Potato and sweetpotato breeding at the international potato center: approaches, outcomes and the way forward

Root and tuber crop breeding is at the front and center of CIP's science program, which seeks to develop and disseminate sustainable agri-food technologies, information and practices to serve objectives including poverty alleviation, income generation, food security and the sustainable use of natural resources. CIP was established in 1971 in Peru, which is part of potato's center of origin and diversity, with an initial mandate on potato and expanding to include sweetpotato in 1986. Potato and sweetpotato are among the top 10 most consumed food staples globally and provide some of the most affordable sources of energy and vital nutrients. Sweetpotato plays a key role in securing food for many households in Africa and South Asia, while potato is important worldwide. Both crops grow in a range of conditions with relatively few inputs and simple agronomic techniques. Potato is adapted to the cooler environments, while sweetpotato grows well in hot climates, and hence, the two crops complement each other. Germplasm enhancement (pre-breeding), the development of new varieties and building capacity for breeding and variety testing in changing climates with emphasis on adaptation, resistance, nutritional quality and resource-use efficiency are CIP's central activities with significant benefits to the poor. Investments in potato and sweetpotato breeding and allied disciplines at CIP have resulted in the release of many varieties some of which have had documented impact in the release countries. Partnership with diverse types of organizations has been key to the centers way of working toward improving livelihoods through crop production in the global South.

Using machine learning for image-based analysis of sweetpotato root sensory attributes

The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as high-throughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained R 2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained R 2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable R 2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers.

Early-Stage Phenotyping of Sweet Potato Virus Disease Caused by Sweet Potato Chlorotic Stunt Virus and Sweet Potato Virus C to Support Breeding

Sweet potato virus disease (SPVD) is a global constraint to sweetpotato (Ipomoea batatas) production, especially under intensive cultivation in the humid tropics such as East Africa. The objectives of this study were to develop a precision SPVD phenotyping protocol, to find new SPVD-resistant genotypes, and to standardize the first stages of screening for SPVD resistance. The first part of the protocol was based on enzyme-linked immunosorbent assay results for sweet potato chlorotic stunt virus (SPCSV) and sweet potato virus C (SPVC) with adjustments to a negative control (uninfected clone Tanzania) and was performed on a prebreeding population (VZ08) comprising 455 clones and 27 check clones graft inoculated under screenhouse conditions. The second part included field studies with 52 selected clones for SPCSV resistance from VZ08 and 8 checks. In screenhouse conditions, the resistant and susceptible check clones performed as expected; 63 clones from VZ08 exhibited lower relative absorbance values for SPCSV and SPVC than inoculated check Tanzania. Field experiments confirmed SPVD resistance of several clones selected by relative absorbance values (nine resistant clones in two locations; that is, 17.3% of the screenhouse selection), supporting the reliability of our method for SPVD-resistance selection. Two clones were promising, exhibiting high storage root yields of 28.7 to 34.9 t ha-1 and SPVD resistance, based on the proposed selection procedure. This modified serological analysis for SPVD-resistance phenotyping might lead to more efficient development of resistant varieties by reducing costs and time at early stages, and provide solid data for marker-assisted selection with a quantitative tool for classifying resistance.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Cruz PC, Borromeo TH, Lalusin AG, Mauleon R, McNally KL, Swamy BPM. Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

Breeding staple crops with increased micronutrient concentration is a sustainable approach to address micronutrient malnutrition. We carried out Multi-Cross QTL analysis and Inclusive Composite Interval Mapping for 11 agronomic, yield and biofortification traits using four connected RILs populations of rice. Overall, MC-156 QTLs were detected for agronomic (115) and biofortification (41) traits, which were higher in number but smaller in effects compared to single population analysis. The MC-QTL analysis was able to detect important QTLs viz: qZn_5.2, qFe_7.1, qGY_10.1, qDF_7.1, qPH_1.1, qNT_4.1, qPT_4.1, qPL_1.2, qTGW_5.1, qGL_3.1 , and qGW_6.1 , which can be used in rice genomics assisted breeding. A major QTL (qZn_5.2 ) for grain Zn concentration has been detected on chromosome 5 that accounted for 13% of R². In all, 26 QTL clusters were identified on different chromosomes. qPH_6.1 epistatically interacted with qZn_5.1 and qGY_6.2 . Most of QTLs were co-located with functionally related candidate genes indicating the accuracy of QTL mapping. The genomic region of qZn_5.2 was co-located with putative genes such as OsZIP5, OsZIP9, and LOC_OS05G40490 that are involved in Zn uptake. These genes included polymorphic functional SNPs, and their promoter regions were enriched with cis-regulatory elements involved in plant growth and development, and biotic and abiotic stress tolerance. Major effect QTL identified for biofortification and agronomic traits can be utilized in breeding for Zn biofortified rice varieties.

Analysis of the Nutritional Composition and Drought Tolerance Traits of Sweet Potato: Selection Criteria for Breeding Lines

Sweet potato is an important world staple with the potential to address hunger and malnutrition. The Agricultural Research Council of South Africa has led sweet potato breeding efforts in the country since 1952 and released several important cultivars. More detailed quality assessments are necessary in addition to general breeding criteria. The present study investigated sweet potato cultivars and elite clones for (1) their nutritional composition; (2) biochemical reaction to drought stress; (3) correlate biochemical reaction to root yield for possible identification of screening methods for drought tolerance in sweet potato. Orange-fleshed cultivars, in particular Bophelo, had superior contents of Fe, Zn, Mg, Ca, Mn, and dietary fiber. Cream-fleshed cultivars, particularly Ndou, were higher in starch and carbohydrate. When sweet potato cultivars were subjected to drought stress, significant changes were noted in most antioxidant enzymes, chlorophyll and proline concentrations, and ¹³C discrimination. All of these showed significant correlations with root yield. For screening of drought tolerance, the determination of nitrate reductase, proline, and chlorophyll concentrations at 60 days after planting are recommended. Ndou was less affected by drought stress. Bophelo and Ndou, which are gaining popularity on the informal market, have superior nutritional value and are good cultivar choices for food security and addressing malnutrition.

Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis

A sample set of 18 sweet potatoes [Ipomoea batatas (L.) Lam] segmented into six registered cultivars and 12 new varieties were evaluated. The 142 tuberous roots were obtained from a sweet potato germplasm bank (BAG-sweet potato; -27.417713768824555 and -49.64874168439556), specifically from plants belonging to a sweet potato breeding program. All samples were characterized according to their morphology, instrumental pulp color, proximate composition, and total dietary fiber. The analytical results were submitted to parametric and non-parametric statistical tests for sample variance data comparison. Moreover, the screening of the cultivars and new varieties was performed by exploratory statistical analysis, factor analysis (FA), and principal component analysis (PCA). From the sixteen independent variables that characterized the samples, the exploratory FA identified thirteen that had a communality greater than 0.7, with 92.08% of assertiveness. The PCA generated 4 principal components able to account for 84.01% of the explanatory variance. So, among the six registered cultivars, SCS372 Marina and SCS370 Luiza showed the capability to be employed as cultivars for production. Among the 12 sweet potato new varieties, samples 17025-13, 17125-10, and 17117 met the requirements for patent and registration. These results will be useful to farmers who wish to use these sweet potatoes in the development of their crops.