The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective.

Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers' priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot's recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers' preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.

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 R2 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 R2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable R2 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.

End-user preferences to enhance prospects for varietal acceptance and adoption in potato breeding in Uganda.

BACKGROUND: Potato varieties have diverse biophysical characteristics, so it is important for breeders to have the capacity to choose those that meet the preferences of end users, such as mealiness, firmness, and taste, among others. Combining user preferences with descriptive information regarding the sensory characteristics of boiled potatoes can contribute to the improvement of consumer-driven varieties. This study aimed to factor in the preferences of end users to improve the prospects for varietal acceptance, adoption, and discrimination among genotypes in potato breeding. RESULTS: The priority quality traits (traits that play the most significant roles in acceptance and adoption) of the boiled potatoes were determined by evaluating gender and livelihood using the G+ tool. The G+ tool is designed to assess gender impact on roots, tubers and bananas (RTB) traits by serving as a validation check to reflect on important gender-based issues in agricultural food systems in order to reduce harm and promote positive impact. Potato genotypes were differentiated by penetration (textural parameters as measured by standard texture probe) and the procedure was repeatable, as there was no significant difference between the cooking replicates at 40 min of cooking. Instrument-based texture parameters, such as penetration peak force (hardness/firmness) and area (area under the curve, which represents energy needed to penetrate) of boiled potato tubers were significantly associated with sensory attributes such as fracturability and hardness in the mouth. An attempt to differentiate genotypes using near-infrared spectroscopy (NIRS) revealed that the average results observed for the calibration for yellow color (r2 = 0.70), homogeneity of color (r2 = 0.48), moisture in mass (r2 = 0.40), and uniformity of texture (r2 = 0.56) suggested that these parameters could be used for initial breeding screening purposes. CONCLUSIONS: The preferred traits of the boiled potato can be integrated into the potato-breeding program/product profile. Near-infrared spectroscopy shows strong potential to predict potato color and the ability of NIRS models to predict some texture attributes is also promising. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Decision tree scoring system to guide selection for consumer preference in sweetpotato breeding trials.

BACKGROUND: Previously, a lexicon and protocol for quantitative descriptive analysis (QDA) was established for the Uganda sweetpotato breeding program. The implication of QDA scores for priority sensory attributes on consumer preference should be determined to interpret results efficiently and make decisions effectively. The present study aimed to develop a gender-responsive decision tree to obtain an overall sweetpotato eating quality score to facilitate demand-led targeted breeding selection. It focused on Kamuli and Hoima districts (Uganda) and uses pre-lease advanced clones ('NKB3', 'NKB105', 'NKB135', 'D11' and 'D20'), released varieties ('NASPOT 8' and 'NAROSPOT 1') and landraces ('Muwulu-Aduduma', 'Umbrella'). RESULTS: Including boiled sweetpotato sensory characteristics, namely mealy, sweet taste, sweetpotato smell, firm and not fibrous, in breeding design would benefit end-users, especially women given their role in varietal selection, food preparation and marketing. 'D20', 'NASPOT 8' and 'NAROSPOT 1' were most liked in both districts. 'NKB3' and 'D11' were the least liked in Hoima, whereas 'Muwulu-Aduduma' was the least liked in Kamuli. There was a positive correlation between color and overall liking (r2 = 0.8) and consumers liked the color (average rating ≥ 6 on a nine-point hedonic scale) of all genotypes. Threshold values (average rating on 11-point scales) for consumer acceptability were identified (sweet taste = 6, sweetpotato aroma and flavor = 6, firmness = 3, and mealiness = 4). A regression decision tree tool was created to calculate an eating quality selection index when screening lines in breeding programs using the values. CONCLUSION: Decision trees that include consumer needs and gender considerations would facilitate demand-led breeding and make varietal selection in sweetpotato breeding programs more effective. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Sensory guided selection criteria for breeding consumer-preferred sweetpotatoes in Uganda.

Prioritizing sensory attributes and consumer evaluation early in breeding trials to screen for end-user preferred traits could improve adoption rates of released genotypes. In this study, a lexicon and protocol for descriptive sensory analysis (DSA) was established for sweetpotato and used to validate an instrumental texture method for which critical values for consumer preference were set. The study comprised several phases: lexicon development during a 4-day workshop; 3-day intensive panel training; follow-up virtual training, evaluation of 12 advanced genotypes and 101 additional samples from two trials in 2021 by DSA and instrumental texture analysis using TPA double compression; and DSA, instrumental texture analysis and consumer acceptability tests on 7 genotypes in on-farm trials. The established sweetpotato lexicon comprising 27 sensory attributes enabled characterization and differentiation of genotypes by sensory profiles. Significant correlation was found between sensory firmness by hand and mouth with TPA peak positive force (r = 0.695 and r = 0.648, respectively) and positive area (r = 0.748, r = 0.715, respectively). D20, NAROSPOT 1, NASPOT 8, and Umbrella were the most liked genotypes in on-farm trials (overall liking = 7). An average peak positive force of 3700 gf was proposed as a minimum texture value for screening sweetpotato genotypes, since it corresponded with at least 46 % of consumers perceiving sweetpotatoes as just-about-right in firmness and a minimum overall liking of 6 on average. Combining DSA with instrumental texture analysis facilitates efficient screening of genotypes in sweetpotato breeding programs.

Effect of Orange-Fleshed Sweet Potato Purée and Wheat Flour Blends on ß-Carotene, Selected Physicochemical and Microbiological Properties of Bread.

Partial substitution of wheat flour with orange-fleshed sweet potato (OFSP) purée in bread can increase vitamin A intake among consumers. The study investigated the influence of wheat flour substitution with 20-50% of OFSP purée on proximate composition, color, ß-carotene, water activity, and microbial keeping quality. The moisture content, crude protein, crude fat, total ash, crude fiber, and carbohydrate in bread ranged from 28.6-32.7%, 9.9-10.6%, 5.0-5.5%, 1.9-3.2%, 1.4-1.8%, and 79.1-80.9%, respectively. ß-carotene, total ash, and crude fiber contents in bread, and Hunter color values a*, b*, chroma, and ∆E significantly increased with the addition of OFSP purée. Total viable counts (TVC), yeast, and molds in bread ranged from 2.82-3.64 log10 cfu/g and 1.48-2.16 log10 cfu/g, respectively, on the last day of storage. Water activity, TVC, and fungal counts were low in sweet potato composite bread as compared to white bread. Total ß-carotene in OFSP bread ranged from 1.9-5.4 mg/100 g (on dry weight). One hundred grams of bread portion enriched with 40% and 50% OFSP purée provides more than 50% of vitamin A dietary requirements to children aged 4-8 years. Incorporation of up to 50% OFSP purée in wheat flour produces a relatively shelf-stable, nutritious, and health-promoting functional bread.

Analysis of ß-amylase gene (Amyß) variation reveals allele association with low enzyme activity and increased firmness in cooked sweetpotato (Ipomoea batatas) from East Africa.

ß-amylase is a thermostable enzyme that hydrolyses starch during cooking of sweetpotato (Ipomoea batatas) storage roots, thereby influencing eating quality. Its activity is known to vary amongst genotypes but the genetic diversity of the beta-amylase gene (Amyß) is not well studied. Amyß has a highly conserved region between exon V and VI, forming part of the enzyme's active site. To determine the gene diversity, a 2.3 kb fragment, including the conserved region of the Amyß gene was sequenced from 25 sweetpotato genotypes. The effect of sequence variation on gene expression, enzyme activity, and firmness in cooked roots was determined. Six genotypes carrying several SNPs within exon V, linked with an AT or ATGATA insertion in intron V were unique and clustered together. The genotypes also shared an A336E substitution in the amino acid sequence, eight residues upstream of a substrate-binding Thr344. The genotypes carrying this allele exhibited low gene expression and low enzyme activity. Enzyme activity was negatively correlated with firmness (R = -0.42) in cooked roots. This is the first report of such an allele, associated with low enzyme activity. These results suggest that genetic variation within the AmyB locus can be utilized to develop markers for firmness in sweetpotato breeding.

Development of a food product profile for boiled and steamed sweetpotato in Uganda for effective breeding.

This study sought to understand user preferences of raw, boiled and steamed sweetpotato, a staple food in Uganda. A sequential methodology involving state of knowledge review, gendered food mapping, processing diagnosis and consumer testing was used in Lira and Kamwenge districts. Preferred raw sweetpotato characteristics were large roots (≥ 3 cm diameter) with a sweet taste, smooth skin and hard texture, while mealiness, sweet taste and good sweetpotato smell were important attributes for boiled sweetpotato. Processors, mostly women, highlighted ease of peeling and sappiness of raw roots. There were gender differences in quality characteristic preferences and perceived importance. The released variety, NASPOT 8, had the highest overall liking in Kamwenge and was well liked in Lira. Penalty analysis of consumer data showed that sweetness and firmness were key drivers of overall liking. The results will support breeding programmes in meeting specific end-user product profiles, selection criteria and uptake of new varieties.