Journal of Integrative Agriculture

EDITORIAL-Remote sensing for agricultural applications

Zhengwei Yang, WU Wen-bin, Liping Di, Berk üstünda?

2017, 16(02): 239-241. DOI: 10.1016/S2095-3119(16)61549-6

Abstract ( ) PDF in ScienceDirect

Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

ZHOU Qing-bo, YU Qiang-yi, LIU Jia, WU Wen-bin, TANG Hua-jun

2017, 16(02): 242-251. DOI: 10.1016/S2095-3119(16)61479-X

Abstract ( ) PDF in ScienceDirect

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese “High Resolution Earth Observation Systems”, China is able to receive superb high-resolution remotely sensed images (GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1’s high resolution (i.e., 2 m/8 m), high revisit cycle (i.e., 4 days), and its visible and near-infrared (VNIR) spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system (CHARMS). However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.

An adaptive Mealy machine model for monitoring crop status

Berk üstünda?

2017, 16(02): 252-265. DOI: 10.1016/S2095-3119(16)61430-2

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Variation in phenological stage is the major nonlinearity in monitoring, modeling and various estimations of agricultural systems. Indices are used as a common means of evaluating agricultural monitoring data from remote sensing and terrestrial observation systems, and many of these indices have linear characteristics. The analysis of and relationships between indices are dependent on the type of plant, but they are also highly variable with respect to its phenological stage. For this reason, variations in the phenological stage affect the performance of spatiotemporal crop status monitoring. We hereby propose an adaptive event-triggered model for monitoring crop status based on remote sensing data and terrestrial observations. In the proposed model, the estimation of phenological stage is a part of predicting crop status, and spatially distributed remote sensing parameters and temporal terrestrial monitoring data are used together as inputs in a state space system model. The temporal data are segmented with respect to the phenological stage-oriented timing of the spatial data, so instead of a generalized discrete state space model, we used logical states combined with analog inputs and adaptive trigger functions, as in the case of a Mealy machine model. This provides the necessary nonlinearity for the state transitions. The results showed that observation parameters have considerably greater significance in crop status monitoring with respect to conventional agricultural data fusion techniques.

Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat

LI He, CHEN Zhong-xin, JIANG Zhi-wei, WU Wen-bin, REN Jian-qiang, LIU Bin, Tuya Hasi

2017, 16(02): 266-285. DOI: 10.1016/S2095-3119(15)61293-X

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Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 (GF-1) wide field of view (WFV) camera, environment and disaster monitoring and forecasting satellite (HJ-1) charge coupled device (CCD), and Landsat-8 operational land imager (OLI) data for estimating the leaf area index (LAI) of winter wheat via reflectance and vegetation indices (VIs). The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations: (1) The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination (R²) between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R² between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors’ images can all be used for cross calibration of the reflectance and VIs. (2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI (R² between 0.703 and 0.849). The linear models associated with the 2-band enhanced vegetation index (EVI2), which feature the highest R² (higher than 0.746) and the lowest root mean square errors (RMSE) (less than 0.21), were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors (RE) of 2.18% and an RMSE of 0.13, while the HJ-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. (3) The inversion errors in the different sensors’ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the HJ-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. (4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

LI Zhen-wang, XIN Xiao-ping, TANG Huan, YANG Fan, CHEN Bao-rui, ZHANG Bao-hui

2017, 16(02): 286-297. DOI: 10.1016/S2095-3119(15)61303-X

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Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests (RF) method aggregates an ensemble of decision trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data.
Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.

Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China

CHEN Xiao-yu, LIN Ya, ZHANG Min, YU Le, LI Hao-chuan, BAI Yu-qi

2017, 16(02): 298-311. DOI: 10.1016/S2095-3119(16)61442-9

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Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer (MODIS) land cover product, global land cover map of 2009 (GlobCover2009), finer resolution observation and monitoring of global cropland (FROM-GC) and 30-m global land cover dataset (GlobeLand30). The temporal coverage of these four datasets are circa 2010. One of the typical agricultural regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects: accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy (OA) of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement (mainly located in the plain ecotope of Shaanxi) and the non-cropland spatial agreement (sparsely distributed in the south and middle of Shaanxi) of the four datasets only makes up 33.96% of the whole province. FROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region. The approaches taken in this study could be used to derive a fused cropland classification dataset.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Claire G. Boryan, Zhengwei Yang, Patrick Willis, Liping Di

2017, 16(02): 312-323. DOI: 10.1016/S2095-3119(16)61396-5

Abstract ( ) PDF in ScienceDirect

Area Sampling Frames (ASFs) are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008–2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota (SD) U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit (PSU) level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service (NASS) ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency (FSA) Common Land Unit (CLU) and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

How do temporal and spectral features matter in crop classification in Heilongjiang Province, China?

HU Qiong, WU Wen-bin, SONG Qian, LU Miao, CHEN Di, YU Qiang-yi, TANG Hua-jun

2017, 16(02): 324-336. DOI: 10.1016/S2095-3119(15)61321-1

Abstract ( ) PDF in ScienceDirect

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer (MODIS) surface reflectance product (MOD09A1) data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index (SI) was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index (NDTI), land surface water index (LSWI) and enhanced vegetation index (EVI) are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy (producers’ accuracy of 94.03% and users’ accuracy of 93.77%) with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.

Mapping regional cropping patterns by using GF-1 WFV sensor data

SONG Qian, ZHOU Qing-bo, WU Wen-bin, HU Qiong, LU Miao, LIU Shu-bin

2017, 16(02): 337-347. DOI: 10.1016/S2095-3119(16)61392-8

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The successful launched Gaofen satellite no. 1 wide field-of-view (GF-1 WFV) camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei’an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest (RF) algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can improve the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

TAO Jian-bin, WU Wen-bin, ZHOU Yong, WANG Yu, JIANG Yan

2017, 16(02): 348-359. DOI: 10.1016/S2095-3119(15)61304-1

Abstract ( ) PDF in ScienceDirect

By employing the unique phenological feature of winter wheat extracted from peak before winter (PBW) and the advantages of moderate resolution imaging spectroradiometer (MODIS) data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak (MP). The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Spatio-temporal changes in rice area at the northern limits of the rice cropping system in China from 1984 to 2013

LI Zhi-peng, LONG Yu-qiao, TANG Peng-qin, TAN Jie-yang, LI Zheng-guo, WU Wen-bin, HU Ya-nan, YANG Peng

2017, 16(02): 360-367. DOI: 10.1016/S2095-3119(16)61365-5

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Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China. However, the spatio-temporal dynamic changes in rice area are still unclear, although they may have important policy implications for environmental protection and adaptation to climate change. In this study, we aimed to identify the dynamic changes of the rice area in Heilongjiang Province of northeastern China by extracting data from multiple Landsat images. The study used ground quadrats selected from Google Earth and the extraction of a confusion matrix to verify the accuracy of extraction. The overall accuracy of the extracted rice area was higher than 95% as a result of using the artificial neural network (ANN) classification method. The results showed that the rice area increased by approximately 2.4×10⁶ ha during the past 30 years at an annual rate of 8.0×10⁴ ha, and most of the increase occurred after 2000. The central latitude of the rice area shifted northwards from 46 to 47°N during the study period, and moved eastwards from 130 to 133°E. The rice expansion area accounted for 98% of the total change in rice area, and rice loss was notably rare. The rice expansion was primarily from dryland. In addition, rice cultivation in marshland and grassland played a minor role in the rice expansion in this region.

Telecoupled land-use changes in distant countries

Jing Sun, TONG Yu-xin, Jianguo Liu

2017, 16(02): 368-376. DOI: 10.1016/S2095-3119(16)61528-9

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International food trade has become a key driving force of agricultural land-use changes in trading countries, which has influenced food production and the global environment. Researchers have studied agricultural land-use changes and related environmental issues across multi-trading countries together, but most studies rely on statistic data without spatial attributes. However, agricultural land-use changes are spatially heterogeneous. Uncovering spatial attributes can reveal more critical information that is of scientific significance and has policy implications for enhancing food security and protecting the environment. Based on an integrated framework of telecoupling (socioeconomic and environmental interactions over distances), we studied spatial attributes of soybean land changes within and among trading countries at the same time. Three distant countries - Brazil, China, and the United States - constitute an excellent example of telecoupled systems through the process of soybean trade. Our results presented the spatial distribution of soybean land changes - highlighting the hotspots of soybean gain and soybean loss, and indicated these changes were spatially clustered, different across multi-spatial scales, and varied among the trading countries. Assisted by the results, global challenges like food security and biodiversity loss within and among trading countries can be targeted and managed efficiently. Our work provides simultaneously spatial information for understanding agricultural land-use changes caused by international food trade globally, highlights the needs of coordination among trading countries, and promotes global sustainability.

Assessment for soil loss by using a scheme of alterative sub-models based on the RUSLE in a Karst Basin of Southwest China

CHEN Hao, Takashi Oguchi, WU Pan

2017, 16(02): 377-388. DOI: 10.1016/S2095-3119(16)61507-1

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Accurate assessment of soil loss caused by rainfall is essential for natural and agricultural resources management. Soil erosion directly affects the environment and human sustainability. In this work, the empirical and contemporary model of revised universal soil loss equation (RUSLE) was applied for simulating the soil erosion rate in a karst catchment using remote sensing data and geographical information systems. A scheme of alterative sub-models was adopted to calculate the rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors in the geographic information system (GIS) environment. A map showing the potential of soil erosion rate was produced by the RUSLE and it indicated the severe soil erosion in the study area. Six classes of erosion rate are distinguished from the map: 1) minimal, 2) low, 3) medium, 4) high, 5) very high, and 6) extremely high. The RUSLE gave a mean annual erosion rate of 30.24 Mg ha^–1 yr^–1 from the 1980s to 2000s. The mean annual erosion rate obtained using RUSLE is consistent with the result of previous research based on in situ measurement from 1980 to 2009. The high performance of the RUSLE model indicates the reliability of the sub-models and possibility of applying the RUSLE on quantitative estimation. The result of the RUSLE model is sensitive to the slope steepness, slope length, vegetation factors and digital elevation model (DEM) resolution. The study suggests that attention should be given to the topographic factors and DEM resolution when applying the RUSLE on quantitative estimation of soil loss.

Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China

BAI Jian-jun, YU Yuan, Liping Di

2017, 16(02): 389-397. DOI: 10.1016/S2095-3119(15)61302-8

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Temperature vegetation dryness index (TVDI) and crop water stress index (CWSI) are two commonly used remote sensing-based agricultural drought indicators. This study explored the applicability of monthly moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST) data for agricultural drought monitoring in the Guanzhong Plain, China in 2003. The data were processed using TVDI, calculated by parameterizing the relationship between the MODIS NDVI and LST data. We compared the effectiveness of TVDI against CWSI, derived from the MOD16 products, for drought monitoring. In addition, the surface soil moisture and monthly precipitation were collected and used for verification of the results. Results from the study showed that: (1) drought conditions measured by TVDI and CWSI had a number of similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity of this region; and (2) both standardized precipitation index (SPI) and SM contents at the depth of 10 and 20 cm had better correlations to CWSI than to TVDI, indicating that there were more statistically significant relationships between CWSI and SPI/SM, and that CWSI is a more reliable indicator for assessing and monitoring droughts in this region.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Ranjay Shrestha, Liping Di, Eugene G. Yu, Lingjun Kang, SHAO Yuan-zheng, BAI Yu-qi

2017, 16(02): 398-407. DOI: 10.1016/S2095-3119(16)61502-2

Abstract ( ) PDF in ScienceDirect

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer (MODIS) weekly normalized difference vegetation index (NDVI) product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels (>90%) masked by the United States Department of Agriculture (USDA) Cropland Data Layer (CDL). The NDVI-based time-series difference between flood years and normal year (median of years 2000–2014) was used to detect flood occurrences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R² value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error (4.8%) of leave-one-out cross validation (LOOCV) along with smaller statistical error indicators (root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE)), further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Liping Di, Eugene G. Yu, Lingjun Kang, Ranjay Shrestha, BAI Yu-qi

2017, 16(02): 408-423. DOI: 10.1016/S2095-3119(16)61499-5

Abstract ( ) PDF in ScienceDirect

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in agricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers’ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation (EO) data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS), for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Creation of gene-specific rice mutants by AvrXa23-based TALENs

WANG Fu-jun, WANG Chun-lian, ZHENG Chong-ke, QIN Teng-fei, GAO Ying, LIU Pi-qing, ZHAO Kai-jun

2017, 16(02): 424-434. DOI: 10.1016/S2095-3119(16)61411-9

Abstract ( ) PDF in ScienceDirect

Transcription activator-like effector (TALE) nucleases (TALENs) are increasingly used as a powerful tool for genome editing in a variety of organisms. We have previously cloned the TALE-coding gene avrXa23 from Xanthomonas oryzae pv. oryzae and developed an AvrXa23-based assembly system for designer TALEs or TALENs. Here, we exploit TALENs to induce mutagenesis of the rice ethylene response factor (ERF) transcription factor OsERF922 for testing the gene-editing efficiency of AvrXa23-based TALENs system. A pair of TALENs (T-KJ9/KJ10) was assembled and their nuclease activities were first confirmed in rice protoplast transient assay. The TALENs-expressing construct pT-KJ9/KJ10 was then used for rice transformation. We observed targeting somatic mutagenesis frequency of 15.0% in positive transgenic rice calli and obtained two mutant plants with nucleotide deletion or insertion at the designer target region. Our work demonstrates that the AvrXa23-based TALENs system can be used for site-specific genome editing in rice.

QTL mapping revealed TaVp-1A conferred pre-harvest sprouting resistance in wheat population Yanda 1817×Beinong 6

ZHOU Sheng-hui, FU Lin, WU Qiu-hong, CHEN Jiao-jiao, CHEN Yong-xing, XIE Jing-zhong, WANG Zhen-zhong, WANG Guo-xin, ZHANG De-yun, LIANG Yong, ZHANG Yan, OU Ming-shan, LIANG Rong-qi, HAN Jun, LIU Zhi-yong

2017, 16(02): 435-444. DOI: 10.1016/S2095-3119(16)61361-8

Abstract ( ) PDF in ScienceDirect

Pre-harvest sprouting (PHS) occurs frequently in most of the wheat cultivation area worldwide, which severely reduces yield and end-use quality, resulting in substantial economic loss. In this study, quantitative trait loci (QTL) for PHS resistance were mapped using an available high-density single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) genetic linkage map developed from a 269 recombinant inbred lines (RILs) population of Yanda 1817×Beinong 6. Using phenotypic data on two locations (Beijing and Shijiazhuang, China) in two years (2012 and 2013 harvesting seasons), five QTLs, designated as QPhs.cau-3A.1, QPhs.cau-3A.2, QPhs.cau-5B, QPhs.cau-4A, and QPhs.cau-6A, for PHS (GP) were detected by inclusive composite interval mapping (ICIM) (LOD≥2.5). Two major QTLs, QPhs.cau-3A.2 and QPhs.cau-5B, were mapped on 3AL and 5BS chromosome arms, explaining 6.29–21.65% and 4.36–5.94% of the phenotypic variance, respectively. Precise mapping and comparative genomic analysis revealed that the TaVp-1A flanking region on 3AL is responsible for QPhs.cau-3A.2. SNP markers flanking QPhs.cau-3A.2 genomic region were developed and could be used for introgression of PHS tolerance into high yielding wheat varieties through marker-assisted selection (MAS).

Identification of a major QTL for flag leaf glaucousness using a high-density SNP marker genetic map in hexaploid wheat

LI Chun-lian, LI Ting-ting, LIU Tian-xiang, SUN Zhong-pei, BAI Gui-hua, JIN Feng, WANG Yong, WANG Zhong-hua

2017, 16(02): 445-453. DOI: 10.1016/S2095-3119(16)61339-4

Abstract ( ) PDF in ScienceDirect

Cuticular wax plays an important role in protecting land plant against biotic and abiotic stresses. Cuticular wax production on plant surface is often visualized by a characteristic glaucous appearance. This study identified quantitative trait loci (QTLs) for wheat (Triticum aestivum L.) flag leaf glaucousness (FLG) using a high-density genetic linkage map developed from a recombinant inbred line (RIL) population derived from the cross Heyne×Lakin by single-seed descent. The map consisted of 2 068 single nucleotide polymorphism (SNP) markers and 157 simple sequence repeat (SSR) markers on all 21 wheat chromosomes and covered a genetic distance of 2 381.19 cM, with an average marker interval of 1.07 cM. Two additive QTLs for FLG were identified on chromosomes 3AL and 2DS with the increasing FLG allele contributed from Lakin. The major QTL on 3AL, QFlg.hwwgr-3AL, explained 17.5–37.8% of the phenotypic variation in different environments. QFlg.hwwgr-3AL was located in a 4.4-cM interval on chromosome 3AL that was flanked by two markers IWA1831 and IWA8374. Another QTL for FLG on 2DS, designated as QFlg.hwwgr-2DS which was identified only in Yangling in 2014 (YL14), was flanked by IWA1939 and Xgwm261 and accounted for 11.3% of the phenotypic variation for FLG. QFlg.hwwgr-3AL and QFlg.hwwgr-2DS showed Additive×Environment (AE) interactions, explaining 3.5 and 4.4% of the phenotypic variance, respectively. Our results indicated that different genes/QTLs may contribute different scores of FLG in a cultivar and that the environment may play a role in FLG.

Genetic dissection of the sensory and textural properties of Chinese white noodles using a specific RIL population

LI Wen-jing, DENG Zhi-ying, CHEN Guang-feng, CHEN Fang, LI Xing-feng, TIAN Ji-chun

2017, 16(02): 454-463. DOI: 10.1016/S2095-3119(16)61412-0

Abstract ( ) PDF in ScienceDirect

To dissect the genetic control of the sensory and textural quality traits of Chinese white noodles, a population of recombinant inbred lines (RILs), derived from the cross of waxy wheat Nuomai 1 (NM1) and Gaocheng 8901 (Gc8901), was used. The RILs were tested in three different environments to determine the role of environmental effects on quantitative trait loci (QTL) analysis. A total of 45 QTLs with additive effects for 17 noodle sensory and textural properties under three environments were mapped on 15 chromosomes. These QTLs showed 4.23–42.68% of the phenotypic variance explained (PVE). Nineteen major QTLs were distributed on chromosomes 1B, 1D, 2A, 3B, 3D, 4A, and 6A, explaining more than 10% of the phenotypic variance (PV). Clusters were detected on chromosomes 2B (3 QTLs), 3B (11 QTLs) and 4A (5 QTLs). The cluster detected on chromosome 4A was close to the Wx-B1 marker. Five co-located QTLs with additive effects were identified on chromosomes 2B, 3D, 4A, 6A, and 7B. The two major QTLs, Qadh.sdau-3B.1 and Qspr.sdau-3B.1, in cluster wPt666008–wPt5870 on chromosome 3B were detected in three different environments, which perhaps can be directly applied to improve the textural properties of noodles. These findings could offer evidence for the selection or development of new wheat varieties with noodle quality using molecular marker-assisted selection (MAS).

Development of SNP markers using RNA-seq technology and tetra-primer ARMS-PCR in sweetpotato

KOU Meng, XU Jia-lei, LI Qiang, LIU Ya-ju, WANG Xin, TANG Wei, YAN Hui, ZHANG Yun-gang, MA Dai-fu

2017, 16(02): 464-470. DOI: 10.1016/S2095-3119(16)61405-3

Abstract ( ) PDF in ScienceDirect

The information of single nucleotide polymorphisms (SNPs) is quite unknown in sweetpotato. In this study, two sweetpotato varieties (Xushu 18 and Xu 781) were sequenced by Illumina technology, as well as de novo transcriptome assembly, functional annotation, and in silico discovery of potential SNP molecular markers. Tetra-primer Amplification Refractory Mutation System PCR (ARMS-PCR) is a simple and sufficient method for detecting different alleles in SNP locus. Total 153 sets of ARMS-PCR primers were designed to validate the putative SNPs from sequences. PCR products from 103 sets of primers were different between Xu 781 and Xushu 18 via agarose gel electrophoresis, and the detection rate was 67.32%. We obtained the expected results from 32 sets of primers between the two genotypes. Furthermore, we ascertained the optimal annealing temperature of 32 sets of primers. These SNPs might be used in genotyping, QTL mapping, or marker-assisted trait selection further in sweetpotato. To our knowledge, this work was the first study to develop SNP markers in sweetpotato by using tetra-primer ARMS-PCR technique. This method was a simple, rapid, and useful technique to develop SNP markers, and will provide a potential and preliminary application in discriminating cultivars in sweetpotato.

Changes of chlorogenic acid content and its synthesis-associated genes expression in Xuehua pear fruit during development

HE Jin-gang, CHENG Yu-dou, GUAN Jun-feng, GE Wen-ya, ZHAO Zhe

2017, 16(02): 471-477. DOI: 10.1016/S2095-3119(16)61496-X

Abstract ( ) PDF in ScienceDirect

According to synthetic pathway of plant chlorogenic acid (CGA), the expression patterns of genes encoding enzymes that are associated with CGA synthesis were studied in normally developed Xuehua pear fruit. The study demonstrated that CGA content in peel and flesh of Xuehua pear decreased as fruit development progressed, with a higher level in peel. The expression levels of PbPAL1, PbPAL2, PbC3H, PbC4H, Pb4CL1, Pb4CL2, Pb4CL6, PbHCT1 and PbHCT3 genes decreased in fruit, which was consistent with the pattern of variation in CGA content. That indicated that these genes might be key genes for influencing fruit CGA synthesis in Xuehua pear. However, Pb4CL7 gene expression profile is not consistent with variation of CGA content, hence, it may not be a key gene involved in CGA synthesis.

A test on adverse selection of farmers in crop insurance: Results from Inner Mongolia, China

ZHAO Yuan-feng, CHAI Zhi-hui, Michael S. Delgado, Paul V. Preckel

2017, 16(02): 478-485. DOI: 10.1016/S2095-3119(16)61440-5

Abstract ( ) PDF in ScienceDirect

Adverse selection is an operating risk of crop insurance. Based on survey data on crop insurance collected by questionnaires in Inner Mongolia, China, the paper uses non-parametric analysis and econometric models to estimate the relationship between conditions for crop production and farmers’ insurance decision in order to test the existence of farmers’ adverse selection. The results show farmers’ adverse selection does exist, but settling a claim by negotiation and premium subsidy from governments at all levels can defuse farmers’ adverse selection under the current system of crop insurance. Risk regionalization, heterogeneous insurance contract and product innovation may decrease adverse selection to some extent.

Extreme meteorological disaster effects on grain production in Jilin Province, China

XU Lei, ZHANG Qiao, ZHANG Jing, ZHAO Liang, SUN Wei, JIN Yun-xiang

2017, 16(02): 486-496. DOI: 10.1016/S2095-3119(15)61285-0

Abstract ( ) PDF in ScienceDirect

Extreme meteorological disaster effects on grain production is mainly determined by the interaction between danger degree of hazard-induced factors and vulnerability degree of hazard-affected bodies. This paper treats physical exposure, sensitivity of the response to the impact, and capabilities of disaster prevention and mitigation as a complex system for vulnerability degree of hazard-affected bodies, which included the external shocks and internal stability mechanism. Hazard-induced factors generate external shocks on grain production systems though exposure and sensitivity of hazard-affected body, and the result can be represented as affected area of grain. By quantile regression model, this paper depicts the quantitative relationship between hazard-induced factors of extreme meteorological disaster and the affected area in the tail of the distribution. Moreover, the model of production function have also been utilized to expound and prove the quantitative relationship between the affected area and final grain output under the internal stability mechanism of the agricultural natural resources endowment, the input factors of agricultural production, and the capacity of defending disaster. The empirical study of this paper finds that impact effects of drought disaster to grain production system presents the basic law of “diminishing marginal loss”, namely, with the constant improvement of the grade of drought, marginal affected area produced by hazard-induced factors will be diminishing. Scenario simulation of extreme drought impact shows that by every 1% reduction in summer average rainfall, grain production of Jilin Province will fell 0.2549% and cut production of grain 14.69% eventually. In response to ensure China’s grain security, the construction of the long-term mechanism of agricultural disaster prevention and mitigation, and the innovation of agricultural risk management tools should be also included in the agricultural policy agenda.

Fine mapping of a novel wax crystal-sparse leaf3 gene in rice

GONG Hong-bing, ZENG Sheng-yuan, XUE Xiang, ZHANG Ya-fang, CHEN Zong-xiang, ZUO Shi-min, LI Chuang, LIN Tian-zi, JING De-dao, YU Bo, QIAN Hua-fei, PAN Xue-biao, SHENG Sheng-lan

2017, 16(02): 497-502. DOI: 10.1016/S2095-3119(16)61470-3

Abstract ( ) PDF in ScienceDirect

Cuticular wax plays an important role in protecting plants against water loss and pathogen infection and in the adaptations to environmental stresses. The genetic mechanism of the biosynthesis and accumulation of epicuticular wax in rice remains largely unknown. Here, we show a spontaneous mutant displaying wax crystal-sparse leaves and decreased content of epicuticular wax that was derived from the cytoplasmic male sterility (CMS) restorer line Zhenhui 714. Compared with the wild type Zhenhui 714, the mutant exhibited hydrophilic features on leaf surface and more sensitivity to drought stress. The mutation also caused lower grain number per panicle and thousand grain weight, leading to the decline of yield. Genetic analysis indicates that the mutation is controlled by a single recessive gene, named wax crystal-sparse leaf3 (wsl3). Using segregation populations derived from crosses of mutant/Zhendao 88 and mutant/Wuyujing 3, respectively, the wsl3 gene was fine-mapped to a 110-kb region between markers c3-16 and c3-22 on chromosome 3. According to the rice reference genome and gene analysis, we conclude that a novel gene/mechanism involved in regulation of rice cuticular wax formation.

Small RNA deep sequencing reveals full-length genome of Citrus yellow vein clearing virus in Chongqing, China

YU Yun-qi, WU Qiong, SU Hua-nan, WANG Xue-feng, CAO Meng-ji, ZHOU Chang-yong

2017, 16(02): 503-508. DOI: 10.1016/S2095-3119(16)61533-2

Abstract ( ) PDF in ScienceDirect

To identity the potential pathogen associated with the yellow vein clearing symptom on lemon trees, the profiles of virus-derived small interfering RNAs from citrus samples were obtained and analyzed by deep sequencing method in this study. Twenty-seven contigs almost cover the full length genome of Citrus yellow vein clearing virus (CYVCV) isolate YN were obtained using the small RNA deep sequencing technology. Analysis showed that this isolate CQ shared the highest nucleotide identity with isolate Y1 (JX040635) and YN (KP313242), both of which belong to the genus Mandarivirus in the family Alphaflexiviridae. Mapping analysis of viral-derived siRNA (vsiRNA) profiles revealed an uneven distribution pattern of their generations along both positive and negative genome strands, and 22- and 21-nt vsiRNAs ranked the majority. BLAST against viroids and other viral databases confirmed that this sample was single-infected only by CYVCV, which indicated that CYVCV was the exact causal agent for the yellow clearing symptom occurring on lemon. This is the first CYVCV isolate detected in Chongqing and the second in China. This result could provide a molecular basis for the investigation of citrus viral diseases to protect citrus health in this region.

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