Cassava (Manihot esculenta Crantz) is a major tropical crop. Regarded as an orphan crop a few decades ago, it now receives considerable attention from governments, industry and agencies investing in agricultural research. As a result, the cassava community generates vast amounts of information and develops useful technologies and products. This positions cassava as a key industrial commodity and a reliable food security staple. Significant genetic gains have been achieved through the early 2000s. Gains are particularly noticeable under better agronomic conditions, as was the case for the green revolution of cereals. However, further gains obtained in the past two decades have not been as impressive. Cassava breeding cycle is long, and its multiplication rate slow. Therefore, it takes no less than 8 years to develop a new variety. Cassava breeding is based on the use of heterozygous progenitors which has important drawbacks. One of them is the impossibility to implement conventional back-crossing. Cassava researchers have recently introgressed a single recessive trait (amylose-free starch) into elite varieties. This was unprecedented in cassava and revealed important problems incorporating single genes into elite varieties. In the absence of backcross, the process required essentially the development of new varieties, which exposed strong effects of undesirable genetic linkages. Breeding approaches to overcome the problems of introgressing single-gene traits have been developed and will be implemented to introduce resistance to cassava mosaic disease into SE Asia breeding populations. These methods will also be useful to exploit recently identified immunity to cassava brown streak disease, a serious problem currently restricted to Africa. Trait introgression is an important process in crop breeding and will be more important in the future as gene discovery and editing identify useful genes. This article consolidates and integrates information from cassava and other crops and proposes guidelines to maximize the returns on investments on this important commodity.

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Excellence in Cassava Breeding: Perspectives for the Future

Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

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Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava ( Manihot esculenta Crantz)

Cassava is crucial for food security of millions of people in sub-Saharan Africa. The crop has great potential to contribute to African development and is increasing its income-earning potential for small-scale farmers and related value chains on the continent. Therefore, it is critical to increase cassava production, as well as its quality attributes. Technological innovations offer great potential to drive this envisioned change. This paper highlights genomic tools and resources available in cassava. The paper also provides a glimpse of how these resources have been used to screen and understand the pattern of cassava genetic diversity on the continent. Here, we reviewed the approaches currently used for phenotyping cassava traits, highlighting the methodologies used to link genotypic and phenotypic information, dissect the genetics architecture of key cassava traits, and identify quantitative trait loci/markers significantly associated with those traits. Additionally, we examined how knowledge acquired is utilized to contribute to crop improvement. We explored major approaches applied in the field of molecular breeding for cassava, their promises, and limitations. We also examined the role of national agricultural research systems as key partners for sustainable cassava production.

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Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

Protein composition, chlorophyll, carotenoids, and cyanide content of cassava leaves (Manihot esculenta Crantz) as influenced by cultivar, plant age, and leaf position.

Cassava is an important crop for food security and industry in the least-developed and developing countries The completion of the cassava genome sequence and identification of large numbers of candidate genes by next-generation sequencing provide extensive resources for cassava molecular breeding and increase the need for rapid and efficient gene function analysis systems in cassava Several plant virus-induced gene silencing (VIGS) systems have been developed as reverse genetic tools for rapid gene function analysis in cassava However, these VIGS vectors could cause severe viral symptoms or inefficient gene silencing In this study, we constructed agroinfection-compatible infectious cDNA clones of cassava common mosaic virus isolate CM (CsCMV-CM, genus Potexvirus, family Alphaflexiviridae) that causes systemic infection with mild symptoms in cassava CsCMV-CM was then modified to a viral vector carrying the Nimble cloning frame, which facilitates the rapid and high-throughput cloning of silencing fragments into the viral genome The CsCMV-based vector successfully silenced phytoene desaturase (PDS) and magnesium chelatase subunit I (ChlI) in different cassava varieties and Nicotiana benthamiana The silencing of the ChlI gene could persist for more than two months This CsCMV-based VIGS system provides a new tool for rapid and efficient gene function studies in cassava

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A cassava common mosaic virus vector for virus-induced gene silencing in cassava

In Asia, cassava (Manihot esculenta) is cultivated by more than 8 million farmers, driving the rural economy of many countries. The International Center for Tropical Agriculture (CIAT), in partnership with national agricultural research institutes (NARIs), instigated breeding and agronomic research in Asia, 1983. The breeding program has successfully released high-yielding cultivars resulting in an average yield increase from 13.0 t ha-1 in 1996 to 21.3 t ha-1 in 2016, with significant economic benefits. Following the success in increasing yields, cassava breeding has turned its focus to higher-value traits, such as waxy cassava, to reach new market niches. More recently, building resistance to invasive pests and diseases has become a top priority due to the emergent threat of cassava mosaic disease (CMD). The agronomic research involves driving profitability with advanced technologies focusing on better agronomic management practices thereby maintaining sustainable production systems. Remote sensing technologies are being tested for trait discovery and large-scale field evaluation of cassava. In summary, cassava breeding in Asia is driven by a combination of food and market demand with technological innovations to increase the productivity. Further, exploration in the potential of data-driven agriculture is needed to empower researchers and producers for sustainable advancement.

Văn Đồng Nguyễn

Papers

Cassava breeding and agronomy in Asia: 50 years of history and future directions