Sugarcane Mapping Research Articles

Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable.

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

In India, the second-largest sugarcane producing country in the world, accurate mapping of sugarcane land is a key to designing targeted agricultural policies. Such a map is not available, however, as it is challenging to reliably identify sugarcane areas using remote sensing due to sugarcane’s phenological characteristics, coupled with a range of cultivation periods for different varieties. To produce a modern sugarcane map for the Bhima Basin in central India, we utilized crowdsourced data and applied supervised machine learning (neural network) and unsupervised classification methods individually and in combination. We highlight four points. First, smartphone crowdsourced data can be used as an alternative ground truth for sugarcane mapping but requires careful correction of potential errors. Second, although the supervised machine learning method performs best for sugarcane mapping, the combined use of both classification methods improves sugarcane mapping precision at the cost of worsening sugarcane recall and missing some actual sugarcane area. Third, machine learning image classification using high-resolution satellite imagery showed significant potential for sugarcane mapping. Fourth, our best estimate of the sugarcane area in the Bhima Basin is twice that shown in government statistics. This study provides useful insights into sugarcane mapping that can improve the approaches taken in other regions.

Remote Sensing Applications in Sugarcane Cultivation: A Review

A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information.

Progress and Prospects of Association Mapping in Sugarcane (Saccharum Species Hybrid), a Complex Polyploid Crop

Sugarcane is one of the most important economic crops and contributes more than 80% of the sugar globally. Developing superior sugarcane varieties is a long-term process due to polyploidy, high chromosome numbers, large complex genome size and also many other practical constraints like long breeding cycle, etc. The recent advancements in molecular markers, next-gen sequencing tools and development of robust statistical models have the potential to lead to affordable genomics-assisted breeding in sugarcane. In order to identify genes linked to trait(s) of interest, association mapping has good prospects as a feasible approach in sugarcane. Association mapping (AM) is a recent technique that identifies quantitative trait loci (QTLs) by investigating marker–trait associations that arise due to the linkage disequilibrium between the genotypic polymorphic loci and the phenotypic variation in a diverse panel of genotypes. In last decade, the understanding of association mapping has increased significantly, due to which focussed efforts are now been made in developing association mapping population, genome-wide association studies and detection of QTLs for several important traits in sugarcane. In contrast to linkage mapping, detecting QTLs through AM approach in sugarcane has certain advantages as historical phenotypic data could be used and there is no need to artificially develop a structured segregating population. The last few years have witnessed a more concerted effort in AM studies in sugarcane where a range of marker tools like SSRs, DArT, GBS, targeted sequence enrichment, etc. were exploited to find out QTLs linked to traits of economic importance. In this review, the recent developments in the area of association mapping in sugarcane and the various methodologies and statistical tools that are being adopted have been discussed.

Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images

Sugarcane is a major crop for sugar and ethanol production and its area has increased substantially in tropical and subtropical regions in recent decades. Updated and accurate sugarcane maps are critical for monitoring sugarcane area and production and assessing its impacts on the society, economy and the environment. To date, no sugarcane mapping tools are available to generate annual maps of sugarcane at the field scale over large regions. In this study, we developed a pixel- and phenology-based mapping tool to produce an annual map of sugarcane at 10-m spatial resolution by analyzing time-series Landsat-7/8, Sentinel-2 and Sentinel-1 images (LC/S2/S1) during August 31, 2017 - July 1, 2019 in Guangxi province, China, which accounts for 65% of sugarcane production of China. First, we generated annual maps of croplands and other land cover types in 2018. Second, we delineated the cropping intensity (single, double and triple cropping in a year) for all cropland pixels in 2018. Third, we identified sugarcane fields in 2018 based on its phenological characteristics. The resultant 2018 sugarcane map has producer, user and overall accuracies of 88%, 96% and 96%, respectively. According to the annual sugarcane map in 2018, there was a total of 8940 km2 sugarcane in Guangxi, which was ~1% higher than the estimate from the Guangxi Agricultural Statistics Report. Finally, we identified green-up dates of those sugarcane fields in 2019, which could be used to support the sugarcane planting and management activities. Our study demonstrates the potential of the pixel- and phenology-based sugarcane mapping tool (both the algorithms and the LC/S2/S1 time series images) in identifying croplands, cropping intensity and sugarcane fields in the complex landscapes with diverse crop types, fragmented crop fields and frequent cloudy weather. The resultant annual maps from this study could be used to assist farms and sugarcane mills for sustainable sugarcane production and environment.

Sugarcane mapping in Paraná State Brazil using MODIS EVI images

Sugarcane cultivated in Brazil deserves attention because it makes the Country the world's largest producer of sugar and ethanol. The aim of this work was to develop and evaluate a methodology for sugarcane mapping in Parana State, Brazil using temporal series of the MODIS EVI, for 2010/2011 to 2013/2014 crop seasons. The methodology included supervised classification Fuzzy ARTMAP, taking as input variables such as terms of harmonics amplitude and phase, and phenological metrics of culture. Area estimates indicated a moderate and strong correlation (rs), ranging from 0.62 to 0.71 comparing with IBGE official data and from 0.79 to 0.87 with the Canasat data. To assess mapping accuracy, Canasat vector maps were used as reference to build the confusion matrix. The method developed based on Fuzzy ARTMAP proved efficient to map and estimate the acreage of sugarcane in the State of Parana, due to digital processing techniques used in homogeneous samples, selection of phenological seasonal metrics, and decomposition of images in accordance with harmonics and supervised training. These together minimized the neural network forecast errors. Results indicate that the methodology is appropriate for sugarcane mapping. DOI: https://doi.org/10.23953/cloud.ijarsg.451

Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms

Sugarcane is an important type of cash crop and plays a crucial role in global sugar production. Clarifying the magnitude of sugarcane planting will likely provide very evident supports for local land use management and policy-making. However, sugarcane growth environment in complex landscapes with frequent rainy weather conditions poses many challenges for its rapid mapping. This study thus tried and used 10-m Sentinel-2 images as well as crop phenology information to map sugarcane in Longzhou county of China in 2018. To minimize the influences of cloudy and rainy conditions, this study firstly fused all available images in each phenology stage to obtain cloud-free remote sensing images of three phenology stage (seedling, elongation and harvest) with the help of Google Earth Engine platform. Then, the study used the fused images to compute the normalized difference vegetation index (NDVI) of each stage. A three-band NDVI dataset along with 4000 training samples and 2000 random validation samples was finally used for sugarcane mapping. To assess the robustness of the three-band NDVI dataset with phenological characteristics for sugarcane mapping, this study employed five classifiers based on machine learning algorithms, including two support vector machine classifiers (Polynomial-SVM and RBF-SVM), a random forest classifier (RF), an artificial neural network classifier (ANN) and a decision tree classifier (CART-DT). Results showed that except for ANN classifier, Polynomial-SVM, RBF-SVM, RF and CART-DT classifiers displayed high accuracy sugarcane resultant maps with producer’s and user’s accuracies of greater than 91%. The ANN classifier tended to overestimate area of sugarcane and underestimate area of forests. Overall performances of five classifiers suggest Polynomial-SVM has the best potential to improve sugarcane mapping at the regional scale. Also, this study observed that most sugarcane (more than 75% of entire study area) tends to grow in flat regions with slope of less than 10°. This study emphasizes the importance of considering phenology in rapid sugarcane mapping, and suggests the potential of fine-resolution Sentinel-2 images and machine learning approaches in high-accuracy land use management and decision-making.

Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

More than 90% of the sugar production in China comes from sugarcane, which is widely grown in South China. Optical image time series have proven to be efficient for sugarcane mapping. There are, however, two limitations associated with previous research: one is that the critical observations during the sugarcane growing season are limited due to frequent cloudy weather in South China; the other is that the classification method requires imagery time series covering the entire growing season, which reduces the time efficiency. The Sentinel-1A (S1A) synthetic aperture radar (SAR) data featuring relatively high spatial-temporal resolution provides an ideal data source for all-weather observations. In this study, we attempted to develop a method for the early season mapping of sugarcane. First, we proposed a framework consisting of two procedures: initial sugarcane mapping using the S1A SAR imagery time series, followed by non-vegetation removal using Sentinel-2 optical imagery. Second, we tested the framework using an incremental classification strategy based on S1A imagery covering the entire 2017–2018 sugarcane season. The study area was in Suixi and Leizhou counties of Zhanjiang city, China. Results indicated that an acceptable accuracy, in terms of Kappa coefficient, can be achieved to a level above 0.902 using time series three months before sugarcane harvest. In general, sugarcane mapping utilizing the combination of VH + VV as well as VH polarization alone outperformed mapping using VV alone. Although the XGBoost classifier with VH + VV polarization achieved a maximum accuracy that was slightly lower than the random forest (RF) classifier, the XGBoost shows promising performance in that it was more robust to overfitting with noisy VV time series and the computation speed was 7.7 times faster than RF classifier. The total sugarcane areas in Suixi and Leizhou for the 2017–2018 harvest year estimated by this study were approximately 598.95 km2 and 497.65 km2, respectively. The relative accuracy of the total sugarcane mapping area was approximately 86.3%.

Mapping the Tres Zapotes Polity

AbstractSeasonal wetlands and exuberant crop growth present challenges to systematic archaeological survey in alluvial settings, particularly in the tropical lowlands. Lidar can reduce the time and cost required to survey such areas by allowing targeting of elevated features not buried under recent alluvium, but demographic interpretation requires estimating post-abandonment alluvial depths. The broad alluvial plain between the Papaloapan delta and the Tuxtla Mountains in southern Veracruz, Mexico, offers a valuable case study, featuring seasonally inundated marshes, lakes, and vast fields of sugar cane, a crop infamously obstructive to lidar mapping. Undertaken to reconstruct demographic and organizational change in the Tres Zapotes polity, this study evaluates the benefits and limitations of lidar for archaeological survey in tropical alluvial settings based on overlap between lidar and systematic pedestrian survey and estimates of alluvial depth obtained by auger testing and underscores the importance of timing for lidar mapping in sugar cane.

Sugarcane Mapping in Tillering Period by Quad-Polarization TerraSAR-X Data

This letter presents preliminary results of sugarcane mapping in its tillering period using quad-polarization data. Three TerraSAR-X images were collected in early, middle, and late tillering periods in the Leizhou Peninsula, South China. Polarization features based on Cloude-Pottier decomposition, such as scattering angle and polarization entropy, were used to analyze sugarcane scattering mechanism temporal behavior and the differences between sugarcane and other typical land-cover types. Results show good separability of sugarcane from other land-cover types. At last, based on common features of sugarcane in tillering period, a sugarcane mapping method was proposed with certain high accuracy and good stability. The results suggest that the tillering period, from early April to late May, is a suitable growth period for sugarcane mapping, and polarization features appear promising for crop mapping and monitoring.

Characterisation of alleles of the sucrose phosphate synthase gene family in sugarcane and their association with sugar-related traits

Sucrose phosphate synthase (SPS) is a key enzyme in the production of sucrose. Five SPS gene families have been identified in monocotyledonous plants including sugarcane. Using SPS family-specific primers to four of the five families (we had previously characterised the fifth gene family), an approximately 400-nt region was amplified from the parents of a sugarcane mapping population, namely the cultivar Q165 and a S. officinarum line IJ76-514. Alignment of the sequences from both parents suggested from one to three genes per SPS gene family, with variable numbers of alleles per gene. Single-dose (SD) single-nucleotide polymorphisms (SNPs) were identified in at least one allele from each SPS gene family and mapped in Q165. For gene families SPS I–IV, SNPs from different alleles in each gene family mapped to different linkage groups within the same homology group (HG), suggesting a single gene per gene family, or multiple genes at a single locus. These map locations were syntenic with SPS gene family locations in sorghum. Two SNPs from different alleles in gene family SPS V were mapped to two different HGs, suggesting two genes in this family; one of the map locations was syntenic with the location of SPS V in sorghum. QTL analysis for sugar-related traits was undertaken with the SD and double-dose SNP markers. SNPs from SPS gene family IV were strongly associated with sugar-related traits, while SNPs from other gene families were associated with agronomic traits, such as stalk weight, diameter, and number. This study provides insight into the evolution of this important polyploid crop as well as highlights the importance of this gene family to sugar production in sugarcane.

A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil

The ability to monitor sugarcane expansion in Brazil, the world’s largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the sugarcane area almost doubled over the last decade in the South-Central region of Brazil. Using remote sensing images, the sugarcane cultivation area was annually monitored and mapped between 2003 and 2012, a period of major sugarcane expansion. The objective of this work was to assess the thematic mapping accuracy of sugarcane, in the crop year 2010/2011, with the novel approach of developing a web platform that integrates different spatial and temporal image resolutions to assist interpreters in classifying a large number of points selected by stratified random sampling. A field campaign confirmed the suitability of the web platform to generate the reference data set. An overall accuracy of 98% with an area estimation error of −0.5% was achieved for the sugarcane map of 2010/11. The accuracy assessment indicated that the map is of excellent quality, offering very accurate sugarcane area estimation for the purpose of agricultural statistics. Moreover, the web platform showed to be very effective in the construction of the reference dataset.

Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas

The aim of this research was to develop a methodology for contributing in the automation of sugarcane mapping over large areas, with time-series of remotely sensed imagery. To this end, two major techniques were combined: Object Based Image Analysis (OBIA) and Data Mining (DM). OBIA was used to represent the knowledge needed to map sugarcane, whereas DM was applied to generate the knowledge model. To derive the image objects, the segmentation algorithm implemented in Definiens Developer® was used. The data mining algorithm used was J48, which generates decision trees (DT) from a previously prepared training set. The study area comprises three municipalities located in the northwest of São Paulo state, all of which are good representatives of the agricultural conditions in the Southern and Southeastern regions of Brazil. A time series of Landsat TM and ETM+ images was acquired in order to represent the wide range of pattern variation along the sugarcane crop cycle. After training, the DT was applied to the Landsat time series, thus generating the desired thematic map with sugarcane ready to harvest. Classification accuracy was calculated over a set of 500 points not previously used during the training stage. Using error matrix analysis and Kappa statistics, tests for statistical significance were derived. The statistics indicated that the classification achieved an overall accuracy of 94% and a Kappa coefficient of 0.87. Results show that the combination of OBIA and DM techniques is very efficient and promising for the sugarcane classification process.

Functional markers for gene mapping and genetic diversity studies in sugarcane

BackgroundThe database of sugarcane expressed sequence tags (EST) offers a great opportunity for developing molecular markers that are directly associated with important agronomic traits. The development of new EST-SSR markers represents an important tool for genetic analysis. In sugarcane breeding programs, functional markers can be used to accelerate the process and select important agronomic traits, especially in the mapping of quantitative traits loci (QTL) and plant resistant pathogens or qualitative resistance loci (QRL). The aim of this work was to develop new simple sequence repeat (SSR) markers in sugarcane using the sugarcane expressed sequence tag (SUCEST database).FindingsA total of 365 EST-SSR molecular markers with trinucleotide motifs were developed and evaluated in a collection of 18 genotypes of sugarcane (15 varieties and 3 species). In total, 287 of the EST-SSRs markers amplified fragments of the expected size and were polymorphic in the analyzed sugarcane varieties. The number of alleles ranged from 2-18, with an average of 6 alleles per locus, while polymorphism information content values ranged from 0.21-0.92, with an average of 0.69. The discrimination power was high for the majority of the EST-SSRs, with an average value of 0.80. Among the markers characterized in this study some have particular interest, those that are related to bacterial defense responses, generation of precursor metabolites and energy and those involved in carbohydrate metabolic process.ConclusionsThese EST-SSR markers presented in this work can be efficiently used for genetic mapping studies of segregating sugarcane populations. The high Polymorphism Information Content (PIC) and Discriminant Power (DP) presented facilitate the QTL identification and marker-assisted selection due the association with functional regions of the genome became an important tool for the sugarcane breeding program.

Conversion of AFLP markers to high-throughput markers in a complex polyploid, sugarcane

The application of DNA markers linked to traits of commercial value in sugarcane may increase the efficiency of sugarcane breeding. The majority of markers generated for quantitative trait locus mapping in sugarcane have been single sequence repeats or AFLPs (amplified fragment length polymorphisms). Since AFLP markers are not adapted for large-scale implementation in plant breeding, our objective was to assess the feasibility of converting AFLP markers to fast, cheap and reliable PCR-based assays in a complex polyploid, sugarcane. Three AFLP markers were selected on the basis of an association to resistance to the fungal pathogen Ustilago scitaminea, the causal agent of smut in sugarcane. We developed an approach which enabled the identification of polymorphisms in these AFLP markers. Towards this goal, we employed GenomeWalking and 454 sequencing to isolate sequences adjacent to the linked AFLP markers and identify SNP (single nucleotide polymorphisms) haplotypes present in the homo(eo)logous chromosomes of sugarcane. One AFLP marker was converted to a cleavage amplified polymorphic sequence marker, another to a SCAR (sequence characteristered amplified region) marker and the final AFLP marker to a SNP PCR-based assay. However, validation of each of the markers in 240 genotypes resulted in 99, 90 and 60% correspondence with the original AFLP marker. These experiments indicate that even in a complex polyploid such as sugarcane, polymorphisms identified by AFLP can be converted to high-throughput marker systems, but due to the complexity this would only be carried out for high-value markers. In some cases, the polymorphisms identified are not transferable to more sequence-specific PCR applications.

A quantitative genetics approach to nitrogen use efficiency in sugarcane

The economic and environmental consequences of inefficient use of nitrogen (N) fertiliser in agricultural crops is of concern worldwide, so new crop varieties with improved nitrogen use efficiency (NUE) are sought. Here, we report the first study of mapping quantitative trait loci (QTL) for nitrogen physiology traits in sugarcane. QTL analysis was undertaken for each parent of a segregating bi-parental sugarcane mapping population. We grew 168 progeny under limiting (0.2 mM NH4NO3) and non-limiting (5.0 mM NH4NO3) N supplies in two glasshouse experiments. Significant marker-trait associations (MTA) were detected in each treatment for shoot dry weight, root dry weight, total shoot N, shoot internal NUE (iNUE; measured as units shoot dry weight per unit N), leaf protein content and glutamine synthetase (GS) activity. MTA for GS activity did not co-locate with other traits except leaf protein content, indicating that variation in GS activity is not linked to plant size or iNUE during early growth. Under high N, there were no significant MTA for iNUE among markers from the male parent, Q165, an Australian commercial cultivar, but six MTA were found for markers inherited from the female parent, IJ76–514, a Saccharum officinarum ancestral variety. The results indicate that variation for iNUE under high N may be lower in commercial varieties than unimproved genotypes. Further, four MTA were consistent with previous field-based research on sugar and biomass production. Our study provides initial evidence that QTL may be incorporated in sugarcane breeding programs targeting improved NUE.

The Complex Genetic Structure of Sugarcane Limits Identification of Additional SNP-Defined Simplex Alleles in Microsatellite Loci

Cultivated sugarcane possesses a large number of chromosomes (typically >80) which can be organised into eight homology groups, each containing approximately 12 homo(eo)logous chromosomes. Currently, microsatellite (SSR) markers are the most easily used markers for marker-assisted selection and other genetic applications. However, only SSR alleles that segregate as simplex and duplex markers can be incorporated into sugarcane maps using populations of ∼300 progeny. Consequently only a subset of possible alleles have been mapped for a given SSR locus and are available for subsequent QTL analyses. Three sugarcane SSR loci, mSSCIR8, mSSCIR17 and mSSCIR18, were amplified, cloned and sequenced from the parents of an Australian sugarcane mapping population, IJ76-514 and . The sequences were examined to identify nucleotide sequence polymorphisms in the flanking regions that could provide additional simplex SSR allele markers. Alignment of the sequences revealed SNP, indel and repeat length variation and the pattern of sequence variation suggested multiple alleles for each SSR, including all of the alleles previously scored by fragment length. While the flanking regions of the SSR loci contained numerous SNPs and indels, none defined new simplex alleles within multiplex fragments and no new SSR simplex alleles were mapped. Furthermore, many of the sequence-defined alleles appear to be spurious and may have arisen from PCR-mediated recombination. These results confirm the difficulties associated with characterising allelic diversity in a polyploid species and the complexity of mapping in sugarcane.

Linkage mapping and genome analysis in a Saccharum interspecific cross using AFLP, SRAP and TRAP markers

Framework genetic linkage maps of two progenitor species of cultivated sugarcane, Saccharum officinarum ‘La Striped’ (2n = 80) and S. spontaneum ‘SES 147B’ (2n = 64) were constructed using amplified fragment length polymorphism (AFLP), sequence related amplified polymorphism (SRAP), and target region amplification polymorphism (TRAP) markers. The mapping population was comprised of 100 F1 progeny derived from the interspecific cross. A total of 344 polymorphic markers were generated from the female (S. officinarum) parent, out of which 247 (72%) were single-dose (segregating in a 1:1 ratio) and 33 (9%) were double-dose (segregating in a 3.3:1 ratio) markers. Sixty-four (19%) markers deviated from Mendelian segregation ratios. In the S. spontaneum genome, out of a total of 306 markers, 221 (72%) were single-dose, 43 (14%) were double-dose, and 42 markers (14%) deviated from Mendelian segregation ratios. Linkage maps with Kosambi map distances were constructed using a LOD score ≥5.0 and a recombination threshold of 0.45. In Saccharum officinarum, 146 markers were linked to form 49 linkage groups (LG) spanning 1732 cM whereas, in S. spontaneum, 121 markers were linked to form 45 LG spanning 1491 cM. The estimated genome size of S. officinarum ‘La Striped’ was 2448 cM whereas that of S. spontaneum ‘SES 147B’ was 3232 cM. Based on the two maps, genome coverage was 69% in S. officinarum and 46% in S. spontaneum. The S. officinarum parent ‘La Striped’ behaved like an auto-allopolyploid whereas S. spontaneum ‘SES 147B’ behaved like a true autopolyploid. Although a large disparity exists between the two genomes, the existence of simple duplex markers, which are heterozygous in both parents and segregate 3:1 in the progeny, indicates that pairing and recombination can occur between the two genomes. The study also revealed that, compared with AFLP, the SRAP and TRAP markers appear less effective at generating a large number of genome-wide markers for linkage mapping in sugarcane. However, SRAP and TRAP markers can be useful for QTL mapping because of their ability to target gene-rich regions of the genome, which is a focus of our future research.

Domestication to Crop Improvement: Genetic Resources for Sorghum and Saccharum (Andropogoneae)

BackgroundBoth sorghum (Sorghum bicolor) and sugarcane (Saccharum officinarum) are members of the Andropogoneae tribe in the Poaceae and are each other's closest relatives amongst cultivated plants. Both are relatively recent domesticates and comparatively little of the genetic potential of these taxa and their wild relatives has been captured by breeding programmes to date. This review assesses the genetic gains made by plant breeders since domestication and the progress in the characterization of genetic resources and their utilization in crop improvement for these two related species.Genetic ResourcesThe genome of sorghum has recently been sequenced providing a great boost to our knowledge of the evolution of grass genomes and the wealth of diversity within S. bicolor taxa. Molecular analysis of the Sorghum genus has identified close relatives of S. bicolor with novel traits, endosperm structure and composition that may be used to expand the cultivated gene pool. Mutant populations (including TILLING populations) provide a useful addition to genetic resources for this species. Sugarcane is a complex polyploid with a large and variable number of copies of each gene. The wild relatives of sugarcane represent a reservoir of genetic diversity for use in sugarcane improvement. Techniques for quantitative molecular analysis of gene or allele copy number in this genetically complex crop have been developed. SNP discovery and mapping in sugarcane has been advanced by the development of high-throughput techniques for ecoTILLING in sugarcane. Genetic linkage maps of the sugarcane genome are being improved for use in breeding selection. The improvement of both sorghum and sugarcane will be accelerated by the incorporation of more diverse germplasm into the domesticated gene pools using molecular tools and the improved knowledge of these genomes.

Characterisation of single nucleotide polymorphisms in sugarcane ESTs

Commercial sugarcane cultivars (Saccharum spp. hybrids) are both polyploid and aneuploid with chromosome numbers in excess of 100; these chromosomes can be assigned to 8 homology groups. To determine the utility of single nucleotide polymorphisms (SNPs) as a means of improving our understanding of the complex sugarcane genome, we developed markers to a suite of SNPs identified in a list of sugarcane ESTs. Analysis of 69 EST contigs showed a median of 9 SNPs per EST and an average of 1 SNP per 50 bp of coding sequence. The quantitative presence of each base at 58 SNP loci within 19 contiguous sequence sets was accurately and reliably determined for 9 sugarcane genotypes, including both commercial cultivars and ancestral species, through the use of quantitative light emission technology in pyrophosphate sequencing. Across the 9 genotypes tested, 47 SNP loci were polymorphic and 11 monomorphic. Base frequency at individual SNP loci was found to vary approximately twofold between Australian sugarcane cultivars and more widely between cultivars and wild species. Base quantity was shown to segregate as expected in the IJ76-514 x Q165 sugarcane mapping population, indicating that SNPs that occur on one or two sugarcane chromosomes have the potential to be mapped. The use of SNP base frequencies from five of the developed markers was able to clearly distinguish all genotypes in the population. The use of SNP base frequencies from a further six markers within an EST contig was able to help establish the likely copy number of the locus in two genotypes tested. This is the first instance of a technology that has been able to provide an insight into the copy number of a specific gene locus in hybrid sugarcane. The identification of specific and numerous haplotypes/alleles present in a genotype by pyrophosphate sequencing or alternative techniques ultimately will provide the basis for identifying associations between specific alleles and phenotype and between allele dosage and phenotype in sugarcane.