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Gender responsiveness in breeding programs to meet client and end user preferences for crops is essential. This case study analyzes the implementation experience of gender-responsive breeding and variety dissemination in Malawi and Ghana, focusing on good practices and challenges encountered. In Malawi, a training-of-trainers approach was employed to share knowledge among trained farmers. In Ghana, a research study was conducted to identify gender-based preferences for sweetpotato to define breeding objectives. The participation of social scientists, food scientists, and sweetpotato breeders in the GREAT (Gender Researchers Equipped for Agricultural Transformation) team provided a multidisciplinary perspective, addressing questions and responses in the field. Research efforts were strengthened by focusing on food quality through the establishment of an analytical laboratory for rapid evaluation of nutrition and food quality, including sugars. This helped develop sensory analytical capacity to better understand quality attributes and market segments, guiding breeding and improving market opportunities for women. Breeding outcomes resulting from gender inclusion led to the release of some sweetpotato varieties meeting end user and consumer preferences, as well as adoption of OFSP varieties by men and women. Other good practices for gender inclusion and responsiveness include providing funds for gender-based research and activities, engaging gender specialists and social scientists in trans-disciplinary teams, designing program activities with gender considerations, and incorporating traits in seed multiplication and dissemination decisions. Application of these gender inclusion practices resulted in adoption and development of acceptable sweetpotato varieties.

Background: Variability in sugar content between raw and cooked sweetpotato storage roots impact nutritional and dietary importance with implications for consumer preference. High-throughput phenotyping is required to breed varieties that satisfy consumer preferences., Results: Near-infrared reflectance spectroscopy (NIRS) calibration curves were developed for analysing sugars in baked storage roots using 147 genotypes from a population segregating for sugar content and other traits. The NIRS prediction curves had high coefficients of determination in calibration (R 2 c ) of 0.96 (glucose), 0.93 (fructose), 0.96 (sucrose), and 0.96 (maltose). The corresponding coefficients of determination for cross-validation (R 2 cv ) were 0.92 (glucose), 0.89 (fructose), 0.96 (sucrose) and 0.93 (maltose) and were similar to the R 2 c for all sugars measured. The ratios of the standard deviation of the reference set to the standard error of cross-validation were greater than three for all sugars. These results confirm the applicability of the NIRS curves in efficiently determining sugar content in baked sweetpotato storage roots. External validation was performed on an additional 70 genotypes. Coefficients of determination (r 2 ) were 0.88 (glucose), 0.88 (fructose), 0.86 (sucrose) and 0.49 (maltose). The results were comparable to those found for the calibration and cross-validation in fructose, glucose, and sucrose, but were moderate for maltose due to the low variability of maltose content in the population., Conclusions: NIRS can be used for screening sugar content in baked sweetpotato storage roots in breeding programs and can be used to assist with the development of improved sweetpotato varieties that better meet consumer preferences. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Background: Little information is available on α- and β-amylase activity and their associations with starch, sugars and other culinary qualities in sweetpotato. The present study aimed to assess sweetpotato storage root α- and β-amylase activity in relation to starch, sugars, β-carotene content and storage root flesh color., Results: α- and β-amylase activity (α-AA and β-AA) were assayed from a Tanzania (T) × Beauregard (B) genetic mapping population in their uncured (raw), cured and stored (approximately 11 weeks) forms during 2016 and 2017. Ceralpha and Betamyl methods, with modifications to suit a high-throughput microplate assay format, were used to quantify α-AA and β-AA, respectively. Storage root dry matter, starch, glucose, fructose, sucrose and β-carotene content were predicted using near infrared reflectance spectroscopy. There was little relationship (r 2  = 0.02-0.08, P ≤ 0.05 in 2016 and r 2  = 0.05-0.11, P ≤ 0.05 in 2017) between α-AA and β-AA. We observed negative linear associations between α-AA and dry matter content and generally no correlations between β-AA and dry matter content. β-AA and sugars were weakly positively correlated. β-AA and β-carotene content were positively correlated (r = 0.3-0.4 in 2016 and 0.3-0.5 in 2017)., Conclusion: Generally, the correlation coefficient for amylase enzyme activity and sugar components of storage roots at harvest increased after curing and during post-harvest storage. The present study is a major step forward in sweetpotato breeding in terms of providing a better understanding of how α- and β-amylase activity are inter-associated with several culinary quality attributes. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

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., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s) 2023.)