RSA5 - Post-harvest and Enhanced Nutrition

Better Nutrition and Income

Leader: Thierry Tran
Collaborators: John Belalcazar, Jorge Luis Luna, María Alejandra Ospina, Jhon Larry Moreno, Andrés Escobar, Cristian Duarte, Maël Clergue, Matthieu Vergnol, Xiaofei Zhang
Partners: CRP-RTB, RTBFoods, CIRAD

The RSA-5 team is developing technologies and strategies to diminish root losses and increase cassava value through specialized starch uses and increased micronutrient potential. We address these challenges through the development of faster protocols for phenotyping the postharvest quality traits of cassava products; classification of cassava genotypes by means of near-infrared spectroscopy (NIRS) analysis; developing a faster method for waxy root (with high branched starch content) identification; and implementation of a harmonized coding system in collaboration with RSA-1.

The expansion of the cassava industry has been a success in several countries over the past 30 years, such as Thailand, Nigeria, Vietnam, and Brazil, thus improving income for millions of small and medium farmers and processors. Cassava has potential to replicate this success in more countries, and indeed is expected to play a key role as a resilient source of carbohydrates to feed the growing populations. The Alliance’s Cassava Program addresses the following challenges toward cassava food processing: (1) identifying high-potential cassava genotypes produced by the Alliance’s breeding program through systematic high- throughput screening of users’ quality traits; (2) increasing cassava availability through decreased postharvest losses; (3) increasing cassava processing at small and medium scale through optimized processing equipment (energy efficiency, robustness, capacity matching the availability of raw material); and (4) increasing the profitability of cassava value chains through minimized production costs, value chain assessments, and market assessments.

In 2020, RSA-5 continued to develop faster protocols for phenotyping postharvest quality traits of cassava products, with the objective of integrating quality criteria assessments early in the selection of improved clones. A new water absorption method (WAB) reduced the time necessary to assess the cooking quality of boiled cassava by half, requiring 30 minutes per sample instead of 60 minutes with the conventional method. The new method was implemented in several projects. Specifically, the “RTBfoods progenitors collection” and two progeny collections (30, 293 and 353 genotypes, respectively) were comprehensively phenotyped using water absorption and additional characteristics, such as texture. For the first time, RSA-5 and RSA-1 were also able to screen water absorption for a large number of clones (3,196) from first-generation progenies (F1C1) biofortified cassava trials thanks to faster phenotyping methods, thus allowing to include cooking quality among the selection criteria for further breeding. This effort resulted in the selection of 389 high-potential biofortified clones for advanced trials (CET).

Cooking time & water absorption

Diversity of cooking time and water absorption (WAB30) among cassava genotypes.

Correlation WAB & Cooking time

Significant correlation between water absorption at 30° (WAB30) and cooking time (R2 = 0.63) and selection criteria.

Prediction of functional quality traits using NIRS

Notable progress was made in predicting postharvest quality traits by means of NIRS, with the first demonstration of correct classification of cassava genotypes into two groups: short- and long-cooking (below and above 30 minutes, respectively). This was the first time a functional quality trait was predicted by NIRS in cassava. Previously, it was possible to predict only compositional data, such as dry matter or beta-carotene content. The prediction accuracy was 80%, which is sufficient for early screening to select short-cooking clones and reject long-cooking ones. Taking only 5 minutes per sample, NIRS analysis promises to be a gamechanger in screening quality traits for breeding, which allows true high-throughput phenotyping (HTPP) and selection of best candidate clones both for agronomic performance and postharvest quality.

NIRS classification

Classification by NIRS of cassava genotypes into two classes: &se;30 min (C1) and >30 min (C2).

Early detection of waxy root trait

A new method was developed for early detection of the waxy (high amylopectin) root trait using young leaves. The protocol involves forcing leaves to overproduce starch by continuous illumination for 24 hours, followed by chlorophyll extraction and iodine staining of the leaves to classify them as waxy (brown color) or non-waxy (purple color). The waxy trait could thus be successfully detected in leaves 3 months after planting. This obviates the need to wait for the formation of roots 6-8 months after planting, thus saving 3-5 months in the selection process. Faster phenotyping protocols are key for increasing capacity to screen for postharvest quality traits and accelerating the development of improved cassava varieties that match users’ expectations and preferences.

Stain for waxy phenotype

Cassava leaves (3 months after planting) colored with 2% iodine solution after chlorophyll extraction. (A) waxy genotype and (B) normal starch genotype.

Genotype traceability

To ensure data traceability, RSA-1 and RSA-5 implemented a harmonized coding system for field trials and phenotyping of postharvest quality traits. The 16-digit code is compatible with the Cassavabase breeding database. The system uses QR codes to link each sample to the Fieldbook data collection app. This has streamlined the collection and uploading of datasets to Cassavabase, further reducing delays in making quality traits data available to RSA-1 and further analyses.

  1. Chirinda N; Trujillo C; Loaiza S; Salazar S; Luna J; Tong Encinas LA; Becerra-López Lavalle LA; Tran T. 2021. Nitrous oxide emissions from cassava fields amended with organic and inorganic fertilizers. Soil Use and Management. Doi: 10.1111/sum.12696
  2. Escobar A; Rondet E; Dahdouh L; Ricci J; Akissoé N; Dufour D; Tran T; Cuq B; Delalonde M. 2020. Identification of critical versus robust processing unit operations determining the physical and biochemical properties of cassava-based semolina (gari). International Journal of Food Science and Technology. Doi:10.1111/ijfs.14857
  3. Luna J; Dufour D; Tran T; Pizarro M; Calle F; Garcia Dominguez M; Hurtado IM; Sanchez T; Ceballos H. 2020. Postharvest physiological deterioration in several cassava genotypes over sequential harvests and effect of pruning prior to harvest. International Journal of Food Science and Technology. Doi:10.1111/ijfs.14711
  4. Moreno JL; Tran T; Cantero-Tubilla B; Lopez-Lopez K; Becerra-Lopez Lavalle L.A; Dufour D. 2020. Physicochemical and physiological changes during the ripening of banana (Musaceae) fruit grown in Colombia. International Journal of Food Science and Technology. Doi:10.1111/ijfs.14851
  5. Ospina MA; Pizarro M; Tran T; Ricci J; Belalcazar J; Luna JL; Londoño LF; Salazar S; Ceballos H; Dufour D; Becerra-López Lavalle LA. 2020. Cyanogenic, carotenoids and protein composition in leaves and roots across seven diverse population found in the world cassava germplasm collection at CIAT, Colombia. International Journal of Food Science and Technology. Doi:10.1111/ijfs.14888
  6. Tran T; Zhang X; Ceballos H; Moreno JL; Luna J; Escobar A; Morante N; Belalcazar B; Becerra-López Lavalle LA; Dufour D. 2020. Correlation of cooking time with water absorption and changes in relative density during boiling of cassava roots. International Journal of Food Science and Technology. Doi:10.1111/ijfs.14769

Contact details

We are located at various places around the world to be close where we want to achieve impact. You can contact us at our HQ in Cali-Colombia, where you can find out more.