of environment factors.  Our results show that the average erosion rate in China is 1.44 t ha–1 yr–1.  More than 60% of the territory in China is influenced by soil erosion limitedly, with an average potential erosion rate less than 0.1 t ha–1 yr–1.  Other unused land and other forested woodlands showed the highest erosion risk.  Our estimates are comparable to those of runoff plot studies.  Our results provide a useful tool for soil loss assessments and ecological environment protections.
    Updating conventional soil maps by mining soil–environment relationships from individual soil polygons
    CHENG Wei, ZHU A-xing, QIN Cheng-zhi, QI Feng
    2019, 18(2): 265-278.  DOI: 10.1016/S2095-3119(18)61938-0
    Abstract ( )   PDF (9735KB) ( )  
    Conventional soil maps contain valuable knowledge on soil–environment relationships.  Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.  Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.  Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.  This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.  The proposed method consists of three major steps.  Firstly, the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.  Secondly, for each environmental covariate, these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.  And lastly, the extracted soil–environment relationships are applied to updating the conventional soil map with new, improved environmental data by adopting a soil land inference model (SoLIM) framework.  This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County, Wisconsin, United States.  The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.  Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy. 
    Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China
    GAO Xue-song, XIAO Yi, DENG Liang-ji, LI Qi-quan, WANG Chang-quan, LI Bing, DENG Ou-ping, ZENG Min
    2019, 18(2): 279-289.  DOI: 10.1016/S2095-3119(18)62069-6
    Abstract ( )   PDF (3400KB) ( )  
    Understanding soil nutrient distributions and the factors affecting them are crucial for fertilizer management and environmental protection in vulnerable ecological regions.  Based on 555 soil samples collected in 2012 in Renshou County, located in the purple soil hilly area of Sichuan Basin, China,  the spatial variability of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) was studied with geostatistical analysis and the relative roles of the affecting factors were quantified using regression analysis.  The means of TN, TP and TK contents were 1.12, 0.82 and 9.64 g kg–1, respectively.  The coefficients of variation ranged from 30.56 to 38.75% and the nugget/sill ratios ranged from 0.45 to 0.61, indicating that the three soil nutrients had moderate variability and spatial dependence.  Two distribution patterns were observed.  TP and TK were associated with patterns of obvious spatial distribution trends while the spatial distribution of TN was characterized by higher variability.  Soil group, land use type and topographic factors explained 26.5, 35.6 and 8.4% of TN variability, respectively, with land use being the dominant factor.  Parent material, soil group, land use type and topographic factors explained 17.5, 10.7, 12.0 and 5.0% of TP variability, respectively, and both parent material and land use type played important roles.  Only parent material and soil type contributed to TK variability and could explain 25.1 and 13.7% of TK variability, respectively.  More attention should focus on adopting reasonable land use types for the purposes of fertilizer management and consider the different roles of the affecting factors at the landscape scale in this purple soil hilly area. 
    Spatial variability of soil bulk density and its controlling factors in an agricultural intensive area of Chengdu Plain, Southwest China
    LI Shan, LI Qi-quan, WANG Chang-quan, LI Bing, GAO Xue-song, LI Yi-ding, WU De-yong
    2019, 18(2): 290-300.  DOI: 10.1016/S2095-3119(18)61930-6
    Abstract ( )   PDF (3344KB) ( )  
    Soil bulk density is a basic but important physic soil property related to soil porosity, soil moisture and hydraulic conductivity, which is crucial to soil quality assessment and land use management.  In this study, we evaluated the spatial variability of soil bulk density in the 0–20, 20–40, 40–60 and 60–100 cm layers as well as its affecting factors in Southwest China’s agricultural intensive area.  Results indicated the mean value of surface soil bulk density (0–20 cm) was 1.26 g cm–3, significantly lower than that of subsoil (20–100 cm).  No statistical difference existed among the subsoil with a mean soil bulk density of 1.54 g cm–3.  Spatially, soil bulk density played a similar spatial pattern in soil profile, whereas obvious differences were found in details.  The nugget effects for soil bulk density in the 0–20 and 20–40 cm layers were 27.22 and 27.02% while 12.06 and 3.46% in the 40–60 and 60–100 cm layers, respectively, gradually decreasing in the soil profile, indicating that the spatial variability of soil bulk density above 40 cm was affected by structural and random factors while dominated by structural factors under 40 cm.  Soil organic matter was the controlling factor on the spatial variability of soil bulk density in each layer.  Land use and elevation were another two dominated factor controlling the spatial variability of soil bulk density in the 0–20 and 40–60 cm layers, respectively.  Soil genus was one of the dominated factors controlling the spatial variability of soil bulk below 40 cm. 
    An integrated method of selecting environmental covariates for predictive soil depth mapping
    LU Yuan-yuan, LIU Feng, ZHAO Yu-guo, SONG Xiao-dong, ZHANG Gan-lin
    2019, 18(2): 301-315.  DOI: 10.1016/S2095-3119(18)61936-7
    Abstract ( )   PDF (20438KB) ( )  
    Environmental covariates are the basis of predictive soil mapping.  Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high.  In this study, we proposed an integrated method to select environmental covariates for predictive soil depth mapping.  First, candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge.  Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates.  Finally, three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate.  We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.  A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth.  The results showed that compared to the set of environmental covariates selected by the three conventional selection methods, the set of environmental covariates selected by the proposed method achieved higher mapping accuracy.  The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R2 value of 0.76, which was 0.08–0.26 higher than the other methods.  The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.
    Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover
    WU Cheng-yong, CAO Guang-chao, CHEN Ke-long, E Chong-yi, MAO Ya-hui, ZHAO Shuang-kai, WANG Qi, SU Xiao-yi, WEI Ya-lan
    2019, 18(2): 316-327.  DOI: 10.1016/S2095-3119(18)61988-4
    Abstract ( )   PDF (6857KB) ( )  
    Soil moisture (SM), which plays a crucial role in studies of the climate, ecology, agriculture and the environment, can be estimated and mapped by remote sensing technology over a wide region.  However, remotely sensed SM is constrained by its estimation accuracy, which mainly stems from the influence of vegetation cover on soil spectra information in mixed pixels.  To overcome the low-accuracy defects of existing surface albedo method for estimating SM, in this paper, Qinghai Lake Basin, an important animal husbandry production area in Qinghai Province, China, was chosen as an empirical research area.  Using the surface albedo computed from moderate resolution imaging spectroradiometer (MODIS) reflectance products and the actual measured SM data, an albedo/vegetation coverage trapezoid feature space was constructed.  Bare soil albedo was extracted from the surface albedo mainly containing information of soil, vegetation, and both albedo models for estimating SM were constructed separately.  The accuracy of the bare soil albedo model (root mean square error=4.20, mean absolute percent error=22.75%, and theil inequality coefficient=0.67) was higher than that of the existing surface albedo model (root mean square error=4.66, mean absolute percent error=25.46% and theil inequality coefficient=0.74).  This result indicated that the bare soil albedo greatly improved the accuracy of SM estimation and mapping.  As this method eliminated the effect of vegetation cover and restored the inherent soil spectra, it not only quantitatively estimates and maps SM at regional scales with high accuracy, but also provides a new way of improving the accuracy of soil organic matter estimation and mapping. 
    Automatic extraction and structuration of soil–environment relationship information from soil survey reports
    WANG De-sheng, LIU Jun-zhi, ZHU A-xing, WANG Shu, ZENG Can-ying, MA Tian-wu
    2019, 18(2): 328-339.  DOI: 10.1016/S2095-3119(18)62071-4
    Abstract ( )   PDF (1092KB) ( )  
    In addition to soil samples, conventional soil maps, and experienced soil surveyors, text about soils (e.g., soil survey reports) is an important potential data source for extracting soil–environment relationships.  Considering that the words describing soil–environment relationships are often mixed with unrelated words, the first step is to extract the needed words and organize them in a structured way.  This paper applies natural language processing (NLP) techniques to automatically extract and structure information from soil survey reports regarding soil–environment relationships.  The method includes two steps: (1) construction of a knowledge frame and (2) information extraction using either a rule-based method or a statistic-based method for different types of information.  For uniformly written text information, the rule-based approach was used to extract information.  These types of variables include slope, elevation, accumulated temperature, annual mean temperature, annual precipitation, and frost-free period.  For information contained in text written in diverse styles, the statistic-based method was adopted.  These types of variables include landform and parent material.  The soil species of China soil survey reports were selected as the experimental dataset.  Precision (P), recall (R), and F1-measure (F1) were used to evaluate the performances of the method.  For the rule-based method, the P values were 1, the R values were above 92%, and the F1 values were above 96% for all the involved variables.  For the method based on the conditional random fields (CRFs), the P, R and F1 values for the parent material were, respectively, 84.15, 83.13, and 83.64%; the values for landform were 88.33, 76.81, and 82.17%, respectively.  To explore the impact of text types on the performance of the CRFs-based method, CRFs models were trained and validated separately by the descriptive texts of soil types and typical profiles.  For parent material, the maximum F1 value for the descriptive text of soil types was 90.7%, while the maximum F1 value for the descriptive text of soil profiles was only 75%.  For landform, the maximum F1 value for the descriptive text of soil types was 85.33%, which was similar to that of the descriptive text of soil profiles (i.e., 85.71%).  These results suggest that NLP techniques are effective for the extraction and structuration of soil–environment relationship information from a text data source.
    Using proximal sensor data for soil salinity management and mapping
    GUO Yan, ZHOU Yin, ZHOU Lian-qing, LIU Ting, WANG Lai-gang, CHENG Yong-zheng, HE Jia, ZHENG Guo-qing
    2019, 18(2): 340-349.  DOI: 10.1016/S1671-2927(00)12104
    Abstract ( )   PDF (5151KB) ( )  
    Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China’s coastline.  However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited.  As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies.  In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones.  The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field.  Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity (ECa) data and the spatial correlations of variation.  We concluded that two management zones are optimal to guide precision management.  Zone A had an average salinity level of about 165 mS m–1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil.  In Zone B, there was a low salinity level with a mean of 89 mS m–1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally.  In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content.  Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization. 
    Crop Science
    Discovery of leaf region and time point related modules and genes in maize (Zea mays L.) leaves by Weighted Gene Co-expression Network analysis (WGCNA) of gene expression profiles of carbon metabolism
    WANG Jing-lu, ZHANG Ying, PAN Xiao-di, DU Jian-jun, MA Li-ming, GUO Xin-yu
    2019, 18(2): 350-360.  DOI: 10.1016/S2095-3119(18)62029-5
    Abstract ( )   PDF (1658KB) ( )  
    Maize (Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves.  However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night.  Hence, we used Weighted Gene Co-expression Network analysis (WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day.  There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points.  Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963 (accession number), and quality control of data was based on Pearson correlation coefficients.  We obtained eight co-expression network modules.  The modules with the highest significance of association with LeafRegion and TimePoint were selected.  Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules.  These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.
    Up-regulation of a homeodomain-leucine zipper gene HD-1 contributes to trichome initiation and development in cotton
    NIU Er-li, CAI Cai-ping, BAO Jiang-hao, WU Shuang, ZHAO Liang, GUO Wang-zhen
    2019, 18(2): 361-371.  DOI: 10.1016/S2095-3119(18)61914-8
    Abstract ( )   PDF (2738KB) ( )  
    Plant trichomes originate from epidermal cells.  In this work, we demonstrated that a homeodomain-leucine zipper (HD-Zip) gene, Gh_A06G1283 (GhHD-1A), was related to the leaf trichome trait in allotetraploid cotton and could be a candidate gene for the T1 locus.  The ortholog of GhHD-1A  in the hairless accession Gossypium barbadense cv. Hai7124 was interrupted by a long terminal repeat (LTR) retrotransposon, while GhHD-1A worked well in the hairy accession Gossypium hirsutum acc. T586.  Sequence and phylogenetic analysis showed that GhHD-1A  belonged to the HD-Zip IV gene family, which mainly regulated epidermis hair development in plants.  Silencing of GhHD-1A  and its homoeologs GhHD-1D in allotetraploid T586 and Hai7124 could significantly reduce the density of leaf hairs and affect the expression levels of other genes related to leaf trichome formation.  Further analysis found that GhHD-1A  mainly regulated trichome initiation on the upper epidermal hairs of leaves in cotton, while the up-regulated expression of GhHD-1A  in different organs/tissues also altered epidermal trichome development.  This study not only helps to unravel the important roles of GhHD-1A  in regulating trichome initiation in cotton, but also provides a reference for exploring the different forms of trichome development in plants.
    GsMAPK4, a positive regulator of soybean tolerance to salinity stress
    QIU You-wen, FENG Zhe, FU Ming-ming, YUAN Xiao-han, LUO Chao-chao, YU Yan-bo, FENG Yanzhong, WEI Qi, LI Feng-lan
    2019, 18(2): 372-380.  DOI: 10.1016/S2095-3119(18)61957-4
    Abstract ( )   PDF (2738KB) ( )  
    Salt stress is one of the major factors affecting plant growth and yield in soybean under saline soil condition.  Despite many studies on salinity tolerance of soybean during the past few decades, the detailed signaling pathways and the signaling molecules for salinity tolerance regulation have not been clarified.  In this study, a proteomic technology based on two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) were used to identify proteins responsible for salinity tolerance in soybean plant.  Real-time quantitative PCR (qRT-PCR) and Western blotting (WB) were used to verify the results of 2-DE/MS.  Based on the results of 2-DE and MS, we selected glucosyltransferase (GsGT4), 4-coumarate, coenzyme A ligase (Gs4CL1), mitogen-activated protein kinase 4 (GsMAPK4), dehydration responsive element binding protein (GsDREB1), and soybean cold-regulated gene (GsSRC1) in the salinity tolerant soybean variety, and GsMAPK4 for subsequent research.  We transformed soybean plants with mitogen-activated-protein kinase 4 (GsMAPK4) and screened the resulting transgenics soybean plants using PCR and WB, which confirmed the expression of GsMAPK4 in transgenic soybean.  GsMAPK4-overexpressed transgenic plants showed significantly increased tolerance to salt stress, suggesting that GsMAPK4 played a pivotal role in salinity tolerance.  Our research will provide new insights for better understanding the salinity tolerance regulation at molecular level.
    Alleviation of arsenic toxicity by phosphate is associated with its regulation of detoxification, defense, and transport gene expression in barley
    Gerald Zvobgo, Jonas Lwalaba Wa Lwalaba, Tichaona Sagonda, James Mutemachani Mapodzeke, Noor Muhammad, Imran Haider Shamsi, ZHANG Guo-ping
    2019, 18(2): 381-394.  DOI: 10.1016/S2095-3119(18)61955-0
    Abstract ( )   PDF (3008KB) ( )  
    Arsenic (As) contamination in soils has posed a severe threat to safe crop production.  The previous studies showed the antagonism between phosphorus (P) and As in plant growth and As uptake, while the mechanisms of alleviating As toxicity by P is not completely clear.  Due to the limiting P condition, it is imperative to understand how low P addition can be used to suppress arsenate As (V) uptake and the subsequent mechanisms involved.  Thus in this study we investigated the effect of P addition on As uptake, anti-oxidative enzyme activity, and anti-oxidant content, and the relative expression of transport, defense, and detoxification genes using two barley genotypes differing in As toxicity tolerance.  P addition significantly reduced As concentration in plant tissues, and caused the great changes in activities of catalase and superoxide dismutase, glutathione content, and the relative expression of examined genes when the plants of the two barley genotypes were exposed to 100 µmol L–1 As, with ZDB160 (As-tolerant) being much more affected than ZDB475 (As-sensitive).  The current results show that P addition can alleviate As toxicity by regulating the expression of As transport, defense, and detoxification genes to a greater extent in As tolerance of barley, suggesting the possibility of controlling As uptake and toxicity by applying low amount of P fertilizers in the As-contaminated soils.
    Effects of potassium deficiency on photosynthesis, chloroplast ultrastructure, ROS, and antioxidant activities in maize (Zea mays L.)
    DU Qi, ZHAO Xin-hua, XIA Le, JIANG Chun-ji, WANG Xiao-guang, HAN Yi, WANG Jing, YU Hai-qiu
    2019, 18(2): 395-406.  DOI: 10.1016/S2095-3119(18)61953-7
    Abstract ( )   PDF (5041KB) ( )  
    Potassium (K) deficiency significantly decreases photosynthesis due to leaf chlorosis induced by accumulation of reactive oxygen species (ROS).  But, the physiological mechanism for adjusting antioxidative defense system to protect leaf function in maize (Zea mays L.) is unknown.  In the present study, four maize inbred lines (K-tolerant, 90-21-3 and 099; K-sensitive, D937 and 835) were used to analyze leaf photosynthesis, anatomical structure, chloroplast ultrastructure, ROS, and antioxidant activities.  The results showed that the chlorophyll content, net photosynthetic rate (Pn), stomatal conductance (Gs), photochemical quenching (qP), and electron transport rate of PSII (ETR) in 90-21-3 and 099 were higher than those in D937 and 835 under K deficiency treatment.  Parameters of leaf anatomical structure in D937 that were significantly changed under K deficiency treatment include smaller thickness of leaf, lower epidermis cells, and vascular bundle area, whereas the vascular bundle area, xylem vessel number, and area in 90-21-3 were significantly larger or higher.  D937 also had seriously damaged chloroplasts and PSII reaction centers along with increased superoxide anion (O2-·) and hydrogen peroxide (H2O2).  Activities of antioxidants, like superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX), were significantly stimulated in 90-21-3 resulting in lower levels of O2-· and H2O2.  These results indicated that the K-tolerant maize promoted antioxidant enzyme activities to maintain ROS homeostasis and suffered less oxidative damage on the photosynthetic apparatus, thereby maintaining regular photosynthesis under K deficiency stress.
    Arbuscular mycorrhizal fungi combined with exogenous calcium improves the growth of peanut (Arachis hypogaea L.) seedlings under continuous cropping
    CUI Li, GUO Feng, ZHANG Jia-lei, YANG Sha, MENG Jing-jing, GENG Yun, WANG Quan, LI Xinguo, WAN Shu-bo
    2019, 18(2): 407-416.  DOI: 10.1016/S2095-3119(19)62611-0
    Abstract ( )   PDF (624KB) ( )  
    The growth and yield of peanut are negatively affected by continuous cropping.  Arbuscular mycorrhizal fungi (AMF) and calcium ions (Ca2+) have been used to improve stress resistance in other plants, but little is known about their roles in peanut seedling growth under continuous cropping.  This study investigated the possible roles of the AMF Glomus mosseae combined with exogenous Ca2+ in improving the physiological responses of peanut seedlings under continuous cropping.  G. mosseae combined with exogenous Ca2+ can enhance plant biomass, Ca2+ level, and total chlorophyll content.  Under exogenous Ca2+ application, the Fv/Fm in arbuscular mycorrhizal (AM) plant leaves was higher than that in the control plants when they were exposed to high irradiance levels.  The peroxidase, superoxide dismutase, and catalase activities in AM plant leaves also reached their maximums, and accordingly, the malondialdehyde content was the lowest compared to other treatments.  Additionally, root activity, and content of total phenolics and flavonoids were significantly increased in AM plant roots treated by Ca2+ compared to either G. mosseae inoculation or Ca2+ treatment alone.  Transcription levels of AhCaM, AhCDPK, AhRAM1, and AhRAM2 were significantly improved in AM plant roots under exogenous Ca2+ treatment.  This implied that exogenous Ca2+ might be involved in the regulation of G. mosseae colonization of peanut plants, and in turn, AM symbiosis might activate the Ca2+ signal transduction pathway.  The combination of AMF and Ca2+ benefitted plant growth and development under continuous cropping, suggesting that it is a promising method to cope with the stress caused by continuous cropping.
    Kiwifruit (Actinidia chinensis) R1R2R3-MYB transcription factor AcMYB3R enhances drought and salinity tolerance in Arabidopsis thaliana
    ZHANG Ya-bin, TANG Wei, WANG Li-huan, HU Ya-wen, LIU Xian-wen, LIU Yong-sheng
    2019, 18(2): 417-427.  DOI: 10.1016/S2095-3119(18)62127-6
    Abstract ( )   PDF (7925KB) ( )  
    Kiwifruit is an important fruit crop that is highly sensitive to environmental stresses, such as drought, heat, cold, water logging and phytopathogens.  Therefore it is indispensable to identify stress-responsive candidate genes in kiwifruit cultivars for the stress resistance improvement.  Here we report the isolation and characterization of a novel kiwifruit R1R2R3-MYB homolog (AcMYB3R) whose expression was induced by drought, salinity and cold stress.  In vitro assays showed that AcMYB3R is a nuclear protein with transcriptional activation activity by binding to the cis-element of the kiwifruit orthologue of G2/M phase-specific gene KNOLLE.  The Arabidopsis transgenic plants overexpressing AcMYB3R showed drastically enhanced tolerance to drought and salt stress.  The expressions of stress-responsive genes such as RD29A, RD29B, COR15A and RD22 were prominently up-regulated by ectopic expression of AcMYB3R.  Our study provides a valuable piece of information for functional genomics studies of kiwifruit and molecular breeding in improving stress tolerance for crop production.
    Protective roles of trehalose in Pleurotus pulmonarius during heat stress response
    LIU Xiu-ming, WU Xiang-li, GAO Wei, QU Ji-bin, CHEN Qiang, HUANG Chen-yang, ZHANG Jin-xia
    2019, 18(2): 428-437.  DOI: 10.1016/S2095-3119(18)62010-6
    Abstract ( )   PDF (948KB) ( )  
    High temperature is one of the major abiotic stresses that limit edible mushroom growth and development.  The understanding of physiological alterations in response to heat stress and the corresponding mechanisms involved is vital for the breeding of heat-resistant edible mushroom strains.  Although trehalose functions as a protectant against abiotic stresses in fungi, the putative role of trehalose in thermotolerance remains to be elucidated.  In this study, we found heat stress inhibited the growth of two Pleurotus pulmonarius strains, heat-sensitive and less-sensitive, and the inhibition was more significant for the sensitive strain.  Heat stress leads to the increase of lipid peroxidation and intracellular trehalose accumulation, with a higher level in the heat-sensitive strain, and this effect is independent of exogenous trehalose application. In addition, a lower concentration of exogenous trehalose application in sensitive strain than in less-sensitive strain was found to alleviate the inhibition of mycelium growth and further increase the intracellular trehalose concentration by heat stress.  Thus, the protective effects of trehalose were more remarkable in the sensitive strain.  The activities of intracellular trehalose metabolic enzymes, i.e., trehalose-6-phosphate synthase, trehalose phosphorylase and neutral trehalase, were determined, and our data indicated that the changes of these enzymes activities in the sensitive strain were more beneficial to accumulate trehalose than that in the less-sensitive strain.
    Animal Science · Veterinary Medicine
    Inhibition of KU70 and KU80 by CRISPR interference, not NgAgo interference, increases the efficiency of homologous recombination in pig fetal fibroblasts
    LI Guo-ling, QUAN Rong, WANG Hao-qiang, RUAN Xiao-fang, MO Jian-xin, ZHONG Cui-li, YANG Huaqiang, LI Zi-cong, GU Ting, LIU De-wu, WU Zhen-fang, CAI Geng-yuan, ZHANG Xian-wei
    2019, 18(2): 438-448.  DOI: 10.1016/S2095-3119(18)62150-1
    Abstract ( )   PDF (765KB) ( )  
    Non-homologous end-joining (NHEJ) is a predominant pathway for the repair of DNA double-strand breaks (DSB).  It inhibits the efficiency of homologous recombination (HR) by competing for DSB targets.  To improve the efficiency of HR, multiple CRISPR interference (CRISPRi) and Natronobacterium gregoryi Argonaute (NgAgo) interference (NgAgoi) systems have been designed for the knockdown of NHEJ key molecules, KU70, KU80, polynucleotide kinase/phosphatase (PNKP), DNA ligase IV (LIG4), and NHEJ1.  Suppression of KU70 and KU80 by CRISPRi dramatically promoted (P<0.05) the efficiency of HR to 1.85- and 1.58-fold, respectively, whereas knockdown of PNKP, LIG4, and NHEJ1 repair factors did not significantly increase (P>0.05) HR efficiency.  Interestingly, although the NgAgoi system significantly suppressed (P<0.05) KU70, KU80, PNKP, LIG4, and NHEJ1 expression, it did not improve (P>0.05) HR efficiency in primary fetal fibroblasts.  Our result showed that both NgAgo and catalytically inactive Cas9 (dCas9) could interfere with the expression of target genes, but the downstream factors appear to be more active following CRISPR-mediated interference than that of NgAgo. 
    miR-34c inhibits proliferation and enhances apoptosis in immature porcine Sertoli cells by targeting the SMAD7 gene
    RAN Mao-liang, WENG Bo, CAO Rong, PENG Fu-zhi, LUO Hui, GAO Hu, CHEN Bin
    2019, 18(2): 449-459.  DOI: 10.1016/S2095-3119(19)62612-2
    Abstract ( )   PDF (4350KB) ( )  
    MicroRNAs (miRNAs) are implicated in swine spermatogenesis via their regulations of cell proliferation, apoptosis, and differentiation.  Recent studies indicated that miR-34c is indispensable in the late steps of spermatogenesis.  However, whether miR-34c plays similar important roles in immature porcine Sertoli cells remain unknown.  In the present study, we conducted two experiments using a completely randomised design to study the function roles of miR-34c.  The results from experiment I demonstrated that the relative expression level of miR-34c in swine testicular tissues increased (P=0.0017) quadratically with increasing age, while the relative expression level of SMAD family member 7 (SMAD7 ) decreased (P=0.0009) with curve.  Furthermore, miR-34c expression levels showed a significant negative correlation (P=0.013) with SMAD7 gene expression levels.  The results from experiment II indicated that miR-34c directly targets the SMAD7 gene using a luciferase reporter assay, and suppresses (P<0.05) SMAD7 mRNA and protein expressions in immature porcine Sertoli cells.  Overexpression of miR-34c inhibited (P<0.05) proliferation and enhanced (P<0.05) apoptosis in the immature porcine Sertoli cells, which was supported by the results from the Cell Counting Kit-8 (CCK-8) assay, the 5-Ethynyl-2´-deoxyuridine (EdU) assay, and the Annexin V-FITC/PI staining assay.  Furthermore, knockdown of SMAD7 via small interfering RNA (siRNA) gave a similar result.  It is concluded that miR-34c inhibits proliferation and enhances apoptosis in immature porcine Sertoli cells by targeting the SMAD7 gene.
    Agricultural Economics and Management
    Maize production under risk: The simultaneous adoption of off-farm income diversification and agricultural credit to manage risk
    Shoaib Akhtar, LI Gu-cheng, Adnan Nazir, Amar Razzaq, Raza Ullah, Muhammad Faisal, Muhammad Asad Ur Rehman Naseer, Muhammad Haseeb Raza
    2019, 18(2): 460-470.  DOI: 10.1016/S2095-3119(18)61968-9
    Abstract ( )   PDF (866KB) ( )  
    Farmers in Pakistan continue to produce maize under various types of risks and adopt several strategies to manage those risks.  This study is the first attempt to investigate the factors affecting the concurrent adoption of off-farm income diversification and agricultural credit which the farmers use to manage the risk to maize production.  We apply bivariate and multinomial probit approaches to the primary data collected from four districts of Punjab Province in Pakistan.  The results show that strong correlations exist between the off-farm diversification and agricultural credit which indicates that the use of one risk management strategy leads to another.  The findings demonstrate that education, livestock number, maize farming experience, perceptions of biological risks and risk-averse nature of the growers significantly encourage the adoption of diversification as a risk management tool while farm size inversely affects the adoption of diversification.  Similarly, in the adoption equation of credit, maize farming experience, farm size, perceptions of price and biological risks and risk attitude of farmers significantly enhance the chances of adopting agricultural credit to manage farm risks.  These findings are important for the relevant stakeholders who seek to offer carefully designed risk minimizing options to the maize farmers. 
    Factors affecting the adoption of on-farm milk safety measures in Northern China - An examination from the perspective of farm size and production type
    YANG Xin-ran, Kevin Z. Chen, KONG Xiang-zhi
    2019, 18(2): 471-481.  DOI: 10.1016/S2095-3119(19)62567-0
    Abstract ( )   PDF (1042KB) ( )  
    The cow stock of smallholder farmers with less than 100 cows still accounted for nearly 50% of total cows in China.  Since the milk scandal occurred in 2008, raw milk safety has become focus for the sound development of the Chinese dairy industry. Adoption of on-farm milk safety measures by smallholders is a key for ensuring milk safety, and these measures are largely voluntary in nature.  The recent survey conducted in northern China reveals that an overall adoption rate of various milk safety measures by smallholders is close to 48% with wide variations across the dairy farmers.  We employ the Poisson regression model to study the determinants of farmers’ adoption of voluntary milk safety measures.  Compared with backyard dairy farmers, dairy complex and scaled dairy farms adopted more milk safety measures.  Moreover, the empirical result indicates that farmers’ adoption of raw milk safety measures is positively affected by farm size.  These findings suggest that the changing dairy production structure towards larger farms and away from backyard dairy farming prompts smallholder dairy farmers to adopt more raw milk safety measures.  This lends some support to the role of recent policy initiatives towards larger farms and away from backyard dairy farming on increasing the farmers’ milk safety practices and reducing on-farm incidence of milk safety.